Hospital Closures Had No Measurable Impact On Local Hospitalization Rates Or Mortality Rates, 2003–11

Hospital closures may accelerate in coming years as provisions of the Affordable Care Act (ACA) put increasing pressure on hospital finances—for example, through reductions in the rate of growth in payments for hospital services and reductions in disproportionate share and graduate medical education funding. ¹ ACA insurance expansion provisions may offset some of this financial pressure. ² However, many policy makers and political leaders worry that hospital closures could have important negative effects for patients seeking access to acute care services, ^3,4 especially for conditions for which timely care has an impact on the outcome.

Whether hospital closures have meaningful adverse consequences for access and patient outcomes is unclear. If hospitals that close have a poor record of quality and safety, and if there are better-performing institutions in close proximity, patients may be better off seeking care elsewhere. Alternatively, if hospital closures occur in areas with few other health care alternatives, patients may be worse off.

There are very few data on the impact of hospital closures nationally: Most previous research focused on primarily rural areas or specific regions of the country. National data on what happens to patients after hospitals close would be very useful in helping federal policy makers and city and state government officials determine how and when to step in to prevent hospital closures.

Given the relatively limited empirical evidence on hospital closures nationwide and their impact on patient outcomes, we sought to answer three questions: First, what are the characteristics of hospitals that closed over the past decade and the communities they served? Second, what is the relationship between hospital closures and patient outcomes (mortality and readmission rates) in a community? And third, does the relationship between hospital closures and patient outcomes differ by the acuity of the medical condition considered or by the rurality of the area in which the closure occurred?

Study Data And Methods

Hospitals

Our primary predictor was hospital closure. To identify closed hospitals, we first used the “landscape change” reports from the American Hospital Association from the period 2005–10. ⁵ These reports document year-to-year changes in the national hospital census and verify the cause for the changes (such as a hospital merger or acquisition, the opening of a new hospital, or a closure). We confirmed the closures using Medicare cost reports for the same time period.

Given the high degree of fidelity between our two sources, we expanded the study period to include closures during 2003–05 and during 2010–11 using solely the Medicare cost reports. Thus, the total study period was from 2003 to 2011.

For each suspected closure identified by one or both of our sources, we confirmed the hospital’s status using multiple additional sources, including state health departments and local newspaper articles describing specific closures. We also examined the number of yearly hospitalizations for each hospital, using acute care claims in Medicare inpatient files. We considered that closures and the year of closure were confirmed if the number of hospitalizations declined to zero at the appropriate time. After identifying all closed hospitals, we used the complete census of US hospitals for the study period to define a comparison group of currently open hospitals.

Hospital Service Areas

To understand the impact of a closure on the community of patients who could feasibly have sought care at the closed hospital, we performed all analyses at the hospital service area (HSA) level. As defined by the Dartmouth Atlas of Health Care , ⁶ an HSA is a local health care market that represents travel patterns for patients receiving primary hospital care. There are 3,436 HSAs in the United States, many of which contain a single hospital.

We assigned each hospital closure to its designated HSA. For comparison, control HSAs were identified using three matching variables: geographic region (one of the ten regions defined by the Centers for Disease Control and Prevention), rurality (defined by rural-urban commuting area code), and baseline risk-adjusted all-cause annual mortality rates.

We identified three control HSAs for each closure HSA to reduce the likelihood of unmeasured confounding, since there might have been differences in outcomes based on rurality or region that would otherwise be inadequately accounted for. To address this and be certain that our results were robust to our choice of controls, we also repeated our analyses using all nonclosure HSAs as the control group.

In addition, we repeated our analysis using the hospital referral region (HRR) as the unit of analysis and comparing HRRs with closures to all other HRRs. This approach had the advantage of examining the larger hospital market. However, it also had the disadvantage of diluting our ability to detect the impact of a closure on access to care, since only a small proportion of patients in an HRR would be expected to be affected by any given closure.

