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extacy
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A peer-reviewed clinical journal serving healthcare professionals working with the Department of Veterans Affairs, the Department of Defense, and the Public Health Service.
HIV free 30 months after stem cell transplant, is the London patient cured?
A patient with HIV remission induced by stem cell transplantation continues to be disease free at the 30-month mark.
The individual, referred to as the London patient, received allogeneic hematopoietic stem cell transplantation (allo-HSCT) for stage IVB Hodgkin lymphoma. The transplant donor was homozygous for the CCR5 delta-32 mutation, which confers immunity to HIV because there’s no point of entry for the virus into immune cells.
After extensive sampling of various tissues, including gut, lymph node, blood, semen, and cerebrospinal fluid (CSF), Ravindra Kumar Gupta, MD, PhD, and colleagues found no detectable virus that was competent to replicate. However, they reported that the testing did detect some “fossilized” remnants of HIV DNA persisting in certain tissues.
The results were shared in a video presentation of the research during the Conference on Retroviruses & Opportunistic Infections, which was presented online this year. CROI organizers chose to hold a virtual meeting because of concerns about the spread of COVID-19.
The London patient’s HIV status had been reported the previous year at CROI 2019, but only blood samples were used in that analysis.
In a commentary accompanying the simultaneously published study in the Lancet, Jennifer Zerbato, PhD, and Sharon Lewin, FRACP, PHD, FAAHMS, asked: “A key question now for the area of HIV cure is how soon can one know if someone has been cured of HIV?
“We will need more than a handful of patients cured of HIV to really understand the duration of follow-up needed and the likelihood of an unexpected late rebound in virus replication,” continued Dr. Zerbato, of the University of Melbourne, and Dr. Lewin, of the Royal Melbourne Hospital and Monash University, also in Melbourne.
In their ongoing analysis of data from the London patient, Dr. Gupta, a virologist at the University of Cambridge (England), and associates constructed a mathematical model that maps the probability for lifetime remission or cure of HIV against several factors, including the degree of chimerism achieved with the stem cell transplant.
In this model, when chimerism reaches 80% in total HIV target cells, the probability of remission for life is 98%; when donor chimerism reaches 90%, the probability of lifetime remission is greater than 99%. Peripheral T-cell chimerism in the London patient has held steady at 99%.
Dr. Gupta and associates obtained some testing opportunistically: A PET-CT scan revealed an axillary lymph node that was biopsied after it was found to have avid radiotracer uptake. Similarly, the CSF sample was obtained in the course of a work-up for some neurologic symptoms that the London patient was having.
In contrast to the first patient who achieved ongoing HIV remission from a pair of stem cell transplants received over 13 years ago – the Berlin patient – the London patient did not receive whole-body radiation, but rather underwent a reduced-intensity conditioning regimen. The London patient experienced a bout of gut graft-versus-host disease (GVHD) about 2 months after his transplant, but has been free of GVHD in the interval. He hasn’t taken cytotoxic agents or any GVHD prophylaxis since 6 months post transplant.
Though there’s no sign of HIV that’s competent to replicate, “the London patient has shown somewhat slow CD4 reconstitution,” said Dr. Gupta and coauthors in discussing the results.
The patient had a reactivation of Epstein-Barr virus (EBV) about 21 months after analytic treatment interruption (ATI) of antiretroviral therapy that was managed without any specific treatment, but he hasn’t experienced any opportunistic infections. However, his CD4 count didn’t rebound to pretransplant levels until 28 months after ATI. At that point, his CD4 count was 430 cells per mcL, or 23.5% of total T cells. The CD4:CD8 ratio was 0.86; normal range is 1.5-2.5.
The researchers used quantitative real-time polymerase chain reaction (rt-PCR) to look for packaging site and envelope (env) DNA fragments, and droplet digital PCR to quantify HIV-1 DNA.
The patient’s HIV-1 plasma load measured at 30 months post ATI on an ultrasensitive assay was below the lower limit of detection (less than 1 copy per mL). Semen viremia measured at 21 months was also below the lower limit of detection, as was CSF measured at 25 months.
Samples were taken from the patient’s rectum, cecum, sigmoid colon, and terminal ileum during a colonoscopy conducted 22 months post ATI; all tested negative for HIV DNA via droplet digital PCR.
The lymph node had large numbers of EBV-positive cells and was positive for HIV-1 env and long-terminal repeat by double-drop PCR, but no integrase DNA was detected. Additionally, no intact proviral DNA was found on assay.
Dr. Gupta and associates speculated that “EBV reactivation could have triggered EBV-specific CD4 and CD8 T-cell responses and proliferation, potentially including CD4 T cells containing HIV-1 DNA.” Supporting this hypothesis, EBV-specific CD8 T-cell responses in peripheral blood were “robust,” and the researchers also saw some CD4 response.
“Similar to the Berlin patient, highly sensitive tests showed very low levels of so-called fossilized HIV-1 DNA in some tissue samples from the London patient. Residual HIV-1 DNA and axillary lymph node tissue could represent a defective clone that expanded during hyperplasia within the lymph note sampled,” noted Dr. Gupta and coauthors.
Responses of CD4 and CD8 T cells to HIV have also remained below the limit of detection, though cytomegalovirus-specific responses persist in the London patient.
As with the Berlin patient, standard enzyme-linked immunosorbent assay (ELISA) testing has remained positive in the London patient. “Standard ELISA testing, therefore, cannot be used as a marker for cure, although more work needs to be done to assess the role of detuned low-avidity antibody assays in defining cure,” noted Dr. Gupta and associates.
The ongoing follow-up plan for the London patient is to obtain viral load testing twice yearly up to 5 years post ATI, and then obtain yearly tests for a total of 10 years. Ongoing testing will confirm the investigators’ belief that “these findings probably represent the second recorded HIV-1 cure after CCR5 delta-32/delta-32 allo-HSCT, with evidence of residual low-level HIV-1 DNA.”
Dr. Zerbato and Dr. Lewin advised cautious optimism and ongoing surveillance: “In view of the many cells sampled in this case, and the absence of any intact virus, is the London patient truly cured? The additional data provided in this follow-up case report is certainly exciting and encouraging but, in the end, only time will tell.”
Dr. Gupta reported being a consultant for ViiV Healthcare and Gilead Sciences; several coauthors also reported financial relationships with pharmaceutical companies. The work was funded by amfAR, the American Foundation for AIDS Research, and the Wellcome Trust. Dr. Lewin reported grants from the National Health and Medical Research Council of Australia, the National Institutes of Health, the American Foundation for AIDS Research, Gilead Sciences, Merck, ViiV Healthcare, Leidos, the Wellcome Trust, the Australian Centre for HIV and Hepatitis Virology Research, and the Melbourne HIV Cure Consortium. Dr. Zerbato reported grants from the Melbourne HIV Cure Consortium,
SOURCE: Gupta R et al. Lancet. 2020 Mar 10. doi: 10.1016/ S2352-3018(20)30069-2.
A patient with HIV remission induced by stem cell transplantation continues to be disease free at the 30-month mark.
The individual, referred to as the London patient, received allogeneic hematopoietic stem cell transplantation (allo-HSCT) for stage IVB Hodgkin lymphoma. The transplant donor was homozygous for the CCR5 delta-32 mutation, which confers immunity to HIV because there’s no point of entry for the virus into immune cells.
After extensive sampling of various tissues, including gut, lymph node, blood, semen, and cerebrospinal fluid (CSF), Ravindra Kumar Gupta, MD, PhD, and colleagues found no detectable virus that was competent to replicate. However, they reported that the testing did detect some “fossilized” remnants of HIV DNA persisting in certain tissues.
The results were shared in a video presentation of the research during the Conference on Retroviruses & Opportunistic Infections, which was presented online this year. CROI organizers chose to hold a virtual meeting because of concerns about the spread of COVID-19.
The London patient’s HIV status had been reported the previous year at CROI 2019, but only blood samples were used in that analysis.
In a commentary accompanying the simultaneously published study in the Lancet, Jennifer Zerbato, PhD, and Sharon Lewin, FRACP, PHD, FAAHMS, asked: “A key question now for the area of HIV cure is how soon can one know if someone has been cured of HIV?
“We will need more than a handful of patients cured of HIV to really understand the duration of follow-up needed and the likelihood of an unexpected late rebound in virus replication,” continued Dr. Zerbato, of the University of Melbourne, and Dr. Lewin, of the Royal Melbourne Hospital and Monash University, also in Melbourne.
In their ongoing analysis of data from the London patient, Dr. Gupta, a virologist at the University of Cambridge (England), and associates constructed a mathematical model that maps the probability for lifetime remission or cure of HIV against several factors, including the degree of chimerism achieved with the stem cell transplant.
In this model, when chimerism reaches 80% in total HIV target cells, the probability of remission for life is 98%; when donor chimerism reaches 90%, the probability of lifetime remission is greater than 99%. Peripheral T-cell chimerism in the London patient has held steady at 99%.
Dr. Gupta and associates obtained some testing opportunistically: A PET-CT scan revealed an axillary lymph node that was biopsied after it was found to have avid radiotracer uptake. Similarly, the CSF sample was obtained in the course of a work-up for some neurologic symptoms that the London patient was having.
In contrast to the first patient who achieved ongoing HIV remission from a pair of stem cell transplants received over 13 years ago – the Berlin patient – the London patient did not receive whole-body radiation, but rather underwent a reduced-intensity conditioning regimen. The London patient experienced a bout of gut graft-versus-host disease (GVHD) about 2 months after his transplant, but has been free of GVHD in the interval. He hasn’t taken cytotoxic agents or any GVHD prophylaxis since 6 months post transplant.
Though there’s no sign of HIV that’s competent to replicate, “the London patient has shown somewhat slow CD4 reconstitution,” said Dr. Gupta and coauthors in discussing the results.
The patient had a reactivation of Epstein-Barr virus (EBV) about 21 months after analytic treatment interruption (ATI) of antiretroviral therapy that was managed without any specific treatment, but he hasn’t experienced any opportunistic infections. However, his CD4 count didn’t rebound to pretransplant levels until 28 months after ATI. At that point, his CD4 count was 430 cells per mcL, or 23.5% of total T cells. The CD4:CD8 ratio was 0.86; normal range is 1.5-2.5.
The researchers used quantitative real-time polymerase chain reaction (rt-PCR) to look for packaging site and envelope (env) DNA fragments, and droplet digital PCR to quantify HIV-1 DNA.
The patient’s HIV-1 plasma load measured at 30 months post ATI on an ultrasensitive assay was below the lower limit of detection (less than 1 copy per mL). Semen viremia measured at 21 months was also below the lower limit of detection, as was CSF measured at 25 months.
Samples were taken from the patient’s rectum, cecum, sigmoid colon, and terminal ileum during a colonoscopy conducted 22 months post ATI; all tested negative for HIV DNA via droplet digital PCR.
The lymph node had large numbers of EBV-positive cells and was positive for HIV-1 env and long-terminal repeat by double-drop PCR, but no integrase DNA was detected. Additionally, no intact proviral DNA was found on assay.
Dr. Gupta and associates speculated that “EBV reactivation could have triggered EBV-specific CD4 and CD8 T-cell responses and proliferation, potentially including CD4 T cells containing HIV-1 DNA.” Supporting this hypothesis, EBV-specific CD8 T-cell responses in peripheral blood were “robust,” and the researchers also saw some CD4 response.
“Similar to the Berlin patient, highly sensitive tests showed very low levels of so-called fossilized HIV-1 DNA in some tissue samples from the London patient. Residual HIV-1 DNA and axillary lymph node tissue could represent a defective clone that expanded during hyperplasia within the lymph note sampled,” noted Dr. Gupta and coauthors.
Responses of CD4 and CD8 T cells to HIV have also remained below the limit of detection, though cytomegalovirus-specific responses persist in the London patient.
As with the Berlin patient, standard enzyme-linked immunosorbent assay (ELISA) testing has remained positive in the London patient. “Standard ELISA testing, therefore, cannot be used as a marker for cure, although more work needs to be done to assess the role of detuned low-avidity antibody assays in defining cure,” noted Dr. Gupta and associates.
The ongoing follow-up plan for the London patient is to obtain viral load testing twice yearly up to 5 years post ATI, and then obtain yearly tests for a total of 10 years. Ongoing testing will confirm the investigators’ belief that “these findings probably represent the second recorded HIV-1 cure after CCR5 delta-32/delta-32 allo-HSCT, with evidence of residual low-level HIV-1 DNA.”
Dr. Zerbato and Dr. Lewin advised cautious optimism and ongoing surveillance: “In view of the many cells sampled in this case, and the absence of any intact virus, is the London patient truly cured? The additional data provided in this follow-up case report is certainly exciting and encouraging but, in the end, only time will tell.”
Dr. Gupta reported being a consultant for ViiV Healthcare and Gilead Sciences; several coauthors also reported financial relationships with pharmaceutical companies. The work was funded by amfAR, the American Foundation for AIDS Research, and the Wellcome Trust. Dr. Lewin reported grants from the National Health and Medical Research Council of Australia, the National Institutes of Health, the American Foundation for AIDS Research, Gilead Sciences, Merck, ViiV Healthcare, Leidos, the Wellcome Trust, the Australian Centre for HIV and Hepatitis Virology Research, and the Melbourne HIV Cure Consortium. Dr. Zerbato reported grants from the Melbourne HIV Cure Consortium,
SOURCE: Gupta R et al. Lancet. 2020 Mar 10. doi: 10.1016/ S2352-3018(20)30069-2.
