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Fresh Press: ACS Surgery News digital August issue is available on the website
This month’s issue features coverage of a presentation by John Morton, MD, FACS, at the ACS National Surgical Quality Improvement Project annual meeting on the remarkable progress made over the past decade on bariatric surgery safety and patient satisfaction. The work in this area of quality improvement continues with the DROP project currently underway. See p
In this month’s From the Washington Office column, Patrick V. Bailey, MD, FACS, keeps the Fellows informed on the advocacy efforts to make sure the 2017 proposed Medicare Physician Fee Schedule does not impose unreasonable data collection burdens on surgeons. See p. 7
Meet our new co-Editors of ACS Surgery News, Karen E. Deveney, MD, FACS and Tyler G. Hughes, MD, FACS! These two surgeons are teaming up to oversee the content and direction of ACS Surgery News, replacing our esteemed former Editor-in-Chief, Layton F. Rikkers, MD, FACS. Dr. Rikkers is a tough act to follow, but our two new Editors are up to the challenge and I hope you will join me in welcoming them both. See p. 8
Use the mobile app to download or view as a pdf.
This month’s issue features coverage of a presentation by John Morton, MD, FACS, at the ACS National Surgical Quality Improvement Project annual meeting on the remarkable progress made over the past decade on bariatric surgery safety and patient satisfaction. The work in this area of quality improvement continues with the DROP project currently underway. See p
In this month’s From the Washington Office column, Patrick V. Bailey, MD, FACS, keeps the Fellows informed on the advocacy efforts to make sure the 2017 proposed Medicare Physician Fee Schedule does not impose unreasonable data collection burdens on surgeons. See p. 7
Meet our new co-Editors of ACS Surgery News, Karen E. Deveney, MD, FACS and Tyler G. Hughes, MD, FACS! These two surgeons are teaming up to oversee the content and direction of ACS Surgery News, replacing our esteemed former Editor-in-Chief, Layton F. Rikkers, MD, FACS. Dr. Rikkers is a tough act to follow, but our two new Editors are up to the challenge and I hope you will join me in welcoming them both. See p. 8
Use the mobile app to download or view as a pdf.
This month’s issue features coverage of a presentation by John Morton, MD, FACS, at the ACS National Surgical Quality Improvement Project annual meeting on the remarkable progress made over the past decade on bariatric surgery safety and patient satisfaction. The work in this area of quality improvement continues with the DROP project currently underway. See p
In this month’s From the Washington Office column, Patrick V. Bailey, MD, FACS, keeps the Fellows informed on the advocacy efforts to make sure the 2017 proposed Medicare Physician Fee Schedule does not impose unreasonable data collection burdens on surgeons. See p. 7
Meet our new co-Editors of ACS Surgery News, Karen E. Deveney, MD, FACS and Tyler G. Hughes, MD, FACS! These two surgeons are teaming up to oversee the content and direction of ACS Surgery News, replacing our esteemed former Editor-in-Chief, Layton F. Rikkers, MD, FACS. Dr. Rikkers is a tough act to follow, but our two new Editors are up to the challenge and I hope you will join me in welcoming them both. See p. 8
Use the mobile app to download or view as a pdf.
FDA official: We’re monitoring DIY artificial pancreas boom
SAN DIEGO – A Food and Drug Administration official told diabetes educators that her agency is carefully monitoring the growth of an unusual development in diabetes care: the do-it-yourself artificial pancreas.
While the homemade insulin pumps are serving a need that has been unmet by manufacturers, the unregulated devices can be dangerous, according to Courtney Lias, PhD, director of the FDA’s Division of Chemistry and Toxicology Devices, who spoke at the annual meeting of the American Association of Diabetes Educators. “As they go toward a larger community, we see that the risk is raised.”
Still, “people are doing this because they feel this is the best way for them to help themselves or their children. We understand why people are doing it, but we want to make sure they do it safely,” Dr. Lias said.
At issue: The need for a “closed loop” artificial pancreas that needs little or no human intervention to measure blood sugar levels and deliver insulin as needed.
While current insulin pumps can deliver basal insulin continuously, users must program them to deliver an insulin bolus after meals or to address high blood sugar. Manufacturers are trying to develop a closed-loop artificial pancreas (also known as a bionic pancreas) that will simplify the process.
On their own, computer experts have been experimenting with jury-rigged homemade do-it-yourself (DIY) systems. “We recognize that for many PWDs [people with diabetes] the available help is not yet enough, so we are not waiting,” according to Dana Lewis and Scott Leibrand, two bloggers on a site called DIYPS.org.
In May 2016, The Wall Street Journal profiled a San Diego third-grader who uses a homemade “robotic pancreas” designed by his software engineer father. “More than 50 people have soldered, tinkered, and written software to make such devices for themselves or their children,” according to the Wall Street Journal report.
In her talk at the American Association of Diabetes Educators meeting, Dr. Lias noted that “there are a lot of questions about whether this is something that should be done.”
The algorithm behind a homemade device is one of area of concern, she said. “Who developed it and who’s responsible for having developed it? You may not understand how the algorithm is developed and what information is behind it. If something goes wrong, there’s no recourse.”
There are also questions about quality control, she noted: “Is there a responsible party for understanding things, for collecting information and making corrections?”
Dr. Lias said physicians should ask these questions if patients say they are using a DIY artificial pancreas: Do you understand exactly what algorithm is being used? Is it right for you? Have you checked the code to ensure it implements the algorithm correctly? Have you double-checked? When new, modified versions of code are shared, have you re-validated your entire system before implementing it?
It’s also important, she said, to note that these devices have not been determined to be safe and effective.
As the FDA monitors these DIY devices, Dr. Lias said, it’s also working to be ready to consider the work of manufacturers who are trying to develop the first commercial artificial pancreas device.
“Artificial pancreas devices do not have to be perfect with zero risk to be beneficial,” she says. “The approval decision is a benefit/risk decision. We make this decision in the context of the high risks that people with diabetes face every day.”
For now, she says, one focus is to make it easier for companies to work together to create the components of an artificial pancreas device.
The FDA is also concerned about what newly diagnosed people with diabetes will do if their devices break down, and they don’t know how to give themselves an insulin injection. “That’s a scenario that we will need to work out,” she said. “We’re talking with manufacturers about how they plan to work with that.”
SAN DIEGO – A Food and Drug Administration official told diabetes educators that her agency is carefully monitoring the growth of an unusual development in diabetes care: the do-it-yourself artificial pancreas.
While the homemade insulin pumps are serving a need that has been unmet by manufacturers, the unregulated devices can be dangerous, according to Courtney Lias, PhD, director of the FDA’s Division of Chemistry and Toxicology Devices, who spoke at the annual meeting of the American Association of Diabetes Educators. “As they go toward a larger community, we see that the risk is raised.”
Still, “people are doing this because they feel this is the best way for them to help themselves or their children. We understand why people are doing it, but we want to make sure they do it safely,” Dr. Lias said.
At issue: The need for a “closed loop” artificial pancreas that needs little or no human intervention to measure blood sugar levels and deliver insulin as needed.
While current insulin pumps can deliver basal insulin continuously, users must program them to deliver an insulin bolus after meals or to address high blood sugar. Manufacturers are trying to develop a closed-loop artificial pancreas (also known as a bionic pancreas) that will simplify the process.
On their own, computer experts have been experimenting with jury-rigged homemade do-it-yourself (DIY) systems. “We recognize that for many PWDs [people with diabetes] the available help is not yet enough, so we are not waiting,” according to Dana Lewis and Scott Leibrand, two bloggers on a site called DIYPS.org.
In May 2016, The Wall Street Journal profiled a San Diego third-grader who uses a homemade “robotic pancreas” designed by his software engineer father. “More than 50 people have soldered, tinkered, and written software to make such devices for themselves or their children,” according to the Wall Street Journal report.
In her talk at the American Association of Diabetes Educators meeting, Dr. Lias noted that “there are a lot of questions about whether this is something that should be done.”
The algorithm behind a homemade device is one of area of concern, she said. “Who developed it and who’s responsible for having developed it? You may not understand how the algorithm is developed and what information is behind it. If something goes wrong, there’s no recourse.”
There are also questions about quality control, she noted: “Is there a responsible party for understanding things, for collecting information and making corrections?”
Dr. Lias said physicians should ask these questions if patients say they are using a DIY artificial pancreas: Do you understand exactly what algorithm is being used? Is it right for you? Have you checked the code to ensure it implements the algorithm correctly? Have you double-checked? When new, modified versions of code are shared, have you re-validated your entire system before implementing it?
It’s also important, she said, to note that these devices have not been determined to be safe and effective.
As the FDA monitors these DIY devices, Dr. Lias said, it’s also working to be ready to consider the work of manufacturers who are trying to develop the first commercial artificial pancreas device.
“Artificial pancreas devices do not have to be perfect with zero risk to be beneficial,” she says. “The approval decision is a benefit/risk decision. We make this decision in the context of the high risks that people with diabetes face every day.”
For now, she says, one focus is to make it easier for companies to work together to create the components of an artificial pancreas device.
The FDA is also concerned about what newly diagnosed people with diabetes will do if their devices break down, and they don’t know how to give themselves an insulin injection. “That’s a scenario that we will need to work out,” she said. “We’re talking with manufacturers about how they plan to work with that.”
SAN DIEGO – A Food and Drug Administration official told diabetes educators that her agency is carefully monitoring the growth of an unusual development in diabetes care: the do-it-yourself artificial pancreas.
While the homemade insulin pumps are serving a need that has been unmet by manufacturers, the unregulated devices can be dangerous, according to Courtney Lias, PhD, director of the FDA’s Division of Chemistry and Toxicology Devices, who spoke at the annual meeting of the American Association of Diabetes Educators. “As they go toward a larger community, we see that the risk is raised.”
Still, “people are doing this because they feel this is the best way for them to help themselves or their children. We understand why people are doing it, but we want to make sure they do it safely,” Dr. Lias said.
At issue: The need for a “closed loop” artificial pancreas that needs little or no human intervention to measure blood sugar levels and deliver insulin as needed.
While current insulin pumps can deliver basal insulin continuously, users must program them to deliver an insulin bolus after meals or to address high blood sugar. Manufacturers are trying to develop a closed-loop artificial pancreas (also known as a bionic pancreas) that will simplify the process.
On their own, computer experts have been experimenting with jury-rigged homemade do-it-yourself (DIY) systems. “We recognize that for many PWDs [people with diabetes] the available help is not yet enough, so we are not waiting,” according to Dana Lewis and Scott Leibrand, two bloggers on a site called DIYPS.org.
In May 2016, The Wall Street Journal profiled a San Diego third-grader who uses a homemade “robotic pancreas” designed by his software engineer father. “More than 50 people have soldered, tinkered, and written software to make such devices for themselves or their children,” according to the Wall Street Journal report.
In her talk at the American Association of Diabetes Educators meeting, Dr. Lias noted that “there are a lot of questions about whether this is something that should be done.”
The algorithm behind a homemade device is one of area of concern, she said. “Who developed it and who’s responsible for having developed it? You may not understand how the algorithm is developed and what information is behind it. If something goes wrong, there’s no recourse.”
There are also questions about quality control, she noted: “Is there a responsible party for understanding things, for collecting information and making corrections?”
Dr. Lias said physicians should ask these questions if patients say they are using a DIY artificial pancreas: Do you understand exactly what algorithm is being used? Is it right for you? Have you checked the code to ensure it implements the algorithm correctly? Have you double-checked? When new, modified versions of code are shared, have you re-validated your entire system before implementing it?
It’s also important, she said, to note that these devices have not been determined to be safe and effective.
As the FDA monitors these DIY devices, Dr. Lias said, it’s also working to be ready to consider the work of manufacturers who are trying to develop the first commercial artificial pancreas device.
“Artificial pancreas devices do not have to be perfect with zero risk to be beneficial,” she says. “The approval decision is a benefit/risk decision. We make this decision in the context of the high risks that people with diabetes face every day.”
For now, she says, one focus is to make it easier for companies to work together to create the components of an artificial pancreas device.
The FDA is also concerned about what newly diagnosed people with diabetes will do if their devices break down, and they don’t know how to give themselves an insulin injection. “That’s a scenario that we will need to work out,” she said. “We’re talking with manufacturers about how they plan to work with that.”
AT AADE 16
Save the Date: AATS Centennial (1917-2017)
April 29 – May 3, 2017
Boston Hynes Convention Center
Boston, MA
President & Annual Meeting Chair
Thoralf M. Sundt, III
Annual Meeting Co-Chairs
Robert D. Jaquiss
Bryan F. Meyers
Reflecting on the Past. Building Our Future. Always Learning.
Please join us in Boston to celebrate the AATS Centennial.
Attendees will commemorate the first 100 years of the AATS and cardiothoracic surgery by enjoying activities and events, as well as viewing historical artifacts and memorabilia.
Celebrate How Far We Have Come: The Centennial officially begins with the Welcome Reception in the Exhibit Hall.
Celebrate Our Leadership: “In the Words of the Presidents” — a commemorative text containing personal reminiscences from most recent AATS past presidents — will be provided to all professional attendees.
Celebrate Our Specialty: The once-in-a-lifetime Centennial Gala will be held at the famed Wang Theatre in the heart of Boston. This black tie affair will include a cocktail reception, sit-down dinner on the stage, and performances from local musicians from prestigious Boston institutes.
Celebrate Our History: “In the Beginning” is a documentary film providing an in-depth look at the formative years of and challenges faced by cardiothoracic surgery. The film includes interviews with past presidents and members of the Centennial Committee.
April 29 – May 3, 2017
Boston Hynes Convention Center
Boston, MA
President & Annual Meeting Chair
Thoralf M. Sundt, III
Annual Meeting Co-Chairs
Robert D. Jaquiss
Bryan F. Meyers
Reflecting on the Past. Building Our Future. Always Learning.
Please join us in Boston to celebrate the AATS Centennial.
Attendees will commemorate the first 100 years of the AATS and cardiothoracic surgery by enjoying activities and events, as well as viewing historical artifacts and memorabilia.
Celebrate How Far We Have Come: The Centennial officially begins with the Welcome Reception in the Exhibit Hall.
Celebrate Our Leadership: “In the Words of the Presidents” — a commemorative text containing personal reminiscences from most recent AATS past presidents — will be provided to all professional attendees.
Celebrate Our Specialty: The once-in-a-lifetime Centennial Gala will be held at the famed Wang Theatre in the heart of Boston. This black tie affair will include a cocktail reception, sit-down dinner on the stage, and performances from local musicians from prestigious Boston institutes.
Celebrate Our History: “In the Beginning” is a documentary film providing an in-depth look at the formative years of and challenges faced by cardiothoracic surgery. The film includes interviews with past presidents and members of the Centennial Committee.
April 29 – May 3, 2017
Boston Hynes Convention Center
Boston, MA
President & Annual Meeting Chair
Thoralf M. Sundt, III
Annual Meeting Co-Chairs
Robert D. Jaquiss
Bryan F. Meyers
Reflecting on the Past. Building Our Future. Always Learning.
Please join us in Boston to celebrate the AATS Centennial.
Attendees will commemorate the first 100 years of the AATS and cardiothoracic surgery by enjoying activities and events, as well as viewing historical artifacts and memorabilia.
Celebrate How Far We Have Come: The Centennial officially begins with the Welcome Reception in the Exhibit Hall.
Celebrate Our Leadership: “In the Words of the Presidents” — a commemorative text containing personal reminiscences from most recent AATS past presidents — will be provided to all professional attendees.
Celebrate Our Specialty: The once-in-a-lifetime Centennial Gala will be held at the famed Wang Theatre in the heart of Boston. This black tie affair will include a cocktail reception, sit-down dinner on the stage, and performances from local musicians from prestigious Boston institutes.
Celebrate Our History: “In the Beginning” is a documentary film providing an in-depth look at the formative years of and challenges faced by cardiothoracic surgery. The film includes interviews with past presidents and members of the Centennial Committee.
Obamacare marketplace shakeout rocks Arizona, Southeast
Some of the Affordable Care Act’s insurance marketplaces are in turmoil as the fourth open enrollment season approaches this fall, but what’s ahead for consumers very much depends on where they live.
Competition on these exchanges will be diminished next year when three of the nation’s largest health insurers – Aetna, UnitedHealthcare and Humana – will sell individual plans in many fewer markets. So too will several Blue Cross and Blue Shield plans in various states. That’s on top of the 16 nonprofit co-ops that have closed since January 2015.
The announcements, however, apply generally only to the individual market. The much larger market of employer-sponsored insurance is not part of the health law exchanges.
Aetna’s exit announcement Aug. 15 that blamed financial losses on its marketplace plans gave Obamacare opponents who have from the start predicted the ACA’s failure a fresh chance to proclaim “I told you so.”
That story line got more complicated Aug. 17 after the Huffington Post reported that Aetna CEO Mark Bertolini sent a letter to the Department of Justice (DOJ) on July 5 threatening to withdraw from the Obamacare marketplaces if the DOJ sued to block his company’s planned merger with Humana. The DOJ did just that a couple weeks later.
But most marketplace consumers won’t see any ill effects from insurers’ withdrawals, according to ACA advocates and independent experts.
“The effect on consumers is going to be mixed around the country,” said Katherine Hempstead, PhD, a senior adviser at the nonpartisan Robert Wood Johnson Foundation. “Most of these marketplaces are not dependent on” the large national carriers.
