A total of 373 studies were imported from various databases for screening: Embase (n = 122), PubMed (n = 112), Scopus (n = 106), and CINAHL Plus (n = 33). After removing duplicates, 237 studies remained. Following a review of titles and abstracts, 66 articles were identified as potentially relevant to rural hospital closures. However, 51 were excluded for the following reasons: lack of empirical data or relevant quantitative/qualitative analysis, focus on non-U.S. settings, examination of hospital financial performance without closure outcomes, or analysis of the impact of rural hospital closures rather than their predictors. After thoroughly reviewing the full texts, 15 manuscripts were ultimately included in the final analysis (see Fig. 1 for PRISMA flow diagram). The detailed metadata for the narrative synthesis of the 15 studies included in this review are provided in Supplementary Table S1.
Summary of the objectives and descriptions of the included studies
This analysis examines 15 studies that investigate the various factors contributing to the closure of rural hospitals in the U.S. The key challenges identified include financial distress, workforce shortages, policy influences, economic and demographic influences, and market dynamics.
Financial distress
Financial distress remains the primary driver of rural hospital closures. Several studies have investigated financial sustainability and developed predictive models to assess financial risk. One study analyzed the financial performance of 1004 rural hospitals between 2011 and 2017, assessing differences in profitability by ownership and designation type, and evaluating the impact of Medicaid expansion on hospital sustainability [33]. Another study introduced and validated a Financial Distress Index (FDI) to predict closure risks among 2466 rural hospitals, categorizing them based on indicators such as unprofitability, equity decline, and insolvency [20]. Research examining Medicare Advantage penetration in rural areas assessed how increased enrollment in these plans affected hospital financial stability and closure risk between 2008 and 2019, using financial distress models to analyze hospital market characteristics [34]. Additionally, a study of rural hospitals’ survival strategies from 2010 to 2018 explored how financially struggling hospitals either closed, merged, or remained operational, emphasizing the role of financial health and market competition [35].
A qualitative study further examined financial distress by analyzing the relationship between community characteristics and hospital closures from 2005 to 2015. This study compared the sociodemographic and market characteristics of closed hospitals with those of operational hospitals, focusing on factors such as population density, market share, unemployment rates, and racial/ethnic composition [36]. These studies provide a comprehensive understanding of rural hospitals’ financial challenges and the strategies they use to respond to financial instability and market pressures.
Workforce shortages
Workforce shortages significantly impact rural healthcare access and contribute to hospital closures. One study identified hospital- and county-level factors affecting the closure of obstetric units in rural areas, using a mixed-methods approach combining multivariate logistic regression with qualitative interviews with administrators from 306 rural hospitals across nine states [37]. Another study examined the effects of labor and delivery unit closures in rural Georgia from 2012 to 2016, incorporating quantitative hospital and regional data with qualitative assessments from newspaper reports. Findings highlighted the disproportionate impact of closures on Black and low-income women [38]. Additionally, research on the urbanization of American surgery projected significant shortages in general surgery, orthopedic surgery, and obstetrics/gynecology for rural hospitals. By analyzing population trends, surgeon certification data, and hospital distribution statistics, the study estimated future recruitment needs and financial implications for rural healthcare facilities [39].
Further qualitative studies investigated the impact of workforce shortages from the perspective of healthcare professionals. One study explored nurses’ experiences working in rural hospitals that closed between 2014 and 2020. Using a retrospective qualitative approach, researchers conducted semi-structured interviews with nurses from two closed hospitals, identifying themes related to pre-closure conditions, closure dynamics, and the impact on nursing staff and communities [40]. These studies collectively highlight the crucial role of workforce shortages in shaping rural hospitals’ ability to maintain essential services.
Policy influences
Few studies have explored the impact of healthcare policies on rural hospital closures. One study examined the relationship between Medicaid expansion and rural hospital viability, testing the hypothesis that expanding Medicaid eligibility reduces uncompensated care costs and strengthens hospital financial stability. Using financial data from 2008 to 2016, the study compared closure rates between states that expanded Medicaid and those that did not [41]. Additionally, a study examined the effects of telehealth policy adoption on rural hospital financial outcomes, assessing whether the expansion of remote healthcare services affected hospital revenue distribution, credit ratings, and closure risks. Using a quasi-experimental design based on staggered adoptions of telehealth parity laws, the study measured changes in patient volume, financial performance, and access to capital [42]. These studies highlight the significant role of healthcare policies, such as Medicaid expansion and telehealth adoption, in influencing rural hospital financial stability and closure risks.
