The affordability of prescription medications continues to be one of the most pressing public health issues in the United States. Many patients reduce their prescribed doses to make medications last longer or do not fill prescriptions because of cost.1 Cost‐related medication underuse affects patients with and without drug insurance coverage,2 and is likely to become even more problematic as employers scale back on drug benefits3 and drug prices continue to increase.4 The landmark Patient Protection and Affordability Act passed in March 2010 does little to address this issue.5

Existing estimates of cost‐related medication underuse come largely from surveys of ambulatory patients. For example, using data from the Medicare Current Beneficiary Survey, Maden et al. estimated that 11% to 15% of patients reduced medication use in the past year because of cost.6 Tseng and colleagues found very similar rates of cost‐related underuse in managed care beneficiaries with diabetes.7

Hospitalized patients, who have a high burden of disease and tend to use more medications than their ambulatory counterparts, may be particularly vulnerable to cost‐related underuse but, thus far, have been subject to little investigation. New medications, which are frequently prescribed at the time of discharge, may exacerbate these issues further and contribute to preventable readmissions. Accordingly, we surveyed a cohort of medical inpatients at a large academic medical center to estimate the prevalence and predictors of cost‐related medication underuse for hospitalized managed care patients, and to identify strategies that patients perceive as helpful to make medications more affordable.

METHODS

RESULTS

During the study period, 483 potentially‐eligible patients were admitted to the general medicine, cardiology, and oncology services. We excluded 167 because they had been discharged prior to being identified, had been surveyed or already declined participation on a prior admission, or were not managed care enrollees (see Appendix A). Of the remaining 316 subjects, 130 participated in the inpatient survey (response rate = 41%); 93 (75%) of these patients were reached by telephone after hospital discharge and completed the postdischarge survey. The baseline characteristics of our respondents are presented in Table 1. Patients had a mean age of 52 years, were 50% male and two‐thirds of white race, represented a range of household incomes, and almost all had employer‐sponsored prescription coverage. Prior to admission, patients took an average of 5 prescription medications and paid an average copayment of $10.80 and $21.60 for each generic and brand‐name prescription, respectively.

Cost‐Related Medication Underuse

Thirty (23%) of the survey respondents reported at least 1 cost‐related medication underuse strategy in the year prior to their hospital admission (Figure 1), most commonly not filling a prescription at all because of cost (n = 26; 20%). Rates of cost‐related underuse were highest for patients of black race, low income, and women (Figure 2).

Figure 1

Figure 2

In unadjusted analyses, black respondents had 4.60 (95% confidence interval [CI], 1.63 to 13.0) times the odds of reporting cost‐related underuse than non‐Hispanic white respondents (Table 2). The association of black race and cost‐related underuse appears to be confounded, in part, by income (adjusted odds ratio for black race was 4.16; 95% CI, 1.34 to 12.86) and the number of medications patients used on a regular basis (adjusted odds ratio for black race was 4.14; 95% CI, 1.44 to 11.96). After controlling for these variables, as well as age and gender, the relationship between race and cost‐related underuse remained statistically significant (adjusted odds ratio 3.39; 95% CI, 1.05 to 11.02) (Table 2).

Table 2.Predictors of Cost‐Related Underuse

Predictor	Unadjusted Odds Ratio (95% CI)	Adjusted Odds Ratio (95% CI)
Abbreviations: CI = confidence interval.
Age (per additional year)	0.98 (0.941.02)	0.97 (0.931.01)
Male (vs female)	0.84 (0.371.90)	1.03 (0.432.48)
Race (vs white race)
Black	4.60 (1.6313.0)	3.39 (1.0511.02)
Other	1.10 (0.363.37)	0.77 (0.202.99)
No. of medications (per additional medication)	1.10 (1.001.20)	1.10 (1.001.22)
High income (vs low income)	0.62 (0.271.42)	0.71 (0.242.07)

Strategies to Help Make Medications More Affordable

Virtually all respondents (n = 123; 95%) endorsed at least one of the proposed strategies to make medications more affordable (Figure 3). A majority felt that lowering cost sharing (94%), or receiving information about lower‐cost medication options (83%) or programs to subsidize medication costs (83%) would be helpful. Approximately 70% of patients stated that speaking to their outpatient physicians might be helpful, although only 14% reported actually speaking with their primary care provider about medication costs in the past year. Results were mixed for other strategies, including speaking with their inpatient physicians.

Figure 3

DISCUSSION

Almost a quarter of the medical inpatients we surveyed had not filled a medication because of cost, or had skipped doses, reduced dosages, or split pills to make their medicines last longer in the prior year. This amount is larger than that found in many prior studies, conducted in outpatient settings, in which 11% to 19% of patients report cost‐related underuse.68, 10, 11 Our results are particularly striking considering that our study cohort consisted exclusively of patients with commercial health insurance, the vast majority of whom also had employer‐sponsored drug coverage. Cost‐related medication underuse may be even more prevalent among hospitalized patients with less generous benefits, including the uninsured and perhaps even beneficiaries of Medicare Part D.

Reductions in medication use because of cost were particularly high among black patients, whose odds of reporting cost‐related underuse were more than 3 times higher than that of patients of non‐Hispanic white race. Race‐related differences in cost‐related underuse have been observed in outpatient studies,68, 12 and may be an important contributor to racial disparities in evidence‐based medication use.1315 These differences may, in part, reflect racial variations in socioeconomic status; lower income patients, who are more likely to be from a racial or ethnic minority, are more sensitive to cost sharing than higher income individuals.16 Consistent with this, the relationship between race and cost‐related underuse in our study was smaller but still highly significant in multivariable models that adjusted for income.

Not surprisingly, the underuse of effective prescription medications is associated with adverse clinical and economic consequences.17 Heisler et al. found that patients who had restricted medications because of cost were 76% more likely to report a decline in their health status than those who had not.18 The health effects of cost‐related underuse are likely to be particularly significant for hospitalized patients, given their high burden of disease and the frequency with which they are prescribed medications at discharge to treat the condition that led to their initial hospitalization. Thus, targeting efforts to address cost‐related underuse patients who are hospitalized may be an efficient method of improving patient health and reducing preventable readmissions. This is consistent with efforts that address, in the inpatient setting, other health issues that are commonly encountered in the ambulatory arena, such as immunizations and smoking cessation.19

Our survey respondents endorsed numerous strategies as being potentially helpful. Predictably, support for lowering copayments was extremely high. While this may not be practical or even desirable for some medications, lowering copayments for highly effective medications, such as statins and antihypertensives, in the context of value‐based insurance design, is an increasingly adopted strategy that has the potential to simultaneously improve clinical outcomes and reduce overall health spending.20, 21

While the majority of patients felt that talking to their outpatient physicians or pharmacists about medication costs might be helpful, the effectiveness of this strategy is unclear. Consistent with prior results,22, 23 the vast majority of the patients we surveyed had not discussed medication costs prior to their admission or after filling newly prescribed medications. Further, although physicians could help reduce drug expenditures in a variety of ways, including the increased ordering of generic drugs,24 many physicians are uncomfortable talking to their patients about costs,25 have limited knowledge about their patients' out‐of‐pocket expenditures, feel that addressing this issue is not their responsibility,26 or do not have resources, such as electronic formulary information, that could facilitate these discussions in an efficient manner.

An alternative strategy may be to provide patients with better education about medication costs. Virtually none of the patients we surveyed knew how much they would pay for their new prescriptions before visiting the pharmacy. These findings are similar to those observed in the outpatient setting,27 and suggest an opportunity to provide patients with information about the cost of their newly and previously prescribed drugs, and to facilitate discussions between patients and inpatient providers about predischarge prescribing decisions, in the same spirit as other predischarge patient education.28 Of course, issues related to transitions of care between the hospital and community setting, and coordination between inpatient and outpatient providers, must be adequately addressed for this strategy to be effective.