Patients

We used Medicare denominator and inpatient files to identify people enrolled in fee-for-service Medicare from January 1, 2002, through December 31, 2012. We assigned beneficiaries to HSAs using their home ZIP codes. We obtained from the Medicare files the following patient characteristics: age, sex, self-reported race/ethnicity, Medicaid eligibility, and medical comorbidities as classified by Anne Elixhauser and coauthors. ⁷

Other Variables

Using the Medicare cost reports and American Hospital Association surveys, we extracted the following hospital characteristics: size, geographic region, ownership, rural-urban commuting area code, teaching status (the ratio of interns or residents to beds), critical access status, safety-net status (in the top 25 percent of the disproportionate-share hospital index), proportion of Medicare patients, and proportion of Medicaid patients. We also calculated markers of hospital financial status from the cost reports, including total margins (net income divided by total revenue) and operating margins (the product of net patient revenue minus total operating expenses, divided by net patient revenue) and costs of various types of uncompensated or undercompensated care.

To understand more about the context in which each closure occurred, we also examined HSA-level variables from the year prior to a closure. Using the Dartmouth Atlas , ⁶ we extracted the following HSA-level characteristics: age, sex, race, and price-adjusted total Medicare Parts A and B reimbursements per enrollee. We used the same source to obtain the following supply-side variables: the numbers of all physicians, all specialists, and primary care physicians per 100,000 residents and of acute care hospital beds per 1,000 residents.

Outcomes

Our primary outcome was all-cause annual mortality at the HSA (population) level, calculated across all beneficiaries residing in the HSAs of interest. Secondary outcomes at the population level included annual hospital admission rates per 1,000 beneficiaries and annual average per beneficiary inpatient spending. We also examined the following outcomes that were limited to patients who were hospitalized: all-cause risk-adjusted thirty-day mortality rates, all-cause risk-adjusted thirty-day readmission rates, length-of-stay, and risk-adjusted inpatient costs per hospitalization. We used the Elixhauser method ⁷ for risk adjustment where appropriate.

We assessed mortality at both the HSA and the hospitalization levels because the rates at the two levels allowed us to examine different issues. Population-based mortality rates offered a more global look at the impact of closures and accounted for deaths that might occur outside of the hospital as a result of increased travel time. In contrast, hospitalization-based mortality rates allowed us to examine the hospital care received by patients before and after their local hospital closed.

Primary Analyses

First, we created a map of each of the closures in our sample. We then compared the characteristics of closed versus open US hospitals using chi-square tests for categorical variables and analysis of variation tests for continuous variables. We used nonparametric Wilcoxon tests to compare the non-normally distributed financial measures of total margin and operating margin.

Second, we compared patient demographic characteristics (age, sex, race/ethnicity, and comorbidities) and basic payment and supply characteristics (Medicare reimbursements per enrollee and supply-side variables) among our three groups of HSAs: HSAs with closures, matched control HSAs without closures, and all HSAs without closures. Appropriate statistical tests were performed to obtain p values.

Third, we created difference-in-differences regression models to test the relationship between hospital closure and the change over time in each of our outcomes of interest (population-level mortality, admissions, and costs; and, for hospitalized patients, thirty-day mortality and readmissions, length-of-stay, and costs). Additionally, as proof of concept, we tested for differences in the change in the proportion of hospitalizations occurring outside patients’ home HSAs after a closure.

Our models included time period (preclosure versus postclosure), closure status for each HSA (closure versus nonclosure), and the interaction between the two as predictors. The models also included patient age, sex, and medical comorbidities for all mortality, length-of-stay, and cost analyses.

We considered the preclosure year to be the calendar year prior to the closure and the postclosure year to be the calendar year following the closure. We excluded the calendar year of the closure.

Correlation within HSAs and matched groups was accounted for by using a random effect for group. This matching strategy allowed us to effectively control for secular trends in any of our outcomes over the decade-long study period, since each HSA with a closure was being directly compared to contemporary controls.

Sensitivity Analyses

We conducted a number of additional analyses. First, to assess whether the impact of closure on mortality varied by condition acuity, we limited our analysis to patients with acute myocardial infarction, stroke, or trauma. ⁸

Second, to determine whether the impact of closure on outcomes differed by rurality or supply of medical services, we subdivided HSAs into rural and urban designations according to their rural-urban commuting area classifications and then repeated our analyses for population-level all-cause mortality rates for each subgroup. We similarly repeated our analyses after stratifying the HSAs by the proportion of beds within an HSA that were removed as a result of a hospital closure, with a particular focus on the HSAs in which all beds were lost because of closure.

Finally, to capture adverse effects that may have taken longer to accrue, we compared annual mortality two years before each closure to mortality two years after the closure.