A patient with HIV remission induced by stem cell transplantation continues to be disease free at the 30-month mark.
The individual, referred to as the London patient, received allogeneic hematopoietic stem cell transplantation (allo-HSCT) for stage IVB Hodgkin lymphoma. The transplant donor was homozygous for the CCR5 delta-32 mutation, which confers immunity to HIV because there’s no point of entry for the virus into immune cells.
After extensive sampling of various tissues, including gut, lymph node, blood, semen, and cerebrospinal fluid (CSF), Ravindra Kumar Gupta, MD, PhD, and colleagues found no detectable virus that was competent to replicate. However, they reported that the testing did detect some “fossilized” remnants of HIV DNA persisting in certain tissues.
The results were shared in a video presentation of the research during the Conference on Retroviruses & Opportunistic Infections, which was presented online this year. CROI organizers chose to hold a virtual meeting because of concerns about the spread of COVID-19.
The London patient’s HIV status had been reported the previous year at CROI 2019, but only blood samples were used in that analysis.
In a commentary accompanying the simultaneously published study in the Lancet, Jennifer Zerbato, PhD, and Sharon Lewin, FRACP, PHD, FAAHMS, asked: “A key question now for the area of HIV cure is how soon can one know if someone has been cured of HIV?
“We will need more than a handful of patients cured of HIV to really understand the duration of follow-up needed and the likelihood of an unexpected late rebound in virus replication,” continued Dr. Zerbato, of the University of Melbourne, and Dr. Lewin, of the Royal Melbourne Hospital and Monash University, also in Melbourne.
In their ongoing analysis of data from the London patient, Dr. Gupta, a virologist at the University of Cambridge (England), and associates constructed a mathematical model that maps the probability for lifetime remission or cure of HIV against several factors, including the degree of chimerism achieved with the stem cell transplant.
In this model, when chimerism reaches 80% in total HIV target cells, the probability of remission for life is 98%; when donor chimerism reaches 90%, the probability of lifetime remission is greater than 99%. Peripheral T-cell chimerism in the London patient has held steady at 99%.
Dr. Gupta and associates obtained some testing opportunistically: A PET-CT scan revealed an axillary lymph node that was biopsied after it was found to have avid radiotracer uptake. Similarly, the CSF sample was obtained in the course of a work-up for some neurologic symptoms that the London patient was having.
In contrast to the first patient who achieved ongoing HIV remission from a pair of stem cell transplants received over 13 years ago – the Berlin patient – the London patient did not receive whole-body radiation, but rather underwent a reduced-intensity conditioning regimen. The London patient experienced a bout of gut graft-versus-host disease (GVHD) about 2 months after his transplant, but has been free of GVHD in the interval. He hasn’t taken cytotoxic agents or any GVHD prophylaxis since 6 months post transplant.
Though there’s no sign of HIV that’s competent to replicate, “the London patient has shown somewhat slow CD4 reconstitution,” said Dr. Gupta and coauthors in discussing the results.
The patient had a reactivation of Epstein-Barr virus (EBV) about 21 months after analytic treatment interruption (ATI) of antiretroviral therapy that was managed without any specific treatment, but he hasn’t experienced any opportunistic infections. However, his CD4 count didn’t rebound to pretransplant levels until 28 months after ATI. At that point, his CD4 count was 430 cells per mcL, or 23.5% of total T cells. The CD4:CD8 ratio was 0.86; normal range is 1.5-2.5.
The researchers used quantitative real-time polymerase chain reaction (rt-PCR) to look for packaging site and envelope (env) DNA fragments, and droplet digital PCR to quantify HIV-1 DNA.
The patient’s HIV-1 plasma load measured at 30 months post ATI on an ultrasensitive assay was below the lower limit of detection (less than 1 copy per mL). Semen viremia measured at 21 months was also below the lower limit of detection, as was CSF measured at 25 months.
Samples were taken from the patient’s rectum, cecum, sigmoid colon, and terminal ileum during a colonoscopy conducted 22 months post ATI; all tested negative for HIV DNA via droplet digital PCR.
The lymph node had large numbers of EBV-positive cells and was positive for HIV-1 env and long-terminal repeat by double-drop PCR, but no integrase DNA was detected. Additionally, no intact proviral DNA was found on assay.
Dr. Gupta and associates speculated that “EBV reactivation could have triggered EBV-specific CD4 and CD8 T-cell responses and proliferation, potentially including CD4 T cells containing HIV-1 DNA.” Supporting this hypothesis, EBV-specific CD8 T-cell responses in peripheral blood were “robust,” and the researchers also saw some CD4 response.
“Similar to the Berlin patient, highly sensitive tests showed very low levels of so-called fossilized HIV-1 DNA in some tissue samples from the London patient. Residual HIV-1 DNA and axillary lymph node tissue could represent a defective clone that expanded during hyperplasia within the lymph note sampled,” noted Dr. Gupta and coauthors.
Responses of CD4 and CD8 T cells to HIV have also remained below the limit of detection, though cytomegalovirus-specific responses persist in the London patient.
As with the Berlin patient, standard enzyme-linked immunosorbent assay (ELISA) testing has remained positive in the London patient. “Standard ELISA testing, therefore, cannot be used as a marker for cure, although more work needs to be done to assess the role of detuned low-avidity antibody assays in defining cure,” noted Dr. Gupta and associates.
The ongoing follow-up plan for the London patient is to obtain viral load testing twice yearly up to 5 years post ATI, and then obtain yearly tests for a total of 10 years. Ongoing testing will confirm the investigators’ belief that “these findings probably represent the second recorded HIV-1 cure after CCR5 delta-32/delta-32 allo-HSCT, with evidence of residual low-level HIV-1 DNA.”
Dr. Zerbato and Dr. Lewin advised cautious optimism and ongoing surveillance: “In view of the many cells sampled in this case, and the absence of any intact virus, is the London patient truly cured? The additional data provided in this follow-up case report is certainly exciting and encouraging but, in the end, only time will tell.”
Dr. Gupta reported being a consultant for ViiV Healthcare and Gilead Sciences; several coauthors also reported financial relationships with pharmaceutical companies. The work was funded by amfAR, the American Foundation for AIDS Research, and the Wellcome Trust. Dr. Lewin reported grants from the National Health and Medical Research Council of Australia, the National Institutes of Health, the American Foundation for AIDS Research, Gilead Sciences, Merck, ViiV Healthcare, Leidos, the Wellcome Trust, the Australian Centre for HIV and Hepatitis Virology Research, and the Melbourne HIV Cure Consortium. Dr. Zerbato reported grants from the Melbourne HIV Cure Consortium,
SOURCE: Gupta R et al. Lancet. 2020 Mar 10. doi: 10.1016/ S2352-3018(20)30069-2.
FROM CROI 2020
Some infected patients could show COVID-19 symptoms after quarantine
Although a 14-day quarantine after exposure to novel coronavirus is “well supported” by evidence, some infected individuals will not become symptomatic until after that period, according to authors of a recent analysis published in Annals of Internal Medicine.
Most individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will develop symptoms by day 12 of the infection, which is within the 14-day period of active monitoring currently recommended by the Centers for Disease Control and Prevention, the authors wrote.
However, an estimated 101 out of 10,000 cases could become symptomatic after the end of that 14-day monitoring period, they cautioned.
“Our analyses do not preclude that estimate from being higher,” said the investigators, led by Stephen A. Lauer, PhD, MD, of Johns Hopkins Bloomberg School of Public Health, Baltimore.
The analysis, based on 181 confirmed cases of coronavirus disease 2019 (COVID-19) that were documented outside of the outbreak epicenter, Wuhan, China, makes “more conservative assumptions” about the window of symptom onset and potential for continued exposure, compared with analyses in previous studies, the researchers wrote.
The estimated incubation period for SARS-CoV-2 in the 181-patient study was a median of 5.1 days, which is comparable with previous estimates based on COVID-19 cases outside of Wuhan and consistent with other known human coronavirus diseases, such as SARS, which had a reported mean incubation period of 5 days, Dr. Lauer and colleagues noted.
Symptoms developed within 11.5 days for 97.5% of patients in the study.
Whether it’s acceptable to have 101 out of 10,000 cases becoming symptomatic beyond the recommended quarantine window depends on two factors, according to the authors. The first is the expected infection risk in the population that is being monitored, and the second is “judgment about the cost of missing cases,” wrote the authors.
In an interview, Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau, Oceanside, N.Y., said that in practical terms, the results suggest that the majority of patients with COVID-19 will be identified within 14 days, with an “outside chance” of an infected individual leaving quarantine and transmitting virus for a short period of time before becoming symptomatic.
“I think the proper message to give those patients [who are asymptomatic upon leaving quarantine] is, ‘after 14 days, we’re pretty sure you’re out of the woods, but should you get any symptoms, immediately requarantine yourself and seek medical care,” he said.
Study coauthor Kyra H. Grantz, a doctoral graduate student at the Johns Hopkins Bloomberg School of Public Health, said that extending a quarantine beyond 14 days might be considered in the highest-risk scenarios, though the benefits of doing so would have to be weighed against the costs to public health and to the individuals under quarantine.
“Our estimate of the incubation period definitely supports the 14-day recommendation that the CDC has been using,” she said in an interview.
Dr. Grantz emphasized that the estimate of 101 out of 10,000 cases developing symptoms after day 14 of active monitoring – representing the 99th percentile of cases – assumes the “most conservative, worst-case scenario” in a population that is fully infected.
“If you’re looking at a following a cohort of 1,000 people whom you think may have been exposed, only a certain percentage will be infected, and only a certain percentage of those will even develop symptoms – before we get to this idea of how many people would we miss,” she said.
The study was supported by the Centers for Disease Control and Prevention, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medical Sciences, and the Alexander von Humboldt Foundation. Four authors reported disclosures related to those entities, and the remaining five reported no conflicts of interest.
SOURCE: Lauer SA et al. Ann Intern Med. 2020 Mar 9. doi:10.1101/2020.02.02.20020016.
Although a 14-day quarantine after exposure to novel coronavirus is “well supported” by evidence, some infected individuals will not become symptomatic until after that period, according to authors of a recent analysis published in Annals of Internal Medicine.
Most individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will develop symptoms by day 12 of the infection, which is within the 14-day period of active monitoring currently recommended by the Centers for Disease Control and Prevention, the authors wrote.
However, an estimated 101 out of 10,000 cases could become symptomatic after the end of that 14-day monitoring period, they cautioned.
“Our analyses do not preclude that estimate from being higher,” said the investigators, led by Stephen A. Lauer, PhD, MD, of Johns Hopkins Bloomberg School of Public Health, Baltimore.
The analysis, based on 181 confirmed cases of coronavirus disease 2019 (COVID-19) that were documented outside of the outbreak epicenter, Wuhan, China, makes “more conservative assumptions” about the window of symptom onset and potential for continued exposure, compared with analyses in previous studies, the researchers wrote.
The estimated incubation period for SARS-CoV-2 in the 181-patient study was a median of 5.1 days, which is comparable with previous estimates based on COVID-19 cases outside of Wuhan and consistent with other known human coronavirus diseases, such as SARS, which had a reported mean incubation period of 5 days, Dr. Lauer and colleagues noted.
Symptoms developed within 11.5 days for 97.5% of patients in the study.
Whether it’s acceptable to have 101 out of 10,000 cases becoming symptomatic beyond the recommended quarantine window depends on two factors, according to the authors. The first is the expected infection risk in the population that is being monitored, and the second is “judgment about the cost of missing cases,” wrote the authors.
In an interview, Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau, Oceanside, N.Y., said that in practical terms, the results suggest that the majority of patients with COVID-19 will be identified within 14 days, with an “outside chance” of an infected individual leaving quarantine and transmitting virus for a short period of time before becoming symptomatic.
“I think the proper message to give those patients [who are asymptomatic upon leaving quarantine] is, ‘after 14 days, we’re pretty sure you’re out of the woods, but should you get any symptoms, immediately requarantine yourself and seek medical care,” he said.
Study coauthor Kyra H. Grantz, a doctoral graduate student at the Johns Hopkins Bloomberg School of Public Health, said that extending a quarantine beyond 14 days might be considered in the highest-risk scenarios, though the benefits of doing so would have to be weighed against the costs to public health and to the individuals under quarantine.
“Our estimate of the incubation period definitely supports the 14-day recommendation that the CDC has been using,” she said in an interview.
Dr. Grantz emphasized that the estimate of 101 out of 10,000 cases developing symptoms after day 14 of active monitoring – representing the 99th percentile of cases – assumes the “most conservative, worst-case scenario” in a population that is fully infected.
“If you’re looking at a following a cohort of 1,000 people whom you think may have been exposed, only a certain percentage will be infected, and only a certain percentage of those will even develop symptoms – before we get to this idea of how many people would we miss,” she said.
