Uncompensated Care is Highest for Rural Hospitals, Particularly in Non-Expansion States.

High levels of uncompensated care impact hospital profitability and may create challenges for rural hospitals at financial risk of closure. We explore 2019 hospital uncompensated care as a percentage of operating expenses and draw comparisons at a state level by Medicaid expansion status and rural classification. We further compare uncompensated care in 2019 to 2014 in rural hospitals by Medicaid expansion implementation timing. We found that, overall, rural hospitals had more uncompensated care than urban hospitals in 2019 (3.81% vs. 3.12%), but there was a larger difference by expansion status (expansion states: 2.55% vs. non-expansion states: 6.28%). In all but seven states, rural hospitals reported higher uncompensated care than urban, and the 14 states with the highest uncompensated care had not expanded Medicaid. We observed that rural hospital uncompensated care in non-expansion states increased between 2014 and 2019, while the most dramatic decrease occurred in late-expansion states.

Permanent Supportive Housing Receipt and Health Care Use Among Adults With Disabilities.

This study assessed whether permanent supportive housing (PSH) participation is associated with health service use among a population of adults with disabilities, including people transitioning into PSH from community and institutional settings. Our primary data sources were 2014 to 2018 secondary data from a PSH program in North Carolina linked to Medicaid claims. We used propensity score weighting to estimate the average treatment effect on the treated of PSH participation. All models were stratified by whether individuals were in institutional or community settings prior to PSH. In weighted analyses, among individuals who were institutionalized prior to PSH, PSH participation was associated with greater hospitalizations and emergency department (ED) visits and fewer primary care visits during the follow-up period, compared with similar individuals who largely remained institutionalized. Individuals who entered PSH from community settings did not have significantly different health service use from similar comparison group members during the 12-month follow-up period.

Predictors of hospital bypass for rural residents seeking common elective surgery.

BACKGROUND: Surgical bypass occurs when rural residents receive surgical care at a nonlocal hospital. Given limited knowledge of current bypass rates, we evaluated rates and predictors of bypass for common procedures. METHODS: We used 2014 to 2016 all-payer claims data from the Healthcare Cost and Utilization Project State Inpatient Databases to study rural patients from 13 states who underwent 1 of 11 common elective surgical procedures. Bypass was measured by whether a patient received elective surgical care at the closest hospital offering the requested procedure or another nonlocal hospital. Bypass probability was then modeled as a function of patient-level and hospital-level characteristics. RESULTS: Of the 121,297 rural elective surgery visits in our sample, 78,268 (64.5%) bypassed their local hospital. Bypass rate was greatest for coronary artery bypass graft or valve replacement (74.8%) and lowest for laparoscopic cholecystectomy (53.7%). In addition, average bypass rate was greatest for surgeries with the highest risk of intraoperative blood loss and postoperative complications. The probability of bypass significantly (P < .001) increased for patients who were younger, privately insured, and lived farther from the closest hospital. In addition, the probability of bypass significantly (P < .001) increased for patients whose local hospital had fewer full-time equivalents, lower operating margin, and fewer recommendations from previous patients. CONCLUSION: Among rural patients seeking elective surgery, bypass of the local hospital was common among both low-risk and high-risk procedures. These findings suggest that there is a substantial amount of bypass, which may negatively impact a hospital's financial performance and, hence, wellbeing of the local community.

Permanent Supportive Housing Tenure Among a Heterogeneous Population of Adults with Disabilities.

People with disabilities can face substantial barriers to living stably in community settings. Evidence shows that permanent supportive housing (PSH), which combines subsidized housing with individualized support services, can improve housing stability among subpopulations of people with disabilities, including those with behavioral health conditions. PSH has also been shown to improve some health outcomes among people with severe mental illness or substance use disorder, but effects varied by participants' program tenure. This study assessed retention in a PSH program serving a broad population of adults with disabilities and identified factors associated with program tenure. Administrative data from 2093 individuals who began participating in a North Carolina PSH program between 2015 and 2018 were analyzed. Participants' unadjusted probability of remaining in a PSH placement at specific time points was estimated, with censoring due to death or the end of the study period (July 2020). Using Cox regression, program tenure was modeled as a function of participant and PSH placement location characteristics. Participants had a 71% probability of remaining in PSH after 2 years. Older age, female gender, and non-Hispanic Black race/ethnicity were associated with lower hazard of PSH departure. Having a severe mental illness diagnosis was associated with greater departure hazard. Level of socioeconomic deprivation and rurality of the PSH placement ZIP code were not associated with departure hazard. PSH programs may be able to successfully retain a heterogeneous population of adults with disabilities, although tenure may vary by participant demographic and clinical characteristics.