A two-tailed p value of less than 0.05 was considered significant. All analyses were performed using SAS software, version 9.2. This project was considered exempt by the Office of Human Research Administration of the Harvard T.H. Chan School of Public Health because of the deidentified nature of the data.

Limitations

Our study had several limitations. First, the use of administrative data limited our ability to control for all clinical confounders. Nonetheless, we doubt whether unmeasured confounders differed over time between closure and nonclosure HSAs in ways that would have meaningfully biased our results.

Second, because we used Medicare data, our findings might not be generalizable to patients of all ages. However, given that Medicare patients are among the most clinically vulnerable, hospital closures are likely to have a bigger impact on them than on younger and healthier patients.

Third, our hospitalization level analyses may have suffered from selection bias, which means that we could have missed small condition-specific changes in out-of-hospital mortality. We tried to address this by also conducting population-level analyses, but neither group of analyses should be interpreted alone.

Fourth, we did not differentiate between hospitals that closed entirely and those that converted to an outpatient center, nursing facility, or other source of nonacute care. Thus, our analysis examined only what occurs when acute care hospital services are discontinued.

Finally, given that our analysis was based on experience, the degree to which it would apply to future hospital closures is uncertain. Still, these findings are useful in providing a broad understanding of the impact of the hospital closures that have occurred during the past decade.

Study Results

Characteristics Of Hospital Closures

We identified 195 hospital closures between 2003 and 2011, shown in online Appendix Exhibit A1. ⁹ Compared to open hospitals, closed hospitals more often were located in the South (41.9 percent of closed hospitals versus 30.5 percent of open hospitals) and in urban areas (70.6 percent versus 45.0 percent), and more often were for profit (42.2 percent versus 25.1 percent; Exhibit 1 ). Again compared to open hospitals, closed hospitals were less likely to be critical access hospitals and more likely to be safety-net hospitals. Not surprisingly, closed hospitals had less favorable total financial and operating margins prior to closure, compared to open hospitals.

Characteristics Of Service Areas With Closures

There were 32,485,906 Medicare beneficiaries in our analysis. At baseline, in hospital service areas with a closure, beneficiaries were slightly older (average age: 79.7 years in HSAs with closures versus 79.4 years in matched HSAs without closures and 79.0 years in all HSAs without closures; Exhibit 2 ). In addition, beneficiaries in HSAs with a closure were more likely to be female and less likely to be white. They were also more likely to be eligible for Medicaid, but they had a similar number of comorbidities.

In the baseline year prior to a hospital closure, HSAs with closures tended to have higher total Medicare reimbursements per enrollee ($9,962 versus $9,881 in matched HSAs without closures and $9,363 in all HSAs without closures; Exhibit 2 ). HSAs with closures and all HSAs without closures had a similar number of all physicians, but those with closures had a higher number of all specialists and a lower number of primary care physicians. None of these differences was significant when we compared HSAs to their matched control HSAs.

HSAs with closures had more acute care hospital beds per 1,000 residents than the matched HSAs without closures, but fewer such beds than all HSAs without closures.

Population-Level Outcomes In Service Areas With And Without Closures

When we compared patients living in HSAs with closures and those living in matched HSAs without closures, we found no significant difference in the change in population-level annual mortality rates from the year before to the year after a closure (5.5 percent preclosure to 5.2 percent postclosure versus 5.4 percent for both preclosure and postclosure, respectively; Exhibit 3 ). Similarly, we found no significant difference in the change in the population-level inpatient admission rate or in the change in inpatient costs per beneficiary.

These results were similar when we compared closure HSAs with all HSAs without closures (Appendix Exhibit A2) ⁹ and when we compared closure HRRs with all HRRs without closures (Appendix Exhibit A3). ⁹

Outcomes After Hospitalization In Service Areas With And Without Closures

When we examined outcomes for patients who had experienced a hospitalization, we found no significant difference in the change in all-cause thirty-day mortality rates between HSAs with closures and matched HSAs without closures ( Exhibit 3 ). There was a greater reduction in the change in readmission rates among closure HSAs (19.4 percent to 18.2 percent versus 18.8 percent to 18.3 percent). Length-of-stay did not change in closure HSAs relative to controls. Nor was there a significant difference in the change in inpatient costs per hospitalization.