The study was supported by the Centers for Disease Control and Prevention, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medical Sciences, and the Alexander von Humboldt Foundation. Four authors reported disclosures related to those entities, and the remaining five reported no conflicts of interest.
SOURCE: Lauer SA et al. Ann Intern Med. 2020 Mar 9. doi:10.1101/2020.02.02.20020016.
Although a 14-day quarantine after exposure to novel coronavirus is “well supported” by evidence, some infected individuals will not become symptomatic until after that period, according to authors of a recent analysis published in Annals of Internal Medicine.
Most individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will develop symptoms by day 12 of the infection, which is within the 14-day period of active monitoring currently recommended by the Centers for Disease Control and Prevention, the authors wrote.
However, an estimated 101 out of 10,000 cases could become symptomatic after the end of that 14-day monitoring period, they cautioned.
“Our analyses do not preclude that estimate from being higher,” said the investigators, led by Stephen A. Lauer, PhD, MD, of Johns Hopkins Bloomberg School of Public Health, Baltimore.
The analysis, based on 181 confirmed cases of coronavirus disease 2019 (COVID-19) that were documented outside of the outbreak epicenter, Wuhan, China, makes “more conservative assumptions” about the window of symptom onset and potential for continued exposure, compared with analyses in previous studies, the researchers wrote.
The estimated incubation period for SARS-CoV-2 in the 181-patient study was a median of 5.1 days, which is comparable with previous estimates based on COVID-19 cases outside of Wuhan and consistent with other known human coronavirus diseases, such as SARS, which had a reported mean incubation period of 5 days, Dr. Lauer and colleagues noted.
Symptoms developed within 11.5 days for 97.5% of patients in the study.
Whether it’s acceptable to have 101 out of 10,000 cases becoming symptomatic beyond the recommended quarantine window depends on two factors, according to the authors. The first is the expected infection risk in the population that is being monitored, and the second is “judgment about the cost of missing cases,” wrote the authors.
In an interview, Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau, Oceanside, N.Y., said that in practical terms, the results suggest that the majority of patients with COVID-19 will be identified within 14 days, with an “outside chance” of an infected individual leaving quarantine and transmitting virus for a short period of time before becoming symptomatic.
“I think the proper message to give those patients [who are asymptomatic upon leaving quarantine] is, ‘after 14 days, we’re pretty sure you’re out of the woods, but should you get any symptoms, immediately requarantine yourself and seek medical care,” he said.
Study coauthor Kyra H. Grantz, a doctoral graduate student at the Johns Hopkins Bloomberg School of Public Health, said that extending a quarantine beyond 14 days might be considered in the highest-risk scenarios, though the benefits of doing so would have to be weighed against the costs to public health and to the individuals under quarantine.
“Our estimate of the incubation period definitely supports the 14-day recommendation that the CDC has been using,” she said in an interview.
Dr. Grantz emphasized that the estimate of 101 out of 10,000 cases developing symptoms after day 14 of active monitoring – representing the 99th percentile of cases – assumes the “most conservative, worst-case scenario” in a population that is fully infected.
“If you’re looking at a following a cohort of 1,000 people whom you think may have been exposed, only a certain percentage will be infected, and only a certain percentage of those will even develop symptoms – before we get to this idea of how many people would we miss,” she said.
The study was supported by the Centers for Disease Control and Prevention, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medical Sciences, and the Alexander von Humboldt Foundation. Four authors reported disclosures related to those entities, and the remaining five reported no conflicts of interest.
SOURCE: Lauer SA et al. Ann Intern Med. 2020 Mar 9. doi:10.1101/2020.02.02.20020016.
FROM ANNALS OF INTERNAL MEDICINE
Key clinical point: Some individuals who are infected with the novel coronavirus could become symptomatic after the active 14-day quarantine period.
Major finding: The median incubation period was 5.1 days, with 97.5% of patients developing symptoms within 11.5 days, implying that 101 of every 10,000 cases (99th percentile) would develop symptoms beyond the quarantine period.
Study details: Analysis of 181 confirmed COVID-19 cases identified outside of the outbreak epicenter, Wuhan, China.
Disclosures: The study was supported by the U.S. Centers for Disease Control and Prevention, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medical Sciences, and the Alexander von Humboldt Foundation. Four authors reported disclosures related to those entities, and the remaining five reported no conflicts of interest.
Source: Lauer SA et al. Ann Intern Med. 2020 Mar 9. doi: 10.1101/2020.02.02.20020016.
Colorectal cancer burden rises in younger age groups
Current trends in the incidence and mortality of colorectal cancer (CRC) in the United States suggest CRC will become a disease that largely affects young and middle-aged adults, according to a report published in CA: A Cancer Journal for Clinicians.
As the second leading cause of cancer-related death in the United States, and with modifiable risk factors accounting for over 50% of cases and deaths, CRC is largely a preventable disease, explained study author Rebecca L. Siegel, of the American Cancer Society, and colleagues.
According to the investigators, CRC incidence dropped by 3.3% per year from 2011 through 2016 among individuals aged 65 years or older, but the opposite was observed for those aged 50-64 years, with rates increasing by 1% per year. The increase was even greater for those younger than 50 years, with an increase of 2.2% per year.
The CRC incidence from 2012 through 2016 was highest among Alaska Natives (89 cases per 100,000 persons) and lowest among Asian/Pacific Islanders (30 cases per 100,000 persons).
“CRC has been the most commonly diagnosed cancer in Alaska Natives since the early 1970s for reasons that are unknown but may include a higher prevalence of risk factors,” the investigators wrote.
The risk of developing CRC is related to several factors, including obesity, vitamin D deficiency, diabetes, smoking, and other dietary factors, the team further explained.
Among those aged 65 years or older, CRC death rates decreased by 3% per year from 2008 through 2017. For those aged 50-64 years, death rates dropped by 0.6% per year. In contrast, death rates rose by 1.3% per year for those younger than 50 years.
“The uptick in young adults, which is most rapid among non-Hispanic whites (2% per year), began around 2004 and was preceded by declines of 1% to 2% per year since at least 1975,” the investigators wrote.
The reduction in incidence and mortality among older adults is partially attributable to higher uptake of CRC screening. According to recent data, CRC screening rates were lower for those aged 50-64 years compared with individuals aged 65 years and older.
Based on current recommendations from the American Cancer Society, CRC screening should begin at age 45, with some higher-risk patients starting at age 40.
“Progress against CRC can be accelerated by increasing access to guideline-recommended screening and high quality treatment, particularly among Alaska Natives, and elucidating causes for rising incidence in young and middle-aged adults,” the investigators concluded.
The authors disclosed financial affiliations with the American Cancer Society, which funded the study, as well as Array Biopharma, Bayer, RGenix, Tesaro, and Seattle Genetics.
SOURCE: Siegel RL et al. CA Cancer J Clin. 2020 Mar 5. doi: 10.3322/caac.21601.
Current trends in the incidence and mortality of colorectal cancer (CRC) in the United States suggest CRC will become a disease that largely affects young and middle-aged adults, according to a report published in CA: A Cancer Journal for Clinicians.
As the second leading cause of cancer-related death in the United States, and with modifiable risk factors accounting for over 50% of cases and deaths, CRC is largely a preventable disease, explained study author Rebecca L. Siegel, of the American Cancer Society, and colleagues.
According to the investigators, CRC incidence dropped by 3.3% per year from 2011 through 2016 among individuals aged 65 years or older, but the opposite was observed for those aged 50-64 years, with rates increasing by 1% per year. The increase was even greater for those younger than 50 years, with an increase of 2.2% per year.
The CRC incidence from 2012 through 2016 was highest among Alaska Natives (89 cases per 100,000 persons) and lowest among Asian/Pacific Islanders (30 cases per 100,000 persons).
“CRC has been the most commonly diagnosed cancer in Alaska Natives since the early 1970s for reasons that are unknown but may include a higher prevalence of risk factors,” the investigators wrote.
The risk of developing CRC is related to several factors, including obesity, vitamin D deficiency, diabetes, smoking, and other dietary factors, the team further explained.
Among those aged 65 years or older, CRC death rates decreased by 3% per year from 2008 through 2017. For those aged 50-64 years, death rates dropped by 0.6% per year. In contrast, death rates rose by 1.3% per year for those younger than 50 years.
“The uptick in young adults, which is most rapid among non-Hispanic whites (2% per year), began around 2004 and was preceded by declines of 1% to 2% per year since at least 1975,” the investigators wrote.
The reduction in incidence and mortality among older adults is partially attributable to higher uptake of CRC screening. According to recent data, CRC screening rates were lower for those aged 50-64 years compared with individuals aged 65 years and older.
Based on current recommendations from the American Cancer Society, CRC screening should begin at age 45, with some higher-risk patients starting at age 40.
“Progress against CRC can be accelerated by increasing access to guideline-recommended screening and high quality treatment, particularly among Alaska Natives, and elucidating causes for rising incidence in young and middle-aged adults,” the investigators concluded.
The authors disclosed financial affiliations with the American Cancer Society, which funded the study, as well as Array Biopharma, Bayer, RGenix, Tesaro, and Seattle Genetics.
SOURCE: Siegel RL et al. CA Cancer J Clin. 2020 Mar 5. doi: 10.3322/caac.21601.
Current trends in the incidence and mortality of colorectal cancer (CRC) in the United States suggest CRC will become a disease that largely affects young and middle-aged adults, according to a report published in CA: A Cancer Journal for Clinicians.
As the second leading cause of cancer-related death in the United States, and with modifiable risk factors accounting for over 50% of cases and deaths, CRC is largely a preventable disease, explained study author Rebecca L. Siegel, of the American Cancer Society, and colleagues.
According to the investigators, CRC incidence dropped by 3.3% per year from 2011 through 2016 among individuals aged 65 years or older, but the opposite was observed for those aged 50-64 years, with rates increasing by 1% per year. The increase was even greater for those younger than 50 years, with an increase of 2.2% per year.
The CRC incidence from 2012 through 2016 was highest among Alaska Natives (89 cases per 100,000 persons) and lowest among Asian/Pacific Islanders (30 cases per 100,000 persons).
“CRC has been the most commonly diagnosed cancer in Alaska Natives since the early 1970s for reasons that are unknown but may include a higher prevalence of risk factors,” the investigators wrote.
The risk of developing CRC is related to several factors, including obesity, vitamin D deficiency, diabetes, smoking, and other dietary factors, the team further explained.
Among those aged 65 years or older, CRC death rates decreased by 3% per year from 2008 through 2017. For those aged 50-64 years, death rates dropped by 0.6% per year. In contrast, death rates rose by 1.3% per year for those younger than 50 years.
“The uptick in young adults, which is most rapid among non-Hispanic whites (2% per year), began around 2004 and was preceded by declines of 1% to 2% per year since at least 1975,” the investigators wrote.
The reduction in incidence and mortality among older adults is partially attributable to higher uptake of CRC screening. According to recent data, CRC screening rates were lower for those aged 50-64 years compared with individuals aged 65 years and older.
Based on current recommendations from the American Cancer Society, CRC screening should begin at age 45, with some higher-risk patients starting at age 40.
“Progress against CRC can be accelerated by increasing access to guideline-recommended screening and high quality treatment, particularly among Alaska Natives, and elucidating causes for rising incidence in young and middle-aged adults,” the investigators concluded.
The authors disclosed financial affiliations with the American Cancer Society, which funded the study, as well as Array Biopharma, Bayer, RGenix, Tesaro, and Seattle Genetics.
SOURCE: Siegel RL et al. CA Cancer J Clin. 2020 Mar 5. doi: 10.3322/caac.21601.
FROM CA: A CANCER JOURNAL FOR CLINICIANS
Frequent tooth brushing may reduce diabetes risk
Oral hygiene may be a key factor in diabetes risk, new data from a Korean national health database suggest.
“Frequent tooth brushing may be an attenuating factor for the risk of new-onset diabetes, and the presence of periodontal disease and increased number of missing teeth may be augmenting factors,” wrote Yoonkyung Chang, MD, of the Department of Neurology, Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, South Korea, and colleagues.
they continued in an article published online in Diabetologia.
Periodontal disease involves inflammatory reactions that affect the surrounding tissues of the teeth. Inflammation, in turn, is an important cause of diabetes because it increases insulin resistance and endothelial dysfunction, Dr. Chang and colleagues explained.
They analyzed data gathered during 2003-2006 from 188,013 individuals from the Korean National Health Insurance System – Health Screening Cohort who had complete data and did not have diabetes at baseline. Oral hygiene behaviors, including frequency of tooth brushing, and dental visits or cleanings, were collected by self-report.
Over a median follow-up of 10 years, there were 31,545 new cases of diabetes, with an estimated overall 10-year event rate of 16.1%. The rate was 17.2% for those with periodontal disease at baseline, compared with 15.8% for those without, which was a significant difference even after adjustments for multiple confounders (hazard ratio, 1.09; P less than .001).
Compared with patients who had no missing teeth, the event rate for new-onset diabetes rose from 15.4% for patients with 1 missing tooth (HR, 1.08; P less than .001) to 21.4% for those with 15 or more missing teeth (HR, 1.21; P less than .001).