Many major metropolitan areas, such as those in California, New York, and Texas, will still have several insurers for individual health insurance consumers to choose from. In Texas, all major metro areas – including Austin, Dallas, Houston, and San Antonio – will have at least three insurers after Aetna and UnitedHealthcare exit.
That’s true also for most urban exchange customers living in the Northwest, the Midwest, and New England.
Most hurt will be marketplace consumers in Arizona, North and South Carolina, Georgia, and parts of Florida, where only one or two insurers will be left when open enrollment season begins Nov. 15.
Remaining insurers might raise their monthly premiums as a result, but more than eight in 10 consumers on the marketplaces who get government subsidies would be insulated. Subsidies increase as premiums rise.
Still, health experts worry that with less competition, insurers may tighten their provider networks and give these consumers fewer choices of hospitals and doctors. That trend started several years ago, and some states have responded with regulations requiring insurers to provide customers with reasonable access to doctors and hospitals in each county where they sell plans.
Nearly 13 million people signed up for Obamacare marketplace policies for 2016. Aetna, UnitedHealthcare, and Humana have 2 million members in total, but their exit from certain states is predicted to affect between 1 million and 1.5 million people who will have to choose new carriers.
While changing plans can force people to find new doctors, it’s also the best way for consumers get the best deals on coverage.
Aetna will exit 11 of 15 states where it sells plans on the exchanges. UnitedHealthcare has said it will quit 22 of 34 states, and Humana will leave 4 of the 15 states where it operates.
In late May, the Kaiser Family Foundation estimated the number of rural counties at risk of having one insurer on the exchanges would triple in 2017. That was before Humana and Aetna detailed their plans. (KHN is an editorially independent project of the foundation.)
Now, “we could be looking at about one in four counties in the U.S. with just one exchange insurer next year, though this could change between now and open enrollment in November,” said Cynthia Cox, associate director for the Kaiser Family Foundation Program for the Study of Health Reform and Private Insurance.
Overshadowed by the big insurers’ withdrawals is the prospect that other carriers will enter markets the three giants are leaving. Smaller insurers Molina and Centene have said they’re doing fine on the exchanges. And Cigna, a larger insurer, has said it will move into some North Carolina counties for 2017.
North Carolina will be left with just one or two plans in most of the state after it loses UnitedHealthcare and Aetna plans. Health insurance experts say three insurers are needed for a healthy competitive market.
“We’ve had a very robust enrollment under the ACA and hope consumers will still see benefits of having coverage even if they have fewer options,” said Ciara Zachary, health policy analyst for the North Carolina Justice Center’s Health Access Coalition.
Rural Americans had few health insurers to choose from even before Obamacare, but some suburban and urban parts of the Southeast will be in the same fix next year. In southeast Florida, consumers in counties near Naples and Fort Myers will have only one marketplace insurer – Florida Blue – next year, unless other insurers step in.
“There are some headwinds, but it’s not a question of whether the market will stabilize but how quickly and how well,” said Dr. Hempstead.
Strong winds are already blowing toward Arizona’s Pinal County, southeast of Phoenix, health care advocates say. Nearly 10,000 people enrolled in Obamacare marketplace policies this year and about 85% received a federal subsidy.
In 2017, Pinal stands to lose its only two insurers – UnitedHealthcare and Blue Cross and Blue Shield of Arizona.
“Clearly this is a big concern for consumers,” said Allen Gjersvig, director of navigator and enrollment services for the Arizona Alliance for Community Health Centers. He said he is hopeful, but not confident, that another insurer will step in.
Neighboring Maricopa County, which includes Phoenix, is expected to have just two relatively small insurers left on its marketplace next year. Mr. Gjersvig said that he questions whether those two – Cigna and Phoenix Health Plan – will have enough doctors and hospitals under contract to handle their new members after larger rival Blue Cross and Blue Shield of Arizona gives up its 40,000 customers.
At least a dozen other counties in Arizona will be left with just one health insurer, he said.
Arizona had eight insurers operating in various parts of the state this year, but four are leaving entirely – Aetna, UnitedHealthcare, Humana, and Health Choice. Two more, Blue Cross Blue Shield and Health Net, are scaling back their participation.
Despite the problems with the marketplaces, Mr. Gjersvig said thousands of people have gained coverage through them and he is confident they will survive.
“We do not see this as a death knell for the marketplace,” he said.
Tammie King, an insurance agent in Columbia, S.C., is less sure how insurer departures will affect consumers in the Palmetto State. Pullouts by UnitedHealthcare and Aetna mean only one carrier in the state in 2017 – Blue Cross and Blue Shield of South Carolina.
That’s a concern in Columbia, S.C., because the Blue Cross plan does not include one of the biggest hospitals, Lexington Medical Center, and its affiliated physicians, she said.
“People will be left unable to see the doctors they are now using,” she added.
Ms. King said she worried the Blue Cross plan will use its monopoly power to further reduce the number of doctors and hospitals in its network and limit its choice of prescription drugs. “You can’t blame them because … they have to do something to control costs,” she said.
This story appears courtesy of Kaiser Health News, a national health policy news service that is part of the nonpartisan Henry J. Kaiser Family Foundation.
Some of the Affordable Care Act’s insurance marketplaces are in turmoil as the fourth open enrollment season approaches this fall, but what’s ahead for consumers very much depends on where they live.
Competition on these exchanges will be diminished next year when three of the nation’s largest health insurers – Aetna, UnitedHealthcare and Humana – will sell individual plans in many fewer markets. So too will several Blue Cross and Blue Shield plans in various states. That’s on top of the 16 nonprofit co-ops that have closed since January 2015.
The announcements, however, apply generally only to the individual market. The much larger market of employer-sponsored insurance is not part of the health law exchanges.
Aetna’s exit announcement Aug. 15 that blamed financial losses on its marketplace plans gave Obamacare opponents who have from the start predicted the ACA’s failure a fresh chance to proclaim “I told you so.”
That story line got more complicated Aug. 17 after the Huffington Post reported that Aetna CEO Mark Bertolini sent a letter to the Department of Justice (DOJ) on July 5 threatening to withdraw from the Obamacare marketplaces if the DOJ sued to block his company’s planned merger with Humana. The DOJ did just that a couple weeks later.
But most marketplace consumers won’t see any ill effects from insurers’ withdrawals, according to ACA advocates and independent experts.
“The effect on consumers is going to be mixed around the country,” said Katherine Hempstead, PhD, a senior adviser at the nonpartisan Robert Wood Johnson Foundation. “Most of these marketplaces are not dependent on” the large national carriers.
Many major metropolitan areas, such as those in California, New York, and Texas, will still have several insurers for individual health insurance consumers to choose from. In Texas, all major metro areas – including Austin, Dallas, Houston, and San Antonio – will have at least three insurers after Aetna and UnitedHealthcare exit.
That’s true also for most urban exchange customers living in the Northwest, the Midwest, and New England.
Most hurt will be marketplace consumers in Arizona, North and South Carolina, Georgia, and parts of Florida, where only one or two insurers will be left when open enrollment season begins Nov. 15.
Remaining insurers might raise their monthly premiums as a result, but more than eight in 10 consumers on the marketplaces who get government subsidies would be insulated. Subsidies increase as premiums rise.
Still, health experts worry that with less competition, insurers may tighten their provider networks and give these consumers fewer choices of hospitals and doctors. That trend started several years ago, and some states have responded with regulations requiring insurers to provide customers with reasonable access to doctors and hospitals in each county where they sell plans.
Nearly 13 million people signed up for Obamacare marketplace policies for 2016. Aetna, UnitedHealthcare, and Humana have 2 million members in total, but their exit from certain states is predicted to affect between 1 million and 1.5 million people who will have to choose new carriers.
While changing plans can force people to find new doctors, it’s also the best way for consumers get the best deals on coverage.
Aetna will exit 11 of 15 states where it sells plans on the exchanges. UnitedHealthcare has said it will quit 22 of 34 states, and Humana will leave 4 of the 15 states where it operates.
In late May, the Kaiser Family Foundation estimated the number of rural counties at risk of having one insurer on the exchanges would triple in 2017. That was before Humana and Aetna detailed their plans. (KHN is an editorially independent project of the foundation.)
Now, “we could be looking at about one in four counties in the U.S. with just one exchange insurer next year, though this could change between now and open enrollment in November,” said Cynthia Cox, associate director for the Kaiser Family Foundation Program for the Study of Health Reform and Private Insurance.
Overshadowed by the big insurers’ withdrawals is the prospect that other carriers will enter markets the three giants are leaving. Smaller insurers Molina and Centene have said they’re doing fine on the exchanges. And Cigna, a larger insurer, has said it will move into some North Carolina counties for 2017.
North Carolina will be left with just one or two plans in most of the state after it loses UnitedHealthcare and Aetna plans. Health insurance experts say three insurers are needed for a healthy competitive market.
“We’ve had a very robust enrollment under the ACA and hope consumers will still see benefits of having coverage even if they have fewer options,” said Ciara Zachary, health policy analyst for the North Carolina Justice Center’s Health Access Coalition.
Rural Americans had few health insurers to choose from even before Obamacare, but some suburban and urban parts of the Southeast will be in the same fix next year. In southeast Florida, consumers in counties near Naples and Fort Myers will have only one marketplace insurer – Florida Blue – next year, unless other insurers step in.
“There are some headwinds, but it’s not a question of whether the market will stabilize but how quickly and how well,” said Dr. Hempstead.
Strong winds are already blowing toward Arizona’s Pinal County, southeast of Phoenix, health care advocates say. Nearly 10,000 people enrolled in Obamacare marketplace policies this year and about 85% received a federal subsidy.
In 2017, Pinal stands to lose its only two insurers – UnitedHealthcare and Blue Cross and Blue Shield of Arizona.
“Clearly this is a big concern for consumers,” said Allen Gjersvig, director of navigator and enrollment services for the Arizona Alliance for Community Health Centers. He said he is hopeful, but not confident, that another insurer will step in.
Neighboring Maricopa County, which includes Phoenix, is expected to have just two relatively small insurers left on its marketplace next year. Mr. Gjersvig said that he questions whether those two – Cigna and Phoenix Health Plan – will have enough doctors and hospitals under contract to handle their new members after larger rival Blue Cross and Blue Shield of Arizona gives up its 40,000 customers.
At least a dozen other counties in Arizona will be left with just one health insurer, he said.
Arizona had eight insurers operating in various parts of the state this year, but four are leaving entirely – Aetna, UnitedHealthcare, Humana, and Health Choice. Two more, Blue Cross Blue Shield and Health Net, are scaling back their participation.
Despite the problems with the marketplaces, Mr. Gjersvig said thousands of people have gained coverage through them and he is confident they will survive.
“We do not see this as a death knell for the marketplace,” he said.
Tammie King, an insurance agent in Columbia, S.C., is less sure how insurer departures will affect consumers in the Palmetto State. Pullouts by UnitedHealthcare and Aetna mean only one carrier in the state in 2017 – Blue Cross and Blue Shield of South Carolina.
That’s a concern in Columbia, S.C., because the Blue Cross plan does not include one of the biggest hospitals, Lexington Medical Center, and its affiliated physicians, she said.
“People will be left unable to see the doctors they are now using,” she added.
Ms. King said she worried the Blue Cross plan will use its monopoly power to further reduce the number of doctors and hospitals in its network and limit its choice of prescription drugs. “You can’t blame them because … they have to do something to control costs,” she said.
This story appears courtesy of Kaiser Health News, a national health policy news service that is part of the nonpartisan Henry J. Kaiser Family Foundation.
Some of the Affordable Care Act’s insurance marketplaces are in turmoil as the fourth open enrollment season approaches this fall, but what’s ahead for consumers very much depends on where they live.
Competition on these exchanges will be diminished next year when three of the nation’s largest health insurers – Aetna, UnitedHealthcare and Humana – will sell individual plans in many fewer markets. So too will several Blue Cross and Blue Shield plans in various states. That’s on top of the 16 nonprofit co-ops that have closed since January 2015.
The announcements, however, apply generally only to the individual market. The much larger market of employer-sponsored insurance is not part of the health law exchanges.
Aetna’s exit announcement Aug. 15 that blamed financial losses on its marketplace plans gave Obamacare opponents who have from the start predicted the ACA’s failure a fresh chance to proclaim “I told you so.”
That story line got more complicated Aug. 17 after the Huffington Post reported that Aetna CEO Mark Bertolini sent a letter to the Department of Justice (DOJ) on July 5 threatening to withdraw from the Obamacare marketplaces if the DOJ sued to block his company’s planned merger with Humana. The DOJ did just that a couple weeks later.
But most marketplace consumers won’t see any ill effects from insurers’ withdrawals, according to ACA advocates and independent experts.
“The effect on consumers is going to be mixed around the country,” said Katherine Hempstead, PhD, a senior adviser at the nonpartisan Robert Wood Johnson Foundation. “Most of these marketplaces are not dependent on” the large national carriers.
Many major metropolitan areas, such as those in California, New York, and Texas, will still have several insurers for individual health insurance consumers to choose from. In Texas, all major metro areas – including Austin, Dallas, Houston, and San Antonio – will have at least three insurers after Aetna and UnitedHealthcare exit.
That’s true also for most urban exchange customers living in the Northwest, the Midwest, and New England.
Most hurt will be marketplace consumers in Arizona, North and South Carolina, Georgia, and parts of Florida, where only one or two insurers will be left when open enrollment season begins Nov. 15.
Remaining insurers might raise their monthly premiums as a result, but more than eight in 10 consumers on the marketplaces who get government subsidies would be insulated. Subsidies increase as premiums rise.
Still, health experts worry that with less competition, insurers may tighten their provider networks and give these consumers fewer choices of hospitals and doctors. That trend started several years ago, and some states have responded with regulations requiring insurers to provide customers with reasonable access to doctors and hospitals in each county where they sell plans.
Nearly 13 million people signed up for Obamacare marketplace policies for 2016. Aetna, UnitedHealthcare, and Humana have 2 million members in total, but their exit from certain states is predicted to affect between 1 million and 1.5 million people who will have to choose new carriers.
While changing plans can force people to find new doctors, it’s also the best way for consumers get the best deals on coverage.
Aetna will exit 11 of 15 states where it sells plans on the exchanges. UnitedHealthcare has said it will quit 22 of 34 states, and Humana will leave 4 of the 15 states where it operates.
In late May, the Kaiser Family Foundation estimated the number of rural counties at risk of having one insurer on the exchanges would triple in 2017. That was before Humana and Aetna detailed their plans. (KHN is an editorially independent project of the foundation.)
Now, “we could be looking at about one in four counties in the U.S. with just one exchange insurer next year, though this could change between now and open enrollment in November,” said Cynthia Cox, associate director for the Kaiser Family Foundation Program for the Study of Health Reform and Private Insurance.
Overshadowed by the big insurers’ withdrawals is the prospect that other carriers will enter markets the three giants are leaving. Smaller insurers Molina and Centene have said they’re doing fine on the exchanges. And Cigna, a larger insurer, has said it will move into some North Carolina counties for 2017.
North Carolina will be left with just one or two plans in most of the state after it loses UnitedHealthcare and Aetna plans. Health insurance experts say three insurers are needed for a healthy competitive market.
“We’ve had a very robust enrollment under the ACA and hope consumers will still see benefits of having coverage even if they have fewer options,” said Ciara Zachary, health policy analyst for the North Carolina Justice Center’s Health Access Coalition.
Rural Americans had few health insurers to choose from even before Obamacare, but some suburban and urban parts of the Southeast will be in the same fix next year. In southeast Florida, consumers in counties near Naples and Fort Myers will have only one marketplace insurer – Florida Blue – next year, unless other insurers step in.
“There are some headwinds, but it’s not a question of whether the market will stabilize but how quickly and how well,” said Dr. Hempstead.
Strong winds are already blowing toward Arizona’s Pinal County, southeast of Phoenix, health care advocates say. Nearly 10,000 people enrolled in Obamacare marketplace policies this year and about 85% received a federal subsidy.
In 2017, Pinal stands to lose its only two insurers – UnitedHealthcare and Blue Cross and Blue Shield of Arizona.
“Clearly this is a big concern for consumers,” said Allen Gjersvig, director of navigator and enrollment services for the Arizona Alliance for Community Health Centers. He said he is hopeful, but not confident, that another insurer will step in.
Neighboring Maricopa County, which includes Phoenix, is expected to have just two relatively small insurers left on its marketplace next year. Mr. Gjersvig said that he questions whether those two – Cigna and Phoenix Health Plan – will have enough doctors and hospitals under contract to handle their new members after larger rival Blue Cross and Blue Shield of Arizona gives up its 40,000 customers.
At least a dozen other counties in Arizona will be left with just one health insurer, he said.
Arizona had eight insurers operating in various parts of the state this year, but four are leaving entirely – Aetna, UnitedHealthcare, Humana, and Health Choice. Two more, Blue Cross Blue Shield and Health Net, are scaling back their participation.
Despite the problems with the marketplaces, Mr. Gjersvig said thousands of people have gained coverage through them and he is confident they will survive.
“We do not see this as a death knell for the marketplace,” he said.