Access to outpatient services in rural communities changes after hospital closure.

OBJECTIVE: Between January 2005 and July 2020, 171 rural hospitals closed across the United States. Little is known about the extent that other providers step in to fill the potential reduction in access from a rural hospital closure. The objective of this analysis is to evaluate the trends of Federally Qualified Health Centers (FQHCs) and Rural Health Clinics (RHCs) in rural areas prior to and following hospital closure. DATA SOURCES/STUDY SETTING: We used publicly available data from Centers for Medicare and Medicaid Provider of Services files, Cecil G. Sheps Center rural hospital closures list, and Small Area Income and Poverty Estimates. STUDY DESIGN: We described the trends over time in the number of hospitals, hospital closures, FQHC sites, and RHCs in rural and urban ZIP codes, 2006-2018. We used two-way fixed effects and pooled generalized linear models with a logit link to estimate the probabilities of having any RHC and any FQHC within 10 straight-line miles. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: Compared to hospitals that never closed, the predicted probability of having any FQHC within 10 miles increased post closure by 5.95 and 11.57 percentage points at 1 year and 5 years, respectively (p < 0.05). The predicted probability of having any RHC within 10 miles was not significantly different following rural hospital closure. A percentage point increase in poverty rate was associated with a 1.98 and a 1.29 percentage point increase in probabilities of having an FQHC or RHC, respectively (p < 0.001). CONCLUSIONS: In areas previously served by a rural hospital, there is a higher probability of new FQHC service-delivery sites post closure. This suggests that some of the potential reductions in access to essential preventive and diagnostic services may be filled by FQHCs. However, many rural communities may have a persistent unmet need for preventive and therapeutic care.

Decline in Inpatient Volume at Rural Hospitals.

PURPOSE: To investigate (1) all-payer inpatient volume changes at rural hospitals and (2) whether trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population. METHODS: We used a retrospective, longitudinal study design. Our study sample consisted of rural hospitals between 2011 and 2017. Inpatient volume was measured as inpatient average daily census (ADC). Additional measured hospital characteristics included census region, Medicare payment type, ownership type, number of beds, local competition, total margin, and whether the hospital was located in a Medicaid expansion state. Measured characteristics of the local patient population included total population size, percent of population aged 65 years or older, and percent of population in poverty. To identify predictors of inpatient volume trends, we fit a linear multiple regression model using generalized estimating equations. FINDINGS: Rural hospitals experienced an average change in ADC of -13% between 2011 and 2017. We found that hospital characteristics (eg, census region, Medicare payment type, ownership type, total margin, whether the hospital was located in a Medicaid expansion state) and patient population characteristics (eg, percent of population in poverty) were significant predictors of inpatient volume trends. CONCLUSIONS: Trends in inpatient volume differ by organizational and geographic characteristics of the hospital and characteristics of the patient population. Researchers and policy makers should continue to explore the causal mechanisms of inpatient volume decline and its role in the financial viability of rural hospitals.

For Rural Hospitals That Merged, Inpatient Charges Decreased and Outpatient Charges Increased: A Pre-/Post-Comparison of Rural Hospitals That Merged and Rural Hospitals That Did Not Merge Between 2005 and 2015.

PURPOSE: To determine whether inpatient and outpatient charges changed at rural hospitals after a merger. METHODS: Hospital mergers were derived from proprietary Irving Levin Associates data through manual review and validation. Hospital-level characteristics were derived from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and US Census data. A difference-in-differences approach was used to determine whether inpatient and outpatient charges changed at rural hospitals after a merger. The comparison group, rural hospitals that did not merge at any point during the sample period, was weighted using inverse probability of treatment weights. Key outcome measures were total inpatient and total outpatient charges (logged). FINDINGS: Hospitals that merged billed 17.73% more inpatient charges and 12.66% more outpatient charges at baseline compared to hospitals that did not merge. Our results indicate that merging was associated with a 3.04% decrease in inpatient charges (P < .001) and a 1.07% increase in outpatient charges (P = .082). Merging was also associated with a 4.38% decrease in total revenue, a 3.58% decrease in net patient revenue, and no change in total inpatient discharges or average daily census. CONCLUSIONS & IMPLICATIONS: Merging was strongly associated with a decrease in inpatient charges and somewhat associated with an increase in outpatient charges for rural hospitals. Future work could build upon this work to determine whether acquirers reduce or eliminate certain services at rural hospitals after a merger, and ultimately how changes in service delivery could impact patients in those rural communities.