Economic and demographic influences
Economic and demographic influences also play a crucial role in rural hospital closures. One study examined whether community sociodemographic factors, such as unemployment and uninsured rates, affected the survival of financially distressed rural hospitals from 2010 to 2019. It analyzed a sample of at-risk hospitals, employing statistical models to identify key predictors of hospital survival [22]. Another study assessed the economic consequences of rural hospital closures on local communities, using a difference-in-differences analysis of 2094 rural counties to examine changes in unemployment rates, labor force participation, per capita income, and healthcare employment before and after hospital closures [43]. These studies underscore the significant impact of economic and demographic factors on rural hospital survival and the broader community.
Market competition
Market competition is another factor influencing rural hospital closures. One study examined the rate of rural hospital closures from 2010 to 2014, identifying financial and market characteristics that differentiated hospitals that closed from those that remained operational. Statistical tests and regression analysis examined the relationship between financial distress, market factors, and hospital closures [23]. Another study evaluated whether rural hospitals affiliated with larger health systems had lower closure risk between 2007 and 2019, using time-dependent survival models to analyze hospital characteristics, market conditions, and utilization patterns [44]. These studies collectively highlight the role of market competition in shaping rural hospital sustainability, emphasizing how hospital affiliations, local economic conditions, and healthcare consolidation trends influence hospital closures.
Sources of data and information
The studies included in this review utilized diverse data sources, including hospital cost reports, national health databases, economic statistics, and qualitative interviews, to analyze rural hospital closures. Several studies primarily relied on the Centers for Medicare and Medicaid Services (CMS) hospital cost reports for financial data. For example, Bai et al. assessed trends in hospital financial viability using CMS hospital cost reports and Small Area Health Insurance Estimates from the U.S. Census Bureau [33]. Similarly, Holmes et al. and Kaufman et al. used CMS Medicare Cost Reports, Provider of Services files, Hospital Service Area files, and Nielsen-Claritas population data to analyze financial distress and market risks contributing to hospital closures [20, 23]. While CMS cost reports provide standardized financial data, they may not fully capture operational challenges faced by small rural hospitals that serve a high proportion of uninsured patients or those outside Medicare programs.
Beyond financial data, economic databases were widely used to assess the broader implications of hospital closures. The Sheps Center for Health Services Research (SCHSR) compiles information on hospital closures and financial distress. This SCHSR dataset was used with the CMS Hospital Cost Report Information System (HCRIS), the American Hospital Directory, and the National Bureau of Economic Research to examine hospital closures, mergers, and financial performance [35]. Additionally, SCHSR data, combined with economic data from the Bureau of Labor Statistics (BLS), the Bureau of Economic Analysis (BEA), the U.S. Federal Reserve, and the U.S. Census Bureau, were utilized to assess the economic impact of hospital closures on local communities [43].
In addition to economic factors, healthcare utilization, and patient outcome data were analyzed to evaluate the causes of hospital closures. Several studies used national health databases, such as the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID), which provided essential financial and utilization metrics for studying Medicaid expansion and hospital survival [22, 41]. Additionally, CMS HCRIS and the American Community Survey (ACS) were used to examine hospital financial trends and rural healthcare access [34].
Several studies looked at data on the hospital workforce and service availability to investigate the effects of workforce shortages on rural hospital closures. The American Hospital Association (AHA) Annual Survey, the American Medical Association (AMA) Master File, and the American Board of Medical Specialties certifications evaluated hospital characteristics, surgeon supply, and urban-rural disparities in surgical workforce demand [39]. Furthermore, the impact of obstetric unit closures was examined using a combination of HCUP SID, the Area Resource File (ARF), and telephone interviews with hospital administrators across nine states [37]. Labor and delivery unit (LDU) closures in rural Georgia were analyzed using data from the Georgia Department of Public Health, the U.S. Census Bureau, and Emory’s MCH Linked Vital Records Data Repository, along with qualitative sources such as newspaper reports [38].
Financial and policy-related datasets were also widely utilized. The Kaiser Family Foundation (KFF) Medicaid expansion database provided insights on state-level Medicaid expansions and their relationship with hospital closures [33]. The Mergent Municipal Fixed Income Database and the Municipal Securities Rulemaking Board (MSRB) analyzed municipal bond data and the financial implications of telehealth policy changes on rural hospitals [42].
Studies examining hospital market competition and mergers used data from Irving Levin Associates (mergers and acquisitions), HCUP Hospital Market Structure Files, and the RAND Corporation state statistics database to explore the role of market forces in rural hospital closures [43, 44]. Finally, qualitative studies relied on semi-structured interviews and community-based data collection. For instance, Smith et al. captured nurses’ perspectives on hospital closures through interviews with healthcare professionals from hospitals that closed in Texas between 2014 and 2015 [40]. Other studies verified hospital closure statuses and rural classifications using direct contact with hospital representatives, websites, and newspaper database searches, and Rural-Urban Commuting Area (RUCA) codes [36].