Tammie King, an insurance agent in Columbia, S.C., is less sure how insurer departures will affect consumers in the Palmetto State. Pullouts by UnitedHealthcare and Aetna mean only one carrier in the state in 2017 – Blue Cross and Blue Shield of South Carolina.
That’s a concern in Columbia, S.C., because the Blue Cross plan does not include one of the biggest hospitals, Lexington Medical Center, and its affiliated physicians, she said.
“People will be left unable to see the doctors they are now using,” she added.
Ms. King said she worried the Blue Cross plan will use its monopoly power to further reduce the number of doctors and hospitals in its network and limit its choice of prescription drugs. “You can’t blame them because … they have to do something to control costs,” she said.
This story appears courtesy of Kaiser Health News, a national health policy news service that is part of the nonpartisan Henry J. Kaiser Family Foundation.
Intervention Decreases Urinary Tract Infections from Catheters
Compared to other healthcare-associated infections, catheter-associated urinary tract infections (CAUTIs) cause relatively low rates of mortality and morbidity, but their prevalence nevertheless leads to a considerable cumulative burden.
Hospitalists can impact CAUTI rates by using a simple bundle of interventions. This idea was recently demonstrated by a quality improvement project addressing high CAUTI rates in the hospital setting. The project was summarized in a paper published in The Joint Commission Journal on Quality and Patient Safety.
The project identified a bundle of primary interventions to reduce CAUTI, which consisted of six elements: the “6 Cs” of CAUTI reduction. These include “consider alternatives,” “culture urine only when indication is clear,” and “connect with a securement device.” The interventions were implemented on one ICU with excellent results and subsequently diffused throughout the healthcare facility using multimedia tools. CAUTI rates decreased by 70%.
“The first steps in CAUTI prevention are to ensure that catheters are placed only when necessary, aseptic technique used for placement, and that they are removed when no longer essential,” says lead author Priya Sampathkumar, MD, Mayo Clinic associate professor of medicine. “Once this has been achieved, if CAUTI rates are still high, a secondary bundle of CAUTI prevention can help to reduce CAUTI further.”
About one in four hospitalized patients have a urinary catheter in place.2 “Hospitalists, therefore, can have a significant impact on CAUTI by being mindful about catheter use and catheter management.” Dr. Sampathkumar says.
References
- Sampathkumar P, Barth JW, Johnson M, et al. Mayo Clinic reduces catheter-associated urinary tract infections. Jt Comm J Qual Patient Saf. 2016;42(6):254-265.
- Catheter-associated urinary tract infections (CAUTI). Centers for Disease Control and Prevention website. Accessed August 8, 2016.
Compared to other healthcare-associated infections, catheter-associated urinary tract infections (CAUTIs) cause relatively low rates of mortality and morbidity, but their prevalence nevertheless leads to a considerable cumulative burden.
Hospitalists can impact CAUTI rates by using a simple bundle of interventions. This idea was recently demonstrated by a quality improvement project addressing high CAUTI rates in the hospital setting. The project was summarized in a paper published in The Joint Commission Journal on Quality and Patient Safety.
The project identified a bundle of primary interventions to reduce CAUTI, which consisted of six elements: the “6 Cs” of CAUTI reduction. These include “consider alternatives,” “culture urine only when indication is clear,” and “connect with a securement device.” The interventions were implemented on one ICU with excellent results and subsequently diffused throughout the healthcare facility using multimedia tools. CAUTI rates decreased by 70%.
“The first steps in CAUTI prevention are to ensure that catheters are placed only when necessary, aseptic technique used for placement, and that they are removed when no longer essential,” says lead author Priya Sampathkumar, MD, Mayo Clinic associate professor of medicine. “Once this has been achieved, if CAUTI rates are still high, a secondary bundle of CAUTI prevention can help to reduce CAUTI further.”
About one in four hospitalized patients have a urinary catheter in place.2 “Hospitalists, therefore, can have a significant impact on CAUTI by being mindful about catheter use and catheter management.” Dr. Sampathkumar says.
References
- Sampathkumar P, Barth JW, Johnson M, et al. Mayo Clinic reduces catheter-associated urinary tract infections. Jt Comm J Qual Patient Saf. 2016;42(6):254-265.
- Catheter-associated urinary tract infections (CAUTI). Centers for Disease Control and Prevention website. Accessed August 8, 2016.
Compared to other healthcare-associated infections, catheter-associated urinary tract infections (CAUTIs) cause relatively low rates of mortality and morbidity, but their prevalence nevertheless leads to a considerable cumulative burden.
Hospitalists can impact CAUTI rates by using a simple bundle of interventions. This idea was recently demonstrated by a quality improvement project addressing high CAUTI rates in the hospital setting. The project was summarized in a paper published in The Joint Commission Journal on Quality and Patient Safety.
The project identified a bundle of primary interventions to reduce CAUTI, which consisted of six elements: the “6 Cs” of CAUTI reduction. These include “consider alternatives,” “culture urine only when indication is clear,” and “connect with a securement device.” The interventions were implemented on one ICU with excellent results and subsequently diffused throughout the healthcare facility using multimedia tools. CAUTI rates decreased by 70%.
“The first steps in CAUTI prevention are to ensure that catheters are placed only when necessary, aseptic technique used for placement, and that they are removed when no longer essential,” says lead author Priya Sampathkumar, MD, Mayo Clinic associate professor of medicine. “Once this has been achieved, if CAUTI rates are still high, a secondary bundle of CAUTI prevention can help to reduce CAUTI further.”
About one in four hospitalized patients have a urinary catheter in place.2 “Hospitalists, therefore, can have a significant impact on CAUTI by being mindful about catheter use and catheter management.” Dr. Sampathkumar says.
References
- Sampathkumar P, Barth JW, Johnson M, et al. Mayo Clinic reduces catheter-associated urinary tract infections. Jt Comm J Qual Patient Saf. 2016;42(6):254-265.
- Catheter-associated urinary tract infections (CAUTI). Centers for Disease Control and Prevention website. Accessed August 8, 2016.
Immunotherapy might treat, prevent malaria
Image courtesy of Ute
Frevert and Margaret Shear
A synthetic version of the protein PD-L2 can treat malaria in mice and protect them from re-infection, according to researchers.
The team’s experiments indicated that PD-L2 determines the severity of malaria infection and is essential for CD4+ T-cell immunity against malaria.
When the researchers administered soluble multimeric PD-L2 to mice, the animals were cured of severe malaria and protected from re-infection months later.
Michelle Wykes, DPhil, of QIMR Berghofer Medical Research Institute in Herston, Queensland, Australia, and her colleagues reported these results in Immunity.
The researchers noted that Plasmodium parasites exploit the interaction between PD-1 and PD-L1 to prevent T cells from fighting malaria, but the role of PD-L2 has not been clear.
With this study, the team found that PD-L2 regulates the PD-1—PD-L1 interaction and might therefore be used to treat malaria.
“We found that, when humans and mice are infected with severe malaria, levels of PD-L2 decrease, and so the T cells aren’t being told to keep fighting the parasites,” Dr Wykes explained.
“We don’t know how malaria manages to block the production of PD-L2. But once we knew how important this protein was for fighting the disease, we developed a synthetic version of it in the laboratory.”
The researchers gave 3 doses of this synthetic PD-L2 to mice that had been infected with a lethal dose of malaria.
“All of these mice were cured of the malaria,” Dr Wykes said. “About 5 months later, we re-infected the same mice with malaria parasites, but, this time, we didn’t give them any more of the synthetic protein. All of the mice were completely protected and didn’t become infected.”
Dr Wykes said these findings could form the basis for new ways to treat malaria in the future.
“[I]f this approach is successful, it should treat all species of malaria parasite,” she noted. “This would be a completely new way of treating malaria—by stimulating a person’s own immune system to destroy the parasites.”
Image courtesy of Ute
Frevert and Margaret Shear
A synthetic version of the protein PD-L2 can treat malaria in mice and protect them from re-infection, according to researchers.
The team’s experiments indicated that PD-L2 determines the severity of malaria infection and is essential for CD4+ T-cell immunity against malaria.
When the researchers administered soluble multimeric PD-L2 to mice, the animals were cured of severe malaria and protected from re-infection months later.
Michelle Wykes, DPhil, of QIMR Berghofer Medical Research Institute in Herston, Queensland, Australia, and her colleagues reported these results in Immunity.
The researchers noted that Plasmodium parasites exploit the interaction between PD-1 and PD-L1 to prevent T cells from fighting malaria, but the role of PD-L2 has not been clear.
With this study, the team found that PD-L2 regulates the PD-1—PD-L1 interaction and might therefore be used to treat malaria.
“We found that, when humans and mice are infected with severe malaria, levels of PD-L2 decrease, and so the T cells aren’t being told to keep fighting the parasites,” Dr Wykes explained.
“We don’t know how malaria manages to block the production of PD-L2. But once we knew how important this protein was for fighting the disease, we developed a synthetic version of it in the laboratory.”
The researchers gave 3 doses of this synthetic PD-L2 to mice that had been infected with a lethal dose of malaria.
“All of these mice were cured of the malaria,” Dr Wykes said. “About 5 months later, we re-infected the same mice with malaria parasites, but, this time, we didn’t give them any more of the synthetic protein. All of the mice were completely protected and didn’t become infected.”
Dr Wykes said these findings could form the basis for new ways to treat malaria in the future.
“[I]f this approach is successful, it should treat all species of malaria parasite,” she noted. “This would be a completely new way of treating malaria—by stimulating a person’s own immune system to destroy the parasites.”
Image courtesy of Ute
Frevert and Margaret Shear
A synthetic version of the protein PD-L2 can treat malaria in mice and protect them from re-infection, according to researchers.
The team’s experiments indicated that PD-L2 determines the severity of malaria infection and is essential for CD4+ T-cell immunity against malaria.
When the researchers administered soluble multimeric PD-L2 to mice, the animals were cured of severe malaria and protected from re-infection months later.
Michelle Wykes, DPhil, of QIMR Berghofer Medical Research Institute in Herston, Queensland, Australia, and her colleagues reported these results in Immunity.
The researchers noted that Plasmodium parasites exploit the interaction between PD-1 and PD-L1 to prevent T cells from fighting malaria, but the role of PD-L2 has not been clear.
With this study, the team found that PD-L2 regulates the PD-1—PD-L1 interaction and might therefore be used to treat malaria.
“We found that, when humans and mice are infected with severe malaria, levels of PD-L2 decrease, and so the T cells aren’t being told to keep fighting the parasites,” Dr Wykes explained.
“We don’t know how malaria manages to block the production of PD-L2. But once we knew how important this protein was for fighting the disease, we developed a synthetic version of it in the laboratory.”
The researchers gave 3 doses of this synthetic PD-L2 to mice that had been infected with a lethal dose of malaria.
“All of these mice were cured of the malaria,” Dr Wykes said. “About 5 months later, we re-infected the same mice with malaria parasites, but, this time, we didn’t give them any more of the synthetic protein. All of the mice were completely protected and didn’t become infected.”
Dr Wykes said these findings could form the basis for new ways to treat malaria in the future.
“[I]f this approach is successful, it should treat all species of malaria parasite,” she noted. “This would be a completely new way of treating malaria—by stimulating a person’s own immune system to destroy the parasites.”
Cancer survivors have ‘normal’ sex lives, survey says
receiving treatment
Photo by Rhoda Baer
A new study suggests cancer survivors and the general population have comparable sex lives, although cancer survivors don’t realize it.
According to a survey of more than 6500 people, cancer survivors over the age of 49 have just as much sex and similar levels of sexual function as individuals of the same age who never had cancer.
However, the cancer survivors were more likely to report being dissatisfied with their sex lives.
“We hope our findings will put cancer survivors’ concerns to rest—showing that they are just as sexually active and function just as well as others their age,” said Sarah Jackson, PhD, of University College London in the UK.
“The next stage of our research will look at why cancer patients feel less satisfied with their sex lives.”
Dr Jackson and her colleagues reported their current findings in Cancer.
The researchers set out to explore differences in sexual activity and function, as well as concerns about sex, between cancer survivors and cancer-free controls in a population-based study.
The team surveyed 3708 women (341 cancer survivors and 3367 controls) and 2982 men (220 cancer survivors and 2762 controls) aged 50 and older. Male and female cancer survivors were significantly older than controls (P<0.001 for both) and reported more comorbidities (P=0.003 for both).
Frequency
There were no significant differences in levels of sexual activity between cancer survivors and controls of either sex.
Among women, 58.2% of cancer survivors and 55.5% of controls reported having any sexual activity in the last year. Among men, the rates were 76.0% and 78.5%, respectively.
Overall, about half of the people surveyed reported having “frequent” sexual intercourse, which was defined as 2 to 3 times a month or more.
This included 49.1% of female cancer survivors, 50.1% of female controls, 49% of male cancer survivors, and 48% of male controls.
Function
The incidence of sexual problems was similar in cancer survivors and controls—both male and female.
For example, around a third of the women said they had problems becoming aroused (31.4% of cancer survivors and 31.8% of controls), and about 40% of the men had erectile dysfunction (40.3% of cancer survivors and 39.3% of controls).
Satisfaction
Despite similar levels of sexual activity and function, cancer survivors were more likely than controls to report feeling dissatisfied with their sex lives.
Among the women, 18.2% of cancer survivors and 11.8% of controls reported dissatisfaction (P=0.034). Among the men, the rates were 30.9% and 19.8%, respectively (P=0.023).
In addition, female cancer survivors were more likely to be concerned about their libido than female controls—10.2% and 7.1%, respectively (P=0.006). But there was no significant difference for the men.
Time from cancer diagnosis
The researchers also found the amount of time from cancer diagnosis was a factor affecting sexual function and concern among women but not men.
Females diagnosed with cancer less than 5 years from the time they were surveyed were more likely than female controls to report difficulty becoming aroused (55.4% and 31.8%, respectively, P=0.016) and achieving orgasm (60.6% and 28.3%, respectively, P<0.001).
The recently diagnosed females were also more likely than controls to be concerned about sexual desire (14.8% and 7.1%, respectively, P=0.007) and orgasmic experience (17.6% and 7.1%, respectively, P=0.042).
“Although some cancer treatments are known to impact on sexual function, this study suggests that the majority of cancer patients have similar sexual function and activity as the general population,” said Martin Ledwick, of Cancer Research UK, which sponsored this study.
“However, cancer patients in the study were more likely to be dissatisfied with their sex lives . . . . This highlights the need for health professionals to make sure they talk about sex with all patients—not just the ones whose sexual function is likely to be affected by their cancer or its treatment.”
receiving treatment
Photo by Rhoda Baer
A new study suggests cancer survivors and the general population have comparable sex lives, although cancer survivors don’t realize it.
According to a survey of more than 6500 people, cancer survivors over the age of 49 have just as much sex and similar levels of sexual function as individuals of the same age who never had cancer.
However, the cancer survivors were more likely to report being dissatisfied with their sex lives.
“We hope our findings will put cancer survivors’ concerns to rest—showing that they are just as sexually active and function just as well as others their age,” said Sarah Jackson, PhD, of University College London in the UK.
“The next stage of our research will look at why cancer patients feel less satisfied with their sex lives.”
Dr Jackson and her colleagues reported their current findings in Cancer.
The researchers set out to explore differences in sexual activity and function, as well as concerns about sex, between cancer survivors and cancer-free controls in a population-based study.
The team surveyed 3708 women (341 cancer survivors and 3367 controls) and 2982 men (220 cancer survivors and 2762 controls) aged 50 and older. Male and female cancer survivors were significantly older than controls (P<0.001 for both) and reported more comorbidities (P=0.003 for both).
Frequency
There were no significant differences in levels of sexual activity between cancer survivors and controls of either sex.
Among women, 58.2% of cancer survivors and 55.5% of controls reported having any sexual activity in the last year. Among men, the rates were 76.0% and 78.5%, respectively.
Overall, about half of the people surveyed reported having “frequent” sexual intercourse, which was defined as 2 to 3 times a month or more.
This included 49.1% of female cancer survivors, 50.1% of female controls, 49% of male cancer survivors, and 48% of male controls.
Function
The incidence of sexual problems was similar in cancer survivors and controls—both male and female.
For example, around a third of the women said they had problems becoming aroused (31.4% of cancer survivors and 31.8% of controls), and about 40% of the men had erectile dysfunction (40.3% of cancer survivors and 39.3% of controls).
Satisfaction
Despite similar levels of sexual activity and function, cancer survivors were more likely than controls to report feeling dissatisfied with their sex lives.
Among the women, 18.2% of cancer survivors and 11.8% of controls reported dissatisfaction (P=0.034). Among the men, the rates were 30.9% and 19.8%, respectively (P=0.023).
In addition, female cancer survivors were more likely to be concerned about their libido than female controls—10.2% and 7.1%, respectively (P=0.006). But there was no significant difference for the men.
Time from cancer diagnosis
The researchers also found the amount of time from cancer diagnosis was a factor affecting sexual function and concern among women but not men.
Females diagnosed with cancer less than 5 years from the time they were surveyed were more likely than female controls to report difficulty becoming aroused (55.4% and 31.8%, respectively, P=0.016) and achieving orgasm (60.6% and 28.3%, respectively, P<0.001).
The recently diagnosed females were also more likely than controls to be concerned about sexual desire (14.8% and 7.1%, respectively, P=0.007) and orgasmic experience (17.6% and 7.1%, respectively, P=0.042).
