Metro–Nonmetro Differences in Adverse Events in Residential Care Communities: Results From the National Post-Acute and Long-Term Care Study

Abstract

More than 1 in 5 older Americans live in rural areas (10.6 million of the 46.2 million aged 65 and older). Long-term care for aging rural populations is a growing challenge in the United States. Research on long-term care services in nonmetro areas has focused almost exclusively on nursing home care, despite growth of residential care alternatives. This paper uses unique facility-level data from the 2020 National Post-acute and Long-term Care Study (NPALS) to examine the relationship of residential care community (RCC) features in metro and nonmetro settings with adverse outcomes (emergency department visits, overnight hospital stays, and falls). Nationally, in 2020, about 13.5% of RCC residents made visits to the emergency department, 8.6% had overnight hospital stays, and 21.3% had falls. Controlling for facility characteristics, RCCs in metro areas had higher risks of overnight hospital stays (p < .001) but lower risks of falls (p = .06).

Keywords

assisted living falls emergency department visits hospitalizations

What this paper adds

• Adverse events in residential care communities.

• Differences in falls, emergency department visits, and overnight hospitalization among residents living in residential care communities in metro and nonmetro locations.

• Association between provider characteristics and adverse events.

Applications of study findings

• Inform policies related to disparities in long-term care in metro and nonmetro settings.

• Estimation of falls, emergency department visits, and overnight hospitalization in residential care settings.

• Improved understanding of provider characteristics associated with adverse events in residential care settings.

Introduction

The United States has long anticipated challenges associated with the care of aging rural populations (United States Congress Senate, 1990). Rural areas have been losing population and aging more rapidly than average for several decades, a trend that has been accompanied by declines in access to services, including health care (Thiede et al., 2017). Poorer economic conditions, a thin health care infrastructure, and limited access to specialty care all increase the likelihood of adverse health outcomes and decrease life expectancy in rural areas (Capriotti et al., 2020; CDC, 2017; Deligiannidis, 2017; Vierboom & Preston, 2020).

Disparities in health care provision between rural and urban settings are observed across acute, post-acute, and long-term care settings. Rural areas experience challenges in access to hospitals and to a well-trained health care workforce, and hospital closures in rural settings have exacerbated these disparities (Burrows et al., 2012; US GAO, 2018). Yet, research on the unique needs and experiences of those aging in rural settings is limited (Jensen et al., 2020).

Other than living with family, nursing homes have historically been the only long-term care option in nonmetro areas (Berry & Kirschner, 2013; Chyr et al., 2020). But many now move into residential care settings at rates higher than nursing home admissions, as nursing home closures have hit rural areas particularly hard (McSweeney-Feld, 2020; Sharma et al., 2021). RCCs provide services and supports to older adults and younger adults with disabilities who cannot live independently in their homes but generally do not require the skilled nursing care provided in nursing homes. RCCs are known by different names in different states and include assisted living communities, board and care homes, congregate care, enriched housing programs, homes for the aged, personal care homes, and shared housing establishments.

As of 2015, about two million older Americans lived in residential care facilities in the community (Toth, et al., 2022). The majority of these (84.6% in 2020) were in metro statistical areas. While occupancy rates declined during the early part of the COVID-19 pandemic, on average RCCs remained about 75% full, compared with an average pre-pandemic occupancy of 86%, and rates were similar across metro and nonmetro locations in both years (authors’ tabulation).

There are no studies on rural–urban differences in the quality of care provided by RCCs, although one study showed no significant rural–urban differences in family satisfaction with care in RCCs (Wood et al., 2021). Studies in the nursing home sector have found significant differences, with rural nursing homes providing lower quality of care than urban nursing homes (Bolin et al., 2006; Bowbliss et al., 2013; Kang et al., 2011; Temkin-Greener et al., 2012). Rural nursing homes have fewer beds, lower staffing levels, and also provide fewer specialized services compared to urban nursing homes (Phillips et al., 2004). Staff in rural nursing homes also have lower levels of education and staff training compared to urban nursing homes (Blustein, 2011; Towsley et al., 2011).