As expected, the proportion of hospitalizations occurring outside a beneficiary’s home HSA increased in closure HSAs compared to matched controls. These results were similar when we compared closure HSAs with all HSAs without closures (Appendix Exhibit A2) ⁹ and when we compared closure HRRs with HRRs without closures (Appendix Exhibit A3). ⁹

Sensitivity Analyses

Next we limited our hospitalization analysis to acute hospital conditions that require prompt medical care, so that added travel times might affect outcomes more dramatically. We still found no significant relationship between the presence of a closure in an HSA and the change in either admission rates or mortality rates for stroke or trauma ( Exhibit 4 ). We found no significant difference in the change in admission rates for acute myocardial infarction, either. However, we did find significant reductions in mortality for acute myocardial infarction in closure HSAs, compared to matched controls (17.7 percent to 13.5 percent versus 15.7 percent to 14.1 percent). We found no difference in outcomes for acute conditions when examining these changes by HRR (Appendix Exhibit A3). ⁹

SOURCE Authors’ analysis of data from Medicare. NOTES Admissions (number per 1,000 residents) relate to the left-hand y axis. Thirty-day risk-adjusted mortality rates (percentages of hospitalizations) relate to the right-hand y axis. Hospital service areas were matched on region, rurality, and baseline risk-adjusted all-cause mortality. Analyses were adjusted for age, sex, and comorbidities. The p values for the difference-in-differences analyses were nonsignificant, with the exception of mortality for acute myocardial infarction (AMI), p = 0.009.

When we limited our analysis to rural HSAs, in which the impact of hospital closure might be more pronounced, we found no significant differences in the change in HSA-level mortality between HSAs with versus those without closure (5.8 percent to 5.3 percent versus 5.8 percent to 5.7 percent; Appendix Exhibit A4). ⁹ However, when we limited our sample to HSAs in which all beds were lost as a result of a closure, we noted a reduction in thirty-day mortality following hospitalization in the closure HSAs relative to controls (9.8 percent to 8.0 percent versus 9.0 percent to 8.5 percent; Appendix Exhibit A5). ⁹

Finally, when we extended the time frame of our analysis to two years before and two years after a closure, we again found no significant difference in the change in population-based mortality rates (5.4 percent to 5.2 percent versus 5.5 percent to 5.3 percent; Appendix Exhibit A6). ⁹

Discussion

In this national study of hospital closures over the past decade, we found no evidence of an association between hospital closures and worsening outcomes for those living in the local community. Indeed, for both the people in the community overall and for those who were hospitalized, living in a hospital service area with a closure had no measurable impact on either hospitalization rates or mortality rates, and it may have had a small positive impact on readmission rates.

When we limited our analyses to time-sensitive medical conditions for which timely acute care is essential, we again found that closures had no detrimental relationship with outcomes. And we found that communities in which closures occurred had a greater improvement in mortality for acute myocardial infarction, compared to those without a closure during the study period.

These findings were consistent in rural areas and in areas in which all hospital beds in an HSA were closed. However, the number of hospitals in rural areas that closed was much smaller, and our power to detect a difference thus was more limited.

There are at least two mechanisms that could explain our findings. First, hospital closures over the past decade could have had a minimal impact on outcomes if the hospitals that closed were of lower quality than those that did not close. Patients who had to travel slightly farther to seek care may have been rewarded by receiving care in a higher-quality hospital, thus offsetting any impact of increased travel time on outcomes—an impact that previous work has shown to be small. ¹⁰ Prior studies have shown that inefficient hospitals, ^11,12 hospitals with low financial margins, ¹³ and hospitals that provide poorly reimbursed services ¹² or that have low productivity ¹⁴ are more likely to close than better-performing hospitals.

It is also possible that hospitals that closed did so because they were unable to continue to compete in the market without achieving economies of scale, either for negotiating contracts or for purchasing. Overall, our findings suggest that existing structures of markets and competition may work reasonably well in selecting hospitals for closure.

Second, there may be relative oversupply of hospital or physician services in areas experiencing a closure, which may minimize the impact of that closure on access or outcomes. Prior studies have shown that hospitals with excess capacity are more likely to close ¹⁴ and that appropriate hospital closures—such as those occurring in oversupplied communities or inefficient delivery settings—may improve market efficiency and local welfare. ^15,16

Indeed, there is evidence to suggest that oversupply of medical services contributes to wasteful use of resources and increased spending without necessarily improving patient outcomes. ^17,18 Consistent with such evidence, we found that HSAs with closures had more physicians (particularly specialists) per 100,000 residents and higher Medicare reimbursements per enrollee, although fewer hospital beds per 1,000 residents, compared to all HSAs without closures.