“Although some cancer treatments are known to impact on sexual function, this study suggests that the majority of cancer patients have similar sexual function and activity as the general population,” said Martin Ledwick, of Cancer Research UK, which sponsored this study.
“However, cancer patients in the study were more likely to be dissatisfied with their sex lives . . . . This highlights the need for health professionals to make sure they talk about sex with all patients—not just the ones whose sexual function is likely to be affected by their cancer or its treatment.”
receiving treatment
Photo by Rhoda Baer
A new study suggests cancer survivors and the general population have comparable sex lives, although cancer survivors don’t realize it.
According to a survey of more than 6500 people, cancer survivors over the age of 49 have just as much sex and similar levels of sexual function as individuals of the same age who never had cancer.
However, the cancer survivors were more likely to report being dissatisfied with their sex lives.
“We hope our findings will put cancer survivors’ concerns to rest—showing that they are just as sexually active and function just as well as others their age,” said Sarah Jackson, PhD, of University College London in the UK.
“The next stage of our research will look at why cancer patients feel less satisfied with their sex lives.”
Dr Jackson and her colleagues reported their current findings in Cancer.
The researchers set out to explore differences in sexual activity and function, as well as concerns about sex, between cancer survivors and cancer-free controls in a population-based study.
The team surveyed 3708 women (341 cancer survivors and 3367 controls) and 2982 men (220 cancer survivors and 2762 controls) aged 50 and older. Male and female cancer survivors were significantly older than controls (P<0.001 for both) and reported more comorbidities (P=0.003 for both).
Frequency
There were no significant differences in levels of sexual activity between cancer survivors and controls of either sex.
Among women, 58.2% of cancer survivors and 55.5% of controls reported having any sexual activity in the last year. Among men, the rates were 76.0% and 78.5%, respectively.
Overall, about half of the people surveyed reported having “frequent” sexual intercourse, which was defined as 2 to 3 times a month or more.
This included 49.1% of female cancer survivors, 50.1% of female controls, 49% of male cancer survivors, and 48% of male controls.
Function
The incidence of sexual problems was similar in cancer survivors and controls—both male and female.
For example, around a third of the women said they had problems becoming aroused (31.4% of cancer survivors and 31.8% of controls), and about 40% of the men had erectile dysfunction (40.3% of cancer survivors and 39.3% of controls).
Satisfaction
Despite similar levels of sexual activity and function, cancer survivors were more likely than controls to report feeling dissatisfied with their sex lives.
Among the women, 18.2% of cancer survivors and 11.8% of controls reported dissatisfaction (P=0.034). Among the men, the rates were 30.9% and 19.8%, respectively (P=0.023).
In addition, female cancer survivors were more likely to be concerned about their libido than female controls—10.2% and 7.1%, respectively (P=0.006). But there was no significant difference for the men.
Time from cancer diagnosis
The researchers also found the amount of time from cancer diagnosis was a factor affecting sexual function and concern among women but not men.
Females diagnosed with cancer less than 5 years from the time they were surveyed were more likely than female controls to report difficulty becoming aroused (55.4% and 31.8%, respectively, P=0.016) and achieving orgasm (60.6% and 28.3%, respectively, P<0.001).
The recently diagnosed females were also more likely than controls to be concerned about sexual desire (14.8% and 7.1%, respectively, P=0.007) and orgasmic experience (17.6% and 7.1%, respectively, P=0.042).
“Although some cancer treatments are known to impact on sexual function, this study suggests that the majority of cancer patients have similar sexual function and activity as the general population,” said Martin Ledwick, of Cancer Research UK, which sponsored this study.
“However, cancer patients in the study were more likely to be dissatisfied with their sex lives . . . . This highlights the need for health professionals to make sure they talk about sex with all patients—not just the ones whose sexual function is likely to be affected by their cancer or its treatment.”
Anemia linked to risk of death after stroke
Anemia may increase the risk of death in older adults who have had a stroke, according to research published in the Journal of the American Heart Association.
An initial analysis of more than 8000 patients showed that anemia was associated with a higher risk of death for up to 1 year following ischemic or hemorrhagic stroke.
A second analysis of nearly 30,000 patients suggested the risk of dying from ischemic stroke is about 2 times higher in patients with anemia than those without it, and the risk of death from hemorrhagic stroke is about 1.5 times higher in anemic patients.
“So there’s the potential for a much poorer outcome if somebody comes in with stroke and they’re also anemic,” said study author Phyo Myint, MD, of the University of Aberdeen in Scotland.
Dr Myint and his colleagues first examined data from the UK Regional Stroke Register. This included 8013 patients with an average age of 78 who were admitted to the hospital with acute stroke between 2003 and 2015.
The team assessed the impact of anemia and hemoglobin levels at admission on death at different time points—inpatient, 7 days, 14 days, 1 month, 3 months, 6 months, and 1 year after stroke.
Anemia was associated with higher odds of death at most of the time points examined. And elevated hemoglobin was associated with a higher risk of death, mainly within the first month.
In addition to analyzing data from the UK Regional Stroke Registry, the researchers systematically reviewed relevant literature published to date. They compiled data from 20 previous studies, increasing the study population to 29,943 stroke patients.
In analyzing these patients, the researchers found that anemia on admission was associated with an increased risk of mortality in both ischemic stroke and hemorrhagic stroke. The odds ratios were 1.97 and 1.46, respectively.
The researchers believe this study emphasizes the impact of anemia on stroke outcomes and the need for increased awareness and interventions for stroke patients with anemia.
“One example of an intervention might be treating the underlying causes of anemia, such as iron deficiency, which is common in this age group,” said study author Raphae Barlas, a medical student at the University of Aberdeen.
“As the study has convincingly demonstrated, anemia does worsen the outcome of stroke, so it is very important that we identify at-risk patients and optimize the management.”
Anemia may increase the risk of death in older adults who have had a stroke, according to research published in the Journal of the American Heart Association.
An initial analysis of more than 8000 patients showed that anemia was associated with a higher risk of death for up to 1 year following ischemic or hemorrhagic stroke.
A second analysis of nearly 30,000 patients suggested the risk of dying from ischemic stroke is about 2 times higher in patients with anemia than those without it, and the risk of death from hemorrhagic stroke is about 1.5 times higher in anemic patients.
“So there’s the potential for a much poorer outcome if somebody comes in with stroke and they’re also anemic,” said study author Phyo Myint, MD, of the University of Aberdeen in Scotland.
Dr Myint and his colleagues first examined data from the UK Regional Stroke Register. This included 8013 patients with an average age of 78 who were admitted to the hospital with acute stroke between 2003 and 2015.
The team assessed the impact of anemia and hemoglobin levels at admission on death at different time points—inpatient, 7 days, 14 days, 1 month, 3 months, 6 months, and 1 year after stroke.
Anemia was associated with higher odds of death at most of the time points examined. And elevated hemoglobin was associated with a higher risk of death, mainly within the first month.
In addition to analyzing data from the UK Regional Stroke Registry, the researchers systematically reviewed relevant literature published to date. They compiled data from 20 previous studies, increasing the study population to 29,943 stroke patients.
In analyzing these patients, the researchers found that anemia on admission was associated with an increased risk of mortality in both ischemic stroke and hemorrhagic stroke. The odds ratios were 1.97 and 1.46, respectively.
The researchers believe this study emphasizes the impact of anemia on stroke outcomes and the need for increased awareness and interventions for stroke patients with anemia.
“One example of an intervention might be treating the underlying causes of anemia, such as iron deficiency, which is common in this age group,” said study author Raphae Barlas, a medical student at the University of Aberdeen.
“As the study has convincingly demonstrated, anemia does worsen the outcome of stroke, so it is very important that we identify at-risk patients and optimize the management.”
Anemia may increase the risk of death in older adults who have had a stroke, according to research published in the Journal of the American Heart Association.
An initial analysis of more than 8000 patients showed that anemia was associated with a higher risk of death for up to 1 year following ischemic or hemorrhagic stroke.
A second analysis of nearly 30,000 patients suggested the risk of dying from ischemic stroke is about 2 times higher in patients with anemia than those without it, and the risk of death from hemorrhagic stroke is about 1.5 times higher in anemic patients.
“So there’s the potential for a much poorer outcome if somebody comes in with stroke and they’re also anemic,” said study author Phyo Myint, MD, of the University of Aberdeen in Scotland.
Dr Myint and his colleagues first examined data from the UK Regional Stroke Register. This included 8013 patients with an average age of 78 who were admitted to the hospital with acute stroke between 2003 and 2015.
The team assessed the impact of anemia and hemoglobin levels at admission on death at different time points—inpatient, 7 days, 14 days, 1 month, 3 months, 6 months, and 1 year after stroke.
Anemia was associated with higher odds of death at most of the time points examined. And elevated hemoglobin was associated with a higher risk of death, mainly within the first month.
In addition to analyzing data from the UK Regional Stroke Registry, the researchers systematically reviewed relevant literature published to date. They compiled data from 20 previous studies, increasing the study population to 29,943 stroke patients.
In analyzing these patients, the researchers found that anemia on admission was associated with an increased risk of mortality in both ischemic stroke and hemorrhagic stroke. The odds ratios were 1.97 and 1.46, respectively.
The researchers believe this study emphasizes the impact of anemia on stroke outcomes and the need for increased awareness and interventions for stroke patients with anemia.
“One example of an intervention might be treating the underlying causes of anemia, such as iron deficiency, which is common in this age group,” said study author Raphae Barlas, a medical student at the University of Aberdeen.
“As the study has convincingly demonstrated, anemia does worsen the outcome of stroke, so it is very important that we identify at-risk patients and optimize the management.”
Music may alleviate cancer patients’ symptoms
Photo by Lars Frantzen
Results of a systematic review suggest music can help alleviate symptoms of anxiety, pain, and fatigue in cancer patients.
The review included more than 50 studies investigating the impact of music therapy—a personalized music experience offered by trained music therapists—and music medicine—listening to pre-recorded music provided by a doctor or nurse—on psychological and physical outcomes in people with cancer.
“We found that music therapy interventions specifically help improve patients’ quality of life,” said study author Joke Bradt, PhD, of Drexel University in Philadelphia, Pennsylvania.
“These are important findings, as these outcomes play an important role in patients’ overall well-being.”
Dr Bradt and her colleagues reported their findings in Cochrane Database of Systematic Reviews.
The researchers examined 52 trials including 3731 cancer patients. The music interventions were classified as music therapy in 23 of the trials and as music medicine in 29 trials.
Analyses suggested that both types of music interventions positively impacted patients. The interventions had a moderate-to-strong effect on anxiety, a strong effect on pain reduction, and a small-to-moderate effect on fatigue.
Small reductions in heart and respiratory rates, as well as lowered blood pressure, were linked to the interventions as well.
In addition, the researchers observed a moderate increase in patients’ quality of life with music therapy but not music medicine.
The team could not determine the effect of music interventions on depression due to the low quality of evidence. And there was no evidence that the interventions improve mood, distress, or physical functioning, but there were few trials investigating these outcomes.
Similarly, the researchers said they could not draw any conclusions about the effect of music interventions on immunologic functioning, coping, resilience, or communication because there were not enough trials evaluating these outcomes.
Still, the researchers hope music interventions will become more widely used, in light of the potential benefits to cancer patients.
“We hope that the findings of this review will encourage healthcare providers in medical settings to seriously consider the use of music therapy in the psychosocial care of people with cancer,” Dr Bradt said.
Photo by Lars Frantzen
Results of a systematic review suggest music can help alleviate symptoms of anxiety, pain, and fatigue in cancer patients.
The review included more than 50 studies investigating the impact of music therapy—a personalized music experience offered by trained music therapists—and music medicine—listening to pre-recorded music provided by a doctor or nurse—on psychological and physical outcomes in people with cancer.
“We found that music therapy interventions specifically help improve patients’ quality of life,” said study author Joke Bradt, PhD, of Drexel University in Philadelphia, Pennsylvania.
“These are important findings, as these outcomes play an important role in patients’ overall well-being.”
Dr Bradt and her colleagues reported their findings in Cochrane Database of Systematic Reviews.
The researchers examined 52 trials including 3731 cancer patients. The music interventions were classified as music therapy in 23 of the trials and as music medicine in 29 trials.
Analyses suggested that both types of music interventions positively impacted patients. The interventions had a moderate-to-strong effect on anxiety, a strong effect on pain reduction, and a small-to-moderate effect on fatigue.
Small reductions in heart and respiratory rates, as well as lowered blood pressure, were linked to the interventions as well.
In addition, the researchers observed a moderate increase in patients’ quality of life with music therapy but not music medicine.
The team could not determine the effect of music interventions on depression due to the low quality of evidence. And there was no evidence that the interventions improve mood, distress, or physical functioning, but there were few trials investigating these outcomes.
Similarly, the researchers said they could not draw any conclusions about the effect of music interventions on immunologic functioning, coping, resilience, or communication because there were not enough trials evaluating these outcomes.
Still, the researchers hope music interventions will become more widely used, in light of the potential benefits to cancer patients.
“We hope that the findings of this review will encourage healthcare providers in medical settings to seriously consider the use of music therapy in the psychosocial care of people with cancer,” Dr Bradt said.
Photo by Lars Frantzen
Results of a systematic review suggest music can help alleviate symptoms of anxiety, pain, and fatigue in cancer patients.
The review included more than 50 studies investigating the impact of music therapy—a personalized music experience offered by trained music therapists—and music medicine—listening to pre-recorded music provided by a doctor or nurse—on psychological and physical outcomes in people with cancer.
“We found that music therapy interventions specifically help improve patients’ quality of life,” said study author Joke Bradt, PhD, of Drexel University in Philadelphia, Pennsylvania.
“These are important findings, as these outcomes play an important role in patients’ overall well-being.”
Dr Bradt and her colleagues reported their findings in Cochrane Database of Systematic Reviews.
The researchers examined 52 trials including 3731 cancer patients. The music interventions were classified as music therapy in 23 of the trials and as music medicine in 29 trials.
Analyses suggested that both types of music interventions positively impacted patients. The interventions had a moderate-to-strong effect on anxiety, a strong effect on pain reduction, and a small-to-moderate effect on fatigue.
Small reductions in heart and respiratory rates, as well as lowered blood pressure, were linked to the interventions as well.
In addition, the researchers observed a moderate increase in patients’ quality of life with music therapy but not music medicine.
The team could not determine the effect of music interventions on depression due to the low quality of evidence. And there was no evidence that the interventions improve mood, distress, or physical functioning, but there were few trials investigating these outcomes.
Similarly, the researchers said they could not draw any conclusions about the effect of music interventions on immunologic functioning, coping, resilience, or communication because there were not enough trials evaluating these outcomes.
Still, the researchers hope music interventions will become more widely used, in light of the potential benefits to cancer patients.
“We hope that the findings of this review will encourage healthcare providers in medical settings to seriously consider the use of music therapy in the psychosocial care of people with cancer,” Dr Bradt said.
State Medicaid Expansion Status
On January 1, 2014, several major provisions of the Affordable Care Act (ACA) took effect, including introduction of the individual mandate for health insurance coverage, opening of the Health Insurance Marketplace, and expansion of Medicaid eligibility to Americans earning up to 133% of the federal poverty level.[1] Nearly 9 million US adults have enrolled in Medicaid since that time, primarily in the 31 states and Washington, DC that have opted into Medicaid expansion.[2, 3] ACA implementation has also had a significant impact on hospital payer mix, primarily by reducing the volume of uncompensated care in Medicaid‐expansion states.[4, 5]
The differential shift in payer mix in Medicaid‐expansion versus nonexpansion states may be relevant to hospitals beyond reimbursement. Medicaid insurance has historically been associated with longer hospitalizations and higher in‐hospital mortality in diverse patient populations, more so than commercial insurance and often even uninsured payer status.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15] The disparity in outcomes between patients with Medicaid versus other insurance persists even after adjustment for disease severity and baseline comorbidities. Insurance type may influence the delivery of inpatient care through variation in access to invasive procedures and adherence to guideline‐concordant medical therapies.[9, 10, 11, 12] Medicaid patients may be more likely than uninsured patients to remain hospitalized pending postacute care placement rather than be discharged home with family support.[16] Medicaid patients are also less likely to leave against medical advice than uninsured patients.[17]
Currently, little is known about the impact of state Medicaid expansion status on length of stay (LOS) or mortality nationally. It is possible that hospitals in Medicaid‐expansion states have experienced relative worsening in LOS and mortality as their share of Medicaid patients has grown. Determining the impact of ACA implementation on payer mix and patient outcomes is particularly important for academic medical centers (AMCs), as they traditionally care for the greatest percentage of both Medicaid and uninsured patients.[18] We sought to characterize the impact of state Medicaid expansion status on payer mix, LOS, and in‐hospital mortality for general medicine patients at AMCs in the United States.
METHODS
The University HealthSystem Consortium (UHC) is an alliance of 117 AMCs and 310 affiliated hospitals, representing >90% of such institutions in the US. We queried the online UHC Clinical Data Base/Resource Manager (CDB/RM) to obtain hospital‐level insurance, LOS, and mortality data for inpatients discharged from a general medicine service between October 1, 2012 and September 30, 2015. We excluded hospitals that were missing data for any month within the study period. No patient‐level data were accessed.