The data and information indicate that financial distress, workforce shortages, policy influences, economic and demographic factors, and market dynamics all contribute to hospital closures in rural areas. Reviewed studies used cross-sectional and longitudinal approaches, though most focused on static risk factors rather than dynamic time-dependent processes. Addressing these challenges requires a comprehensive policy and economic intervention to sustain rural healthcare systems.
Key factors analyzed to identify the causes of rural hospital closures
Several studies included in this review investigated various factors related to hospitals, finances, markets, and policies to understand the reasons behind hospital closures and financial distress, particularly in rural areas. Most papers included hospital-level and regional economic indicators, while some focused on financial distress or healthcare accessibility issues. A few studies examined the impact of hospital closures on the labor market, focusing on changes in employment and per capita income.
Common hospital characteristics included ownership type (public, private, non-profit, or for-profit), system affiliation, bed count, occupancy rate, and accreditation status. Financial indicators were extensively analyzed, with studies often considering operating margin, total margin, debt service coverage, reinvestment levels, and payer mix (including Medicaid, Medicare, private insurance, and self-pay). Additionally, some studies examined telehealth adoption, particularly in states with parity laws, as a potential factor supporting hospital sustainability.
Market characteristics varied across studies but often included competition intensity (typically measured using the Herfindahl-Hirschman Index), median household income, unemployment rates, and demographic factors such as age distribution, racial composition, and insurance coverage rates. Geographic factors were frequently examined, including distance to the nearest hospital, access to obstetric services, and availability of medical professionals such as OB-GYNs, nurse-midwives, and family physicians.
Economic conditions were another critical consideration, with studies incorporating per capita income, participation in disability programs, subprime credit scores, and bankruptcy filings. Several papers also analyzed the impact of Medicaid expansion status on hospital closures. Furthermore, one study used an exclusively qualitative approach to describe the contributors to rural hospital closures, the processes involved, and the outcomes from the perspective of licensed rural nurses during a hospital’s closure [40]. Additionally, some studies utilized newspaper reports to document hospital closures, their contributing factors, and the local community’s responses, providing qualitative insights into the broader implications of these shutdowns. Despite differences in study designs, researchers consistently analyzed financial vulnerability, market competition, and regional economic conditions to identify the reasons for rural hospital closures.
Methodological approaches employed in the included studies
The included studies utilized various quantitative and qualitative methodologies to examine the key factors related to rural hospital closures. Longitudinal and regression-based approaches were commonly used to assess financial viability and predict distress. For instance, longitudinal analyses combined with descriptive and regression techniques were used to evaluate trends in financial sustainability [33]. Logistic regression models incorporating financial, organizational, and market characteristics were developed to predict financial distress and closure risks, including validating an FDI to monitor hospital stability [39].
Multinomial logit models and difference-in-differences (DiD) analyses were widely employed to assess the effects of financial distress, market competition, and policy changes. One study used multinomial logit models to evaluate within-market and out-of-market mergers among rural hospitals [35]. At the same time, another applied a DiD approach to analyze economic shifts following hospital closures [43]. Additionally, DiD was used to investigate the financial effects of telehealth adoption and Medicaid expansion under the Affordable Care Act (ACA) on rural hospital sustainability [41, 42].
Retrospective cohort studies and survival models were also used to examine hospital closures. A study utilizing fixed-effects regression and Cox proportional hazards models explored the relationship between Medicare Advantage penetration and rural hospital financial distress [34]. Another study applied a time-dependent survival model to compare closure risks between independent and multihospital-affiliated rural hospitals [44]. Community sociodemographic factors influencing hospital survival were analyzed using Wilcoxon rank-sum tests and multilevel Weibull proportional-hazards regression [22].
Quantitative analyzes projecting future surgeon availability were used in several studies focused on healthcare workforce shortages and closures. One study quantitatively analyzed and projected future surgeon availability based on population and workforce trends [39]. A mixed-methods study integrating multivariate logistic regression and qualitative analysis examined factors contributing to rural obstetric unit closures and their impact on prenatal care access [37]. Similarly, closures of labor and delivery units (LDUs) in rural Georgia were analyzed using quantitative and qualitative methods, highlighting disparities affecting Black women [38].