Our study has several notable limitations. It had a relatively small sample size and low response rate. Respondents may have differed systematically from non‐respondents, and we were unable to compare the characteristics of both populations. Further, we studied commercially insured inpatients on internal medicine services at an academic medical center, and thus our results may not be generalizable to patients hospitalized in other settings, or with different types of insurance coverage, including the uninsured. The primary outcome of our study was to determine self‐reported cost‐related underuse. While we used validated measures,8 it is possible that patients who reported reducing their medication use in response to cost may not have actually done so. We did not collect information on education or health literacy, nor did we have access to detailed information about our respondents' pharmacy benefit design structures; these important factors may have confounded our analyses, and/or may have been mediators of our observed results, and should be evaluated further in future studies. We did not have adequate statistical power to evaluate whether patients using specific classes of medications were particularly prone to cost‐related underuse.

Despite these limitations, our study is the first, to our knowledge, to evaluate the impact of medication costs on use in a cohort of hospitalized individuals. The high levels of cost‐related underuse that we observed is concerning. Our results support calls for the further development of interventions to address high medication costs and for the consideration of novel approaches to assist patients around the time of hospital discharge.

The affordability of prescription medications continues to be one of the most pressing public health issues in the United States. Many patients reduce their prescribed doses to make medications last longer or do not fill prescriptions because of cost.1 Cost‐related medication underuse affects patients with and without drug insurance coverage,2 and is likely to become even more problematic as employers scale back on drug benefits3 and drug prices continue to increase.4 The landmark Patient Protection and Affordability Act passed in March 2010 does little to address this issue.5

Existing estimates of cost‐related medication underuse come largely from surveys of ambulatory patients. For example, using data from the Medicare Current Beneficiary Survey, Maden et al. estimated that 11% to 15% of patients reduced medication use in the past year because of cost.6 Tseng and colleagues found very similar rates of cost‐related underuse in managed care beneficiaries with diabetes.7

Hospitalized patients, who have a high burden of disease and tend to use more medications than their ambulatory counterparts, may be particularly vulnerable to cost‐related underuse but, thus far, have been subject to little investigation. New medications, which are frequently prescribed at the time of discharge, may exacerbate these issues further and contribute to preventable readmissions. Accordingly, we surveyed a cohort of medical inpatients at a large academic medical center to estimate the prevalence and predictors of cost‐related medication underuse for hospitalized managed care patients, and to identify strategies that patients perceive as helpful to make medications more affordable.

METHODS

RESULTS

During the study period, 483 potentially‐eligible patients were admitted to the general medicine, cardiology, and oncology services. We excluded 167 because they had been discharged prior to being identified, had been surveyed or already declined participation on a prior admission, or were not managed care enrollees (see Appendix A). Of the remaining 316 subjects, 130 participated in the inpatient survey (response rate = 41%); 93 (75%) of these patients were reached by telephone after hospital discharge and completed the postdischarge survey. The baseline characteristics of our respondents are presented in Table 1. Patients had a mean age of 52 years, were 50% male and two‐thirds of white race, represented a range of household incomes, and almost all had employer‐sponsored prescription coverage. Prior to admission, patients took an average of 5 prescription medications and paid an average copayment of $10.80 and $21.60 for each generic and brand‐name prescription, respectively.

Cost‐Related Medication Underuse

Thirty (23%) of the survey respondents reported at least 1 cost‐related medication underuse strategy in the year prior to their hospital admission (Figure 1), most commonly not filling a prescription at all because of cost (n = 26; 20%). Rates of cost‐related underuse were highest for patients of black race, low income, and women (Figure 2).

Figure 1

Figure 2

In unadjusted analyses, black respondents had 4.60 (95% confidence interval [CI], 1.63 to 13.0) times the odds of reporting cost‐related underuse than non‐Hispanic white respondents (Table 2). The association of black race and cost‐related underuse appears to be confounded, in part, by income (adjusted odds ratio for black race was 4.16; 95% CI, 1.34 to 12.86) and the number of medications patients used on a regular basis (adjusted odds ratio for black race was 4.14; 95% CI, 1.44 to 11.96). After controlling for these variables, as well as age and gender, the relationship between race and cost‐related underuse remained statistically significant (adjusted odds ratio 3.39; 95% CI, 1.05 to 11.02) (Table 2).

Table 2.Predictors of Cost‐Related Underuse

Predictor	Unadjusted Odds Ratio (95% CI)	Adjusted Odds Ratio (95% CI)
Abbreviations: CI = confidence interval.
Age (per additional year)	0.98 (0.941.02)	0.97 (0.931.01)
Male (vs female)	0.84 (0.371.90)	1.03 (0.432.48)
Race (vs white race)
Black	4.60 (1.6313.0)	3.39 (1.0511.02)
Other	1.10 (0.363.37)	0.77 (0.202.99)
No. of medications (per additional medication)	1.10 (1.001.20)	1.10 (1.001.22)
High income (vs low income)	0.62 (0.271.42)	0.71 (0.242.07)

Strategies to Help Make Medications More Affordable

Virtually all respondents (n = 123; 95%) endorsed at least one of the proposed strategies to make medications more affordable (Figure 3). A majority felt that lowering cost sharing (94%), or receiving information about lower‐cost medication options (83%) or programs to subsidize medication costs (83%) would be helpful. Approximately 70% of patients stated that speaking to their outpatient physicians might be helpful, although only 14% reported actually speaking with their primary care provider about medication costs in the past year. Results were mixed for other strategies, including speaking with their inpatient physicians.

Figure 3

DISCUSSION

Almost a quarter of the medical inpatients we surveyed had not filled a medication because of cost, or had skipped doses, reduced dosages, or split pills to make their medicines last longer in the prior year. This amount is larger than that found in many prior studies, conducted in outpatient settings, in which 11% to 19% of patients report cost‐related underuse.68, 10, 11 Our results are particularly striking considering that our study cohort consisted exclusively of patients with commercial health insurance, the vast majority of whom also had employer‐sponsored drug coverage. Cost‐related medication underuse may be even more prevalent among hospitalized patients with less generous benefits, including the uninsured and perhaps even beneficiaries of Medicare Part D.

Reductions in medication use because of cost were particularly high among black patients, whose odds of reporting cost‐related underuse were more than 3 times higher than that of patients of non‐Hispanic white race. Race‐related differences in cost‐related underuse have been observed in outpatient studies,68, 12 and may be an important contributor to racial disparities in evidence‐based medication use.1315 These differences may, in part, reflect racial variations in socioeconomic status; lower income patients, who are more likely to be from a racial or ethnic minority, are more sensitive to cost sharing than higher income individuals.16 Consistent with this, the relationship between race and cost‐related underuse in our study was smaller but still highly significant in multivariable models that adjusted for income.

Not surprisingly, the underuse of effective prescription medications is associated with adverse clinical and economic consequences.17 Heisler et al. found that patients who had restricted medications because of cost were 76% more likely to report a decline in their health status than those who had not.18 The health effects of cost‐related underuse are likely to be particularly significant for hospitalized patients, given their high burden of disease and the frequency with which they are prescribed medications at discharge to treat the condition that led to their initial hospitalization. Thus, targeting efforts to address cost‐related underuse patients who are hospitalized may be an efficient method of improving patient health and reducing preventable readmissions. This is consistent with efforts that address, in the inpatient setting, other health issues that are commonly encountered in the ambulatory arena, such as immunizations and smoking cessation.19

Our survey respondents endorsed numerous strategies as being potentially helpful. Predictably, support for lowering copayments was extremely high. While this may not be practical or even desirable for some medications, lowering copayments for highly effective medications, such as statins and antihypertensives, in the context of value‐based insurance design, is an increasingly adopted strategy that has the potential to simultaneously improve clinical outcomes and reduce overall health spending.20, 21

While the majority of patients felt that talking to their outpatient physicians or pharmacists about medication costs might be helpful, the effectiveness of this strategy is unclear. Consistent with prior results,22, 23 the vast majority of the patients we surveyed had not discussed medication costs prior to their admission or after filling newly prescribed medications. Further, although physicians could help reduce drug expenditures in a variety of ways, including the increased ordering of generic drugs,24 many physicians are uncomfortable talking to their patients about costs,25 have limited knowledge about their patients' out‐of‐pocket expenditures, feel that addressing this issue is not their responsibility,26 or do not have resources, such as electronic formulary information, that could facilitate these discussions in an efficient manner.