Capital Expenditures Increased at Rural Hospitals That Merged Between 2012 and 2015.

EXECUTIVE SUMMARY: The number of rural hospital mergers has increased substantially in recent years. A commonly reported reason for merging is to increase access to capital. However, no empirical evidence exists to show whether capital expenditures increased at rural hospitals after a merger. We used a difference-in-differences approach to determine whether total capital expenditures changed at rural hospitals after a merger. The comparison group (rural hospitals that did not merge during the 2012 through 2015 study period) was weighted using inverse probability of treatment weights. The key outcome measure was logged total capital expenditures.Merging resulted in a 26% increase in capital expenditures and also was associated with a significant improvement in plant age. The postmerger improvement in plant age may have been partially attributable to merger-related accounting changes and partially attributable to increased capital expenses, possibly on long-term asset renovations and replacement.These findings suggest that through mergers, rural hospital board members and executives who have accepted or are considering a merger may improve a hospital's ability to increase capital expenditures. Further, increased capital investments in rural hospitals may be an important signal to the community that the acquirer intends to keep the rural hospital open and continue providing some volume and level of services within the community. Future research should determine how capital is spent after a merger.

Rural Hospital Mergers Increased Between 2005 and 2016-What Did Those Hospitals Look Like?

The objective of this study is to determine whether key hospital-level financial and market characteristics are associated with whether rural hospitals merge. Hospital merger status was derived from proprietary Irving Levin Associates data for 2005 through 2016 and hospital-level characteristics from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and U.S. Census data for 2004 through 2016. A discrete-time hazard analysis using generalized estimating equations was used to determine whether factors were associated with merging between 2005 and 2016. Factors included measures of profitability, operational efficiency, capital structure, utilization, and market competitiveness. Between 2005 and 2016, 11% (n = 326) of rural hospitals were involved in at least one merger. Rural hospital mergers have increased in recent years, with more than two-thirds (n = 261) occurring after 2011. The types of rural hospitals that merged during the sample period differed from nonmerged rural hospitals. Rural hospitals with higher odds of merging were less profitable, for-profit, larger, and were less likely to be able to cover current debt. Additional factors associated with higher odds of merging were reporting older plant age, not providing obstetrics, being closer to the nearest large hospital, and not being in the West region. By quantifying the hazard of characteristics associated with whether rural hospitals merged between 2005 and 2016, these findings suggest it is possible to determine leading indicators of rural mergers. This work may serve as a foundation for future research to determine the impact of mergers on rural hospitals.

What Characteristics Influence Whether Rural Beneficiaries Receiving Care From Urban Hospitals Return Home for Skilled Nursing Care?

PURPOSE: Skilled nursing care (SNC) provides Medicare beneficiaries short-term rehabilitation from an acute event. The purpose of this study is to assess beneficiary, market, and hospital factors associated with beneficiaries receiving care near home. METHODS: The population includes Medicare beneficiaries who live in a rural area and received acute care from an urban facility in 2013. "Near home" was defined 3 different ways based on distances from the beneficiary's home to the nearest source of SNC. Results include unadjusted means and odds ratios from logistic regression. FINDINGS: About 69% of rural beneficiaries receiving acute care in an urban location returned near home for SNC. Beneficiaries returning home were white (odds ratio [OR] black: 0.69; other race: 0.79); male (OR: 1.07); older (OR age 85+ [vs 65-69]: 1.14); farther from SNC (OR: 1.01 per mile); closer to acute care (OR: 0.28, logged miles); and received acute care from hospitals that did not own a skilled nursing facility (owned OR: 0.77) and hospitals with: no swing bed (swing bed OR: 0.47), high case mix (OR: 3.04), and nonprofit status (for-profit OR: 0.85). Results varied somewhat across definitions of "near home." CONCLUSIONS: Rural Medicare beneficiaries who received acute care far from home were more likely to receive SNC far from home. Because Medicare beneficiaries have the choice of where to receive SNC, policy makers may consider ensuring that new payment models do not incentivize provision of SNC away from home.