Our outcomes of interest were the proportion of discharges by primary payer (Medicare, commercial, Medicaid, uninsured, or other [eg, Tri‐Care or Workers' Compensation]), as well as the LOS index and mortality index. Both indices were defined as the ratio of the observed to expected values. To determine the expected LOS and mortality, the UHC 2015 risk adjustment models were applied to all cases, adjusting for variables such as patient demographics, low socioeconomic status, admit source and status, severity of illness, and comorbid conditions, as described by International Classification of Diseases, Ninth Revision codes. These models have been validated and are used for research and quality benchmarking for member institutions.[19]
We next stratified hospitals according to state Medicaid expansion status. We defined Medicaid‐expansion states as those that had expanded Medicaid by the end of the study period: Arizona, Arkansas, California, Colorado, Connecticut, Illinois, Indiana, Iowa, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Nevada, New Hampshire, New Jersey, New Mexico, New York, Ohio, Oregon, Pennsylvania, Rhode Island, Washington, Washington DC, and West Virginia. Nonexpansion states included Alabama, Florida, Georgia, Kansas, Louisiana, Missouri, Nebraska, North Carolina, South Carolina, Tennessee, Texas, Utah, Virginia, and Wisconsin. We excluded 12 states due to incomplete data: Alaska, Delaware, Hawaii, Idaho, North Dakota, Maine, Mississippi, Montana, Oklahoma, South Dakota, Vermont, and Wyoming.
We then identified our pre‐ and post‐ACA implementation periods. Medicaid coverage expansion took effect in all expansion states on January 1, 2014, with the exception of Michigan (April 1, 2014), New Hampshire (August 15, 2014), Pennsylvania (January 1, 2015), and Indiana (February 1, 2015).[3] We therefore defined October 1, 2012 to December 31, 2013 as the pre‐ACA implementation period and January 1, 2014 to September 30, 2015 as the post‐ACA implementation period for all states except for Michigan, New Hampshire, Pennsylvania, and Indiana. For these 4 states, we customized the pre‐ and post‐ACA implementation periods to their respective dates of Medicaid expansion; for New Hampshire, we designated October 1, 2012 to July 31, 2014 as the pre‐ACA implementation period and September 1, 2014 to September 30, 2015 as the post‐ACA implementation period, as we were unable to distinguish before versus after data in August 2014 based on the midmonth expansion of Medicaid.
After stratifying hospitals into groups based on whether they were located in Medicaid‐expansion or nonexpansion states, the proportion of discharges by payer was compared between pre‐ and post‐ACA implementation periods both graphically by quarter and using linear regression models weighted for the number of cases from each hospital. Next, for both Medicaid‐expansion and nonexpansion hospitals, LOS index and mortality index were compared before and after ACA implementation using linear regression models weighted for the number of cases from each hospital, both overall and by payer. Difference‐in‐differences estimations were then completed to compare the proportion of discharges by payer, LOS index, and mortality index between Medicaid‐expansion and nonexpansion hospitals before and after ACA implementation. Post hoc linear regression analyses were completed to evaluate the effect of clustering by state level strata on payer mix and LOS and mortality indices. A 2‐sided P value of <0.05 was considered statistically significant. Data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 4,258,952 discharges among general medicine patients from 211 hospitals in 38 states and Washington, DC between October 1, 2012, and September 30, 2015. This included 3,144,488 discharges from 156 hospitals in 24 Medicaid‐expansion states and Washington, DC and 1,114,464 discharges from 55 hospitals in 14 nonexpansion states.
Figure 1 shows the trends in payer mix over time for hospitals in both Medicaid‐expansion and nonexpansion states. As summarized in Table 1, hospitals in Medicaid‐expansion states experienced a significant 3.7‐percentage point increase in Medicaid discharges (P = 0.013) and 2.9‐percentage point decrease in uninsured discharges (P < 0.001) after ACA implementation. This represented an approximately 19% jump and 60% drop in Medicaid and uninsured discharges, respectively. Hospitals in nonexpansion states saw no significant change in the proportion of discharges by payer after ACA implementation. In the difference‐in‐differences analysis, there was a trend toward a greater change in the proportion of Medicaid discharges pre‐ to post‐ACA implementation among hospitals in Medicaid‐expansion states compared to hospitals in nonexpansion states (mean difference‐in‐differences 4.1%, 95% confidence interval [CI]: 0.3%, 8.6%, P = 0.070).
Medicaid‐expansion n=156 hospitals; 3,144,488 cases | Non‐expansion n=55 hospitals; 1,114,464 cases | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Pre‐ACA Implementation (1,453,090 Cases) | Post‐ACA Implementation (1,691,398 Cases) | Mean Difference | P Value | Pre‐ACA Implementation (455,440 Cases) | Post‐ACA Implementation (659,024 Cases) | Mean Difference | P Value | Mean Difference‐in‐Differences | P Value | |
| ||||||||||
Payer mix, % (95% CI) | ||||||||||
Medicare | 48.6 (46.2, 51.0)* | 48.3 (45.9, 50.7) | 0.3 (3.6, 3.1) | 0.865 | 44.3 (40.7, 47.7)* | 45.3 (41.9, 48.6) | 1.0 (3.8, 5.8) | 0.671 | 1.3 (7.1, 4.5) | 0.655 |
Commercial | 23.1 (21.4, 24.7) | 23.2 (21.8, 24.6) | 0.2 (2.0, 2.3) | 0.882 | 21.5 (18.5, 24.6) | 22.7 (19.7, 25.8) | 1.2 (3.0, 5.4) | 0.574 | 1.0 (5.7, 3.6) | 0.662 |
Medicaid | 19.6 (17.6, 21.6) | 23.3 (21.2, 25.5) | 3.7 (0.8, 6.6) | 0.013 | 19.4 (16.9, 21.9) | 19.0 (16.5, 21.4) | 0.4 (3.8, 3.0) | 0.812 | 4.1 (0.3, 8.6) | 0.070 |
Uninsured | 5.0 (4.0, 5.9) | 2.0 (1.7, 2.3) | 2.9 (3.9, 2.0) | <0.001 | 10.9 (8.1, 13.7) | 9.4 (7.0, 11.7) | 1.5 (5.1, 2.1) | 0.407 | 1.4 (5.1, 2.2) | 0.442 |
Other | 3.8 (2.6, 4.9) | 3.1 (2.0, 4.3) | 0.7 (2.3, 1.0) | 0.435 | 4.0 (2.9, 5.0) | 3.7 (2.6, 4.7) | 0.3 (1.7, 1.1) | 0.662 | 0.3 (2.5, 1.8) | 0.762 |
LOS index, mean (95% CI) | ||||||||||
Overall | 1.017 (0.996, 1.038) | 1.006 (0.981, 1.031) | 0.011 (0.044, 0.021) | 0.488 | 1.008 (0.974, 1.042) | 0.995 (0.961, 1.029) | 0.013 (0.061, 0.034) | 0.574 | 0.002 (0.055, 0.059) | 0.943 |
Medicare | 1.012 (0.989, 1.035) | 0.999 (0.971, 1.027) | 0.013 (0.049, 0.023) | 0.488 | 0.982 (0.946, 1.017) | 0.979 (0.944, 1.013) | 0.003 (0.052, 0.046) | 0.899 | 0.010 (0.070, 0.051) | 0.754 |
Commercial | 0.993 (0.974, 1.012) | 0.977 (0.955, 0.998) | 0.016 (0.045, 0.013) | 0.271 | 1.009 (0.978, 1.039) | 0.986 (0.956, 1.016) | 0.022 (0.065, 0.020) | 0.298 | 0.006 (0.044, 0.057) | 0.809 |
Medicaid | 1.059 (1.036, 1.082) | 1.043 (1.018, 1.067) | 0.016 (0.049, 0.017) | 0.349 | 1.064 (1.020, 1.108) | 1.060 (1.015, 1.106) | 0.004 (0.066, 0.059) | 0.911 | 0.012 (0.082, 0.057) | 0.727 |
Uninsured | 0.960 (0.933, 0.988) | 0.925 (0.890, 0.961) | 0.035 (0.080, 0.010) | 0.126 | 0.972 (0.935, 1.009) | 0.944 (0.909, 0.979) | 0.028 (0.078, 0.022) | 0.273 | 0.007 (0.074, 0.060) | 0.835 |
Other | 0.988 (0.960, 1.017) | 0.984 (0.952, 1.015) | 0.005 (0.047, 0.037) | 0.822 | 1.022 (0.973, 1.071) | 0.984 (0.944, 1.024) | 0.038 (0.100, 0.024) | 0.232 | 0.033 (0.042, 0.107) | 0.386 |
Mortality index, mean (95% CI) | ||||||||||
Overall | 1.000 (0.955, 1.045) | 0.878 (0.836, 0.921) | 0.122 (0.183, 0.061) | <0.001 | 0.997 (0.931, 1.062) | 0.850 (0.800, 0.900) | 0.147 (0.227, 0.066) | 0.001 | 0.025 (0.076, 0.125) | 0.628 |
Medicare | 0.990 (0.942, 1.038) | 0.871 (0.826, 0.917) | 0.119 (0.185, 0.053) | <0.001 | 1.000 (0.925, 1.076) | 0.844 (0.788, 0.900) | 0.156 (0.249, 0.064) | 0.001 | 0.038 (0.075, 0.150) | 0.513 |
Commercial | 1.045 (0.934, 1.155) | 0.908 (0.842, 0.975) | 0.136 (0.264, 0.008) | 0.037 | 1.023 (0.935, 1.111) | 0.820 (0.758, 0.883) | 0.203 (0.309, 0.096) | <0.001 | 0.067 (0.099, 0.232) | 0.430 |
Medicaid | 0.894 (0.845, 0.942) | 0.786 (0.748, 0.824) | 0.107 (0.168, 0.046) | 0.001 | 0.937 (0.861, 1.013) | 0.789 (0.733, 0.844) | 0.148 (0.242, 0.055) | 0.002 | 0.041 (0.069, 0.151) | 0.464 |
Uninsured | 1.172 (1.007, 1.337)∥ | 1.136 (0.968, 1.303) | 0.037 (0.271, 0.197) | 0.758 | 0.868 (0.768, 0.968)∥ | 0.850 (0.761, 0.939) | 0.017 (0.149, 0.115) | 0.795 | 0.019 (0.287, 0.248) | 0.887 |
Other | 1.376 (1.052, 1.700)# | 1.156 (0.910, 1.402) | 0.220 (0.624, 0.184) | 0.285 | 1.009 (0.868, 1.150) # | 0.874 (0.682, 1.066) | 0.135 (0.369, 0.099) | 0.254 | 0.085 (0.555, 0.380) | 0.720 |

Table 1 shows that the overall LOS index remained unchanged pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.017 to 1.006, P = 0.488) and nonexpansion hospitals (1.008 to 0.995, P = 0.574). LOS indices for each payer type also remained unchanged. The overall mortality index significantly improved pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.000 to 0.878, P < 0.001) and nonexpansion hospitals (0.997 to 0.850, P = 0.001). Among both Medicaid‐expansion and nonexpansion hospitals, the mortality index significantly improved for Medicare, commercial, and Medicaid discharges but not for uninsured or other discharges. In the difference‐in‐differences analysis, the changes in LOS indices and mortality indices pre‐ to post‐ACA implementation did not differ significantly between hospitals in Medicaid‐expansion versus nonexpansion states.
In post hoc linear regression analyses of payer mix and LOS and mortality indices clustered by state‐level strata, point estimates were minimally changed. Although 95% CIs were slightly wider, statistical significance was unchanged from our primary analyses (data not shown).
DISCUSSION
We found that ACA implementation had a significant impact on payer mix for general medicine patients at AMCs in the United States, primarily by increasing the number of Medicaid beneficiaries and by decreasing the number of uninsured patients in Medicaid‐expansion states. State Medicaid expansion status did not appear to influence either LOS or in‐hospital mortality.
Our study offers some of the longest‐term data currently available on the impact of ACA implementation on payer mix trends and encompasses more states than others have previously. Although we uniquely focused on general medicine patients at AMCs, our results are similar to those seen for US hospitals overall. Nikpay and colleagues evaluated payer mix trends for non‐Medicare adult inpatient stays in 16 states through the second quarter of 2014 using the Healthcare Cost and Utilization Project database through the Agency for Healthcare Research and Quality.[4] They found a relative 20% increase and 50% decrease in Medicaid and uninsured discharges in Medicaid‐expansion states, along with nonsignificant changes in nonexpansion states. Hempstead and Cantor assessed payer mix for non‐Medicare discharges using state hospital association data from 21 states through the fourth quarter of 2014 and found a significant increase in Medicaid patients as well as a nearly significant decrease in uninsured patients in expansion states relative to nonexpansion states.[5] The Department of Health and Human Services also reported that uninsured/self‐pay discharges fell substantially (65%73%) in Medicaid‐expansion states by the end of 2014, with slight decreases in nonexpansion states.[20]
In contrast to our hypothesis, the overall LOS and in‐hospital mortality indices were not influenced by state Medicaid expansion status. From a purely mathematical standpoint, the contribution of Medicaid patients to the overall LOS and mortality indices may have been eclipsed by Medicare and commercially insured patients, who represented a higher proportion of total discharges. The lack of impact of state Medicaid expansion status on overall LOS and mortality indices did not appear to occur as a result of indices for Medicaid patients trending toward the mean. As predicted based on observational studies, Medicaid patients in our study tended to have a higher LOS index than those with other insurance types. Medicaid patients actually tended to have a lower mortality index in our analysis; the reason for this latter finding is unclear and in contrast to other published studies.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21]
To our knowledge, no other studies have evaluated the effect of payer mix changes under the ACA on inpatient outcomes. However, new evidence is emerging on outpatient outcomes. Low‐income adults in Medicaid‐expansion states have reported greater gains in access to primary care services and in the diagnosis of certain chronic health conditions than those in nonexpansion states as a result of ACA implementation.[22, 23] Such improvements in the outpatient setting might be expected to reduce patient acuity on admission. However, they would not necessarily translate to relative improvements in LOS or mortality indices for Medicaid‐expansion hospitals, as the UHC risk adjustment models controlled for disease severity on admission.
Similarly, few studies have assessed the impact of payer mix changes under previous state Medicaid expansions on inpatient outcomes. After Massachusetts expanded Medicaid and enacted near‐universal healthcare coverage in 2006, a minimal LOS reduction of just 0.05 days was observed.[24] New York expanded Medicaid eligibility to nondisabled childless adults with incomes below 100% of the federal poverty level in September 2001, whereas Arizona did so in November 2001 and Maine in October 2002. A study comparing outcomes in these states to 4 neighboring nonexpansion states found a relative reduction in annual all‐cause mortality of 6.1% population wide; however, it did not assess in‐hospital mortality.[25] The Oregon Health Insurance Experiment that randomized low‐income adults to expanded Medicaid coverage or not in 2008 has also reported on outpatient rather than inpatient outcomes.[26]
Our findings have potential implications for health policymakers. That Medicaid expansion status had a neutral effect on both LOS and mortality indices in our analysis should be reassuring for states contemplating Medicaid expansion in the future. Our results also highlight the need for further efforts to reduce disparities in inpatient care based on payer status. For example, although Medicare, commercially insured, and Medicaid patients witnessed significant improvements in mortality indices pre‐ to post‐ACA implementation in hospitals in both Medicaid‐expansion and nonexpansion states, uninsured patients did not.
This study has several limitations. First, our analysis of the impact of ACA implementation on payer mix did not account for concurrent socioeconomic trends that may have influenced insurance coverage across the United States. However, the main goal of this analysis was to demonstrate that changes in payer mix did in fact occur over time, to provide rationale for our subsequent LOS and mortality analyses. Second, we could not control for variation in the design and implementation of Medicaid expansions across states as permitted under the federal Section 1115 waiver process. Third, we only had access to hospital‐level data through the UHC CDB/RM, rather than individual patient data. We attempted to mitigate this limitation by weighting data according to the number of cases per hospital. Lastly, additional patient‐level factors that may influence LOS or mortality may not be included in the UHC risk adjustment models.
In summary, the differential shift in payer mix between Medicaid‐expansion and nonexpansion states did not influence overall LOS or in‐hospital mortality for general medicine patients at AMCs in the United States. Additional research could help to determine the impact of ACA implementation on other patient outcomes that may be dependent on insurance status, such as readmissions or hospital‐acquired complications.
Disclosures: M.E.A. conceived of the study concept and design, assisted with data acquisition, and drafted the manuscript. J.J.G. assisted with study design and made critical revisions to the manuscript. D.A. assisted with study design and made critical revisions to the manuscript. R.P. assisted with study design and made critical revisions to the manuscript. M.L. assisted with study design and data acquisition and made critical revisions to the manuscript. C.D.J. assisted with study design, performed data analyses, and made critical revisions to the manuscript. A modified abstract was presented in poster format at the University HealthSystem Consortium Annual Conference held September 30 to October 2, 2015 in Orlando, Florida, as well as at the Society of Hospital Medicine Research, Innovations, and Vignettes 2016 Annual Meeting held March 69, 2016, in San Diego, California. The authors report no conflicts of interest.
- Department of Health and Human Services. Key features of the Affordable Care Act by year. Available at: http://www.hhs.gov/healthcare/facts‐and‐features/key‐features‐of‐aca‐by‐year/index.html#2014. Accessed April 4, 2016.