An alternative strategy may be to provide patients with better education about medication costs. Virtually none of the patients we surveyed knew how much they would pay for their new prescriptions before visiting the pharmacy. These findings are similar to those observed in the outpatient setting,27 and suggest an opportunity to provide patients with information about the cost of their newly and previously prescribed drugs, and to facilitate discussions between patients and inpatient providers about predischarge prescribing decisions, in the same spirit as other predischarge patient education.28 Of course, issues related to transitions of care between the hospital and community setting, and coordination between inpatient and outpatient providers, must be adequately addressed for this strategy to be effective.

Our study has several notable limitations. It had a relatively small sample size and low response rate. Respondents may have differed systematically from non‐respondents, and we were unable to compare the characteristics of both populations. Further, we studied commercially insured inpatients on internal medicine services at an academic medical center, and thus our results may not be generalizable to patients hospitalized in other settings, or with different types of insurance coverage, including the uninsured. The primary outcome of our study was to determine self‐reported cost‐related underuse. While we used validated measures,8 it is possible that patients who reported reducing their medication use in response to cost may not have actually done so. We did not collect information on education or health literacy, nor did we have access to detailed information about our respondents' pharmacy benefit design structures; these important factors may have confounded our analyses, and/or may have been mediators of our observed results, and should be evaluated further in future studies. We did not have adequate statistical power to evaluate whether patients using specific classes of medications were particularly prone to cost‐related underuse.

Despite these limitations, our study is the first, to our knowledge, to evaluate the impact of medication costs on use in a cohort of hospitalized individuals. The high levels of cost‐related underuse that we observed is concerning. Our results support calls for the further development of interventions to address high medication costs and for the consideration of novel approaches to assist patients around the time of hospital discharge.

The affordability of prescription medications continues to be one of the most pressing public health issues in the United States. Many patients reduce their prescribed doses to make medications last longer or do not fill prescriptions because of cost.1 Cost‐related medication underuse affects patients with and without drug insurance coverage,2 and is likely to become even more problematic as employers scale back on drug benefits3 and drug prices continue to increase.4 The landmark Patient Protection and Affordability Act passed in March 2010 does little to address this issue.5

Existing estimates of cost‐related medication underuse come largely from surveys of ambulatory patients. For example, using data from the Medicare Current Beneficiary Survey, Maden et al. estimated that 11% to 15% of patients reduced medication use in the past year because of cost.6 Tseng and colleagues found very similar rates of cost‐related underuse in managed care beneficiaries with diabetes.7

Hospitalized patients, who have a high burden of disease and tend to use more medications than their ambulatory counterparts, may be particularly vulnerable to cost‐related underuse but, thus far, have been subject to little investigation. New medications, which are frequently prescribed at the time of discharge, may exacerbate these issues further and contribute to preventable readmissions. Accordingly, we surveyed a cohort of medical inpatients at a large academic medical center to estimate the prevalence and predictors of cost‐related medication underuse for hospitalized managed care patients, and to identify strategies that patients perceive as helpful to make medications more affordable.

METHODS

RESULTS

During the study period, 483 potentially‐eligible patients were admitted to the general medicine, cardiology, and oncology services. We excluded 167 because they had been discharged prior to being identified, had been surveyed or already declined participation on a prior admission, or were not managed care enrollees (see Appendix A). Of the remaining 316 subjects, 130 participated in the inpatient survey (response rate = 41%); 93 (75%) of these patients were reached by telephone after hospital discharge and completed the postdischarge survey. The baseline characteristics of our respondents are presented in Table 1. Patients had a mean age of 52 years, were 50% male and two‐thirds of white race, represented a range of household incomes, and almost all had employer‐sponsored prescription coverage. Prior to admission, patients took an average of 5 prescription medications and paid an average copayment of $10.80 and $21.60 for each generic and brand‐name prescription, respectively.

Cost‐Related Medication Underuse

Thirty (23%) of the survey respondents reported at least 1 cost‐related medication underuse strategy in the year prior to their hospital admission (Figure 1), most commonly not filling a prescription at all because of cost (n = 26; 20%). Rates of cost‐related underuse were highest for patients of black race, low income, and women (Figure 2).

Figure 1

Figure 2

In unadjusted analyses, black respondents had 4.60 (95% confidence interval [CI], 1.63 to 13.0) times the odds of reporting cost‐related underuse than non‐Hispanic white respondents (Table 2). The association of black race and cost‐related underuse appears to be confounded, in part, by income (adjusted odds ratio for black race was 4.16; 95% CI, 1.34 to 12.86) and the number of medications patients used on a regular basis (adjusted odds ratio for black race was 4.14; 95% CI, 1.44 to 11.96). After controlling for these variables, as well as age and gender, the relationship between race and cost‐related underuse remained statistically significant (adjusted odds ratio 3.39; 95% CI, 1.05 to 11.02) (Table 2).

Table 2.Predictors of Cost‐Related Underuse

Predictor	Unadjusted Odds Ratio (95% CI)	Adjusted Odds Ratio (95% CI)
Abbreviations: CI = confidence interval.
Age (per additional year)	0.98 (0.941.02)	0.97 (0.931.01)
Male (vs female)	0.84 (0.371.90)	1.03 (0.432.48)
Race (vs white race)
Black	4.60 (1.6313.0)	3.39 (1.0511.02)
Other	1.10 (0.363.37)	0.77 (0.202.99)
No. of medications (per additional medication)	1.10 (1.001.20)	1.10 (1.001.22)
High income (vs low income)	0.62 (0.271.42)	0.71 (0.242.07)

Strategies to Help Make Medications More Affordable

Virtually all respondents (n = 123; 95%) endorsed at least one of the proposed strategies to make medications more affordable (Figure 3). A majority felt that lowering cost sharing (94%), or receiving information about lower‐cost medication options (83%) or programs to subsidize medication costs (83%) would be helpful. Approximately 70% of patients stated that speaking to their outpatient physicians might be helpful, although only 14% reported actually speaking with their primary care provider about medication costs in the past year. Results were mixed for other strategies, including speaking with their inpatient physicians.

Figure 3

DISCUSSION

Almost a quarter of the medical inpatients we surveyed had not filled a medication because of cost, or had skipped doses, reduced dosages, or split pills to make their medicines last longer in the prior year. This amount is larger than that found in many prior studies, conducted in outpatient settings, in which 11% to 19% of patients report cost‐related underuse.68, 10, 11 Our results are particularly striking considering that our study cohort consisted exclusively of patients with commercial health insurance, the vast majority of whom also had employer‐sponsored drug coverage. Cost‐related medication underuse may be even more prevalent among hospitalized patients with less generous benefits, including the uninsured and perhaps even beneficiaries of Medicare Part D.

Reductions in medication use because of cost were particularly high among black patients, whose odds of reporting cost‐related underuse were more than 3 times higher than that of patients of non‐Hispanic white race. Race‐related differences in cost‐related underuse have been observed in outpatient studies,68, 12 and may be an important contributor to racial disparities in evidence‐based medication use.1315 These differences may, in part, reflect racial variations in socioeconomic status; lower income patients, who are more likely to be from a racial or ethnic minority, are more sensitive to cost sharing than higher income individuals.16 Consistent with this, the relationship between race and cost‐related underuse in our study was smaller but still highly significant in multivariable models that adjusted for income.

Not surprisingly, the underuse of effective prescription medications is associated with adverse clinical and economic consequences.17 Heisler et al. found that patients who had restricted medications because of cost were 76% more likely to report a decline in their health status than those who had not.18 The health effects of cost‐related underuse are likely to be particularly significant for hospitalized patients, given their high burden of disease and the frequency with which they are prescribed medications at discharge to treat the condition that led to their initial hospitalization. Thus, targeting efforts to address cost‐related underuse patients who are hospitalized may be an efficient method of improving patient health and reducing preventable readmissions. This is consistent with efforts that address, in the inpatient setting, other health issues that are commonly encountered in the ambulatory arena, such as immunizations and smoking cessation.19

Our survey respondents endorsed numerous strategies as being potentially helpful. Predictably, support for lowering copayments was extremely high. While this may not be practical or even desirable for some medications, lowering copayments for highly effective medications, such as statins and antihypertensives, in the context of value‐based insurance design, is an increasingly adopted strategy that has the potential to simultaneously improve clinical outcomes and reduce overall health spending.20, 21

While the majority of patients felt that talking to their outpatient physicians or pharmacists about medication costs might be helpful, the effectiveness of this strategy is unclear. Consistent with prior results,22, 23 the vast majority of the patients we surveyed had not discussed medication costs prior to their admission or after filling newly prescribed medications. Further, although physicians could help reduce drug expenditures in a variety of ways, including the increased ordering of generic drugs,24 many physicians are uncomfortable talking to their patients about costs,25 have limited knowledge about their patients' out‐of‐pocket expenditures, feel that addressing this issue is not their responsibility,26 or do not have resources, such as electronic formulary information, that could facilitate these discussions in an efficient manner.