Comparative statistical analyses were applied to assess financial and market-related risks associated with rural hospital closures. Pearson’s chi-square and Wilcoxon rank-sum tests were used to evaluate financial and market characteristics influencing hospital closure rates, with logistic regression used to predict financial distress [23]. Multilevel logistic regression compared closed and financially distressed but operational hospitals, emphasizing the role of community characteristics in hospital sustainability [36]. Finally, qualitative research contributed insights into the social and professional impacts of rural hospital closures, including a cross-sectional, retrospective study documenting rural nurses’ experiences and challenges during hospital closures [40]. The studies reviewed used various quantitative and qualitative methods to investigate the reasons behind rural hospital closures. Researchers commonly employed longitudinal and regression-based techniques, such as logistic regression, multinomial logit models, difference-in-differences analyses, and survival models, to predict financial distress and evaluate the effects of policy changes and economic shifts.
Factors contributing to rural hospital closures identified from the included studies
The included studies identified various factors contributing to rural hospital closures and categorized them into five major categories, as summarized in Table 3. Overall, rural hospital closures are primarily driven by financial distress, declining patient volumes, and adverse economic conditions. Factors contributing to financial instability include low profitability, narrowing profit margins, a high dependency on Medicare and Medicaid reimbursements, and lower Medicare payments than private insurers. Economic downturns, characterized by rising unemployment and declining incomes, further exacerbate this financial distress. One study found that adverse economic conditions are a precursor to hospital closures [43].
Table 3 Summary of factors contributing to rural hospital closures
Structural changes in healthcare, such as the shift of revenue from rural to urban hospitals facilitated by the expansion of telehealth, have reduced profit margins and credit ratings for rural hospitals. Service line closures, particularly in obstetric and surgical care, have been linked to low birth volumes, high rates of uninsured or self-pay patients, inadequate Medicaid reimbursements, and shortages of healthcare providers. The difficulty in recruiting surgeons and anesthesiologists due to financial constraints has further compromised hospital viability.
Additional factors that increase the risk of closure include market competition, small population sizes, and low reimbursement rates, especially for hospitals operating as for-profit entities or lacking Critical Access Hospital (CAH) designation. Affiliation with multi-hospital systems, proprietary ownership, and reduced market share also contribute to financial distress and hospital closures. Moreover, high rates of uninsured patients, the absence of Medicaid expansion, and increased uncompensated care burdens heighten financial instability.
Policy changes, lack of community support, administrative mismanagement, and the loss of specialty services further increase the risks of closure. Community characteristics such as lower market share, high population density, and proximity to other hospitals, particularly in areas with higher Black and Hispanic populations, also influence hospital closures. These existing studies collectively identified financial distress, market competition, workforce shortages, policy limitations, and community demographics as key drivers of rural hospital closures.
Limitations identified in the included studies on rural hospital closures
The research on rural hospital closures and their financial viability has several notable limitations. One significant issue is measurement errors, particularly in hospital profitability data derived from unaudited Medicare cost reports and administrative datasets, which may introduce inaccuracies. Additionally, potential reporting errors in these datasets further complicate financial assessments. Many studies define rural hospital markets using commuting zones or county-level data; however, these approaches may not fully capture competitive boundaries or localized economic effects. Another concern is the generalizability of the findings, as results are often limited to specific states or time periods, making them less applicable to hospitals operating under different market conditions or policy environments.
Furthermore, the observational design of many studies hinders the establishment of causal relationships, such as those between Medicare Advantage penetration and financial outcomes or between telehealth adoption and hospital performance. Certain analyses were constrained by small sample sizes, particularly in studies on labor and delivery unit closures, where missing data on maternal care levels and patient demographics limited the scope of the findings. Workforce projections are also subject to uncertainty due to assumptions about population growth and specialization trends. Additionally, the exclusion of certain surgical specialties may have led to an underestimation of workforce shortages.
Studies examining hospital affiliation and closure risk often struggle to differentiate between formal and informal system memberships, and some fail to consider ownership type as a contributing factor. Many studies relied on retrospective designs, which may introduce recall bias into participants’ accounts of hospital closures. Furthermore, the reliance on self-reported survey responses raises concerns about reporting bias. While stepwise regression methods help isolate predictors, they may also limit the generalizability of findings. Several studies also did not fully account for policy changes, such as Medicaid expansion, which could significantly impact hospital sustainability. Additionally, some studies excluded government-run rural hospitals because of their unique financing mechanisms, potentially leading to an overestimation of financial viability among the hospitals studied.
Finally, the inability to observe real-time decision-making processes among hospital executives, along with fiscal year misalignments with calendar years, may have compromised data accuracy in studies assessing financial distress and closure determinants. These limitations underscore the complexity of evaluating rural hospital viability and suggest that future research should integrate more comprehensive data sources, employ refined methodological approaches, and consider broader healthcare outcomes.