The Effect of the Magnet Recognition Signal on Hospital Financial Performance.

EXECUTIVE SUMMARY: The objective of this study was to investigate the effect of the Magnet Recognition (MR) signal on hospital financial performance. MR is a quality designation granted by the American Nurses Credentialing Center (ANCC). Growing evidence shows that MR hospitals are associated with various interrelated positive outcomes that have been theorized to affect hospital financial performance.In this study, which covered the period from 2000 to 2010, we applied a pre-post research design using a longitudinal, unbalanced panel of MR hospitals and hospitals that had never received MR designation located in urban areas in the United States. We obtained data for this analysis from Medicare's Hospital Cost Report Information System, the American Hospital Association Annual Survey Database, the Health Resources & Services Administration's Area Resource File, and the ANCC website. Propensity score matching was used to construct the final study sample. We then applied a difference-in-difference model with hospital fixed effects to the matched hospital sample to test the effect of the MR signal, while controlling for both hospital and market characteristics.According to signaling theory, signals aim to reduce the imbalance of information between two parties, such as patients and providers. The MR signal was found to have a significant positive effect on hospital financial performance. These findings support claims in the literature that the nonfinancial benefits resulting from MR lead to improved financial performance. In the current healthcare environment in which reimbursement is increasingly tied to delivery of quality care, healthcare executives may be encouraged to pursue MR to help hospitals maintain their financial viability while improving quality of care.

How Would Rural Hospitals Be Affected by Loss of the Affordable Care Act's Medicare Low-Volume Hospital Adjustment?

PURPOSE: The low-volume hospital (LVH) payment adjustment established in the Patient Protection and Affordable Care Act (ACA) of 2010 is scheduled to sunset on October 1, 2017. The purpose of this analysis was: (1) to estimate the effect of the ACA LVH adjustment on qualifying hospitals' profitability margins; and (2) to examine hospital and market characteristics of the hospitals that would be most adversely affected by the loss of the ACA LVH adjustment. METHODS: 2004-2015 data from the Hospital Cost Report Information System, Hospital Market Service Area File and Nielsen-Claritas Pop-Facts file were used to estimate difference-in-difference regression models with hospital-level random effects in order to determine whether the ACA LVH adjustment improved qualifying rural hospitals' profitability margins. Recycled predictions estimated the effect of losing the ACA LVH adjustment on profitability margins. Bivariate analyses explored associations between the predicted profitability margins and hospital and market characteristics. FINDINGS: The ACA LVH adjustment significantly improved Sole Community Hospitals' Medicare inpatient margins in the year they received the adjustment, and it had a large but statistically insignificant effect on the profitability margins of other rural hospitals. Hospitals that would be the most adversely affected by loss of the ACA LVH adjustment were more likely to be small, located in the South, and in high-poverty markets with higher proportions of black and uninsured individuals. CONCLUSIONS: Elimination of the ACA LVH adjustment would have differential effects on subgroups of hospitals, and those located in markets serving historically underserved populations would be the most adversely affected.

Predicting Financial Distress and Closure in Rural Hospitals.

PURPOSE: Annual rates of rural hospital closure have been increasing since 2010, and hospitals that close have poor financial performance relative to those that remain open. This study develops and validates a latent index of financial distress to forecast the probability of financial distress and closure within 2 years for rural hospitals. METHODS: Hospital and community characteristics are used to predict the risk of financial distress 2 years in the future. Financial and community data were drawn for 2,466 rural hospitals from 2000 through 2013. We tested and validated a model predicting a latent index of financial distress (FDI), measured by unprofitability, equity decline, insolvency, and closure. Using the predicted FDI score, hospitals are assigned to high, medium-high, medium-low, and low risk of financial distress for use by practitioners. FINDINGS: The FDI forecasts 8.01% of rural hospitals to be at high risk of financial distress in 2015, 16.3% as mid-high, 46.8% as mid-low, and 28.9% as low risk. The rate of closure for hospitals in the high-risk category is 4 times the rate in the mid-high category and 28 times that in the mid-low category. The ability of the FDI to discriminate hospitals experiencing financial distress is supported by a c-statistic of .74 in a validation sample. CONCLUSION: This methodology offers improved specificity and predictive power relative to existing measures of financial distress applied to rural hospitals. This risk assessment tool may inform programs at the federal, state, and local levels that provide funding or support to rural hospitals.