An alternative strategy may be to provide patients with better education about medication costs. Virtually none of the patients we surveyed knew how much they would pay for their new prescriptions before visiting the pharmacy. These findings are similar to those observed in the outpatient setting,27 and suggest an opportunity to provide patients with information about the cost of their newly and previously prescribed drugs, and to facilitate discussions between patients and inpatient providers about predischarge prescribing decisions, in the same spirit as other predischarge patient education.28 Of course, issues related to transitions of care between the hospital and community setting, and coordination between inpatient and outpatient providers, must be adequately addressed for this strategy to be effective.

Our study has several notable limitations. It had a relatively small sample size and low response rate. Respondents may have differed systematically from non‐respondents, and we were unable to compare the characteristics of both populations. Further, we studied commercially insured inpatients on internal medicine services at an academic medical center, and thus our results may not be generalizable to patients hospitalized in other settings, or with different types of insurance coverage, including the uninsured. The primary outcome of our study was to determine self‐reported cost‐related underuse. While we used validated measures,8 it is possible that patients who reported reducing their medication use in response to cost may not have actually done so. We did not collect information on education or health literacy, nor did we have access to detailed information about our respondents' pharmacy benefit design structures; these important factors may have confounded our analyses, and/or may have been mediators of our observed results, and should be evaluated further in future studies. We did not have adequate statistical power to evaluate whether patients using specific classes of medications were particularly prone to cost‐related underuse.

Despite these limitations, our study is the first, to our knowledge, to evaluate the impact of medication costs on use in a cohort of hospitalized individuals. The high levels of cost‐related underuse that we observed is concerning. Our results support calls for the further development of interventions to address high medication costs and for the consideration of novel approaches to assist patients around the time of hospital discharge.

In the United States, general medical inpatient care is increasingly provided by hospital‐based physicians, also called hospitalists.1 The field of pediatrics is no exception, and by 2005 there were an estimated 1000 pediatric hospitalists in the workforce.2 Current numbers are likely to be greater than 2500, as the need for pediatric hospitalists has grown considerably.

At the same time, the quality of care delivered by the United States health system has come under increased scrutiny. In 2001, the Institute of Medicine, in its report on the quality of healthcare in America, concluded that between the care we have and what we could have lies not just a gap but a chasm.3 Meanwhile, the cost of healthcare delivery continues to increase. The pressure to deliver cost‐effective, high quality care is among the more important forces driving the proliferation of hospitalists.4

Over the last decade, data supporting the role of hospitalists in improving quality of care for adult patients has continued to accumulate.58 A 2007 retrospective cohort study by Lindenaur et al.7 included nearly 77,000 adult patients and found small reductions in length of stay without adverse effects on mortality or readmission rates, and a 2009 systematic review by Peterson6 included 33 studies and concluded that in general inpatient care of general medical patients by hospitalist physicians leads to decreased hospital cost and length of stay. A 2002 study by Meltzer et al.8 is also interesting, suggesting that improvements in costs and short‐term mortality are related to the disease‐specific experience of hospitalists.

Similar data for pediatric hospitalists has been slower to emerge. A systematic review of the literature by Landrigan et al., which included studies through 2004, concluded that [R]esearch suggests that pediatric hospitalists decrease costs and length of stay . The quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking.9 Since the publication of that review, there have been multiple studies which have sought to evaluate the quality of pediatric hospitalist systems. This review was undertaken to synthesize this new information, and to determine the effect of pediatric hospitalist systems on quality of care.

METHODS

A review of the available English language literature on the Medline database was undertaken in November of 2010 to answer the question, What are the differences in quality of care and outcomes of inpatient medical care provided by hospitalists versus non‐hospitalists in the pediatric population? Care metrics of interest were categorized according to the Society of Hospital Medicine's recommendations for measuring hospital performance.10

Search terms used (with additional medical subject headings [MeSH] terms in parenthesis) were hospital medicine (hospitalist), pediatrics (child health, child welfare), cost (cost and cost analysis), quality (quality indicators, healthcare), outcomes (outcome assessment, healthcare; outcomes and process assessment, healthcare); volume, patient satisfaction, length of stay, productivity (efficiency), provider satisfaction (attitude of health personnel, job satisfaction), mortality, and readmission rate (patient readmission). The citing articles search tool was used to identify other articles that potentially could meet criteria. Finally, references cited in the selected articles, as well as in excluded literature reviews, were searched for additional articles.

Articles were deemed eligible if they were published in a peer‐reviewed journal, if they had a comparative experimental design for hospitalists versus non‐hospitalists, and if they dealt exclusively with pediatric hospitalists. Noncomparative studies were excluded, as were studies that pertained to settings besides that of an inpatient pediatrics ward, such as pediatric intensive care units or emergency rooms. The search algorithm is diagrammed in Figure 1.

Figure 1

The selected articles were reviewed for the relevant outcome measures. The quality of each article was assessed using the Oxford Centre for Evidence‐Based Medicine levels of evidence,11 a widely accepted standard for critical analysis of studies. Levels of evidence are assigned to studies, from 1a (systematic reviews of randomized controlled trials) to 5 (expert opinion only). Well‐conducted prospective cohort studies receive a rating of 2c; those with wide confidence intervals due to small sample size receive a minus () modifier. This system does not specifically address survey studies, which were therefore not assigned a level of evidence.

RESULTS

The screening process yielded 92 possible relevant articles, which were then reviewed individually (by G.M.M.) by title and abstract. A total of 81 articles were excluded, including 48 studies that were either noncomparative or descriptive in nature. Ten of the identified articles were reviews and did not contain primary data. Nine studies were not restricted to the pediatric population. Also excluded were 7 studies that did not have outcomes related to quality (eg, billing performance), and 7 studies of hospitalists in settings besides general pediatric wards (eg, pediatric intensive care units). Ten studies were thus identified. The cited reference tool was used to identify an additional article which met criteria, yielding 11 total articles that were included in the review.

Five of the identified studies published prior to 2005 were previously reviewed by Landrigan et al.9 Since then, 6 additional studies of similar nature have been published and were included here. Articles that met criteria but appeared in an earlier review are included in Table 1; new articles appear in Table 2. The results of all 11 articles were included for this discussion.

DISCUSSION

Of the 11 studies selected for this review, 10 measured length of stay as an outcome, with the majority favoring hospitalists but with mixed results. Three of these studies, those by Dwight et al.,12 Bellet and Whitaker,13 and Landrigan et al.,14 demonstrated 11% to 14% improvement for hospitalist services compared to community pediatricians. Boyd et al.,21 however, found exactly the opposite result, and 2 studies by Conway and Keren20 and Ogershok et al.16 found no difference in length of stay. Two more studies found benefits restricted to certain conditions: Wells et al.15 found 32% shorter lengths of stay for asthma, but not for other conditions; Srivastava et al.17 found a 13% reduction in length of stay for asthma and 11% for dehydration, but none for viral illnesses or when all conditions were combined. Bekmezian et al.18 found shorter lengths of stay on a hospitalist service for hematology and gastroenterology patients, and Simon et al.19 attribute a general trend of decreasing lengths of stay on a surgical service to the implementation of hospital comanagement for a small percentage of patients.

The most common quality measures studied were patient satisfaction, readmission rates, and mortality. Only 1 study by Ogershok et al.16 reported on patient satisfaction and found few differences between hospitalists and community pediatricians. Readmission rate were reported by 6 studies. Bellet and Whitaker13 found a higher readmission rate for pediatric hospitalists, Bekmezian et al.18 found a lower rate but on a subspecialty service. The study with the greatest power for this analysis, by Landrigan et al.14 with nearly 18,000 patients, found no difference, and neither did another 3 studies. Unsurprisingly, no study detected differences in mortality; it would be extremely difficult to adequately power a study to do so in the general pediatric setting, where mortality is rare.