Professional dental cleaning did not have a significant effect after multivariate analysis. However, the number of daily tooth brushings by the individual did. Compared with brushing 0-1 times/day, those who brushed 3 or more times/day had a significantly lower risk for new-onset diabetes (HR, 0.92; P less than .001).
In subgroup analyses, periodontal disease was more strongly associated with new-onset diabetes in adults aged 51 years and younger (HR, 1.14), compared with those who were 52 years or older (HR, 1.06).
The study was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education. The authors reported no relevant financial relationships.
This article first appeared on Medscape.com.
Oral hygiene may be a key factor in diabetes risk, new data from a Korean national health database suggest.
“Frequent tooth brushing may be an attenuating factor for the risk of new-onset diabetes, and the presence of periodontal disease and increased number of missing teeth may be augmenting factors,” wrote Yoonkyung Chang, MD, of the Department of Neurology, Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, South Korea, and colleagues.
they continued in an article published online in Diabetologia.
Periodontal disease involves inflammatory reactions that affect the surrounding tissues of the teeth. Inflammation, in turn, is an important cause of diabetes because it increases insulin resistance and endothelial dysfunction, Dr. Chang and colleagues explained.
They analyzed data gathered during 2003-2006 from 188,013 individuals from the Korean National Health Insurance System – Health Screening Cohort who had complete data and did not have diabetes at baseline. Oral hygiene behaviors, including frequency of tooth brushing, and dental visits or cleanings, were collected by self-report.
Over a median follow-up of 10 years, there were 31,545 new cases of diabetes, with an estimated overall 10-year event rate of 16.1%. The rate was 17.2% for those with periodontal disease at baseline, compared with 15.8% for those without, which was a significant difference even after adjustments for multiple confounders (hazard ratio, 1.09; P less than .001).
Compared with patients who had no missing teeth, the event rate for new-onset diabetes rose from 15.4% for patients with 1 missing tooth (HR, 1.08; P less than .001) to 21.4% for those with 15 or more missing teeth (HR, 1.21; P less than .001).
Professional dental cleaning did not have a significant effect after multivariate analysis. However, the number of daily tooth brushings by the individual did. Compared with brushing 0-1 times/day, those who brushed 3 or more times/day had a significantly lower risk for new-onset diabetes (HR, 0.92; P less than .001).
In subgroup analyses, periodontal disease was more strongly associated with new-onset diabetes in adults aged 51 years and younger (HR, 1.14), compared with those who were 52 years or older (HR, 1.06).
The study was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education. The authors reported no relevant financial relationships.
This article first appeared on Medscape.com.
Oral hygiene may be a key factor in diabetes risk, new data from a Korean national health database suggest.
“Frequent tooth brushing may be an attenuating factor for the risk of new-onset diabetes, and the presence of periodontal disease and increased number of missing teeth may be augmenting factors,” wrote Yoonkyung Chang, MD, of the Department of Neurology, Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, South Korea, and colleagues.
they continued in an article published online in Diabetologia.
Periodontal disease involves inflammatory reactions that affect the surrounding tissues of the teeth. Inflammation, in turn, is an important cause of diabetes because it increases insulin resistance and endothelial dysfunction, Dr. Chang and colleagues explained.
They analyzed data gathered during 2003-2006 from 188,013 individuals from the Korean National Health Insurance System – Health Screening Cohort who had complete data and did not have diabetes at baseline. Oral hygiene behaviors, including frequency of tooth brushing, and dental visits or cleanings, were collected by self-report.
Over a median follow-up of 10 years, there were 31,545 new cases of diabetes, with an estimated overall 10-year event rate of 16.1%. The rate was 17.2% for those with periodontal disease at baseline, compared with 15.8% for those without, which was a significant difference even after adjustments for multiple confounders (hazard ratio, 1.09; P less than .001).
Compared with patients who had no missing teeth, the event rate for new-onset diabetes rose from 15.4% for patients with 1 missing tooth (HR, 1.08; P less than .001) to 21.4% for those with 15 or more missing teeth (HR, 1.21; P less than .001).
Professional dental cleaning did not have a significant effect after multivariate analysis. However, the number of daily tooth brushings by the individual did. Compared with brushing 0-1 times/day, those who brushed 3 or more times/day had a significantly lower risk for new-onset diabetes (HR, 0.92; P less than .001).
In subgroup analyses, periodontal disease was more strongly associated with new-onset diabetes in adults aged 51 years and younger (HR, 1.14), compared with those who were 52 years or older (HR, 1.06).
The study was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education. The authors reported no relevant financial relationships.
This article first appeared on Medscape.com.
Novel coronavirus may cause environmental contamination through fecal shedding
The toilet bowl, sink, and bathroom door handle of an isolation room housing a patient with the novel coronavirus tested positive for the virus, raising the possibility that viral shedding in the stool could represent another route of transmission, investigators reported.
Air outlet fans and other room sites also tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), though an anteroom, a corridor, and most personal protective equipment (PPE) worn by health care providers tested negative, according to the researchers, led by Sean Wei Xiang Ong, MBBS, of the National Centre for Infectious Diseases, Singapore.
Taken together, these findings suggest a “need for strict adherence to environmental and hand hygiene” to combat significant environmental contamination through respiratory droplets and fecal shedding, Dr. Ong and colleagues wrote in JAMA.
Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau in New York, said these results demonstrate that SARS-CoV-2 is “clearly capable” of contaminating bathroom sinks and toilets.
“That wouldn’t have been the first place I would have thought of, before this study,” he said in an interview. “You need to pay attention to cleaning the bathrooms, which we obviously do, but that’s an important reminder.”
The report by Dr. Ong and coauthors included a total of three patients housed in airborne infection isolation rooms in a dedicated SARS-CoV-2 outbreak center in Singapore. For each patient, surface samples were taken from 26 sites in the isolation room, an anteroom, and a bathroom. Samples were also taken from PPE on physicians as they left the patient rooms.
Samples for the first patient, taken right after routine cleaning, were all negative, according to researchers. That room was sampled twice, on days 4 and 10 of the illness, while the patient was still symptomatic. Likewise, for the second patient, postcleaning samples were negative; those samples were taken 2 days after cleaning.
However, for the third patient, samples were taken before routine cleaning. In this case, Dr. Ong and colleagues said 13 of 15 room sites (87%) were positive, including air outlet fans, while 3 of 5 toilet sites (60%) were positive as well, though no contamination was found in the anteroom, corridor, or in air samples.
That patient had two stool samples that were positive for SARS-CoV-2, but no diarrhea, authors said, and had upper respiratory tract involvement without pneumonia.
The fact that swabs of the air exhaust outlets tested positive suggests that virus-laden droplets could be “displaced by airflows” and end up on vents or other equipment, Dr. Ong and coauthors reported.
All PPE samples tested negative, except for the front of one shoe.
“The risk of transmission from contaminated footwear is likely low, as evidenced by negative results in the anteroom and corridor,” they wrote.
While this study included only a small number of patients, Dr. Glatt said the findings represent an important and useful contribution to the literature on coronavirus disease 2019 (COVID-19).
“Every day we’re getting more information, and each little piece of the puzzle helps us in the overall management of individuals with COVID-19,” he said in the interview. “They’re adding to our ability to manage, control, and mitigate further spread of the disease.”
Funding for the study came from the National Medical Research Council in Singapore and DSO National Laboratories. Dr. Ong and colleagues reported no conflicts of interest.
SOURCE: Ong SWX et al. JAMA. 2020 Mar 4. doi: 10.1001/jama.2020.3227.
The toilet bowl, sink, and bathroom door handle of an isolation room housing a patient with the novel coronavirus tested positive for the virus, raising the possibility that viral shedding in the stool could represent another route of transmission, investigators reported.
Air outlet fans and other room sites also tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), though an anteroom, a corridor, and most personal protective equipment (PPE) worn by health care providers tested negative, according to the researchers, led by Sean Wei Xiang Ong, MBBS, of the National Centre for Infectious Diseases, Singapore.
Taken together, these findings suggest a “need for strict adherence to environmental and hand hygiene” to combat significant environmental contamination through respiratory droplets and fecal shedding, Dr. Ong and colleagues wrote in JAMA.
Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau in New York, said these results demonstrate that SARS-CoV-2 is “clearly capable” of contaminating bathroom sinks and toilets.
“That wouldn’t have been the first place I would have thought of, before this study,” he said in an interview. “You need to pay attention to cleaning the bathrooms, which we obviously do, but that’s an important reminder.”
The report by Dr. Ong and coauthors included a total of three patients housed in airborne infection isolation rooms in a dedicated SARS-CoV-2 outbreak center in Singapore. For each patient, surface samples were taken from 26 sites in the isolation room, an anteroom, and a bathroom. Samples were also taken from PPE on physicians as they left the patient rooms.
Samples for the first patient, taken right after routine cleaning, were all negative, according to researchers. That room was sampled twice, on days 4 and 10 of the illness, while the patient was still symptomatic. Likewise, for the second patient, postcleaning samples were negative; those samples were taken 2 days after cleaning.
However, for the third patient, samples were taken before routine cleaning. In this case, Dr. Ong and colleagues said 13 of 15 room sites (87%) were positive, including air outlet fans, while 3 of 5 toilet sites (60%) were positive as well, though no contamination was found in the anteroom, corridor, or in air samples.
That patient had two stool samples that were positive for SARS-CoV-2, but no diarrhea, authors said, and had upper respiratory tract involvement without pneumonia.
The fact that swabs of the air exhaust outlets tested positive suggests that virus-laden droplets could be “displaced by airflows” and end up on vents or other equipment, Dr. Ong and coauthors reported.
All PPE samples tested negative, except for the front of one shoe.
“The risk of transmission from contaminated footwear is likely low, as evidenced by negative results in the anteroom and corridor,” they wrote.
While this study included only a small number of patients, Dr. Glatt said the findings represent an important and useful contribution to the literature on coronavirus disease 2019 (COVID-19).
“Every day we’re getting more information, and each little piece of the puzzle helps us in the overall management of individuals with COVID-19,” he said in the interview. “They’re adding to our ability to manage, control, and mitigate further spread of the disease.”
Funding for the study came from the National Medical Research Council in Singapore and DSO National Laboratories. Dr. Ong and colleagues reported no conflicts of interest.
SOURCE: Ong SWX et al. JAMA. 2020 Mar 4. doi: 10.1001/jama.2020.3227.
The toilet bowl, sink, and bathroom door handle of an isolation room housing a patient with the novel coronavirus tested positive for the virus, raising the possibility that viral shedding in the stool could represent another route of transmission, investigators reported.
Air outlet fans and other room sites also tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), though an anteroom, a corridor, and most personal protective equipment (PPE) worn by health care providers tested negative, according to the researchers, led by Sean Wei Xiang Ong, MBBS, of the National Centre for Infectious Diseases, Singapore.
Taken together, these findings suggest a “need for strict adherence to environmental and hand hygiene” to combat significant environmental contamination through respiratory droplets and fecal shedding, Dr. Ong and colleagues wrote in JAMA.
Aaron Eli Glatt, MD, chair of medicine at Mount Sinai South Nassau in New York, said these results demonstrate that SARS-CoV-2 is “clearly capable” of contaminating bathroom sinks and toilets.
“That wouldn’t have been the first place I would have thought of, before this study,” he said in an interview. “You need to pay attention to cleaning the bathrooms, which we obviously do, but that’s an important reminder.”
The report by Dr. Ong and coauthors included a total of three patients housed in airborne infection isolation rooms in a dedicated SARS-CoV-2 outbreak center in Singapore. For each patient, surface samples were taken from 26 sites in the isolation room, an anteroom, and a bathroom. Samples were also taken from PPE on physicians as they left the patient rooms.
Samples for the first patient, taken right after routine cleaning, were all negative, according to researchers. That room was sampled twice, on days 4 and 10 of the illness, while the patient was still symptomatic. Likewise, for the second patient, postcleaning samples were negative; those samples were taken 2 days after cleaning.
However, for the third patient, samples were taken before routine cleaning. In this case, Dr. Ong and colleagues said 13 of 15 room sites (87%) were positive, including air outlet fans, while 3 of 5 toilet sites (60%) were positive as well, though no contamination was found in the anteroom, corridor, or in air samples.
That patient had two stool samples that were positive for SARS-CoV-2, but no diarrhea, authors said, and had upper respiratory tract involvement without pneumonia.
The fact that swabs of the air exhaust outlets tested positive suggests that virus-laden droplets could be “displaced by airflows” and end up on vents or other equipment, Dr. Ong and coauthors reported.
All PPE samples tested negative, except for the front of one shoe.
“The risk of transmission from contaminated footwear is likely low, as evidenced by negative results in the anteroom and corridor,” they wrote.
While this study included only a small number of patients, Dr. Glatt said the findings represent an important and useful contribution to the literature on coronavirus disease 2019 (COVID-19).
“Every day we’re getting more information, and each little piece of the puzzle helps us in the overall management of individuals with COVID-19,” he said in the interview. “They’re adding to our ability to manage, control, and mitigate further spread of the disease.”
Funding for the study came from the National Medical Research Council in Singapore and DSO National Laboratories. Dr. Ong and colleagues reported no conflicts of interest.
SOURCE: Ong SWX et al. JAMA. 2020 Mar 4. doi: 10.1001/jama.2020.3227.