- Centers for Medicare and Medicaid Services. Medicaid enrollment data collected through MBES. Available at: https://www.medicaid.gov/medicaid‐chip‐program‐information/program‐information/medicaid‐and‐chip‐enrollment‐data/medicaid‐enrollment‐data‐collected‐through‐mbes.html. Accessed April 4, 2016.
- The Henry J. Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. Available at: http://kff.org/health‐reform/state‐indicator/state‐activity‐around‐expanding‐medicaid‐under‐the‐affordable‐care‐act. Accessed April 4, 2016.
- Affordable Care Act Medicaid expansion reduced uninsured hospital stays in 2014. Health Aff (Millwood). 2016;35(1):106–110. , , .
- State Medicaid expansion and changes in hospital volume according to payer. N Engl J Med. 2016;374(2):196–198. , .
- Understanding predictors of prolonged hospitalizations among general medicine patients: a guide and preliminary analysis. J Hosp Med. 2015;10(9):623–626. , , , , , .
- Impact of insurance and hospital ownership on hospital length of stay among patients with ambulatory care‐sensitive conditions. Ann Fam Med. 2011;9:489–495. , , , .
- Insurance status and hospital care for myocardial infarction, stroke, and pneumonia. J Hosp Med. 2010;5:452–459. , , .
- Payment source, quality of care, and outcomes in patients hospitalized with heart failure. J Am Coll Cardiol. 2011;58(14):1465–1471. , , , , , .
- The inpatient experience and predictors of length of stay for patients hospitalized with systolic heart failure: comparison by commercial, Medicaid, and Medicare payer type. J Med Econ. 2013;16(1):43–54. , , , , .
- Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes. Ann Intern Med. 2006;145:739–748. , , et al.
- Association of insurance status with inpatient treatment for coronary artery disease: findings from the Get with the Guidelines Program. Am Heart J. 2010;159:1026–1036. , , , et al.
- Primary payer status affects mortality for major surgical operations. Ann Surg. 2010;252:544–551. , , , et al.
- Medicaid payer status is associated with in‐hospital morbidity and resource utilization following primary total joint arthroplasty. J Bone Joint Surg Am. 2014;96(21):e180. , , .
- The quality of care delivered to patients within the same hospital varies by insurance type. Health Aff (Millwood). 2013;32(10):1731–1739. , , .
- Effect of insurance status on postacute care among working age stroke survivors. Neurology. 2012;78(20):1590–1595. , , , , .
- Hospitalizations in which patients leave the hospital against medical advice (AMA), 2007. HCUP statistical brief #78. August 2009. Rockville, MD: Agency for Healthcare Research and Quality; 2009. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb78.pdf. Accessed May 12, 2016. , , , .
- Characteristics of Medicaid and uninsured hospitalizations, 2012. HCUP statistical brief #182. Rockville, MD: Agency for Healthcare Research and Quality; 2014. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb182‐Medicaid‐Uninsured‐Hospitalizations‐2012.pdf. Accessed March 9, 2016. , , , .
- Agency for Healthcare Research and Quality. Mortality measurement: mortality risk adjustment methodology for University HealthSystem Consortium. Available at: http://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf. Accessed May 10, 2016.
- Department of Health and Human Services. Insurance expansion, hospital uncompensated care, and the Affordable Care Act. Available at: https://aspe.hhs.gov/pdf‐report/insurance‐expansion‐hospital‐uncompensated‐care‐and‐affordable‐care‐act. Accessed May 27, 2016.
- Our flawed but beneficial Medicaid program. N Engl J Med. 2011;364(16):e31. , , , .
- Changes in self‐reported insurance coverage, access to care, and health under the Affordable Care Act. JAMA. 2015;314(4):366–374. , , , .
- Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid Expansions: a quasi‐experimental study. Ann Intern Med. 2016;164(12):795–803. , .
- The impact of health care reform on hospital and preventive care: evidence from Massachusetts. J Public Econ. 2012;96(11–12):909–929. , .
- Mortality and access to care among adults after state Medicaid expansions. N Engl J Med. 2012;367:1025–1034. , , .
- The Oregon Experiment—effects of Medicaid on clinical outcomes. N Engl J Med. 2013;368(18):1713–1722. , , , et al.
On January 1, 2014, several major provisions of the Affordable Care Act (ACA) took effect, including introduction of the individual mandate for health insurance coverage, opening of the Health Insurance Marketplace, and expansion of Medicaid eligibility to Americans earning up to 133% of the federal poverty level.[1] Nearly 9 million US adults have enrolled in Medicaid since that time, primarily in the 31 states and Washington, DC that have opted into Medicaid expansion.[2, 3] ACA implementation has also had a significant impact on hospital payer mix, primarily by reducing the volume of uncompensated care in Medicaid‐expansion states.[4, 5]
The differential shift in payer mix in Medicaid‐expansion versus nonexpansion states may be relevant to hospitals beyond reimbursement. Medicaid insurance has historically been associated with longer hospitalizations and higher in‐hospital mortality in diverse patient populations, more so than commercial insurance and often even uninsured payer status.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15] The disparity in outcomes between patients with Medicaid versus other insurance persists even after adjustment for disease severity and baseline comorbidities. Insurance type may influence the delivery of inpatient care through variation in access to invasive procedures and adherence to guideline‐concordant medical therapies.[9, 10, 11, 12] Medicaid patients may be more likely than uninsured patients to remain hospitalized pending postacute care placement rather than be discharged home with family support.[16] Medicaid patients are also less likely to leave against medical advice than uninsured patients.[17]
Currently, little is known about the impact of state Medicaid expansion status on length of stay (LOS) or mortality nationally. It is possible that hospitals in Medicaid‐expansion states have experienced relative worsening in LOS and mortality as their share of Medicaid patients has grown. Determining the impact of ACA implementation on payer mix and patient outcomes is particularly important for academic medical centers (AMCs), as they traditionally care for the greatest percentage of both Medicaid and uninsured patients.[18] We sought to characterize the impact of state Medicaid expansion status on payer mix, LOS, and in‐hospital mortality for general medicine patients at AMCs in the United States.
METHODS
The University HealthSystem Consortium (UHC) is an alliance of 117 AMCs and 310 affiliated hospitals, representing >90% of such institutions in the US. We queried the online UHC Clinical Data Base/Resource Manager (CDB/RM) to obtain hospital‐level insurance, LOS, and mortality data for inpatients discharged from a general medicine service between October 1, 2012 and September 30, 2015. We excluded hospitals that were missing data for any month within the study period. No patient‐level data were accessed.
Our outcomes of interest were the proportion of discharges by primary payer (Medicare, commercial, Medicaid, uninsured, or other [eg, Tri‐Care or Workers' Compensation]), as well as the LOS index and mortality index. Both indices were defined as the ratio of the observed to expected values. To determine the expected LOS and mortality, the UHC 2015 risk adjustment models were applied to all cases, adjusting for variables such as patient demographics, low socioeconomic status, admit source and status, severity of illness, and comorbid conditions, as described by International Classification of Diseases, Ninth Revision codes. These models have been validated and are used for research and quality benchmarking for member institutions.[19]
We next stratified hospitals according to state Medicaid expansion status. We defined Medicaid‐expansion states as those that had expanded Medicaid by the end of the study period: Arizona, Arkansas, California, Colorado, Connecticut, Illinois, Indiana, Iowa, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Nevada, New Hampshire, New Jersey, New Mexico, New York, Ohio, Oregon, Pennsylvania, Rhode Island, Washington, Washington DC, and West Virginia. Nonexpansion states included Alabama, Florida, Georgia, Kansas, Louisiana, Missouri, Nebraska, North Carolina, South Carolina, Tennessee, Texas, Utah, Virginia, and Wisconsin. We excluded 12 states due to incomplete data: Alaska, Delaware, Hawaii, Idaho, North Dakota, Maine, Mississippi, Montana, Oklahoma, South Dakota, Vermont, and Wyoming.
We then identified our pre‐ and post‐ACA implementation periods. Medicaid coverage expansion took effect in all expansion states on January 1, 2014, with the exception of Michigan (April 1, 2014), New Hampshire (August 15, 2014), Pennsylvania (January 1, 2015), and Indiana (February 1, 2015).[3] We therefore defined October 1, 2012 to December 31, 2013 as the pre‐ACA implementation period and January 1, 2014 to September 30, 2015 as the post‐ACA implementation period for all states except for Michigan, New Hampshire, Pennsylvania, and Indiana. For these 4 states, we customized the pre‐ and post‐ACA implementation periods to their respective dates of Medicaid expansion; for New Hampshire, we designated October 1, 2012 to July 31, 2014 as the pre‐ACA implementation period and September 1, 2014 to September 30, 2015 as the post‐ACA implementation period, as we were unable to distinguish before versus after data in August 2014 based on the midmonth expansion of Medicaid.
After stratifying hospitals into groups based on whether they were located in Medicaid‐expansion or nonexpansion states, the proportion of discharges by payer was compared between pre‐ and post‐ACA implementation periods both graphically by quarter and using linear regression models weighted for the number of cases from each hospital. Next, for both Medicaid‐expansion and nonexpansion hospitals, LOS index and mortality index were compared before and after ACA implementation using linear regression models weighted for the number of cases from each hospital, both overall and by payer. Difference‐in‐differences estimations were then completed to compare the proportion of discharges by payer, LOS index, and mortality index between Medicaid‐expansion and nonexpansion hospitals before and after ACA implementation. Post hoc linear regression analyses were completed to evaluate the effect of clustering by state level strata on payer mix and LOS and mortality indices. A 2‐sided P value of <0.05 was considered statistically significant. Data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 4,258,952 discharges among general medicine patients from 211 hospitals in 38 states and Washington, DC between October 1, 2012, and September 30, 2015. This included 3,144,488 discharges from 156 hospitals in 24 Medicaid‐expansion states and Washington, DC and 1,114,464 discharges from 55 hospitals in 14 nonexpansion states.
Figure 1 shows the trends in payer mix over time for hospitals in both Medicaid‐expansion and nonexpansion states. As summarized in Table 1, hospitals in Medicaid‐expansion states experienced a significant 3.7‐percentage point increase in Medicaid discharges (P = 0.013) and 2.9‐percentage point decrease in uninsured discharges (P < 0.001) after ACA implementation. This represented an approximately 19% jump and 60% drop in Medicaid and uninsured discharges, respectively. Hospitals in nonexpansion states saw no significant change in the proportion of discharges by payer after ACA implementation. In the difference‐in‐differences analysis, there was a trend toward a greater change in the proportion of Medicaid discharges pre‐ to post‐ACA implementation among hospitals in Medicaid‐expansion states compared to hospitals in nonexpansion states (mean difference‐in‐differences 4.1%, 95% confidence interval [CI]: 0.3%, 8.6%, P = 0.070).
Medicaid‐expansion n=156 hospitals; 3,144,488 cases | Non‐expansion n=55 hospitals; 1,114,464 cases | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Pre‐ACA Implementation (1,453,090 Cases) | Post‐ACA Implementation (1,691,398 Cases) | Mean Difference | P Value | Pre‐ACA Implementation (455,440 Cases) | Post‐ACA Implementation (659,024 Cases) | Mean Difference | P Value | Mean Difference‐in‐Differences | P Value | |
| ||||||||||
Payer mix, % (95% CI) | ||||||||||
Medicare | 48.6 (46.2, 51.0)* | 48.3 (45.9, 50.7) | 0.3 (3.6, 3.1) | 0.865 | 44.3 (40.7, 47.7)* | 45.3 (41.9, 48.6) | 1.0 (3.8, 5.8) | 0.671 | 1.3 (7.1, 4.5) | 0.655 |
Commercial | 23.1 (21.4, 24.7) | 23.2 (21.8, 24.6) | 0.2 (2.0, 2.3) | 0.882 | 21.5 (18.5, 24.6) | 22.7 (19.7, 25.8) | 1.2 (3.0, 5.4) | 0.574 | 1.0 (5.7, 3.6) | 0.662 |
Medicaid | 19.6 (17.6, 21.6) | 23.3 (21.2, 25.5) | 3.7 (0.8, 6.6) | 0.013 | 19.4 (16.9, 21.9) | 19.0 (16.5, 21.4) | 0.4 (3.8, 3.0) | 0.812 | 4.1 (0.3, 8.6) | 0.070 |
Uninsured | 5.0 (4.0, 5.9) | 2.0 (1.7, 2.3) | 2.9 (3.9, 2.0) | <0.001 | 10.9 (8.1, 13.7) | 9.4 (7.0, 11.7) | 1.5 (5.1, 2.1) | 0.407 | 1.4 (5.1, 2.2) | 0.442 |
Other | 3.8 (2.6, 4.9) | 3.1 (2.0, 4.3) | 0.7 (2.3, 1.0) | 0.435 | 4.0 (2.9, 5.0) | 3.7 (2.6, 4.7) | 0.3 (1.7, 1.1) | 0.662 | 0.3 (2.5, 1.8) | 0.762 |
LOS index, mean (95% CI) | ||||||||||
Overall | 1.017 (0.996, 1.038) | 1.006 (0.981, 1.031) | 0.011 (0.044, 0.021) | 0.488 | 1.008 (0.974, 1.042) | 0.995 (0.961, 1.029) | 0.013 (0.061, 0.034) | 0.574 | 0.002 (0.055, 0.059) | 0.943 |
Medicare | 1.012 (0.989, 1.035) | 0.999 (0.971, 1.027) | 0.013 (0.049, 0.023) | 0.488 | 0.982 (0.946, 1.017) | 0.979 (0.944, 1.013) | 0.003 (0.052, 0.046) | 0.899 | 0.010 (0.070, 0.051) | 0.754 |
Commercial | 0.993 (0.974, 1.012) | 0.977 (0.955, 0.998) | 0.016 (0.045, 0.013) | 0.271 | 1.009 (0.978, 1.039) | 0.986 (0.956, 1.016) | 0.022 (0.065, 0.020) | 0.298 | 0.006 (0.044, 0.057) | 0.809 |
Medicaid | 1.059 (1.036, 1.082) | 1.043 (1.018, 1.067) | 0.016 (0.049, 0.017) | 0.349 | 1.064 (1.020, 1.108) | 1.060 (1.015, 1.106) | 0.004 (0.066, 0.059) | 0.911 | 0.012 (0.082, 0.057) | 0.727 |
Uninsured | 0.960 (0.933, 0.988) | 0.925 (0.890, 0.961) | 0.035 (0.080, 0.010) | 0.126 | 0.972 (0.935, 1.009) | 0.944 (0.909, 0.979) | 0.028 (0.078, 0.022) | 0.273 | 0.007 (0.074, 0.060) | 0.835 |
Other | 0.988 (0.960, 1.017) | 0.984 (0.952, 1.015) | 0.005 (0.047, 0.037) | 0.822 | 1.022 (0.973, 1.071) | 0.984 (0.944, 1.024) | 0.038 (0.100, 0.024) | 0.232 | 0.033 (0.042, 0.107) | 0.386 |
Mortality index, mean (95% CI) | ||||||||||
Overall | 1.000 (0.955, 1.045) | 0.878 (0.836, 0.921) | 0.122 (0.183, 0.061) | <0.001 | 0.997 (0.931, 1.062) | 0.850 (0.800, 0.900) | 0.147 (0.227, 0.066) | 0.001 | 0.025 (0.076, 0.125) | 0.628 |
Medicare | 0.990 (0.942, 1.038) | 0.871 (0.826, 0.917) | 0.119 (0.185, 0.053) | <0.001 | 1.000 (0.925, 1.076) | 0.844 (0.788, 0.900) | 0.156 (0.249, 0.064) | 0.001 | 0.038 (0.075, 0.150) | 0.513 |
Commercial | 1.045 (0.934, 1.155) | 0.908 (0.842, 0.975) | 0.136 (0.264, 0.008) | 0.037 | 1.023 (0.935, 1.111) | 0.820 (0.758, 0.883) | 0.203 (0.309, 0.096) | <0.001 | 0.067 (0.099, 0.232) | 0.430 |
Medicaid | 0.894 (0.845, 0.942) | 0.786 (0.748, 0.824) | 0.107 (0.168, 0.046) | 0.001 | 0.937 (0.861, 1.013) | 0.789 (0.733, 0.844) | 0.148 (0.242, 0.055) | 0.002 | 0.041 (0.069, 0.151) | 0.464 |
Uninsured | 1.172 (1.007, 1.337)∥ | 1.136 (0.968, 1.303) | 0.037 (0.271, 0.197) | 0.758 | 0.868 (0.768, 0.968)∥ | 0.850 (0.761, 0.939) | 0.017 (0.149, 0.115) | 0.795 | 0.019 (0.287, 0.248) | 0.887 |
Other | 1.376 (1.052, 1.700)# | 1.156 (0.910, 1.402) | 0.220 (0.624, 0.184) | 0.285 | 1.009 (0.868, 1.150) # | 0.874 (0.682, 1.066) | 0.135 (0.369, 0.099) | 0.254 | 0.085 (0.555, 0.380) | 0.720 |

Table 1 shows that the overall LOS index remained unchanged pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.017 to 1.006, P = 0.488) and nonexpansion hospitals (1.008 to 0.995, P = 0.574). LOS indices for each payer type also remained unchanged. The overall mortality index significantly improved pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.000 to 0.878, P < 0.001) and nonexpansion hospitals (0.997 to 0.850, P = 0.001). Among both Medicaid‐expansion and nonexpansion hospitals, the mortality index significantly improved for Medicare, commercial, and Medicaid discharges but not for uninsured or other discharges. In the difference‐in‐differences analysis, the changes in LOS indices and mortality indices pre‐ to post‐ACA implementation did not differ significantly between hospitals in Medicaid‐expansion versus nonexpansion states.