To What Extent do Community Characteristics Explain Differences in Closure among Financially Distressed Rural Hospitals?

From January 2005 through December 2015, 105 rural hospitals closed. This study examined associations between community characteristics and rural hospital closure. Compared with other rural hospitals that were at high risk of financial distress but remained open over the same time period, closed rural hospitals had a smaller market share (p < .0001) despite being in areas with higher population density (p < .05), were located nearer to another hospital (p < .0001), and were located in markets that had a higher rate of unemployment (p < .05) and a higher percentage of Black (p < .05) and Hispanic (p < .01) residents. These results have three implications for rural health policy: rural hospital closures may disproportionately affect racial and ethnic minorities, community characteristics in combination with other factors make it likely that rural hospital closures will continue, and rural hospital closures illuminate the need for new models of reimbursement and health care delivery to meet the needs of rural communities.

Medicaid Expansion Affects Rural And Urban Hospitals Differently.

Rural hospitals differ from urban hospitals in many ways. For example, rural hospitals are more reliant on public payers and have lower operating margins. In addition, enrollment in the health insurance Marketplaces of the Affordable Care Act (ACA) has varied across rural and urban areas. This study employed a difference-in-differences approach to evaluate the average effect of Medicaid expansion in 2014 on payer mix and profitability for urban and rural hospitals, controlling for secular trends. For both types of hospitals, we found that Medicaid expansion was associated with increases in Medicaid-covered discharges. However, the increases in Medicaid revenue were greater among rural hospitals than urban hospitals, and the decrease in the proportion of costs for uncompensated care were greater among urban hospitals than rural hospitals. This preliminary analysis of the early effects of Medicaid expansion suggests that its financial impacts may be different for hospitals in urban and rural locations.

The Rising Rate of Rural Hospital Closures.

PURPOSE: Since 2010, the rate of rural hospital closures has increased significantly. This study is a preliminary look at recent closures and a formative step in research to understand the causes and the impact on rural communities. METHODS: The 2009 financial performance and market characteristics of rural hospitals that closed from 2010 through 2014 were compared to rural hospitals that remained open during the same period, stratified by critical access hospitals (CAHs) and other rural hospitals (ORHs). Differences were tested using Pearson's chi-square (categorical variables) and Wilcoxon rank test of medians. The relationships between negative operating margin and (1) market factors and (2) utilization/staffing factors were explored using logistic regression. FINDINGS: In 2009, CAHs that subsequently closed from 2010 through 2014 had, in general, lower levels of profitability, liquidity, equity, patient volume, and staffing. In addition, ORHs that closed had smaller market shares and operated in markets with smaller populations compared to ORHs that remained open. Odds of unprofitability were associated with both market and utilization factors. Although half of the closed hospitals ceased providing health services altogether, the remainder have since converted to an alternative health care delivery model. CONCLUSIONS: Financial and market characteristics appear to be associated with closure of rural hospitals from 2010 through 2014, suggesting that it is possible to identify hospitals at risk of closure. As closure rates show no sign of abating, it is important to study the drivers of distress in rural hospitals, as well as the potential for alternative health care delivery models.

Uncompensated Care Burden May Mean Financial Vulnerability For Rural Hospitals In States That Did Not Expand Medicaid.

The implementation of the Affordable Care Act has led to a large decrease in the number of uninsured people. Yet uncompensated care will still occur, particularly in states where eligibility for Medicaid is not expanded. We compared rural hospitals in Medicaid expansion and nonexpansion states in terms of the amount of uncompensated care they provided and their profitability and market characteristics in 2013. We found that rural hospitals in expansion states provided more dollars of uncompensated care than those in nonexpansion states and that the difference was at least partly driven by greater uncompensated costs associated with public programs such as Medicaid. We found higher dollar values of unrecoverable debt and charity care among non-critical access rural hospitals in nonexpansion states than among those in expansion states. Compared to hospitals in expansion states, those in nonexpansion states provided greater amounts of uncompensated care as a percentage of revenues and appeared to be more financially vulnerable; thus, these hospitals may be more likely to experience financial pressure or losses. Policy makers need to formulate strategies for maintaining access to care for rural populations residing in nonexpansion states.