FROM JAMA
Best definition of malnutrition varies by cancer type
For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.
The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.
“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.
With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.
The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.
Combining these three assessments led to seven hierarchical nutritional status categories:
- Severe malnutrition – BMI less than 18.5 kg/m2 and greater than 10% weight loss
- ESPEN 1 – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- ESPEN 2 – BMI less than 18.5 kg/m2
- NSQIP – BMI greater than 20 kg/m2 (if younger than 70 years) or 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- Mild malnutrition – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older)
- Obese – BMI at least 30 kg/m2
- No malnutrition.
The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
Results
Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.
Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).
Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).
Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).
While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.
Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.
For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
Interpreting and applying the results
“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.
He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.
The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.
“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”
Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.
“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”
While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.
“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”
There was no external funding for this study, and the investigators reported no conflicts of interest.
SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.
For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.
The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.
“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.
With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.
The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.
Combining these three assessments led to seven hierarchical nutritional status categories:
- Severe malnutrition – BMI less than 18.5 kg/m2 and greater than 10% weight loss
- ESPEN 1 – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- ESPEN 2 – BMI less than 18.5 kg/m2
- NSQIP – BMI greater than 20 kg/m2 (if younger than 70 years) or 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- Mild malnutrition – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older)
- Obese – BMI at least 30 kg/m2
- No malnutrition.
The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
Results
Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.
Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).
Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).
Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).
While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.
Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.
For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
Interpreting and applying the results
“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.
He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.
The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.
“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”
Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.
“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”
While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.
“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”
There was no external funding for this study, and the investigators reported no conflicts of interest.
SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.
For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.
The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.
“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.
With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.
The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.
Combining these three assessments led to seven hierarchical nutritional status categories:
- Severe malnutrition – BMI less than 18.5 kg/m2 and greater than 10% weight loss
- ESPEN 1 – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- ESPEN 2 – BMI less than 18.5 kg/m2
- NSQIP – BMI greater than 20 kg/m2 (if younger than 70 years) or 22 kg/m2 (if 70 years or older) plus greater than 10% weight loss
- Mild malnutrition – BMI 18.5-20 kg/m2 (if younger than 70 years) or less than 22 kg/m2 (if 70 years or older)
- Obese – BMI at least 30 kg/m2
- No malnutrition.
The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
Results
Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.
Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).
Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).
Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).
While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.
Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.
For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
Interpreting and applying the results
“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.
He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.
The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.
“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”
Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.
“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”
While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.
“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”
There was no external funding for this study, and the investigators reported no conflicts of interest.
SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.
FROM THE JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS
Telehealth seen as a key tool to help fight COVID-19
Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.
Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).
Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.
The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”
While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.
The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.
In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.
An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.
In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.
However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.
The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.
The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.
The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.
Are healthcare systems prepared?
Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.
It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.
Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”
Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.
He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”
Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”
Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.
If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”
Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.
Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.
This article first appeared on Medscape.com.
Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.
Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).
Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.
The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”
While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.
The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.
In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.
An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.
In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.
However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.
The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.
The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.
The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.
Are healthcare systems prepared?
Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.
It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.
Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”
Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.
He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”
Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”
Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.
If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”
Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.
Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.
This article first appeared on Medscape.com.
Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.
Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).
Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.
The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”
While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.
The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.
In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.
An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.
In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.
However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.
The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.
The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.
The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.
Are healthcare systems prepared?
Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.
It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.
Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”
Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.
He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”
Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”
Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.
If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”
Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.
Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.
This article first appeared on Medscape.com.
A Nervous Recipient of a “Tongue Lashing”
Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.
Case Presentation
A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.
On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.
A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.
Diagnosis
Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.1
Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.2 Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.3 While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.4 While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.
A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.5 While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.
Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.6 Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.7 Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.8 They generally occur late in the course of disease, and the time between appearance and death is usually short.8 Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.
Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,9 and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.10
Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.
Management
The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.11
Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.
1. Khot KP, Deshmane S, Choudhari S. Human papilloma virus in oral squamous cell carcinoma-the enigma unraveled. Clin J Dent Res. 2016;19(1):17-23.
2. Bolognia JL, Jorizzo JL, Rapini RP, eds. Neoplasms of the skin. In: Bolognia JL, Jorizzo JL, Rapini RP, eds. Dermatology. Vol 2. St. Louis, MO: Mosby; 2007:1627-1901.
3. Tatusov M, Reddy S, Federman DG. Pyogenic granuloma: yet another motorcycle peril. Postgrad Med. 2012;124(6):124-126.
4. Yuan K, Jin YT, Lin MT. The detection and comparison of angiogenesis-associated factors in pyogenic granuloma by immunohistochemistry. J Periodontol. 2000;71(5):701-709.
5. Krishnan V, Shunmugavelu K. A clinical challenging situation of intra oral fibroma mimicking pyogenic granuloma. J Pan African Med. 2015;22(1):263.
6. Nallasivam KU, Sudha BR. Oral mucocele: review of literature and a case report. J Pharm Bioallied Sci. 2015;7(suppl 2):S731-S733.
7. Zachariades N. Neoplasms metastatic to the mouth, jaws, and surrounding tissues. J Craniomaxillofac Surg. 1989;17(6):283-290.
8. Irani S. Metastasis to the oral soft tissues: a review of 412 cases. J Int Soc Prev Community Dent. 2016;6(5):393-401.
9. Shadman N, Ebrahimi SF, Jafari S, Eslami M. Peripheral giant cell granuloma: a review of 123 cases. Dent Res J (Isfahan). 2009;6(1):47-50.
10. Poonacha KS, Shigli AL, Shirol D. Peripheral ossifying fibroma: a clinical report. Contemp Clin Dent. 2010;1(1):54-56.
11. Gilmore A, Kelsberg G, Safranek G. Clinical inquiries. What’s the best treatment for pyogenic granuloma? J Fam Pract. 2010;59(1):40-42.
Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.
Case Presentation
A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.
On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.
A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.
Diagnosis
Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.1
Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.2 Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.3 While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.4 While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.
A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.5 While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.
Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.6 Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.7 Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.8 They generally occur late in the course of disease, and the time between appearance and death is usually short.8 Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.
Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,9 and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.10
Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.
Management
The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.11
Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.
Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.
Case Presentation
A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.
On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.
A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.
Diagnosis
Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.1
Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.2 Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.3 While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.4 While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.
A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.5 While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.
Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.6 Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.7 Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.8 They generally occur late in the course of disease, and the time between appearance and death is usually short.8 Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.
Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,9 and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.10
Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.
Management
The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.11
Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.
1. Khot KP, Deshmane S, Choudhari S. Human papilloma virus in oral squamous cell carcinoma-the enigma unraveled. Clin J Dent Res. 2016;19(1):17-23.
2. Bolognia JL, Jorizzo JL, Rapini RP, eds. Neoplasms of the skin. In: Bolognia JL, Jorizzo JL, Rapini RP, eds. Dermatology. Vol 2. St. Louis, MO: Mosby; 2007:1627-1901.
3. Tatusov M, Reddy S, Federman DG. Pyogenic granuloma: yet another motorcycle peril. Postgrad Med. 2012;124(6):124-126.
4. Yuan K, Jin YT, Lin MT. The detection and comparison of angiogenesis-associated factors in pyogenic granuloma by immunohistochemistry. J Periodontol. 2000;71(5):701-709.
5. Krishnan V, Shunmugavelu K. A clinical challenging situation of intra oral fibroma mimicking pyogenic granuloma. J Pan African Med. 2015;22(1):263.
6. Nallasivam KU, Sudha BR. Oral mucocele: review of literature and a case report. J Pharm Bioallied Sci. 2015;7(suppl 2):S731-S733.
7. Zachariades N. Neoplasms metastatic to the mouth, jaws, and surrounding tissues. J Craniomaxillofac Surg. 1989;17(6):283-290.
8. Irani S. Metastasis to the oral soft tissues: a review of 412 cases. J Int Soc Prev Community Dent. 2016;6(5):393-401.
9. Shadman N, Ebrahimi SF, Jafari S, Eslami M. Peripheral giant cell granuloma: a review of 123 cases. Dent Res J (Isfahan). 2009;6(1):47-50.
10. Poonacha KS, Shigli AL, Shirol D. Peripheral ossifying fibroma: a clinical report. Contemp Clin Dent. 2010;1(1):54-56.
11. Gilmore A, Kelsberg G, Safranek G. Clinical inquiries. What’s the best treatment for pyogenic granuloma? J Fam Pract. 2010;59(1):40-42.
1. Khot KP, Deshmane S, Choudhari S. Human papilloma virus in oral squamous cell carcinoma-the enigma unraveled. Clin J Dent Res. 2016;19(1):17-23.
2. Bolognia JL, Jorizzo JL, Rapini RP, eds. Neoplasms of the skin. In: Bolognia JL, Jorizzo JL, Rapini RP, eds. Dermatology. Vol 2. St. Louis, MO: Mosby; 2007:1627-1901.
3. Tatusov M, Reddy S, Federman DG. Pyogenic granuloma: yet another motorcycle peril. Postgrad Med. 2012;124(6):124-126.
4. Yuan K, Jin YT, Lin MT. The detection and comparison of angiogenesis-associated factors in pyogenic granuloma by immunohistochemistry. J Periodontol. 2000;71(5):701-709.
5. Krishnan V, Shunmugavelu K. A clinical challenging situation of intra oral fibroma mimicking pyogenic granuloma. J Pan African Med. 2015;22(1):263.
6. Nallasivam KU, Sudha BR. Oral mucocele: review of literature and a case report. J Pharm Bioallied Sci. 2015;7(suppl 2):S731-S733.
7. Zachariades N. Neoplasms metastatic to the mouth, jaws, and surrounding tissues. J Craniomaxillofac Surg. 1989;17(6):283-290.
8. Irani S. Metastasis to the oral soft tissues: a review of 412 cases. J Int Soc Prev Community Dent. 2016;6(5):393-401.
9. Shadman N, Ebrahimi SF, Jafari S, Eslami M. Peripheral giant cell granuloma: a review of 123 cases. Dent Res J (Isfahan). 2009;6(1):47-50.
10. Poonacha KS, Shigli AL, Shirol D. Peripheral ossifying fibroma: a clinical report. Contemp Clin Dent. 2010;1(1):54-56.
11. Gilmore A, Kelsberg G, Safranek G. Clinical inquiries. What’s the best treatment for pyogenic granuloma? J Fam Pract. 2010;59(1):40-42.
Refractive Outcomes for Cataract Surgery With Toric Intraocular Lenses at a Veterans Affairs Medical Center
Cataract surgery is one of the most common ambulatory procedures performed in the US.1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.4
Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).5 However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.6 Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.7-9
Previous studies of resident-performed cataract surgery using toric IOLs6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.7,14-16
Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.13 The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.
Methods
The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.
Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.17 In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.17,18
Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.19,20
Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.
Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.21 Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.14 An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.22-24
A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.
Results
Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.
The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.
While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.
A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).
Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.14 For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.
Discussion
To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.6,10-13
In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.19,25
Limitations
The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.
Conclusions
In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.
1. Schein OD, Cassard SD, Tielsch JM, Gower EW. Cataract surgery among Medicare beneficiaries. Ophthalmic Epidemiol. 2012;19(5):257-264.
2. Congdon N, O’Colmain B, Klaver CC, et al. Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol. 2004;122(4):477-485.
3. Congdon N, Vingerling JR, Klein BE, et al. Prevalence of cataract and pseudophakia/aphakia among adults in the United States. Arch Ophthalmol. 2004;122(4):487-494.
4. National Eye Institute. Cataract tables: cataract defined. https://www.nei.nih.gov/learn-about-eye-health/resources-for-health-educators/eye-health-data-and-statistics/cataract-data-and-statistics/cataract-tables. Updated February 7, 2020. Accessed February 10, 2020.
5. Ostri C, Falck L, Boberg-Ans G, Kessel L. The need for toric intra-ocular lens implantation in public ophthalmology departments. Acta Ophthalmol. 2015;93(5):e396-e397.
6. Sundy M, McKnight D, Eck C, Rieger F 3rd. Visual acuity outcomes of toric lens implantation in patients undergoing cataract surgery at a residency training program. Mo Med. 2016;113(1):40-43.
7. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of methodologies using estimated or measured values of total corneal astigmatism for toric intraocular lens power calculation. J Refract Surg. 2017;33(12):794-800.
8. Reitblat O, Levy A, Kleinmann G, Abulafia A, Assia EI. Effect of posterior corneal astigmatism on power calculation and alignment of toric intraocular lenses: comparison of methodologies. J Cataract Refract Surg. 2016;42(2):217-225.
9. Aristodemou P, Knox Cartwright NE, Sparrow JM, Johnston RL. Formula choice: Hoffer Q, Holladay 1, or SRK/T and refractive outcomes in 8108 eyes after cataract surgery with biometry by partial coherence interferometry. J Cataract Refract Surg. 2011;37(1):63-71.
10. Moreira HR, Hatch KM, Greenberg PB. Benchmarking outcomes in resident-performed cataract surgery with toric intraocular lenses [published correction appears in: Clin Experiment Ophthalmol. 2013;41(8):819]. Clin Exp Ophthalmol. 2013;41(6):624-626.
11. Retzlaff JA, Sanders DR, Kraff MC. Development of the SRK/T intraocular lens implant power calculation formula [published correction appears in: J Cataract Refract Surg. 1990;16(4):528]. J Cataract Refract Surg. 1990;16(3):333-340.