As the older population continues to increase and with Medicaid’s rebalancing efforts to shift funding from institutional to home- and community-based long-term care services, it is expected that the number of people in assisted living and other residential care settings will increase (Caffrey & Sengupta, 2018; Eiken et al., 2018). Understanding the residential care organization, services, and characteristics that are associated with potentially adverse events may add to our understanding of how the effectiveness of long-term services and supports vary across the United States. In this paper, we examine differences in rates of adverse events between metro and nonmetro residential care communities according to provider level indicators of quality of care, controlling for differences across settings in the residential care populations.

Methods

Data Source

The data come from the residential care component of the 2020 NPALS, a biennial study that uses a combination of administrative data for home health agencies, hospices, inpatient rehab facilities, long-term care hospitals, and nursing homes and primary survey data on adult day services centers and residential care communities. The residential care component of NPALS provides national- and state-level estimates and characteristics of RCCs and the residents who receive care; resident characteristics are aggregated at the provider level. Fieldwork was conducted between November 2020 and July 2021, presenting many challenges to data collection (Ward et al., 2021), but provided an opportunity to collect COVID-19-related data and assess if there were any differences in use and characteristics from pre-COVID waves.

There is no standard federal definition, and the nomenclature used to describe RCCs varies between and within states (Mollica et al., 2007). RCCs were eligible to participate in the NPALS if they were licensed, registered, certified, listed, or otherwise regulated by their state and served primarily an adult population, but did not exclusively serve the severely mentally ill or those with intellectual or developmental disabilities (MRDD). Facilities were included if they had four or more beds; at least one resident; and provided at least two meals a day; around-the-clock onsite supervision; and help with activities of daily living (e.g., bathing, eating, and dressing) or health-related services (e.g., medication management) (NCHS 2022).

For the 2020 NPALS, a sample of 11,600 RCCs was selected from the sampling frame of 44,200 communities acquired from licensing agencies in each of the 50 states and the District of Columbia; 4,312 of them completed the survey questionnaire, for a weighted response rate (for differential probabilities of selection) of 45% (NCHS 2022). This is lower than earlier waves (2012, 2014, 2016) of the NPALS but higher than the most recent wave, fielded pre-pandemic (2018).

Variables

Adverse events like falls, medication errors, preventable health care–related infections, and pressure ulcers can be common in long-term care settings and may require emergency care or hospitalizations (Andersson et al., 2018; Choi et al., 2023; Levinson 2014). For this study, three measures of adverse events were selected: the number of residents that had 1) an emergency department visit; 2) an overnight hospital stay; or 3) a fall in the 90 days prior to the interview. Location was classified using the Office of Management and Budget (OMB) three category designation: metropolitan, micropolitan, and neither (OMB 2010). Using OMB’s definition, all places are considered metropolitan if they belong to a metropolitan statistical area (MSA), and all areas that are not part of an MSA (micropolitan, neither) are considered nonmetro.

Among provider characteristics that contribute to adverse events in long-term care settings, lack of adequate, well-trained, and competent staff is well recognized (Andersson et al., 2018; Kiljunen et al., 2017). Consistent with prior studies (e.g., Rome & Harris-Kojetin, 2016), we use average staff hours per resident day (HPRD) to a maximum of 24 hours, to represent staffing levels. We examine staff categories (RN, LPN, aide, social worker, and activities director/staff) separately since training requirements vary by staff type.

Earlier studies have shown that the incidence of falls and overnight hospital stays increased with facility size, so we include a variable to distinguish RCCs with 4–25, 26–50, and 50+ beds. Bed occupancy rate was measured as the ratio of the number of residents to the total number of available beds. Research also suggests that for-profit nursing homes have poorer quality of care for residents than nonprofit nursing homes resulting in higher incidence of adverse outcomes (Mor et al., 2009; Paul et al., 2016; Span, 2012). Ownership was measured as for-profit or nonprofit status. Since RCCs that provide hospice services also may have sicker residents who are more likely to have adverse events, a binary variable was used to indicate RCCs that provided hospice services.

Characteristics of the resident population are important risk factors for adverse events, especially in settings that serve older, more frail populations with greater levels of functional limitation (Cameron et al., 2018; Datta et al., 2018). Older people with Alzheimer’s disease and other dementias have a higher risk of emergency department visits, overnight hospital stays, and falls among other adverse events (Park-Lee & Sengupta, 2019). Resident characteristics used in this analysis include: the percentage of residents using Medicaid to pay for long-term care services; aged 85 and older; who are female; non-Hispanic White, and with Alzheimer’s disease.