The effect of relative experience of hospitalist physicians is uncertain. Boyd et al.21 speculated that 1 possible cause for the decreased lengths of stay and costs associated with their faculty group compared to hospitalists may have been due to the increased experience of the faculty group. Unfortunately, they were unable to generate statistical significance due to the small numbers of physicians in the study. In contrast, the hospitalists in the report by Dwight et al.12 had decreased lengths of stay but were less experienced. In the adult literature, the study by Meltzer et al.8 suggests that improved outcomes from hospitalist systems may not become apparent for 1 or more years after implementation, but none of the pediatric studies included in our review specifically address this issue. This leaves the possibility open that the hospitalist systems evaluated in some studies had insufficient time in which to develop increased efficiencies.

There were several limitations to our studies. First, due to the heterogeneity and methodological variations among the included studies, we were unable to perform a meta‐analysis. Second, the overall quality of evidence is limited due to the lack of randomized control trials. Third, a lack of agreement on appropriate quality markers has limited the study of quality of care. Published reports continue to focus on financial measures, such as length of stay, despite the recommendation in the previous review by Landrigan et al.9 that such studies would be of limited value. Finally, the current variability of hospitalist models and lack of study of factors that might influence outcomes makes comparisons difficult.

Despite these limitations, several interesting trends emerge from these studies. One such trend is that the more recent studies highlight that simple classification of hospitalist system versus traditional system fails to measure the complexity and nuance of care delivery. The 2006 study by Boyd et al.21 is especially notable because it showed the opposite effect of previous studies, namely, an increase in length of stay and costs for hospitalists at St Joseph's Medical Center in Phoenix, Arizona. In this study, the traditional faculty group was employed by the hospital, and the hospitalist group was a private practice model. The authors suggest that their faculty physicians were therefore operating like hospitalists in that almost all of their time was focused on inpatient care while they were on service. They also had a limited number of general pediatricians, who attended in the inpatient setting, who were more experienced than the private practice groups. Also, the authors theorize that their faculty may have had a closer working relationship with their residents due to additional service responsibilities and locations of the faculty group onsite. Further study of the care models utilized by faculty and hospitalist practices at St Joseph's and other hospitals may reveal important insights about improving the quality and efficiency of inpatient pediatric care in general.

Though there is a clear trend in the adult literature indicating that the use of hospitalists results in superior quality of care, there is less evidence for pediatric systems. The aforementioned previous review by Landrigan et al.9, in 2006 concluded that emerging research suggests that pediatric hospitalist systems decrease cost and length of stay, but also the quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking. Data from the 6 additional studies presented here lend limited support to the first hypothesis, and the presence of only 1 negative study is not sufficient to undermine it.

While data on quality markers such as readmission rate or mortality remain elusive, the 2 studies by Conway et al.20, 22 attempt to evaluate quality by comparing the use of evidence‐based therapies by hospitalists and community pediatricians. Though the use of objective PHIS data for UTI in 2009 did not confirm the conclusion suggested by the 2006 provider survey study, the attempt to find measurable outcomes such as the use of evidence‐based therapies is a start but we need more metrics, including rigorous patient outcome metrics, to define the quality of our care systems. Before the effect of hospitalist systems on quality is fully understood, more work will need to be done defining metrics for comparison.

Unfortunately, over 5 years since the previous review by Landrigan et al.9 called for increased focus on inpatient quality and understanding how to improve, the sophistication of our measurement of pediatric inpatient quality and understanding of the mechanisms underlying improvement is still in its infancy. We propose a solution at multiple levels.

First, the investment in research comparing system‐level interventions (eg, discharge process A vs discharge process B) must be increased. This investment increased significantly due to the over $1 billion in Recovery Act funding for comparative effectiveness research.23 However, the future investment in comparative effectiveness research, often called patient‐centered outcomes research, and proportion of investment focused on delivery system interventions is unclear. We propose that the investment in comparing delivery system interventions is essential to improving not only hospital medicine systems, but, more importantly, the healthcare system broadly. In addition, research investment needs to focus on reliably implementing proven interventions in systems of care, and evaluating both the effects on patient outcomes and cost, and the contextual factors associated with successful implementation.24 A hospital medicine example would be the comparison of the implementation of a guideline for a common disease across a set of hospitals. One could perform a prospective observational design, in which one compares high intensity versus low intensity intervention and assesses the baseline characteristics of the hospital systems, to understand their association with successful implementation and, ultimately, patient outcomes. One could also perform a clustered randomized design.

Second, the development and implementation of pediatric quality of care measures, including in the inpatient setting, needs to increase rapidly. The Children's Health Insurance Program (CHIP) and its focus on an initial core set of quality measures that expands over time, through an investment in measure development and validation, is an opportunity for pediatric hospital medicine. Inpatient measures should be a focus of measure development and implementation. We must move beyond a limited set of inpatient measures to a broader set focused on issues such as patient safety, hospital‐acquired infections, outcomes for common illnesses, and transitions of care. We also need better measures for important pediatric populations, such as children with complex medical conditions.25

Third, our understanding of the mechanisms leading to improvement in hospital medicine systems needs to be developed. Studies of hospital medicine systems should move past simple binary comparisons of hospitalist systems versus traditional systems to understand the effect on patient outcomes and cost of factors such as years of experience, volume of patients seen overall and with a specific condition, staffing model, training, quality improvement knowledge and application, and health information systems. These factors may be additive or multiplicative to the performance of inpatient systems once put into place, but these hypotheses need to be tested.

Fourth, individual hospitalists and their groups must focus on quality measurement and improvement in quality and value delivered. At Cincinnati, we have a portfolio of quality and value projects derived from our strategic objectives, illustrated in Figure 2. The projects have leaders and teams to drive improvement and measure results. Increasingly, we are able to publish these results in peer‐reviewed journals. On a quarterly basis, we review the portfolio via a dashboard and/or run and control charts. We establish new projects and set new goals on at least an annual basis. It is important to note that at the beginning of the 2010‐2011 fiscal year, almost all initiatives identified as priorities were yellow or red. Our group is now planning new initiatives and goals for next year. This is one method applicable to our setting, but a focus on quality and value and measuring results needs to be part of every hospital medicine program. As payer focus on value increases, this will be essential to demonstrate how a hospitalist group improves outcomes and adds value.

Figure 2

CONCLUSION

This review suggests that the use of hospitalists can improve the quality of inpatient care in the pediatric population, but this is not a universal finding and, most importantly, the mechanisms of improvement are poorly understood. We propose 4 components to address these issues so that a systematic review 5 years from now would be much more robust. These are: 1) increased investment in research comparing system‐level interventions and reliable implementation; 2) further development and implementation of pediatric quality of care measures in the inpatient setting; 3) understanding the mechanisms and factors leading to improvement in hospital medicine systems; and 4) an increased focus on quality measurement, and improvement in quality and value delivered by all individual hospitalists and their groups.

In the United States, general medical inpatient care is increasingly provided by hospital‐based physicians, also called hospitalists.1 The field of pediatrics is no exception, and by 2005 there were an estimated 1000 pediatric hospitalists in the workforce.2 Current numbers are likely to be greater than 2500, as the need for pediatric hospitalists has grown considerably.

At the same time, the quality of care delivered by the United States health system has come under increased scrutiny. In 2001, the Institute of Medicine, in its report on the quality of healthcare in America, concluded that between the care we have and what we could have lies not just a gap but a chasm.3 Meanwhile, the cost of healthcare delivery continues to increase. The pressure to deliver cost‐effective, high quality care is among the more important forces driving the proliferation of hospitalists.4

Over the last decade, data supporting the role of hospitalists in improving quality of care for adult patients has continued to accumulate.58 A 2007 retrospective cohort study by Lindenaur et al.7 included nearly 77,000 adult patients and found small reductions in length of stay without adverse effects on mortality or readmission rates, and a 2009 systematic review by Peterson6 included 33 studies and concluded that in general inpatient care of general medical patients by hospitalist physicians leads to decreased hospital cost and length of stay. A 2002 study by Meltzer et al.8 is also interesting, suggesting that improvements in costs and short‐term mortality are related to the disease‐specific experience of hospitalists.