Hospitalization rates nationwide are falling each year. ^19,20 Therefore, the HSAs with closures may have had an adequate supply of medical services even after a hospital closure to provide care to patients who reside in them.

It is important to note that we did not examine the totality of the effects of a hospital closure on a community. Instead, we focused only on changes in patient outcomes associated with closures. There may be other negative effects associated with hospital closure, including changes in unemployment and other community-level economic measures, particularly when the closure is of the sole hospital in a community. ^21,22 For example, qualitative studies have demonstrated that hospital closure may be associated with feelings of abandonment and social isolation, increased transportation challenges, and concern and uncertainty about how to access health services among adults in a community with a closure. ^23–25

Our findings are consistent with prior work identifying predictors of hospital closure ^11–14 and emergency department closure. ²⁶ However, these studies did not examine the impact of closure on patient outcomes. Our findings are also consistent with a previous study that examined emergency department closures (some of which were in the context of a broader hospital closure) and that found no impact on patient outcomes. ¹⁰ Our study extended the results from previous studies to a much larger patient population and a greater variety of clinical conditions.

One study had different results than ours: Thomas Buchmueller and coauthors found higher mortality from acute myocardial infarction and unintentional injuries after hospital closure. ⁴ However, that study included only hospitals in a single county.

Conclusion

In this national study of hospital closures, we found no significant relationship between a hospital’s shutting its doors and detrimental patient outcomes for Medicare beneficiaries living in those communities. These findings may offer reassurance to policy makers and clinical leaders who are concerned about the potential acceleration of hospital closures as a result of health care reform.

ACKNOWLEDGMENTS

Karen Joynt was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (Grant No. 1K23HL109177-01). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript. Robert Wild and Jie Zheng, both of the Harvard T.H. Chan School of Public Health, contributed to the analysis of data for this project. Both received routine compensation for employment. The views expressed herein are those of the authors only and do not express the views of the US government.