In post hoc linear regression analyses of payer mix and LOS and mortality indices clustered by state‐level strata, point estimates were minimally changed. Although 95% CIs were slightly wider, statistical significance was unchanged from our primary analyses (data not shown).
DISCUSSION
We found that ACA implementation had a significant impact on payer mix for general medicine patients at AMCs in the United States, primarily by increasing the number of Medicaid beneficiaries and by decreasing the number of uninsured patients in Medicaid‐expansion states. State Medicaid expansion status did not appear to influence either LOS or in‐hospital mortality.
Our study offers some of the longest‐term data currently available on the impact of ACA implementation on payer mix trends and encompasses more states than others have previously. Although we uniquely focused on general medicine patients at AMCs, our results are similar to those seen for US hospitals overall. Nikpay and colleagues evaluated payer mix trends for non‐Medicare adult inpatient stays in 16 states through the second quarter of 2014 using the Healthcare Cost and Utilization Project database through the Agency for Healthcare Research and Quality.[4] They found a relative 20% increase and 50% decrease in Medicaid and uninsured discharges in Medicaid‐expansion states, along with nonsignificant changes in nonexpansion states. Hempstead and Cantor assessed payer mix for non‐Medicare discharges using state hospital association data from 21 states through the fourth quarter of 2014 and found a significant increase in Medicaid patients as well as a nearly significant decrease in uninsured patients in expansion states relative to nonexpansion states.[5] The Department of Health and Human Services also reported that uninsured/self‐pay discharges fell substantially (65%73%) in Medicaid‐expansion states by the end of 2014, with slight decreases in nonexpansion states.[20]
In contrast to our hypothesis, the overall LOS and in‐hospital mortality indices were not influenced by state Medicaid expansion status. From a purely mathematical standpoint, the contribution of Medicaid patients to the overall LOS and mortality indices may have been eclipsed by Medicare and commercially insured patients, who represented a higher proportion of total discharges. The lack of impact of state Medicaid expansion status on overall LOS and mortality indices did not appear to occur as a result of indices for Medicaid patients trending toward the mean. As predicted based on observational studies, Medicaid patients in our study tended to have a higher LOS index than those with other insurance types. Medicaid patients actually tended to have a lower mortality index in our analysis; the reason for this latter finding is unclear and in contrast to other published studies.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21]
To our knowledge, no other studies have evaluated the effect of payer mix changes under the ACA on inpatient outcomes. However, new evidence is emerging on outpatient outcomes. Low‐income adults in Medicaid‐expansion states have reported greater gains in access to primary care services and in the diagnosis of certain chronic health conditions than those in nonexpansion states as a result of ACA implementation.[22, 23] Such improvements in the outpatient setting might be expected to reduce patient acuity on admission. However, they would not necessarily translate to relative improvements in LOS or mortality indices for Medicaid‐expansion hospitals, as the UHC risk adjustment models controlled for disease severity on admission.
Similarly, few studies have assessed the impact of payer mix changes under previous state Medicaid expansions on inpatient outcomes. After Massachusetts expanded Medicaid and enacted near‐universal healthcare coverage in 2006, a minimal LOS reduction of just 0.05 days was observed.[24] New York expanded Medicaid eligibility to nondisabled childless adults with incomes below 100% of the federal poverty level in September 2001, whereas Arizona did so in November 2001 and Maine in October 2002. A study comparing outcomes in these states to 4 neighboring nonexpansion states found a relative reduction in annual all‐cause mortality of 6.1% population wide; however, it did not assess in‐hospital mortality.[25] The Oregon Health Insurance Experiment that randomized low‐income adults to expanded Medicaid coverage or not in 2008 has also reported on outpatient rather than inpatient outcomes.[26]
Our findings have potential implications for health policymakers. That Medicaid expansion status had a neutral effect on both LOS and mortality indices in our analysis should be reassuring for states contemplating Medicaid expansion in the future. Our results also highlight the need for further efforts to reduce disparities in inpatient care based on payer status. For example, although Medicare, commercially insured, and Medicaid patients witnessed significant improvements in mortality indices pre‐ to post‐ACA implementation in hospitals in both Medicaid‐expansion and nonexpansion states, uninsured patients did not.
This study has several limitations. First, our analysis of the impact of ACA implementation on payer mix did not account for concurrent socioeconomic trends that may have influenced insurance coverage across the United States. However, the main goal of this analysis was to demonstrate that changes in payer mix did in fact occur over time, to provide rationale for our subsequent LOS and mortality analyses. Second, we could not control for variation in the design and implementation of Medicaid expansions across states as permitted under the federal Section 1115 waiver process. Third, we only had access to hospital‐level data through the UHC CDB/RM, rather than individual patient data. We attempted to mitigate this limitation by weighting data according to the number of cases per hospital. Lastly, additional patient‐level factors that may influence LOS or mortality may not be included in the UHC risk adjustment models.
In summary, the differential shift in payer mix between Medicaid‐expansion and nonexpansion states did not influence overall LOS or in‐hospital mortality for general medicine patients at AMCs in the United States. Additional research could help to determine the impact of ACA implementation on other patient outcomes that may be dependent on insurance status, such as readmissions or hospital‐acquired complications.
Disclosures: M.E.A. conceived of the study concept and design, assisted with data acquisition, and drafted the manuscript. J.J.G. assisted with study design and made critical revisions to the manuscript. D.A. assisted with study design and made critical revisions to the manuscript. R.P. assisted with study design and made critical revisions to the manuscript. M.L. assisted with study design and data acquisition and made critical revisions to the manuscript. C.D.J. assisted with study design, performed data analyses, and made critical revisions to the manuscript. A modified abstract was presented in poster format at the University HealthSystem Consortium Annual Conference held September 30 to October 2, 2015 in Orlando, Florida, as well as at the Society of Hospital Medicine Research, Innovations, and Vignettes 2016 Annual Meeting held March 69, 2016, in San Diego, California. The authors report no conflicts of interest.
On January 1, 2014, several major provisions of the Affordable Care Act (ACA) took effect, including introduction of the individual mandate for health insurance coverage, opening of the Health Insurance Marketplace, and expansion of Medicaid eligibility to Americans earning up to 133% of the federal poverty level.[1] Nearly 9 million US adults have enrolled in Medicaid since that time, primarily in the 31 states and Washington, DC that have opted into Medicaid expansion.[2, 3] ACA implementation has also had a significant impact on hospital payer mix, primarily by reducing the volume of uncompensated care in Medicaid‐expansion states.[4, 5]
The differential shift in payer mix in Medicaid‐expansion versus nonexpansion states may be relevant to hospitals beyond reimbursement. Medicaid insurance has historically been associated with longer hospitalizations and higher in‐hospital mortality in diverse patient populations, more so than commercial insurance and often even uninsured payer status.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15] The disparity in outcomes between patients with Medicaid versus other insurance persists even after adjustment for disease severity and baseline comorbidities. Insurance type may influence the delivery of inpatient care through variation in access to invasive procedures and adherence to guideline‐concordant medical therapies.[9, 10, 11, 12] Medicaid patients may be more likely than uninsured patients to remain hospitalized pending postacute care placement rather than be discharged home with family support.[16] Medicaid patients are also less likely to leave against medical advice than uninsured patients.[17]
Currently, little is known about the impact of state Medicaid expansion status on length of stay (LOS) or mortality nationally. It is possible that hospitals in Medicaid‐expansion states have experienced relative worsening in LOS and mortality as their share of Medicaid patients has grown. Determining the impact of ACA implementation on payer mix and patient outcomes is particularly important for academic medical centers (AMCs), as they traditionally care for the greatest percentage of both Medicaid and uninsured patients.[18] We sought to characterize the impact of state Medicaid expansion status on payer mix, LOS, and in‐hospital mortality for general medicine patients at AMCs in the United States.
METHODS
The University HealthSystem Consortium (UHC) is an alliance of 117 AMCs and 310 affiliated hospitals, representing >90% of such institutions in the US. We queried the online UHC Clinical Data Base/Resource Manager (CDB/RM) to obtain hospital‐level insurance, LOS, and mortality data for inpatients discharged from a general medicine service between October 1, 2012 and September 30, 2015. We excluded hospitals that were missing data for any month within the study period. No patient‐level data were accessed.
Our outcomes of interest were the proportion of discharges by primary payer (Medicare, commercial, Medicaid, uninsured, or other [eg, Tri‐Care or Workers' Compensation]), as well as the LOS index and mortality index. Both indices were defined as the ratio of the observed to expected values. To determine the expected LOS and mortality, the UHC 2015 risk adjustment models were applied to all cases, adjusting for variables such as patient demographics, low socioeconomic status, admit source and status, severity of illness, and comorbid conditions, as described by International Classification of Diseases, Ninth Revision codes. These models have been validated and are used for research and quality benchmarking for member institutions.[19]
We next stratified hospitals according to state Medicaid expansion status. We defined Medicaid‐expansion states as those that had expanded Medicaid by the end of the study period: Arizona, Arkansas, California, Colorado, Connecticut, Illinois, Indiana, Iowa, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Nevada, New Hampshire, New Jersey, New Mexico, New York, Ohio, Oregon, Pennsylvania, Rhode Island, Washington, Washington DC, and West Virginia. Nonexpansion states included Alabama, Florida, Georgia, Kansas, Louisiana, Missouri, Nebraska, North Carolina, South Carolina, Tennessee, Texas, Utah, Virginia, and Wisconsin. We excluded 12 states due to incomplete data: Alaska, Delaware, Hawaii, Idaho, North Dakota, Maine, Mississippi, Montana, Oklahoma, South Dakota, Vermont, and Wyoming.
We then identified our pre‐ and post‐ACA implementation periods. Medicaid coverage expansion took effect in all expansion states on January 1, 2014, with the exception of Michigan (April 1, 2014), New Hampshire (August 15, 2014), Pennsylvania (January 1, 2015), and Indiana (February 1, 2015).[3] We therefore defined October 1, 2012 to December 31, 2013 as the pre‐ACA implementation period and January 1, 2014 to September 30, 2015 as the post‐ACA implementation period for all states except for Michigan, New Hampshire, Pennsylvania, and Indiana. For these 4 states, we customized the pre‐ and post‐ACA implementation periods to their respective dates of Medicaid expansion; for New Hampshire, we designated October 1, 2012 to July 31, 2014 as the pre‐ACA implementation period and September 1, 2014 to September 30, 2015 as the post‐ACA implementation period, as we were unable to distinguish before versus after data in August 2014 based on the midmonth expansion of Medicaid.
After stratifying hospitals into groups based on whether they were located in Medicaid‐expansion or nonexpansion states, the proportion of discharges by payer was compared between pre‐ and post‐ACA implementation periods both graphically by quarter and using linear regression models weighted for the number of cases from each hospital. Next, for both Medicaid‐expansion and nonexpansion hospitals, LOS index and mortality index were compared before and after ACA implementation using linear regression models weighted for the number of cases from each hospital, both overall and by payer. Difference‐in‐differences estimations were then completed to compare the proportion of discharges by payer, LOS index, and mortality index between Medicaid‐expansion and nonexpansion hospitals before and after ACA implementation. Post hoc linear regression analyses were completed to evaluate the effect of clustering by state level strata on payer mix and LOS and mortality indices. A 2‐sided P value of <0.05 was considered statistically significant. Data analyses were performed using Stata 12.0 (StataCorp, College Station, TX).
RESULTS
We identified 4,258,952 discharges among general medicine patients from 211 hospitals in 38 states and Washington, DC between October 1, 2012, and September 30, 2015. This included 3,144,488 discharges from 156 hospitals in 24 Medicaid‐expansion states and Washington, DC and 1,114,464 discharges from 55 hospitals in 14 nonexpansion states.
Figure 1 shows the trends in payer mix over time for hospitals in both Medicaid‐expansion and nonexpansion states. As summarized in Table 1, hospitals in Medicaid‐expansion states experienced a significant 3.7‐percentage point increase in Medicaid discharges (P = 0.013) and 2.9‐percentage point decrease in uninsured discharges (P < 0.001) after ACA implementation. This represented an approximately 19% jump and 60% drop in Medicaid and uninsured discharges, respectively. Hospitals in nonexpansion states saw no significant change in the proportion of discharges by payer after ACA implementation. In the difference‐in‐differences analysis, there was a trend toward a greater change in the proportion of Medicaid discharges pre‐ to post‐ACA implementation among hospitals in Medicaid‐expansion states compared to hospitals in nonexpansion states (mean difference‐in‐differences 4.1%, 95% confidence interval [CI]: 0.3%, 8.6%, P = 0.070).
Medicaid‐expansion n=156 hospitals; 3,144,488 cases | Non‐expansion n=55 hospitals; 1,114,464 cases | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Pre‐ACA Implementation (1,453,090 Cases) | Post‐ACA Implementation (1,691,398 Cases) | Mean Difference | P Value | Pre‐ACA Implementation (455,440 Cases) | Post‐ACA Implementation (659,024 Cases) | Mean Difference | P Value | Mean Difference‐in‐Differences | P Value | |
| ||||||||||
Payer mix, % (95% CI) | ||||||||||
Medicare | 48.6 (46.2, 51.0)* | 48.3 (45.9, 50.7) | 0.3 (3.6, 3.1) | 0.865 | 44.3 (40.7, 47.7)* | 45.3 (41.9, 48.6) | 1.0 (3.8, 5.8) | 0.671 | 1.3 (7.1, 4.5) | 0.655 |
Commercial | 23.1 (21.4, 24.7) | 23.2 (21.8, 24.6) | 0.2 (2.0, 2.3) | 0.882 | 21.5 (18.5, 24.6) | 22.7 (19.7, 25.8) | 1.2 (3.0, 5.4) | 0.574 | 1.0 (5.7, 3.6) | 0.662 |
Medicaid | 19.6 (17.6, 21.6) | 23.3 (21.2, 25.5) | 3.7 (0.8, 6.6) | 0.013 | 19.4 (16.9, 21.9) | 19.0 (16.5, 21.4) | 0.4 (3.8, 3.0) | 0.812 | 4.1 (0.3, 8.6) | 0.070 |
Uninsured | 5.0 (4.0, 5.9) | 2.0 (1.7, 2.3) | 2.9 (3.9, 2.0) | <0.001 | 10.9 (8.1, 13.7) | 9.4 (7.0, 11.7) | 1.5 (5.1, 2.1) | 0.407 | 1.4 (5.1, 2.2) | 0.442 |
Other | 3.8 (2.6, 4.9) | 3.1 (2.0, 4.3) | 0.7 (2.3, 1.0) | 0.435 | 4.0 (2.9, 5.0) | 3.7 (2.6, 4.7) | 0.3 (1.7, 1.1) | 0.662 | 0.3 (2.5, 1.8) | 0.762 |
LOS index, mean (95% CI) | ||||||||||
Overall | 1.017 (0.996, 1.038) | 1.006 (0.981, 1.031) | 0.011 (0.044, 0.021) | 0.488 | 1.008 (0.974, 1.042) | 0.995 (0.961, 1.029) | 0.013 (0.061, 0.034) | 0.574 | 0.002 (0.055, 0.059) | 0.943 |
Medicare | 1.012 (0.989, 1.035) | 0.999 (0.971, 1.027) | 0.013 (0.049, 0.023) | 0.488 | 0.982 (0.946, 1.017) | 0.979 (0.944, 1.013) | 0.003 (0.052, 0.046) | 0.899 | 0.010 (0.070, 0.051) | 0.754 |
Commercial | 0.993 (0.974, 1.012) | 0.977 (0.955, 0.998) | 0.016 (0.045, 0.013) | 0.271 | 1.009 (0.978, 1.039) | 0.986 (0.956, 1.016) | 0.022 (0.065, 0.020) | 0.298 | 0.006 (0.044, 0.057) | 0.809 |
Medicaid | 1.059 (1.036, 1.082) | 1.043 (1.018, 1.067) | 0.016 (0.049, 0.017) | 0.349 | 1.064 (1.020, 1.108) | 1.060 (1.015, 1.106) | 0.004 (0.066, 0.059) | 0.911 | 0.012 (0.082, 0.057) | 0.727 |
Uninsured | 0.960 (0.933, 0.988) | 0.925 (0.890, 0.961) | 0.035 (0.080, 0.010) | 0.126 | 0.972 (0.935, 1.009) | 0.944 (0.909, 0.979) | 0.028 (0.078, 0.022) | 0.273 | 0.007 (0.074, 0.060) | 0.835 |
Other | 0.988 (0.960, 1.017) | 0.984 (0.952, 1.015) | 0.005 (0.047, 0.037) | 0.822 | 1.022 (0.973, 1.071) | 0.984 (0.944, 1.024) | 0.038 (0.100, 0.024) | 0.232 | 0.033 (0.042, 0.107) | 0.386 |
Mortality index, mean (95% CI) | ||||||||||
Overall | 1.000 (0.955, 1.045) | 0.878 (0.836, 0.921) | 0.122 (0.183, 0.061) | <0.001 | 0.997 (0.931, 1.062) | 0.850 (0.800, 0.900) | 0.147 (0.227, 0.066) | 0.001 | 0.025 (0.076, 0.125) | 0.628 |
Medicare | 0.990 (0.942, 1.038) | 0.871 (0.826, 0.917) | 0.119 (0.185, 0.053) | <0.001 | 1.000 (0.925, 1.076) | 0.844 (0.788, 0.900) | 0.156 (0.249, 0.064) | 0.001 | 0.038 (0.075, 0.150) | 0.513 |
Commercial | 1.045 (0.934, 1.155) | 0.908 (0.842, 0.975) | 0.136 (0.264, 0.008) | 0.037 | 1.023 (0.935, 1.111) | 0.820 (0.758, 0.883) | 0.203 (0.309, 0.096) | <0.001 | 0.067 (0.099, 0.232) | 0.430 |
Medicaid | 0.894 (0.845, 0.942) | 0.786 (0.748, 0.824) | 0.107 (0.168, 0.046) | 0.001 | 0.937 (0.861, 1.013) | 0.789 (0.733, 0.844) | 0.148 (0.242, 0.055) | 0.002 | 0.041 (0.069, 0.151) | 0.464 |
Uninsured | 1.172 (1.007, 1.337)∥ | 1.136 (0.968, 1.303) | 0.037 (0.271, 0.197) | 0.758 | 0.868 (0.768, 0.968)∥ | 0.850 (0.761, 0.939) | 0.017 (0.149, 0.115) | 0.795 | 0.019 (0.287, 0.248) | 0.887 |
Other | 1.376 (1.052, 1.700)# | 1.156 (0.910, 1.402) | 0.220 (0.624, 0.184) | 0.285 | 1.009 (0.868, 1.150) # | 0.874 (0.682, 1.066) | 0.135 (0.369, 0.099) | 0.254 | 0.085 (0.555, 0.380) | 0.720 |

Table 1 shows that the overall LOS index remained unchanged pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.017 to 1.006, P = 0.488) and nonexpansion hospitals (1.008 to 0.995, P = 0.574). LOS indices for each payer type also remained unchanged. The overall mortality index significantly improved pre‐ to post‐ACA implementation for both Medicaid‐expansion (1.000 to 0.878, P < 0.001) and nonexpansion hospitals (0.997 to 0.850, P = 0.001). Among both Medicaid‐expansion and nonexpansion hospitals, the mortality index significantly improved for Medicare, commercial, and Medicaid discharges but not for uninsured or other discharges. In the difference‐in‐differences analysis, the changes in LOS indices and mortality indices pre‐ to post‐ACA implementation did not differ significantly between hospitals in Medicaid‐expansion versus nonexpansion states.