Similar data for pediatric hospitalists has been slower to emerge. A systematic review of the literature by Landrigan et al., which included studies through 2004, concluded that [R]esearch suggests that pediatric hospitalists decrease costs and length of stay . The quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking.9 Since the publication of that review, there have been multiple studies which have sought to evaluate the quality of pediatric hospitalist systems. This review was undertaken to synthesize this new information, and to determine the effect of pediatric hospitalist systems on quality of care.

METHODS

A review of the available English language literature on the Medline database was undertaken in November of 2010 to answer the question, What are the differences in quality of care and outcomes of inpatient medical care provided by hospitalists versus non‐hospitalists in the pediatric population? Care metrics of interest were categorized according to the Society of Hospital Medicine's recommendations for measuring hospital performance.10

Search terms used (with additional medical subject headings [MeSH] terms in parenthesis) were hospital medicine (hospitalist), pediatrics (child health, child welfare), cost (cost and cost analysis), quality (quality indicators, healthcare), outcomes (outcome assessment, healthcare; outcomes and process assessment, healthcare); volume, patient satisfaction, length of stay, productivity (efficiency), provider satisfaction (attitude of health personnel, job satisfaction), mortality, and readmission rate (patient readmission). The citing articles search tool was used to identify other articles that potentially could meet criteria. Finally, references cited in the selected articles, as well as in excluded literature reviews, were searched for additional articles.

Articles were deemed eligible if they were published in a peer‐reviewed journal, if they had a comparative experimental design for hospitalists versus non‐hospitalists, and if they dealt exclusively with pediatric hospitalists. Noncomparative studies were excluded, as were studies that pertained to settings besides that of an inpatient pediatrics ward, such as pediatric intensive care units or emergency rooms. The search algorithm is diagrammed in Figure 1.

Figure 1

The selected articles were reviewed for the relevant outcome measures. The quality of each article was assessed using the Oxford Centre for Evidence‐Based Medicine levels of evidence,11 a widely accepted standard for critical analysis of studies. Levels of evidence are assigned to studies, from 1a (systematic reviews of randomized controlled trials) to 5 (expert opinion only). Well‐conducted prospective cohort studies receive a rating of 2c; those with wide confidence intervals due to small sample size receive a minus () modifier. This system does not specifically address survey studies, which were therefore not assigned a level of evidence.

RESULTS

The screening process yielded 92 possible relevant articles, which were then reviewed individually (by G.M.M.) by title and abstract. A total of 81 articles were excluded, including 48 studies that were either noncomparative or descriptive in nature. Ten of the identified articles were reviews and did not contain primary data. Nine studies were not restricted to the pediatric population. Also excluded were 7 studies that did not have outcomes related to quality (eg, billing performance), and 7 studies of hospitalists in settings besides general pediatric wards (eg, pediatric intensive care units). Ten studies were thus identified. The cited reference tool was used to identify an additional article which met criteria, yielding 11 total articles that were included in the review.

Five of the identified studies published prior to 2005 were previously reviewed by Landrigan et al.9 Since then, 6 additional studies of similar nature have been published and were included here. Articles that met criteria but appeared in an earlier review are included in Table 1; new articles appear in Table 2. The results of all 11 articles were included for this discussion.

DISCUSSION

Of the 11 studies selected for this review, 10 measured length of stay as an outcome, with the majority favoring hospitalists but with mixed results. Three of these studies, those by Dwight et al.,12 Bellet and Whitaker,13 and Landrigan et al.,14 demonstrated 11% to 14% improvement for hospitalist services compared to community pediatricians. Boyd et al.,21 however, found exactly the opposite result, and 2 studies by Conway and Keren20 and Ogershok et al.16 found no difference in length of stay. Two more studies found benefits restricted to certain conditions: Wells et al.15 found 32% shorter lengths of stay for asthma, but not for other conditions; Srivastava et al.17 found a 13% reduction in length of stay for asthma and 11% for dehydration, but none for viral illnesses or when all conditions were combined. Bekmezian et al.18 found shorter lengths of stay on a hospitalist service for hematology and gastroenterology patients, and Simon et al.19 attribute a general trend of decreasing lengths of stay on a surgical service to the implementation of hospital comanagement for a small percentage of patients.

The most common quality measures studied were patient satisfaction, readmission rates, and mortality. Only 1 study by Ogershok et al.16 reported on patient satisfaction and found few differences between hospitalists and community pediatricians. Readmission rate were reported by 6 studies. Bellet and Whitaker13 found a higher readmission rate for pediatric hospitalists, Bekmezian et al.18 found a lower rate but on a subspecialty service. The study with the greatest power for this analysis, by Landrigan et al.14 with nearly 18,000 patients, found no difference, and neither did another 3 studies. Unsurprisingly, no study detected differences in mortality; it would be extremely difficult to adequately power a study to do so in the general pediatric setting, where mortality is rare.

The effect of relative experience of hospitalist physicians is uncertain. Boyd et al.21 speculated that 1 possible cause for the decreased lengths of stay and costs associated with their faculty group compared to hospitalists may have been due to the increased experience of the faculty group. Unfortunately, they were unable to generate statistical significance due to the small numbers of physicians in the study. In contrast, the hospitalists in the report by Dwight et al.12 had decreased lengths of stay but were less experienced. In the adult literature, the study by Meltzer et al.8 suggests that improved outcomes from hospitalist systems may not become apparent for 1 or more years after implementation, but none of the pediatric studies included in our review specifically address this issue. This leaves the possibility open that the hospitalist systems evaluated in some studies had insufficient time in which to develop increased efficiencies.

There were several limitations to our studies. First, due to the heterogeneity and methodological variations among the included studies, we were unable to perform a meta‐analysis. Second, the overall quality of evidence is limited due to the lack of randomized control trials. Third, a lack of agreement on appropriate quality markers has limited the study of quality of care. Published reports continue to focus on financial measures, such as length of stay, despite the recommendation in the previous review by Landrigan et al.9 that such studies would be of limited value. Finally, the current variability of hospitalist models and lack of study of factors that might influence outcomes makes comparisons difficult.

Despite these limitations, several interesting trends emerge from these studies. One such trend is that the more recent studies highlight that simple classification of hospitalist system versus traditional system fails to measure the complexity and nuance of care delivery. The 2006 study by Boyd et al.21 is especially notable because it showed the opposite effect of previous studies, namely, an increase in length of stay and costs for hospitalists at St Joseph's Medical Center in Phoenix, Arizona. In this study, the traditional faculty group was employed by the hospital, and the hospitalist group was a private practice model. The authors suggest that their faculty physicians were therefore operating like hospitalists in that almost all of their time was focused on inpatient care while they were on service. They also had a limited number of general pediatricians, who attended in the inpatient setting, who were more experienced than the private practice groups. Also, the authors theorize that their faculty may have had a closer working relationship with their residents due to additional service responsibilities and locations of the faculty group onsite. Further study of the care models utilized by faculty and hospitalist practices at St Joseph's and other hospitals may reveal important insights about improving the quality and efficiency of inpatient pediatric care in general.

Though there is a clear trend in the adult literature indicating that the use of hospitalists results in superior quality of care, there is less evidence for pediatric systems. The aforementioned previous review by Landrigan et al.9, in 2006 concluded that emerging research suggests that pediatric hospitalist systems decrease cost and length of stay, but also the quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking. Data from the 6 additional studies presented here lend limited support to the first hypothesis, and the presence of only 1 negative study is not sufficient to undermine it.

While data on quality markers such as readmission rate or mortality remain elusive, the 2 studies by Conway et al.20, 22 attempt to evaluate quality by comparing the use of evidence‐based therapies by hospitalists and community pediatricians. Though the use of objective PHIS data for UTI in 2009 did not confirm the conclusion suggested by the 2006 provider survey study, the attempt to find measurable outcomes such as the use of evidence‐based therapies is a start but we need more metrics, including rigorous patient outcome metrics, to define the quality of our care systems. Before the effect of hospitalist systems on quality is fully understood, more work will need to be done defining metrics for comparison.

Unfortunately, over 5 years since the previous review by Landrigan et al.9 called for increased focus on inpatient quality and understanding how to improve, the sophistication of our measurement of pediatric inpatient quality and understanding of the mechanisms underlying improvement is still in its infancy. We propose a solution at multiple levels.