NOTES

• 1 Centers for Medicare and Medicaid Services . Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules . Fed Regist . 2013 ; 78 ( 160 ): 50495 – 1040 . Medline, Google Scholar
• 2 DeLeire T , Joynt K , McDonald R . Impact of insurance expansion on hospital uncompensated care costs in 2014 [Internet]. Washington (DC) : Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation ; 2014 Sep 24 [cited 2015 Mar 3 ]. (ASPE Issue Brief). Available from: http://aspe.hhs.gov/health/reports/2014/uncompensatedcare/ib_uncompensatedcare.pdf Google Scholar
• 3 Bazzoli GJ , Lee W , Hsieh HM , Mobley LR . The effects of safety net hospital closures and conversions on patient travel distance to hospital services . Health Serv Res . 2012 ; 47 ( 1 Pt 1 ): 129 – 50 . Crossref, Medline, Google Scholar
• 4 Buchmueller TC , Jacobson M , Wold C . How far to the hospital? The effect of hospital closures on access to care . J Health Econ . 2006 ; 25 ( 4 ): 740 – 61 . Crossref, Medline, Google Scholar
• 5 American Hospital Association . New report on opened, closed, and merged hospitals [Internet]. Washington (DC) : AHA ; 2013 Dec 19 [cited 2015 Mar 30 ]. Available from: http://www.ahadataviewer.com/ Google Scholar
• 6 Dartmouth Institute for Health Policy and Clinical Practice . Dartmouth atlas of health care [Internet]. Lebanon (NH) : The Institute ; [cited 2015 Mar 3 ]. Available from: http://www.dartmouthatlas.org/ Google Scholar
• 7 Elixhauser A , Steiner C , Harris DR , Coffey RM . Comorbidity measures for use with administrative data . Med Care . 1998 ; 36 ( 1 ): 8 – 27 . Crossref, Medline, Google Scholar
• 8 The International Classification of Diseases , Ninth Revision, Clinical Modification (ICD-9-CM), codes are as follows: for acute myocardial infarction, 410xx except for 41002, 41012, 41022, 41032, 41042, 41052, 41062, 41072, 41082, and 41092; for stroke, 430, 431, 4321, 4329, 43301, 43311, 43321, 43331, 43391, 43401, 43411, and 43491; for trauma, 800–949.
• 9 To access the Appendix, click on the Appendix link in the box to the right of the article online.
• 10 Hsia RY , Kanzaria HK , Srebotnjak T , Maselli J , McCulloch C , Auerbach AD . Is emergency department closure resulting in increased distance to the nearest emergency department associated with increased inpatient mortality? Ann Emerg Med . 2012 ; 60 ( 6 ): 707 – 15 . Crossref, Medline, Google Scholar
• 11 Deily ME , McKay NL , Dorner FH . Exit and inefficiency: the effects of ownership type . J Hum Resour . 2000 ; 35 ( 4 ): 734 – 47 . Crossref, Google Scholar
• 12 Ciliberto F , Lindrooth RC . Exit from the hospital industry . Econ Inq . 2007 ; 45 ( 1 ): 71 – 81 . Crossref, Google Scholar
• 13 Ly DP , Jha AK , Epstein AM . The association between hospital margins, quality of care, and closure or other change in operating status . J Gen Intern Med . 2011 ; 26 ( 11 ): 1291 – 6 . Crossref, Medline, Google Scholar
• 14 Harrison TD . Consolidations and closures: an empirical analysis of exits from the hospital industry . Health Econ . 2007 ; 16 ( 5 ): 457 – 74 . Crossref, Medline, Google Scholar
• 15 Capps C , Dranove D , Lindrooth RC . Hospital closure and economic efficiency . J Health Econ . 2010 ; 29 ( 1 ): 87 – 109 . Crossref, Medline, Google Scholar
• 16 Lindrooth RC , Lo Sasso AT , Bazzoli GJ . The effect of urban hospital closure on markets . J Health Econ . 2003 ; 22 ( 5 ): 691 – 712 . Crossref, Medline, Google Scholar
• 17 Goodman DC , Grumbach K . Does having more physicians lead to better health system performance? JAMA . 2008 ; 299 ( 3 ): 335 – 7 . Crossref, Medline, Google Scholar
• 18 Nyweide DJ , Anthony DL , Chang CH , Goodman D . Seniors’ perceptions of health care not closely associated with physician supply . Health Aff (Millwood) . 2011 ; 30 ( 2 ): 219 – 27 . Go to the article, Google Scholar
• 19 Chen J , Normand SL , Wang Y , Drye EE , Schreiner GC , Krumholz HM . Recent declines in hospitalizations for acute myocardial infarction for Medicare fee-for-service beneficiaries: progress and continuing challenges . Circulation . 2010 ; 121 ( 11 ): 1322 – 8 . Crossref, Medline, Google Scholar
• 20 Chen J , Normand SL , Wang Y , Krumholz HM . National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998–2008 . JAMA . 2011 ; 306 ( 15 ): 1669 – 78 . Crossref, Medline, Google Scholar
• 21 Probst JC , Samuels ME , Hussey JR , Berry DE , Ricketts TC . Economic impact of hospital closure on small rural counties, 1984 to 1988: demonstration of a comparative analysis approach . J Rural Health . 1999 ; 15 ( 4 ): 375 – 90 . Crossref, Medline, Google Scholar
• 22 Holmes GM , Slifkin RT , Randolph RK , Poley S . The effect of rural hospital closures on community economic health . Health Serv Res . 2006 ; 41 ( 2 ): 467 – 85 . Crossref, Medline, Google Scholar
• 23 Muus KJ , Ludtke RL , Gibbens B . Community perceptions of rural hospital closure . J Community Health . 1995 ; 20 ( 1 ): 65 – 73 . Crossref, Medline, Google Scholar
• 24 Countouris M , Gilmore S , Yonas M . Exploring the impact of a community hospital closure on older adults: a focus group study . Health Place . 2014 ; 26 : 143 – 8 . Crossref, Medline, Google Scholar
• 25 Romero D , Kwan A , Swearingen J , Nestler S , Cohen N . Impact of the closure of a large urban medical center: a qualitative assessment (part I) . J Community Health . 2012 ; 37 ( 5 ): 982 – 94 . Crossref, Medline, Google Scholar
• 26 Hsia RY , Kellermann AL , Shen Y-C . Factors associated with closures of emergency departments in the United States . JAMA . 2011 ; 305 ( 19 ): 1978 – 85 . Crossref, Medline, Google Scholar