12. Roensch MA, Charton JW, Blomquist PH, Aggarwal NK, McCulley JP. Resident experience with toric and multifocal intraocular lenses in a public county hospital system. J Cataract Refract Surg. 2012;38(5):793-798.
13. Pouyeh B, Galor A, Junk AK, et al. Surgical and refractive outcomes of cataract surgery with toric intraocular lens implantation at a resident-teaching institution. J Cataract Refract Surg. 2011;37(9):1623-1628.
14. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of astigmatic prediction errors associated with new calculation methods for toric intraocular lenses. J Cataract Refract Surg. 2017;43(3):340-347.
15. Abulafia A, Hill WE, Franchina M, Barrett GD. Comparison of methods to predict residual astigmatism after intraocular lens implantation. J Refract Surg. 2015;31(10):699-707.
16. Abulafia A, Barrett GD, Kleinmann G, et al. Prediction of refractive outcomes with toric intraocular lens implantation. J Cataract Refract Surg. 2015;41(5):936-944.
17. Wang Q, Jiang W, Lin T, Wu X, Lin H, Chen W. Meta-analysis of accuracy of intraocular lens power calculation formulas in short eyes. Clin Exp Ophthalmol. 2018;46(4):356-363.
18. Melles RB, Holladay JT, Chang WJ. Accuracy of intraocular lens calculation formulas. Ophthalmology. 2018;125(2):169-178.
19. Hoffer KJ. The Hoffer Q formula: a comparison of theoretic and regression formulas. J Cataract Refract Surg. 1993;19(6):700-712.
20. Cooke DL, Cooke TL. Comparison of 9 intraocular lens power calculation formulas. J Cataract Refract Surg. 2016;42(8):1157-1164.
21. American Society of Cataract and Refractive Surgery. Barrett toric calculator. www.ascrs.org/barrett-toric-calculator. Accessed February 5, 2020.
22. Holladay JT, Pettit G. Improving toric intraocular lens calculations using total surgically induced astigmatism for a 2.5 mm temporal incision. J Cataract Refract Surg. 2019;45(3):272-283.
23. Canovas C, Alarcon A, Rosén R, et al. New algorithm for toric intraocular lens power calculation considering the posterior corneal astigmatism. J Cataract Refract Surg. 2018;44(2):168-174.
24. Visser N, Berendschot TT, Bauer NJ, Nuijts RM. Vector analysis of corneal and refractive astigmatism changes following toric pseudophakic and toric phakic IOL implantation. Invest Ophthalmol Vis Sci. 2012;53(4):1865-1873.
25. Narváez J, Zimmerman G, Stulting RD, Chang DH. Accuracy of intraocular lens power prediction using the Hoffer Q, Holladay 1, Holladay 2, and SRK/T formulas. J Cataract Refract Surg. 2006;32(12):2050-2053.
Cataract surgery is one of the most common ambulatory procedures performed in the US.1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.4
Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).5 However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.6 Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.7-9
Previous studies of resident-performed cataract surgery using toric IOLs6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.7,14-16
Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.13 The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.
Methods
The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.
Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.17 In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.17,18
Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.19,20
Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.
Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.21 Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.14 An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.22-24
A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.
Results
Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.
The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.
While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.
A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).
Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.14 For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.
Discussion
To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.6,10-13
In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.19,25
Limitations
The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.
Conclusions
In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.
Cataract surgery is one of the most common ambulatory procedures performed in the US.1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.4
Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).5 However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.6 Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.7-9
Previous studies of resident-performed cataract surgery using toric IOLs6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.7,14-16
Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.13 The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.
Methods
The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.
Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.17 In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.17,18
Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.19,20
Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.
Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.21 Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.14 An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.22-24
A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.
Results
Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.
The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.
While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.
A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).
Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.14 For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.
Discussion
To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.6,10-13
In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.19,25
Limitations
The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.
Conclusions
In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.
1. Schein OD, Cassard SD, Tielsch JM, Gower EW. Cataract surgery among Medicare beneficiaries. Ophthalmic Epidemiol. 2012;19(5):257-264.
2. Congdon N, O’Colmain B, Klaver CC, et al. Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol. 2004;122(4):477-485.
3. Congdon N, Vingerling JR, Klein BE, et al. Prevalence of cataract and pseudophakia/aphakia among adults in the United States. Arch Ophthalmol. 2004;122(4):487-494.
4. National Eye Institute. Cataract tables: cataract defined. https://www.nei.nih.gov/learn-about-eye-health/resources-for-health-educators/eye-health-data-and-statistics/cataract-data-and-statistics/cataract-tables. Updated February 7, 2020. Accessed February 10, 2020.
5. Ostri C, Falck L, Boberg-Ans G, Kessel L. The need for toric intra-ocular lens implantation in public ophthalmology departments. Acta Ophthalmol. 2015;93(5):e396-e397.
6. Sundy M, McKnight D, Eck C, Rieger F 3rd. Visual acuity outcomes of toric lens implantation in patients undergoing cataract surgery at a residency training program. Mo Med. 2016;113(1):40-43.
7. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of methodologies using estimated or measured values of total corneal astigmatism for toric intraocular lens power calculation. J Refract Surg. 2017;33(12):794-800.
8. Reitblat O, Levy A, Kleinmann G, Abulafia A, Assia EI. Effect of posterior corneal astigmatism on power calculation and alignment of toric intraocular lenses: comparison of methodologies. J Cataract Refract Surg. 2016;42(2):217-225.
9. Aristodemou P, Knox Cartwright NE, Sparrow JM, Johnston RL. Formula choice: Hoffer Q, Holladay 1, or SRK/T and refractive outcomes in 8108 eyes after cataract surgery with biometry by partial coherence interferometry. J Cataract Refract Surg. 2011;37(1):63-71.
10. Moreira HR, Hatch KM, Greenberg PB. Benchmarking outcomes in resident-performed cataract surgery with toric intraocular lenses [published correction appears in: Clin Experiment Ophthalmol. 2013;41(8):819]. Clin Exp Ophthalmol. 2013;41(6):624-626.
11. Retzlaff JA, Sanders DR, Kraff MC. Development of the SRK/T intraocular lens implant power calculation formula [published correction appears in: J Cataract Refract Surg. 1990;16(4):528]. J Cataract Refract Surg. 1990;16(3):333-340.
12. Roensch MA, Charton JW, Blomquist PH, Aggarwal NK, McCulley JP. Resident experience with toric and multifocal intraocular lenses in a public county hospital system. J Cataract Refract Surg. 2012;38(5):793-798.
13. Pouyeh B, Galor A, Junk AK, et al. Surgical and refractive outcomes of cataract surgery with toric intraocular lens implantation at a resident-teaching institution. J Cataract Refract Surg. 2011;37(9):1623-1628.
14. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of astigmatic prediction errors associated with new calculation methods for toric intraocular lenses. J Cataract Refract Surg. 2017;43(3):340-347.
15. Abulafia A, Hill WE, Franchina M, Barrett GD. Comparison of methods to predict residual astigmatism after intraocular lens implantation. J Refract Surg. 2015;31(10):699-707.
16. Abulafia A, Barrett GD, Kleinmann G, et al. Prediction of refractive outcomes with toric intraocular lens implantation. J Cataract Refract Surg. 2015;41(5):936-944.
17. Wang Q, Jiang W, Lin T, Wu X, Lin H, Chen W. Meta-analysis of accuracy of intraocular lens power calculation formulas in short eyes. Clin Exp Ophthalmol. 2018;46(4):356-363.
18. Melles RB, Holladay JT, Chang WJ. Accuracy of intraocular lens calculation formulas. Ophthalmology. 2018;125(2):169-178.
19. Hoffer KJ. The Hoffer Q formula: a comparison of theoretic and regression formulas. J Cataract Refract Surg. 1993;19(6):700-712.
20. Cooke DL, Cooke TL. Comparison of 9 intraocular lens power calculation formulas. J Cataract Refract Surg. 2016;42(8):1157-1164.
21. American Society of Cataract and Refractive Surgery. Barrett toric calculator. www.ascrs.org/barrett-toric-calculator. Accessed February 5, 2020.
22. Holladay JT, Pettit G. Improving toric intraocular lens calculations using total surgically induced astigmatism for a 2.5 mm temporal incision. J Cataract Refract Surg. 2019;45(3):272-283.
23. Canovas C, Alarcon A, Rosén R, et al. New algorithm for toric intraocular lens power calculation considering the posterior corneal astigmatism. J Cataract Refract Surg. 2018;44(2):168-174.
24. Visser N, Berendschot TT, Bauer NJ, Nuijts RM. Vector analysis of corneal and refractive astigmatism changes following toric pseudophakic and toric phakic IOL implantation. Invest Ophthalmol Vis Sci. 2012;53(4):1865-1873.
25. Narváez J, Zimmerman G, Stulting RD, Chang DH. Accuracy of intraocular lens power prediction using the Hoffer Q, Holladay 1, Holladay 2, and SRK/T formulas. J Cataract Refract Surg. 2006;32(12):2050-2053.
1. Schein OD, Cassard SD, Tielsch JM, Gower EW. Cataract surgery among Medicare beneficiaries. Ophthalmic Epidemiol. 2012;19(5):257-264.
2. Congdon N, O’Colmain B, Klaver CC, et al. Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol. 2004;122(4):477-485.
3. Congdon N, Vingerling JR, Klein BE, et al. Prevalence of cataract and pseudophakia/aphakia among adults in the United States. Arch Ophthalmol. 2004;122(4):487-494.
4. National Eye Institute. Cataract tables: cataract defined. https://www.nei.nih.gov/learn-about-eye-health/resources-for-health-educators/eye-health-data-and-statistics/cataract-data-and-statistics/cataract-tables. Updated February 7, 2020. Accessed February 10, 2020.
5. Ostri C, Falck L, Boberg-Ans G, Kessel L. The need for toric intra-ocular lens implantation in public ophthalmology departments. Acta Ophthalmol. 2015;93(5):e396-e397.
6. Sundy M, McKnight D, Eck C, Rieger F 3rd. Visual acuity outcomes of toric lens implantation in patients undergoing cataract surgery at a residency training program. Mo Med. 2016;113(1):40-43.
7. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of methodologies using estimated or measured values of total corneal astigmatism for toric intraocular lens power calculation. J Refract Surg. 2017;33(12):794-800.
8. Reitblat O, Levy A, Kleinmann G, Abulafia A, Assia EI. Effect of posterior corneal astigmatism on power calculation and alignment of toric intraocular lenses: comparison of methodologies. J Cataract Refract Surg. 2016;42(2):217-225.
9. Aristodemou P, Knox Cartwright NE, Sparrow JM, Johnston RL. Formula choice: Hoffer Q, Holladay 1, or SRK/T and refractive outcomes in 8108 eyes after cataract surgery with biometry by partial coherence interferometry. J Cataract Refract Surg. 2011;37(1):63-71.
10. Moreira HR, Hatch KM, Greenberg PB. Benchmarking outcomes in resident-performed cataract surgery with toric intraocular lenses [published correction appears in: Clin Experiment Ophthalmol. 2013;41(8):819]. Clin Exp Ophthalmol. 2013;41(6):624-626.
11. Retzlaff JA, Sanders DR, Kraff MC. Development of the SRK/T intraocular lens implant power calculation formula [published correction appears in: J Cataract Refract Surg. 1990;16(4):528]. J Cataract Refract Surg. 1990;16(3):333-340.
12. Roensch MA, Charton JW, Blomquist PH, Aggarwal NK, McCulley JP. Resident experience with toric and multifocal intraocular lenses in a public county hospital system. J Cataract Refract Surg. 2012;38(5):793-798.
13. Pouyeh B, Galor A, Junk AK, et al. Surgical and refractive outcomes of cataract surgery with toric intraocular lens implantation at a resident-teaching institution. J Cataract Refract Surg. 2011;37(9):1623-1628.
14. Ferreira TB, Ribeiro P, Ribeiro FJ, O’Neill JG. Comparison of astigmatic prediction errors associated with new calculation methods for toric intraocular lenses. J Cataract Refract Surg. 2017;43(3):340-347.
15. Abulafia A, Hill WE, Franchina M, Barrett GD. Comparison of methods to predict residual astigmatism after intraocular lens implantation. J Refract Surg. 2015;31(10):699-707.
16. Abulafia A, Barrett GD, Kleinmann G, et al. Prediction of refractive outcomes with toric intraocular lens implantation. J Cataract Refract Surg. 2015;41(5):936-944.
17. Wang Q, Jiang W, Lin T, Wu X, Lin H, Chen W. Meta-analysis of accuracy of intraocular lens power calculation formulas in short eyes. Clin Exp Ophthalmol. 2018;46(4):356-363.
18. Melles RB, Holladay JT, Chang WJ. Accuracy of intraocular lens calculation formulas. Ophthalmology. 2018;125(2):169-178.
19. Hoffer KJ. The Hoffer Q formula: a comparison of theoretic and regression formulas. J Cataract Refract Surg. 1993;19(6):700-712.
20. Cooke DL, Cooke TL. Comparison of 9 intraocular lens power calculation formulas. J Cataract Refract Surg. 2016;42(8):1157-1164.