First, the investment in research comparing system‐level interventions (eg, discharge process A vs discharge process B) must be increased. This investment increased significantly due to the over $1 billion in Recovery Act funding for comparative effectiveness research.23 However, the future investment in comparative effectiveness research, often called patient‐centered outcomes research, and proportion of investment focused on delivery system interventions is unclear. We propose that the investment in comparing delivery system interventions is essential to improving not only hospital medicine systems, but, more importantly, the healthcare system broadly. In addition, research investment needs to focus on reliably implementing proven interventions in systems of care, and evaluating both the effects on patient outcomes and cost, and the contextual factors associated with successful implementation.24 A hospital medicine example would be the comparison of the implementation of a guideline for a common disease across a set of hospitals. One could perform a prospective observational design, in which one compares high intensity versus low intensity intervention and assesses the baseline characteristics of the hospital systems, to understand their association with successful implementation and, ultimately, patient outcomes. One could also perform a clustered randomized design.

Second, the development and implementation of pediatric quality of care measures, including in the inpatient setting, needs to increase rapidly. The Children's Health Insurance Program (CHIP) and its focus on an initial core set of quality measures that expands over time, through an investment in measure development and validation, is an opportunity for pediatric hospital medicine. Inpatient measures should be a focus of measure development and implementation. We must move beyond a limited set of inpatient measures to a broader set focused on issues such as patient safety, hospital‐acquired infections, outcomes for common illnesses, and transitions of care. We also need better measures for important pediatric populations, such as children with complex medical conditions.25

Third, our understanding of the mechanisms leading to improvement in hospital medicine systems needs to be developed. Studies of hospital medicine systems should move past simple binary comparisons of hospitalist systems versus traditional systems to understand the effect on patient outcomes and cost of factors such as years of experience, volume of patients seen overall and with a specific condition, staffing model, training, quality improvement knowledge and application, and health information systems. These factors may be additive or multiplicative to the performance of inpatient systems once put into place, but these hypotheses need to be tested.

Fourth, individual hospitalists and their groups must focus on quality measurement and improvement in quality and value delivered. At Cincinnati, we have a portfolio of quality and value projects derived from our strategic objectives, illustrated in Figure 2. The projects have leaders and teams to drive improvement and measure results. Increasingly, we are able to publish these results in peer‐reviewed journals. On a quarterly basis, we review the portfolio via a dashboard and/or run and control charts. We establish new projects and set new goals on at least an annual basis. It is important to note that at the beginning of the 2010‐2011 fiscal year, almost all initiatives identified as priorities were yellow or red. Our group is now planning new initiatives and goals for next year. This is one method applicable to our setting, but a focus on quality and value and measuring results needs to be part of every hospital medicine program. As payer focus on value increases, this will be essential to demonstrate how a hospitalist group improves outcomes and adds value.

Figure 2

CONCLUSION

This review suggests that the use of hospitalists can improve the quality of inpatient care in the pediatric population, but this is not a universal finding and, most importantly, the mechanisms of improvement are poorly understood. We propose 4 components to address these issues so that a systematic review 5 years from now would be much more robust. These are: 1) increased investment in research comparing system‐level interventions and reliable implementation; 2) further development and implementation of pediatric quality of care measures in the inpatient setting; 3) understanding the mechanisms and factors leading to improvement in hospital medicine systems; and 4) an increased focus on quality measurement, and improvement in quality and value delivered by all individual hospitalists and their groups.

In the United States, general medical inpatient care is increasingly provided by hospital‐based physicians, also called hospitalists.1 The field of pediatrics is no exception, and by 2005 there were an estimated 1000 pediatric hospitalists in the workforce.2 Current numbers are likely to be greater than 2500, as the need for pediatric hospitalists has grown considerably.

At the same time, the quality of care delivered by the United States health system has come under increased scrutiny. In 2001, the Institute of Medicine, in its report on the quality of healthcare in America, concluded that between the care we have and what we could have lies not just a gap but a chasm.3 Meanwhile, the cost of healthcare delivery continues to increase. The pressure to deliver cost‐effective, high quality care is among the more important forces driving the proliferation of hospitalists.4

Over the last decade, data supporting the role of hospitalists in improving quality of care for adult patients has continued to accumulate.58 A 2007 retrospective cohort study by Lindenaur et al.7 included nearly 77,000 adult patients and found small reductions in length of stay without adverse effects on mortality or readmission rates, and a 2009 systematic review by Peterson6 included 33 studies and concluded that in general inpatient care of general medical patients by hospitalist physicians leads to decreased hospital cost and length of stay. A 2002 study by Meltzer et al.8 is also interesting, suggesting that improvements in costs and short‐term mortality are related to the disease‐specific experience of hospitalists.

Similar data for pediatric hospitalists has been slower to emerge. A systematic review of the literature by Landrigan et al., which included studies through 2004, concluded that [R]esearch suggests that pediatric hospitalists decrease costs and length of stay . The quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking.9 Since the publication of that review, there have been multiple studies which have sought to evaluate the quality of pediatric hospitalist systems. This review was undertaken to synthesize this new information, and to determine the effect of pediatric hospitalist systems on quality of care.

METHODS

A review of the available English language literature on the Medline database was undertaken in November of 2010 to answer the question, What are the differences in quality of care and outcomes of inpatient medical care provided by hospitalists versus non‐hospitalists in the pediatric population? Care metrics of interest were categorized according to the Society of Hospital Medicine's recommendations for measuring hospital performance.10

Search terms used (with additional medical subject headings [MeSH] terms in parenthesis) were hospital medicine (hospitalist), pediatrics (child health, child welfare), cost (cost and cost analysis), quality (quality indicators, healthcare), outcomes (outcome assessment, healthcare; outcomes and process assessment, healthcare); volume, patient satisfaction, length of stay, productivity (efficiency), provider satisfaction (attitude of health personnel, job satisfaction), mortality, and readmission rate (patient readmission). The citing articles search tool was used to identify other articles that potentially could meet criteria. Finally, references cited in the selected articles, as well as in excluded literature reviews, were searched for additional articles.

Articles were deemed eligible if they were published in a peer‐reviewed journal, if they had a comparative experimental design for hospitalists versus non‐hospitalists, and if they dealt exclusively with pediatric hospitalists. Noncomparative studies were excluded, as were studies that pertained to settings besides that of an inpatient pediatrics ward, such as pediatric intensive care units or emergency rooms. The search algorithm is diagrammed in Figure 1.

Figure 1

The selected articles were reviewed for the relevant outcome measures. The quality of each article was assessed using the Oxford Centre for Evidence‐Based Medicine levels of evidence,11 a widely accepted standard for critical analysis of studies. Levels of evidence are assigned to studies, from 1a (systematic reviews of randomized controlled trials) to 5 (expert opinion only). Well‐conducted prospective cohort studies receive a rating of 2c; those with wide confidence intervals due to small sample size receive a minus () modifier. This system does not specifically address survey studies, which were therefore not assigned a level of evidence.

RESULTS

The screening process yielded 92 possible relevant articles, which were then reviewed individually (by G.M.M.) by title and abstract. A total of 81 articles were excluded, including 48 studies that were either noncomparative or descriptive in nature. Ten of the identified articles were reviews and did not contain primary data. Nine studies were not restricted to the pediatric population. Also excluded were 7 studies that did not have outcomes related to quality (eg, billing performance), and 7 studies of hospitalists in settings besides general pediatric wards (eg, pediatric intensive care units). Ten studies were thus identified. The cited reference tool was used to identify an additional article which met criteria, yielding 11 total articles that were included in the review.

Five of the identified studies published prior to 2005 were previously reviewed by Landrigan et al.9 Since then, 6 additional studies of similar nature have been published and were included here. Articles that met criteria but appeared in an earlier review are included in Table 1; new articles appear in Table 2. The results of all 11 articles were included for this discussion.