In post hoc linear regression analyses of payer mix and LOS and mortality indices clustered by state‐level strata, point estimates were minimally changed. Although 95% CIs were slightly wider, statistical significance was unchanged from our primary analyses (data not shown).
DISCUSSION
We found that ACA implementation had a significant impact on payer mix for general medicine patients at AMCs in the United States, primarily by increasing the number of Medicaid beneficiaries and by decreasing the number of uninsured patients in Medicaid‐expansion states. State Medicaid expansion status did not appear to influence either LOS or in‐hospital mortality.
Our study offers some of the longest‐term data currently available on the impact of ACA implementation on payer mix trends and encompasses more states than others have previously. Although we uniquely focused on general medicine patients at AMCs, our results are similar to those seen for US hospitals overall. Nikpay and colleagues evaluated payer mix trends for non‐Medicare adult inpatient stays in 16 states through the second quarter of 2014 using the Healthcare Cost and Utilization Project database through the Agency for Healthcare Research and Quality.[4] They found a relative 20% increase and 50% decrease in Medicaid and uninsured discharges in Medicaid‐expansion states, along with nonsignificant changes in nonexpansion states. Hempstead and Cantor assessed payer mix for non‐Medicare discharges using state hospital association data from 21 states through the fourth quarter of 2014 and found a significant increase in Medicaid patients as well as a nearly significant decrease in uninsured patients in expansion states relative to nonexpansion states.[5] The Department of Health and Human Services also reported that uninsured/self‐pay discharges fell substantially (65%73%) in Medicaid‐expansion states by the end of 2014, with slight decreases in nonexpansion states.[20]
In contrast to our hypothesis, the overall LOS and in‐hospital mortality indices were not influenced by state Medicaid expansion status. From a purely mathematical standpoint, the contribution of Medicaid patients to the overall LOS and mortality indices may have been eclipsed by Medicare and commercially insured patients, who represented a higher proportion of total discharges. The lack of impact of state Medicaid expansion status on overall LOS and mortality indices did not appear to occur as a result of indices for Medicaid patients trending toward the mean. As predicted based on observational studies, Medicaid patients in our study tended to have a higher LOS index than those with other insurance types. Medicaid patients actually tended to have a lower mortality index in our analysis; the reason for this latter finding is unclear and in contrast to other published studies.[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21]
To our knowledge, no other studies have evaluated the effect of payer mix changes under the ACA on inpatient outcomes. However, new evidence is emerging on outpatient outcomes. Low‐income adults in Medicaid‐expansion states have reported greater gains in access to primary care services and in the diagnosis of certain chronic health conditions than those in nonexpansion states as a result of ACA implementation.[22, 23] Such improvements in the outpatient setting might be expected to reduce patient acuity on admission. However, they would not necessarily translate to relative improvements in LOS or mortality indices for Medicaid‐expansion hospitals, as the UHC risk adjustment models controlled for disease severity on admission.
Similarly, few studies have assessed the impact of payer mix changes under previous state Medicaid expansions on inpatient outcomes. After Massachusetts expanded Medicaid and enacted near‐universal healthcare coverage in 2006, a minimal LOS reduction of just 0.05 days was observed.[24] New York expanded Medicaid eligibility to nondisabled childless adults with incomes below 100% of the federal poverty level in September 2001, whereas Arizona did so in November 2001 and Maine in October 2002. A study comparing outcomes in these states to 4 neighboring nonexpansion states found a relative reduction in annual all‐cause mortality of 6.1% population wide; however, it did not assess in‐hospital mortality.[25] The Oregon Health Insurance Experiment that randomized low‐income adults to expanded Medicaid coverage or not in 2008 has also reported on outpatient rather than inpatient outcomes.[26]
Our findings have potential implications for health policymakers. That Medicaid expansion status had a neutral effect on both LOS and mortality indices in our analysis should be reassuring for states contemplating Medicaid expansion in the future. Our results also highlight the need for further efforts to reduce disparities in inpatient care based on payer status. For example, although Medicare, commercially insured, and Medicaid patients witnessed significant improvements in mortality indices pre‐ to post‐ACA implementation in hospitals in both Medicaid‐expansion and nonexpansion states, uninsured patients did not.
This study has several limitations. First, our analysis of the impact of ACA implementation on payer mix did not account for concurrent socioeconomic trends that may have influenced insurance coverage across the United States. However, the main goal of this analysis was to demonstrate that changes in payer mix did in fact occur over time, to provide rationale for our subsequent LOS and mortality analyses. Second, we could not control for variation in the design and implementation of Medicaid expansions across states as permitted under the federal Section 1115 waiver process. Third, we only had access to hospital‐level data through the UHC CDB/RM, rather than individual patient data. We attempted to mitigate this limitation by weighting data according to the number of cases per hospital. Lastly, additional patient‐level factors that may influence LOS or mortality may not be included in the UHC risk adjustment models.
In summary, the differential shift in payer mix between Medicaid‐expansion and nonexpansion states did not influence overall LOS or in‐hospital mortality for general medicine patients at AMCs in the United States. Additional research could help to determine the impact of ACA implementation on other patient outcomes that may be dependent on insurance status, such as readmissions or hospital‐acquired complications.
Disclosures: M.E.A. conceived of the study concept and design, assisted with data acquisition, and drafted the manuscript. J.J.G. assisted with study design and made critical revisions to the manuscript. D.A. assisted with study design and made critical revisions to the manuscript. R.P. assisted with study design and made critical revisions to the manuscript. M.L. assisted with study design and data acquisition and made critical revisions to the manuscript. C.D.J. assisted with study design, performed data analyses, and made critical revisions to the manuscript. A modified abstract was presented in poster format at the University HealthSystem Consortium Annual Conference held September 30 to October 2, 2015 in Orlando, Florida, as well as at the Society of Hospital Medicine Research, Innovations, and Vignettes 2016 Annual Meeting held March 69, 2016, in San Diego, California. The authors report no conflicts of interest.
- Department of Health and Human Services. Key features of the Affordable Care Act by year. Available at: http://www.hhs.gov/healthcare/facts‐and‐features/key‐features‐of‐aca‐by‐year/index.html#2014. Accessed April 4, 2016.
- Centers for Medicare and Medicaid Services. Medicaid enrollment data collected through MBES. Available at: https://www.medicaid.gov/medicaid‐chip‐program‐information/program‐information/medicaid‐and‐chip‐enrollment‐data/medicaid‐enrollment‐data‐collected‐through‐mbes.html. Accessed April 4, 2016.
- The Henry J. Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. Available at: http://kff.org/health‐reform/state‐indicator/state‐activity‐around‐expanding‐medicaid‐under‐the‐affordable‐care‐act. Accessed April 4, 2016.
- Affordable Care Act Medicaid expansion reduced uninsured hospital stays in 2014. Health Aff (Millwood). 2016;35(1):106–110. , , .
- State Medicaid expansion and changes in hospital volume according to payer. N Engl J Med. 2016;374(2):196–198. , .
- Understanding predictors of prolonged hospitalizations among general medicine patients: a guide and preliminary analysis. J Hosp Med. 2015;10(9):623–626. , , , , , .
- Impact of insurance and hospital ownership on hospital length of stay among patients with ambulatory care‐sensitive conditions. Ann Fam Med. 2011;9:489–495. , , , .
- Insurance status and hospital care for myocardial infarction, stroke, and pneumonia. J Hosp Med. 2010;5:452–459. , , .
- Payment source, quality of care, and outcomes in patients hospitalized with heart failure. J Am Coll Cardiol. 2011;58(14):1465–1471. , , , , , .
- The inpatient experience and predictors of length of stay for patients hospitalized with systolic heart failure: comparison by commercial, Medicaid, and Medicare payer type. J Med Econ. 2013;16(1):43–54. , , , , .
- Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes. Ann Intern Med. 2006;145:739–748. , , et al.
- Association of insurance status with inpatient treatment for coronary artery disease: findings from the Get with the Guidelines Program. Am Heart J. 2010;159:1026–1036. , , , et al.
- Primary payer status affects mortality for major surgical operations. Ann Surg. 2010;252:544–551. , , , et al.
- Medicaid payer status is associated with in‐hospital morbidity and resource utilization following primary total joint arthroplasty. J Bone Joint Surg Am. 2014;96(21):e180. , , .
- The quality of care delivered to patients within the same hospital varies by insurance type. Health Aff (Millwood). 2013;32(10):1731–1739. , , .
- Effect of insurance status on postacute care among working age stroke survivors. Neurology. 2012;78(20):1590–1595. , , , , .
- Hospitalizations in which patients leave the hospital against medical advice (AMA), 2007. HCUP statistical brief #78. August 2009. Rockville, MD: Agency for Healthcare Research and Quality; 2009. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb78.pdf. Accessed May 12, 2016. , , , .
- Characteristics of Medicaid and uninsured hospitalizations, 2012. HCUP statistical brief #182. Rockville, MD: Agency for Healthcare Research and Quality; 2014. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb182‐Medicaid‐Uninsured‐Hospitalizations‐2012.pdf. Accessed March 9, 2016. , , , .
- Agency for Healthcare Research and Quality. Mortality measurement: mortality risk adjustment methodology for University HealthSystem Consortium. Available at: http://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf. Accessed May 10, 2016.
- Department of Health and Human Services. Insurance expansion, hospital uncompensated care, and the Affordable Care Act. Available at: https://aspe.hhs.gov/pdf‐report/insurance‐expansion‐hospital‐uncompensated‐care‐and‐affordable‐care‐act. Accessed May 27, 2016.
- Our flawed but beneficial Medicaid program. N Engl J Med. 2011;364(16):e31. , , , .
- Changes in self‐reported insurance coverage, access to care, and health under the Affordable Care Act. JAMA. 2015;314(4):366–374. , , , .
- Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid Expansions: a quasi‐experimental study. Ann Intern Med. 2016;164(12):795–803. , .
- The impact of health care reform on hospital and preventive care: evidence from Massachusetts. J Public Econ. 2012;96(11–12):909–929. , .
- Mortality and access to care among adults after state Medicaid expansions. N Engl J Med. 2012;367:1025–1034. , , .
- The Oregon Experiment—effects of Medicaid on clinical outcomes. N Engl J Med. 2013;368(18):1713–1722. , , , et al.
- Department of Health and Human Services. Key features of the Affordable Care Act by year. Available at: http://www.hhs.gov/healthcare/facts‐and‐features/key‐features‐of‐aca‐by‐year/index.html#2014. Accessed April 4, 2016.
- Centers for Medicare and Medicaid Services. Medicaid enrollment data collected through MBES. Available at: https://www.medicaid.gov/medicaid‐chip‐program‐information/program‐information/medicaid‐and‐chip‐enrollment‐data/medicaid‐enrollment‐data‐collected‐through‐mbes.html. Accessed April 4, 2016.
- The Henry J. Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. Available at: http://kff.org/health‐reform/state‐indicator/state‐activity‐around‐expanding‐medicaid‐under‐the‐affordable‐care‐act. Accessed April 4, 2016.
- Affordable Care Act Medicaid expansion reduced uninsured hospital stays in 2014. Health Aff (Millwood). 2016;35(1):106–110. , , .
- State Medicaid expansion and changes in hospital volume according to payer. N Engl J Med. 2016;374(2):196–198. , .
- Understanding predictors of prolonged hospitalizations among general medicine patients: a guide and preliminary analysis. J Hosp Med. 2015;10(9):623–626. , , , , , .
- Impact of insurance and hospital ownership on hospital length of stay among patients with ambulatory care‐sensitive conditions. Ann Fam Med. 2011;9:489–495. , , , .
- Insurance status and hospital care for myocardial infarction, stroke, and pneumonia. J Hosp Med. 2010;5:452–459. , , .
- Payment source, quality of care, and outcomes in patients hospitalized with heart failure. J Am Coll Cardiol. 2011;58(14):1465–1471. , , , , , .
- The inpatient experience and predictors of length of stay for patients hospitalized with systolic heart failure: comparison by commercial, Medicaid, and Medicare payer type. J Med Econ. 2013;16(1):43–54. , , , , .
- Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes. Ann Intern Med. 2006;145:739–748. , , et al.
- Association of insurance status with inpatient treatment for coronary artery disease: findings from the Get with the Guidelines Program. Am Heart J. 2010;159:1026–1036. , , , et al.
- Primary payer status affects mortality for major surgical operations. Ann Surg. 2010;252:544–551. , , , et al.
- Medicaid payer status is associated with in‐hospital morbidity and resource utilization following primary total joint arthroplasty. J Bone Joint Surg Am. 2014;96(21):e180. , , .
- The quality of care delivered to patients within the same hospital varies by insurance type. Health Aff (Millwood). 2013;32(10):1731–1739. , , .
- Effect of insurance status on postacute care among working age stroke survivors. Neurology. 2012;78(20):1590–1595. , , , , .
- Hospitalizations in which patients leave the hospital against medical advice (AMA), 2007. HCUP statistical brief #78. August 2009. Rockville, MD: Agency for Healthcare Research and Quality; 2009. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb78.pdf. Accessed May 12, 2016. , , , .
- Characteristics of Medicaid and uninsured hospitalizations, 2012. HCUP statistical brief #182. Rockville, MD: Agency for Healthcare Research and Quality; 2014. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb182‐Medicaid‐Uninsured‐Hospitalizations‐2012.pdf. Accessed March 9, 2016. , , , .
- Agency for Healthcare Research and Quality. Mortality measurement: mortality risk adjustment methodology for University HealthSystem Consortium. Available at: http://archive.ahrq.gov/professionals/quality‐patient‐safety/quality‐resources/tools/mortality/Meurer.pdf. Accessed May 10, 2016.
- Department of Health and Human Services. Insurance expansion, hospital uncompensated care, and the Affordable Care Act. Available at: https://aspe.hhs.gov/pdf‐report/insurance‐expansion‐hospital‐uncompensated‐care‐and‐affordable‐care‐act. Accessed May 27, 2016.
- Our flawed but beneficial Medicaid program. N Engl J Med. 2011;364(16):e31. , , , .
- Changes in self‐reported insurance coverage, access to care, and health under the Affordable Care Act. JAMA. 2015;314(4):366–374. , , , .
- Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid Expansions: a quasi‐experimental study. Ann Intern Med. 2016;164(12):795–803. , .
- The impact of health care reform on hospital and preventive care: evidence from Massachusetts. J Public Econ. 2012;96(11–12):909–929. , .
- Mortality and access to care among adults after state Medicaid expansions. N Engl J Med. 2012;367:1025–1034. , , .
- The Oregon Experiment—effects of Medicaid on clinical outcomes. N Engl J Med. 2013;368(18):1713–1722. , , , et al.