21. American Society of Cataract and Refractive Surgery. Barrett toric calculator. www.ascrs.org/barrett-toric-calculator. Accessed February 5, 2020.
22. Holladay JT, Pettit G. Improving toric intraocular lens calculations using total surgically induced astigmatism for a 2.5 mm temporal incision. J Cataract Refract Surg. 2019;45(3):272-283.
23. Canovas C, Alarcon A, Rosén R, et al. New algorithm for toric intraocular lens power calculation considering the posterior corneal astigmatism. J Cataract Refract Surg. 2018;44(2):168-174.
24. Visser N, Berendschot TT, Bauer NJ, Nuijts RM. Vector analysis of corneal and refractive astigmatism changes following toric pseudophakic and toric phakic IOL implantation. Invest Ophthalmol Vis Sci. 2012;53(4):1865-1873.
25. Narváez J, Zimmerman G, Stulting RD, Chang DH. Accuracy of intraocular lens power prediction using the Hoffer Q, Holladay 1, Holladay 2, and SRK/T formulas. J Cataract Refract Surg. 2006;32(12):2050-2053.
Demographic Profile and Service-Connection Trends of Posttraumatic Stress Disorder and Traumatic Brain Injury in US Veterans Pre- and Post-9/11
The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.
Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.5 Although delayed onset of PTSD in the absence of prior symptoms is rare,6,7 its incidence rises with increasing frequency of exposure to traumatic events8,9 and over time.10
According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”8 TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.2 According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars3,19 compared with 10% to 30% in previous wars.3,19,20
When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.21 The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.22 The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.22
The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.23 Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.22
The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.
Methods
This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.
Study Population
The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.
Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).
Statistical Analysis
Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ2 tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.
Results
PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).
Distribution by Gender and Age
The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.
Distribution by Race and Marital Status
The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.
Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.
Geographic Distribution
Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.
Service-Connected Disability
Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.
Discussion
The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.
The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.24 In post-9/11 military conflicts, 81% of injuries were explosion related.24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.24-26
Increases in PTSD Diagnosis
Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.25-27
More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.14 Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.8-10
The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.28 An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.
The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).29 A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.5-7
Prevalence
A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts3,20 and 10% to 30% of veterans from previous wars.3,19 These rates are higher than estimates from allied forces from other countries.19 Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.3,11,19,30-38 The prevalence of PTSD is 2 to 4 times higher among the US veterans19,39 when compared with that of civilians.40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).19
This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.31
In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.
For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.42
Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.40 Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.
Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.43 One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.44 In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.44
Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.
Limitations
Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.
Conclusions
This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.
Acknowledgments
This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.
1. Bagalman E. Traumatic brain injury among veterans. http://www.ncsl.org/documents/statefed/health/TBI_Vets2013.pdf. Published January 4, 2013. Accessed February 3, 2020.
2. Veterans Health Administration, Support Service Center. Workload files fiscal year 2008-fiscal year 2012. [Source not verified.]
3. Tanielian T, Jaycox LH, eds. Invisible Wounds of War: Psychological and Cognitive Injuries, Their Consequences, and Services to Assist Recovery. Santa Monica, CA: RAND Corporation; 2008.
4. Bagalman E. Health care for veterans: traumatic brain injury. https://fas.org/sgp/crs/misc/R40941.pdf. Published March 9, 2015. Accessed February 4, 2020.
5. Ikin JF, Sim MR, McKenzie DP, et al. Anxiety, post-traumatic stress disorder and depression in Korean War veterans 50 years after the war. Br J Psychiatry. 2007;190(6):475-483.
6. Andrews B, Brewin CR, Philpott R, Stewart L. Delayed-onset posttraumatic stress disorder: a systematic review of the evidence. Am J Psychiatry. 2007;164(9):1319-1326.
7. Frueh BC, Grubaugh AL, Yeager DE, Magruder KM. Delayed-onset post-traumatic stress disorder among war veterans in primary care clinics. Br J Psychiatry. 2009;194(6):515-520.
8. McAllister TW. Neurobiological consequences of traumatic brain injury. Dialogues Clin Neurosci. 2011;13(3):287-300.
9. Schlenger WE, Kulka RA, Fairbank JA, et al. The prevalence of posttraumatic stress disorder in the Vietnam generation: a multimethod, multisource assessment of psychiatric disorder. J Trauma Stress. 1992;5(3):333-363.
10. Friedman MJ, Resick PA, Bryant RA, Strain J, Horowitz M, Spiegel D. Classification of trauma and stressor-related disorders in DSM-5. Depress Anxiety. 2011;28(9):737-749.
11. Lew HL, Otis JD, Tun C, Kerns RD, Clark ME, Cifu DX. Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: polytrauma clinical triad. J Rehabil Res Dev. 2009;46(6):697-702.
12. Carlson K, Kehle S, Meis L, et al. The Assessment and Treatment of Individuals with History of Traumatic Brain Injury and Post-Traumatic Stress Disorder: A Systematic Review of the Evidence. Washington, DC: US Department of Veterans Affairs; 2009.
13. Gironda RJ, Clark ME, Ruff RL, et al. Traumatic brain injury, polytrauma, and pain: challenges and treatment strategies for the polytrauma rehabilitation. Rehabil Psychol. 2009;54(3):247-258.
14. Hoge CW, McGurk D, Thomas JL, Cox AL, Engel CC, Castro CA. Mild traumatic brain injury in U.S. soldiers returning from Iraq. N Engl J Med. 2008;358(5):453-463.
15. Bazarian JJ, Cernak I, Noble-Haeusslein L, Potolicchio S, Temkin N. Long-term neurologic outcomes after traumatic brain injury. J Head Trauma Rehabil. 2009;24(6):439-451.
16. Peskind ER, Brody D, Cernak I, McKee A, Ruff RL. Military- and sports-related mild traumatic brain injury: clinical presentation, management, and long-term consequences. J Clin Psychiatry. 2013;74(2):180-188.
17. Riggio S. Traumatic brain injury and its neurobehavioral sequelae. Neurol Clin. 2011;29(1):35-47, vii.
18. Helmick KM, Spells CA, Malik SZ, Davies CA, Marion DW, Hinds SR. Traumatic brain injury in the US military: epidemiology and key clinical and research programs. Brain Imaging Behav. 2015;9(3):358-366.
19. Richardson LK, Frueh BC, Acierno R. Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Aust N Z J Psychiatry. 2010;44(1):4-19.
20. Thompson WW, Gottesman II, Zalewski C. Reconciling disparate prevalence rates of PTSD in large samples of US male Vietnam veterans and their controls. BMC Psychiatry. 2006;6:19.
21. Frueh BC, Elhai JD, Gold PB, et al Disability compensation seeking among veterans evaluated for posttraumatic stress disorder. Psychiatr Serv. 2003;54(1):84-91.
22. Thakur H, Oni O, Singh V, et al. Increases in the service connection disability and treatment costs associated with posttraumatic stress disorder and/or traumatic brain injury in United States veterans pre- and post-9/11: the strong need for a novel therapeutic approach. Epidemiology (Sunnyvale). 2018;8(4):353.
23. Schlenger WE, Kulka RA, Fairbank JA, et al. The prevalence of post-traumatic stress disorder in the Vietnam generation: a multimethod, multisource assessment of psychiatric disorder. J Trauma Stress. 1992;5(3):333-363.
24. Belmont PJ, Schoenfeld AJ, Goodman G. Epidemiology of combat wounds in Operation Iraqi Freedom and Operation Enduring Freedom: orthopaedic burden of disease. J Surg Orthop Adv. 2010;19(1):2-7.
25. Owens BD, Kragh JG Jr, Wenke JC, Macaitis J, Wade CE, Holcomb JB. Combat wounds in Operation Iraqi Freedom and Operation Enduring Freedom. J Trauma. 2008;64(2):295-299.
26. Defense Health Agency, Defense and Veterans Brain Injury Center. DOD worldwide numbers for TBI since 2000. https://dvbic.dcoe.mil/dod-worldwide-numbers-tbi. Updated February 14, 2020. Accessed February 14, 2020.
27. Armed Forces Health Surveillance Center. Deployment-related conditions of special surveillance interest, U.S. armed forces, by month and service, January 2003-December 2012 (data as of 22 January 2013). MSMR. 2013;20(1):16-19.
28. Harvey JH, Stein SK, Scott PK. Fifty years of grief: accounts and reported psychological reactions of Normandy invasion veterans. J Narrative Life History. 1995;5(4):321-332.
29. US Department of Veterans Affairs. Polytrauma/TBI system of care. https://www.polytrauma.va.gov/system-of-care/index.asp. Updated June 3, 2015. Accessed February 4, 2020.
30. Wolfe J, Erickson DJ, Sharkansky EJ, King DW, King LA. Course and predictors of posttraumatic stress disorder among Gulf War veterans: a prospective analysis. J Consult Clin Psychol. 1999;67(4):520-528.
31. Breslau N, Davis GC, Peterson EL, Schultz L. Psychiatric sequelae of posttraumatic stress disorder in women. Arch Gen Psychiatry. 1997;54(1):81-87.
32. Kessler RC, Sonnega A, Bromet E, Hughes M, Nelson CB. Posttraumatic stress disorder in the National Comorbidity Survey. Arch Gen Psychiatry. 1995;52(12):1048-1060.
33. Wolfe J, Kimerling R. Gender issues in the assessment of posttraumatic stress disorder. In: Wilson J, Keane TM, eds. Assessing Psychological Trauma and PTSD. New York: Guilford; 2004:192-238.
34. Engel CC Jr, Engel AL, Campbell SJ, McFall ME, Russo J, Katon W. Posttraumatic stress disorder symptoms and precombat sexual and physical abuse in Desert Storm veterans. J Nerv Ment Dis. 1993;181(11):683-688.
35. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Profile of veterans: 2016 data from the American Community Survey. https://www.va.gov/vetdata/docs/SpecialReports/Profile_of_Veterans_2016.pdf. Published February 2018. Accessed February 4, 2020.
36. US Department of Commerce Economics and Statistics Administration, US Census Bureau, Geography Division. 2010 population distribution in the United States and Puerto Rico. https://www2.census.gov/geo/maps/dc10_thematic/2010_Nighttime_PopDist/2010_Nighttime_PopDist_Page_Map.pdf. Accessed February 4, 2020.
37. Cifu DX, Taylor BC, Carne WF, et al. Traumatic brain injury, posttraumatic stress disorder, and pain diagnoses in OIF/OEF/OND veterans. J Rehabil Res Dev. 2013;50(9):1169-1176.
38. Dohrenwend BP, Turner JB, Turse NA, Adams BG, Koenen KC, Marshall R. The psychological risks of Vietnam for U.S. veterans: a revisit with new data and methods. Science. 2006;313(5789):979-982.
39. Magruder KM, Frueh BC, Knapp RG, et al. Prevalence of posttraumatic stress disorder in Veterans Affairs primary care clinics. Gen Hosp Psychiatry. 2005;27(3):169-179.
40. Norris FH. Epidemiology of trauma: frequency and impact of different potentially traumatic events on different demographic groups. J Consult Clin Psychol. 1992;60(3):409-418.
41. Resnick HS, Kilpatrick DG, Dansky BS, Saunders BE, Best CL. Prevalence of civilian trauma and posttraumatic stress disorder in a representative national sample of women. J Consult Clin Psychol. 1993;61(6):984-991.
42. Najavits LM. The problem of dropout from “gold standard” PTSD therapies. F1000Prime Rep. 2015;7:43.
43. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Trends in veterans with a service-connected disability: 1985 to 2014. https://www.va.gov/vetdata/docs/QuickFacts/SCD_trends_FINAL_2014.PDF. Published June 2015. Accessed February 4, 2020.
44. US Department of Veterans Affairs, Office of Inspector General. Review of state variances in VA disability compensation payments. Report 05-00765-137. https://www.va.gov/oig/52/reports/2005/VAOIG-05-00765-137.pdf. Published May 19, 2015. Accessed February 4, 2020.
45. McNally RJ. Progress and controversy in the study of posttraumatic stress disorder. Annu Rev Psychol. 2003;54:229-252.
46. Freeman T, Powell M, Kimbrell T. Measuring symptom exaggeration in veterans with chronic posttraumatic stress disorder. Psychiatry Res. 2008;158(3):374-380.
47. Frueh BC, Elhai JD, Grubaugh AL, et al. Documented combat exposure of US veterans seeking treatment for combat-related post-traumatic stress disorder. Br J Psychiatry. 2005;186(6):467-475.
48. Frueh BC, Hamner MB, Cahill SP, Gold PB, Hamlin KL. Apparent symptom overreporting in combat veterans evaluated for PTSD. Clin Psychol Rev. 2000;20(7):853-885.
49. Sparr L, Pankratz LD. Factitious posttraumatic stress disorder. Am J Psychiatry. 1983;140(8):1016-1019.
50. Baggaley M. ‘Military Munchausen’s’: assessment of factitious claims of military service in psychiatric patients. Psychiatr Bull. 1998;22(3):153-154.
The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.
Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.5 Although delayed onset of PTSD in the absence of prior symptoms is rare,6,7 its incidence rises with increasing frequency of exposure to traumatic events8,9 and over time.10
According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”8 TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.2 According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars3,19 compared with 10% to 30% in previous wars.3,19,20
When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.21 The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.22 The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.22
The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.23 Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.22
The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.