DISCUSSION

Of the 11 studies selected for this review, 10 measured length of stay as an outcome, with the majority favoring hospitalists but with mixed results. Three of these studies, those by Dwight et al.,12 Bellet and Whitaker,13 and Landrigan et al.,14 demonstrated 11% to 14% improvement for hospitalist services compared to community pediatricians. Boyd et al.,21 however, found exactly the opposite result, and 2 studies by Conway and Keren20 and Ogershok et al.16 found no difference in length of stay. Two more studies found benefits restricted to certain conditions: Wells et al.15 found 32% shorter lengths of stay for asthma, but not for other conditions; Srivastava et al.17 found a 13% reduction in length of stay for asthma and 11% for dehydration, but none for viral illnesses or when all conditions were combined. Bekmezian et al.18 found shorter lengths of stay on a hospitalist service for hematology and gastroenterology patients, and Simon et al.19 attribute a general trend of decreasing lengths of stay on a surgical service to the implementation of hospital comanagement for a small percentage of patients.

The most common quality measures studied were patient satisfaction, readmission rates, and mortality. Only 1 study by Ogershok et al.16 reported on patient satisfaction and found few differences between hospitalists and community pediatricians. Readmission rate were reported by 6 studies. Bellet and Whitaker13 found a higher readmission rate for pediatric hospitalists, Bekmezian et al.18 found a lower rate but on a subspecialty service. The study with the greatest power for this analysis, by Landrigan et al.14 with nearly 18,000 patients, found no difference, and neither did another 3 studies. Unsurprisingly, no study detected differences in mortality; it would be extremely difficult to adequately power a study to do so in the general pediatric setting, where mortality is rare.

The effect of relative experience of hospitalist physicians is uncertain. Boyd et al.21 speculated that 1 possible cause for the decreased lengths of stay and costs associated with their faculty group compared to hospitalists may have been due to the increased experience of the faculty group. Unfortunately, they were unable to generate statistical significance due to the small numbers of physicians in the study. In contrast, the hospitalists in the report by Dwight et al.12 had decreased lengths of stay but were less experienced. In the adult literature, the study by Meltzer et al.8 suggests that improved outcomes from hospitalist systems may not become apparent for 1 or more years after implementation, but none of the pediatric studies included in our review specifically address this issue. This leaves the possibility open that the hospitalist systems evaluated in some studies had insufficient time in which to develop increased efficiencies.

There were several limitations to our studies. First, due to the heterogeneity and methodological variations among the included studies, we were unable to perform a meta‐analysis. Second, the overall quality of evidence is limited due to the lack of randomized control trials. Third, a lack of agreement on appropriate quality markers has limited the study of quality of care. Published reports continue to focus on financial measures, such as length of stay, despite the recommendation in the previous review by Landrigan et al.9 that such studies would be of limited value. Finally, the current variability of hospitalist models and lack of study of factors that might influence outcomes makes comparisons difficult.

Despite these limitations, several interesting trends emerge from these studies. One such trend is that the more recent studies highlight that simple classification of hospitalist system versus traditional system fails to measure the complexity and nuance of care delivery. The 2006 study by Boyd et al.21 is especially notable because it showed the opposite effect of previous studies, namely, an increase in length of stay and costs for hospitalists at St Joseph's Medical Center in Phoenix, Arizona. In this study, the traditional faculty group was employed by the hospital, and the hospitalist group was a private practice model. The authors suggest that their faculty physicians were therefore operating like hospitalists in that almost all of their time was focused on inpatient care while they were on service. They also had a limited number of general pediatricians, who attended in the inpatient setting, who were more experienced than the private practice groups. Also, the authors theorize that their faculty may have had a closer working relationship with their residents due to additional service responsibilities and locations of the faculty group onsite. Further study of the care models utilized by faculty and hospitalist practices at St Joseph's and other hospitals may reveal important insights about improving the quality and efficiency of inpatient pediatric care in general.

Though there is a clear trend in the adult literature indicating that the use of hospitalists results in superior quality of care, there is less evidence for pediatric systems. The aforementioned previous review by Landrigan et al.9, in 2006 concluded that emerging research suggests that pediatric hospitalist systems decrease cost and length of stay, but also the quality of care in pediatric hospitalist systems is unclear, because rigorous metrics to evaluate quality are lacking. Data from the 6 additional studies presented here lend limited support to the first hypothesis, and the presence of only 1 negative study is not sufficient to undermine it.

While data on quality markers such as readmission rate or mortality remain elusive, the 2 studies by Conway et al.20, 22 attempt to evaluate quality by comparing the use of evidence‐based therapies by hospitalists and community pediatricians. Though the use of objective PHIS data for UTI in 2009 did not confirm the conclusion suggested by the 2006 provider survey study, the attempt to find measurable outcomes such as the use of evidence‐based therapies is a start but we need more metrics, including rigorous patient outcome metrics, to define the quality of our care systems. Before the effect of hospitalist systems on quality is fully understood, more work will need to be done defining metrics for comparison.

Unfortunately, over 5 years since the previous review by Landrigan et al.9 called for increased focus on inpatient quality and understanding how to improve, the sophistication of our measurement of pediatric inpatient quality and understanding of the mechanisms underlying improvement is still in its infancy. We propose a solution at multiple levels.

First, the investment in research comparing system‐level interventions (eg, discharge process A vs discharge process B) must be increased. This investment increased significantly due to the over $1 billion in Recovery Act funding for comparative effectiveness research.23 However, the future investment in comparative effectiveness research, often called patient‐centered outcomes research, and proportion of investment focused on delivery system interventions is unclear. We propose that the investment in comparing delivery system interventions is essential to improving not only hospital medicine systems, but, more importantly, the healthcare system broadly. In addition, research investment needs to focus on reliably implementing proven interventions in systems of care, and evaluating both the effects on patient outcomes and cost, and the contextual factors associated with successful implementation.24 A hospital medicine example would be the comparison of the implementation of a guideline for a common disease across a set of hospitals. One could perform a prospective observational design, in which one compares high intensity versus low intensity intervention and assesses the baseline characteristics of the hospital systems, to understand their association with successful implementation and, ultimately, patient outcomes. One could also perform a clustered randomized design.

Second, the development and implementation of pediatric quality of care measures, including in the inpatient setting, needs to increase rapidly. The Children's Health Insurance Program (CHIP) and its focus on an initial core set of quality measures that expands over time, through an investment in measure development and validation, is an opportunity for pediatric hospital medicine. Inpatient measures should be a focus of measure development and implementation. We must move beyond a limited set of inpatient measures to a broader set focused on issues such as patient safety, hospital‐acquired infections, outcomes for common illnesses, and transitions of care. We also need better measures for important pediatric populations, such as children with complex medical conditions.25

Third, our understanding of the mechanisms leading to improvement in hospital medicine systems needs to be developed. Studies of hospital medicine systems should move past simple binary comparisons of hospitalist systems versus traditional systems to understand the effect on patient outcomes and cost of factors such as years of experience, volume of patients seen overall and with a specific condition, staffing model, training, quality improvement knowledge and application, and health information systems. These factors may be additive or multiplicative to the performance of inpatient systems once put into place, but these hypotheses need to be tested.

Fourth, individual hospitalists and their groups must focus on quality measurement and improvement in quality and value delivered. At Cincinnati, we have a portfolio of quality and value projects derived from our strategic objectives, illustrated in Figure 2. The projects have leaders and teams to drive improvement and measure results. Increasingly, we are able to publish these results in peer‐reviewed journals. On a quarterly basis, we review the portfolio via a dashboard and/or run and control charts. We establish new projects and set new goals on at least an annual basis. It is important to note that at the beginning of the 2010‐2011 fiscal year, almost all initiatives identified as priorities were yellow or red. Our group is now planning new initiatives and goals for next year. This is one method applicable to our setting, but a focus on quality and value and measuring results needs to be part of every hospital medicine program. As payer focus on value increases, this will be essential to demonstrate how a hospitalist group improves outcomes and adds value.

Figure 2

CONCLUSION

This review suggests that the use of hospitalists can improve the quality of inpatient care in the pediatric population, but this is not a universal finding and, most importantly, the mechanisms of improvement are poorly understood. We propose 4 components to address these issues so that a systematic review 5 years from now would be much more robust. These are: 1) increased investment in research comparing system‐level interventions and reliable implementation; 2) further development and implementation of pediatric quality of care measures in the inpatient setting; 3) understanding the mechanisms and factors leading to improvement in hospital medicine systems; and 4) an increased focus on quality measurement, and improvement in quality and value delivered by all individual hospitalists and their groups.

Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.

Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.

Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.