Methods
This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.
Study Population
The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.
Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).
Statistical Analysis
Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ2 tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.
Results
PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).
Distribution by Gender and Age
The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.
Distribution by Race and Marital Status
The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.
Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.
Geographic Distribution
Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.
Service-Connected Disability
Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.
Discussion
The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.
The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.24 In post-9/11 military conflicts, 81% of injuries were explosion related.24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.24-26
Increases in PTSD Diagnosis
Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.25-27
More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.14 Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.8-10
The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.28 An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.
The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).29 A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.5-7
Prevalence
A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts3,20 and 10% to 30% of veterans from previous wars.3,19 These rates are higher than estimates from allied forces from other countries.19 Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.3,11,19,30-38 The prevalence of PTSD is 2 to 4 times higher among the US veterans19,39 when compared with that of civilians.40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).19
This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.31
In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.
For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.42
Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.40 Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.
Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.43 One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.44 In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.44
Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.
Limitations
Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.
Conclusions
This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.
Acknowledgments
This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.
The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.
Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.5 Although delayed onset of PTSD in the absence of prior symptoms is rare,6,7 its incidence rises with increasing frequency of exposure to traumatic events8,9 and over time.10
According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”8 TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.2 According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars3,19 compared with 10% to 30% in previous wars.3,19,20
When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.21 The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.22 The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.22
The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.23 Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.22
The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.
Methods
This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.
Study Population
The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.
Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).
Statistical Analysis
Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ2 tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.
Results
PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).
Distribution by Gender and Age
The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.
Distribution by Race and Marital Status
The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.
Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.
Geographic Distribution
Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.
Service-Connected Disability
Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.
Discussion
The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.
The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.24 In post-9/11 military conflicts, 81% of injuries were explosion related.24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.24-26
Increases in PTSD Diagnosis
Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.25-27
More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.14 Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.8-10
The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.28 An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.
The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).29 A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.5-7
Prevalence
A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts3,20 and 10% to 30% of veterans from previous wars.3,19 These rates are higher than estimates from allied forces from other countries.19 Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.3,11,19,30-38 The prevalence of PTSD is 2 to 4 times higher among the US veterans19,39 when compared with that of civilians.40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).19
This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.31
In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.
For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.42
Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.40 Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.
Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.43 One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.44 In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.44
Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.
Limitations
Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.
Conclusions
This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.
Acknowledgments
This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.
1. Bagalman E. Traumatic brain injury among veterans. http://www.ncsl.org/documents/statefed/health/TBI_Vets2013.pdf. Published January 4, 2013. Accessed February 3, 2020.
2. Veterans Health Administration, Support Service Center. Workload files fiscal year 2008-fiscal year 2012. [Source not verified.]
3. Tanielian T, Jaycox LH, eds. Invisible Wounds of War: Psychological and Cognitive Injuries, Their Consequences, and Services to Assist Recovery. Santa Monica, CA: RAND Corporation; 2008.
4. Bagalman E. Health care for veterans: traumatic brain injury. https://fas.org/sgp/crs/misc/R40941.pdf. Published March 9, 2015. Accessed February 4, 2020.
5. Ikin JF, Sim MR, McKenzie DP, et al. Anxiety, post-traumatic stress disorder and depression in Korean War veterans 50 years after the war. Br J Psychiatry. 2007;190(6):475-483.
6. Andrews B, Brewin CR, Philpott R, Stewart L. Delayed-onset posttraumatic stress disorder: a systematic review of the evidence. Am J Psychiatry. 2007;164(9):1319-1326.
7. Frueh BC, Grubaugh AL, Yeager DE, Magruder KM. Delayed-onset post-traumatic stress disorder among war veterans in primary care clinics. Br J Psychiatry. 2009;194(6):515-520.
8. McAllister TW. Neurobiological consequences of traumatic brain injury. Dialogues Clin Neurosci. 2011;13(3):287-300.
9. Schlenger WE, Kulka RA, Fairbank JA, et al. The prevalence of posttraumatic stress disorder in the Vietnam generation: a multimethod, multisource assessment of psychiatric disorder. J Trauma Stress. 1992;5(3):333-363.
10. Friedman MJ, Resick PA, Bryant RA, Strain J, Horowitz M, Spiegel D. Classification of trauma and stressor-related disorders in DSM-5. Depress Anxiety. 2011;28(9):737-749.
11. Lew HL, Otis JD, Tun C, Kerns RD, Clark ME, Cifu DX. Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: polytrauma clinical triad. J Rehabil Res Dev. 2009;46(6):697-702.
12. Carlson K, Kehle S, Meis L, et al. The Assessment and Treatment of Individuals with History of Traumatic Brain Injury and Post-Traumatic Stress Disorder: A Systematic Review of the Evidence. Washington, DC: US Department of Veterans Affairs; 2009.
13. Gironda RJ, Clark ME, Ruff RL, et al. Traumatic brain injury, polytrauma, and pain: challenges and treatment strategies for the polytrauma rehabilitation. Rehabil Psychol. 2009;54(3):247-258.
14. Hoge CW, McGurk D, Thomas JL, Cox AL, Engel CC, Castro CA. Mild traumatic brain injury in U.S. soldiers returning from Iraq. N Engl J Med. 2008;358(5):453-463.
15. Bazarian JJ, Cernak I, Noble-Haeusslein L, Potolicchio S, Temkin N. Long-term neurologic outcomes after traumatic brain injury. J Head Trauma Rehabil. 2009;24(6):439-451.
16. Peskind ER, Brody D, Cernak I, McKee A, Ruff RL. Military- and sports-related mild traumatic brain injury: clinical presentation, management, and long-term consequences. J Clin Psychiatry. 2013;74(2):180-188.
17. Riggio S. Traumatic brain injury and its neurobehavioral sequelae. Neurol Clin. 2011;29(1):35-47, vii.
18. Helmick KM, Spells CA, Malik SZ, Davies CA, Marion DW, Hinds SR. Traumatic brain injury in the US military: epidemiology and key clinical and research programs. Brain Imaging Behav. 2015;9(3):358-366.
19. Richardson LK, Frueh BC, Acierno R. Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Aust N Z J Psychiatry. 2010;44(1):4-19.
20. Thompson WW, Gottesman II, Zalewski C. Reconciling disparate prevalence rates of PTSD in large samples of US male Vietnam veterans and their controls. BMC Psychiatry. 2006;6:19.
21. Frueh BC, Elhai JD, Gold PB, et al Disability compensation seeking among veterans evaluated for posttraumatic stress disorder. Psychiatr Serv. 2003;54(1):84-91.
22. Thakur H, Oni O, Singh V, et al. Increases in the service connection disability and treatment costs associated with posttraumatic stress disorder and/or traumatic brain injury in United States veterans pre- and post-9/11: the strong need for a novel therapeutic approach. Epidemiology (Sunnyvale). 2018;8(4):353.
23. Schlenger WE, Kulka RA, Fairbank JA, et al. The prevalence of post-traumatic stress disorder in the Vietnam generation: a multimethod, multisource assessment of psychiatric disorder. J Trauma Stress. 1992;5(3):333-363.
24. Belmont PJ, Schoenfeld AJ, Goodman G. Epidemiology of combat wounds in Operation Iraqi Freedom and Operation Enduring Freedom: orthopaedic burden of disease. J Surg Orthop Adv. 2010;19(1):2-7.
25. Owens BD, Kragh JG Jr, Wenke JC, Macaitis J, Wade CE, Holcomb JB. Combat wounds in Operation Iraqi Freedom and Operation Enduring Freedom. J Trauma. 2008;64(2):295-299.
26. Defense Health Agency, Defense and Veterans Brain Injury Center. DOD worldwide numbers for TBI since 2000. https://dvbic.dcoe.mil/dod-worldwide-numbers-tbi. Updated February 14, 2020. Accessed February 14, 2020.
27. Armed Forces Health Surveillance Center. Deployment-related conditions of special surveillance interest, U.S. armed forces, by month and service, January 2003-December 2012 (data as of 22 January 2013). MSMR. 2013;20(1):16-19.
28. Harvey JH, Stein SK, Scott PK. Fifty years of grief: accounts and reported psychological reactions of Normandy invasion veterans. J Narrative Life History. 1995;5(4):321-332.
29. US Department of Veterans Affairs. Polytrauma/TBI system of care. https://www.polytrauma.va.gov/system-of-care/index.asp. Updated June 3, 2015. Accessed February 4, 2020.
30. Wolfe J, Erickson DJ, Sharkansky EJ, King DW, King LA. Course and predictors of posttraumatic stress disorder among Gulf War veterans: a prospective analysis. J Consult Clin Psychol. 1999;67(4):520-528.
31. Breslau N, Davis GC, Peterson EL, Schultz L. Psychiatric sequelae of posttraumatic stress disorder in women. Arch Gen Psychiatry. 1997;54(1):81-87.
32. Kessler RC, Sonnega A, Bromet E, Hughes M, Nelson CB. Posttraumatic stress disorder in the National Comorbidity Survey. Arch Gen Psychiatry. 1995;52(12):1048-1060.
33. Wolfe J, Kimerling R. Gender issues in the assessment of posttraumatic stress disorder. In: Wilson J, Keane TM, eds. Assessing Psychological Trauma and PTSD. New York: Guilford; 2004:192-238.
34. Engel CC Jr, Engel AL, Campbell SJ, McFall ME, Russo J, Katon W. Posttraumatic stress disorder symptoms and precombat sexual and physical abuse in Desert Storm veterans. J Nerv Ment Dis. 1993;181(11):683-688.
35. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Profile of veterans: 2016 data from the American Community Survey. https://www.va.gov/vetdata/docs/SpecialReports/Profile_of_Veterans_2016.pdf. Published February 2018. Accessed February 4, 2020.
36. US Department of Commerce Economics and Statistics Administration, US Census Bureau, Geography Division. 2010 population distribution in the United States and Puerto Rico. https://www2.census.gov/geo/maps/dc10_thematic/2010_Nighttime_PopDist/2010_Nighttime_PopDist_Page_Map.pdf. Accessed February 4, 2020.
37. Cifu DX, Taylor BC, Carne WF, et al. Traumatic brain injury, posttraumatic stress disorder, and pain diagnoses in OIF/OEF/OND veterans. J Rehabil Res Dev. 2013;50(9):1169-1176.
38. Dohrenwend BP, Turner JB, Turse NA, Adams BG, Koenen KC, Marshall R. The psychological risks of Vietnam for U.S. veterans: a revisit with new data and methods. Science. 2006;313(5789):979-982.
39. Magruder KM, Frueh BC, Knapp RG, et al. Prevalence of posttraumatic stress disorder in Veterans Affairs primary care clinics. Gen Hosp Psychiatry. 2005;27(3):169-179.
40. Norris FH. Epidemiology of trauma: frequency and impact of different potentially traumatic events on different demographic groups. J Consult Clin Psychol. 1992;60(3):409-418.
41. Resnick HS, Kilpatrick DG, Dansky BS, Saunders BE, Best CL. Prevalence of civilian trauma and posttraumatic stress disorder in a representative national sample of women. J Consult Clin Psychol. 1993;61(6):984-991.
42. Najavits LM. The problem of dropout from “gold standard” PTSD therapies. F1000Prime Rep. 2015;7:43.
43. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Trends in veterans with a service-connected disability: 1985 to 2014. https://www.va.gov/vetdata/docs/QuickFacts/SCD_trends_FINAL_2014.PDF. Published June 2015. Accessed February 4, 2020.
44. US Department of Veterans Affairs, Office of Inspector General. Review of state variances in VA disability compensation payments. Report 05-00765-137. https://www.va.gov/oig/52/reports/2005/VAOIG-05-00765-137.pdf. Published May 19, 2015. Accessed February 4, 2020.
45. McNally RJ. Progress and controversy in the study of posttraumatic stress disorder. Annu Rev Psychol. 2003;54:229-252.
46. Freeman T, Powell M, Kimbrell T. Measuring symptom exaggeration in veterans with chronic posttraumatic stress disorder. Psychiatry Res. 2008;158(3):374-380.
47. Frueh BC, Elhai JD, Grubaugh AL, et al. Documented combat exposure of US veterans seeking treatment for combat-related post-traumatic stress disorder. Br J Psychiatry. 2005;186(6):467-475.
48. Frueh BC, Hamner MB, Cahill SP, Gold PB, Hamlin KL. Apparent symptom overreporting in combat veterans evaluated for PTSD. Clin Psychol Rev. 2000;20(7):853-885.
49. Sparr L, Pankratz LD. Factitious posttraumatic stress disorder. Am J Psychiatry. 1983;140(8):1016-1019.
50. Baggaley M. ‘Military Munchausen’s’: assessment of factitious claims of military service in psychiatric patients. Psychiatr Bull. 1998;22(3):153-154.
1. Bagalman E. Traumatic brain injury among veterans. http://www.ncsl.org/documents/statefed/health/TBI_Vets2013.pdf. Published January 4, 2013. Accessed February 3, 2020.
2. Veterans Health Administration, Support Service Center. Workload files fiscal year 2008-fiscal year 2012. [Source not verified.]
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