Item Non-Response and Missing Data

Of the 4,312 RCCs that participated in the study, 10% (10.8% metro and 7.5% nonmetro settings) did not respond to questions about resident emergency department visits and overnight hospital stays. Among independent variables, 11% (12.0% metro and 9.3% nonmetro) did not have information about the number of Alzheimer’s disease residents and about 8% (8.2% metro and 5.8% nonmetro) did not respond to staffing questions. Cases with missing data on any of the variables were excluded from the bivariate and multivariate analyses; this resulted in an analytic sample of 3,550 RCCs. In order to evaluate whether this introduced bias in our analysis, we compared our results for samples with and without missing data. There were no significant demographic differences between the two groups, and results using the larger sample with a flag for missing data showed no differences in the direction or level of significance in the associations found in the data and no significant coefficients for missing data flags.

Analysis

We first compared differences in facility and resident characteristics across metro and nonmetro locations. We then used multivariate analyses to assess whether metro/nonmetro location was related to having falls, overnight hospitalizations, and emergency department visits among residential care residents. After comparing model residuals for several different specifications (Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial models, zero-truncated Poisson, and zero-truncated negative binomial models), the negative binomial regression model had the best fit for the over dispersed count outcome variables in this study. The outcome variables were counts of overnight hospital stays, emergency department visits, and falls. The models were adjusted by controlling for selected resident and RCC characteristics and used number of residents as exposure in the negative binomial models. All statistical significance tests were 2-sided, with p < .05 as the level of statistical significance. Results are presented as incidence rate ratios (IRRs) which are exponentials of the coefficients in the model (Hilbe, 2011). Analyses were done using the nbreg command in Stata 17.0 (StataCorp, 2021).

Results

The majority (85%) of RCCs were in metropolitan areas, with almost 22,000 metropolitan RCCs serving 541,700 residents nationally. About 4,000 nonmetropolitan residential communities served 86,000 residents. Nationally, in 2020, about 13.5% (84,900) of RCC residents visited an emergency department, 8.6% (54,200) had overnight hospital stays, and 21.1% (132,300) had a fall in the last 90 days (Table 1). Overall, on average 3 residents had emergency department visits, 2 residents had overnight hospitalizations, and 5 residents had falls. The average number of residents who had emergency department visits and overnight hospitalizations was higher in metro than nonmetro areas.

Table 1.

Percent, Mean, and Range of Adverse Events, by Metro–Nonmetro Status.

N (weighted)	Total	Metro	Nonmetro	p-value
N (weighted)	3550 (25,970)	2674 (21,977)	876 (3,993)	p-value
Adverse events
Percent of residents who had emergency department visits	13.5	13.6	13.4	0.82
Mean number of emergency department visits (range)	3.3 (0–92)	3.3 (0–92)	2.9 (0–65)	0.03
Percent of residents who had overnight hospitalizations	8.6	8.9	7.1	0.00
Mean number of overnight hospitalizations (range)	2.1 (0–75)	2.2 (0–75)	1.5 (0–20)	0.00
Percent of residents who had falls	21.1	21.0	21.7	0.63
Mean number of falls (range)	5.1 (0–144)	5.2 (0–144)	4.7 (0–86)	0.22

Source: NCHS, National Post-acute and Long-term Care Study, 2020.

Table 2 shows resident and provider characteristics, by metro–nonmetro location. Resident populations only significantly differ in the percentage that are non-Hispanic White (94.2% in nonmetro RCCs compared with 86.7% in metro ones) and the use of Medicaid, with a higher percentage in nonmetro facilities (27.5%) compared to metro (16.6%). About 4 in 10 residents were diagnosed with Alzheimer’s disease in both metro (42.6%) and nonmetro settings (41.0%), and about half of residents had difficulty walking and were aged 85 and older, regardless of the setting. Almost 7 out of 10 residents were female in both settings.

Table 2.

Sample Characteristics, by Metro–Nonmetro Status.

N (weighted)	Total	Metro	Nonmetro	p-value
	3550 (25,970)	2674 (21,977)	876 (3,993)
	Estimate (%)
Resident characteristics
Percent of residents with Alzheimer’s disease	42.4	42.6	41.0	0.39
Percent of residents having difficulty walking	50.3	50.4	49.3	0.59
Percent of residents aged 85 and older	49.3	49.0	50.9	0.32
Percent of non-Hispanic white residents	87.8	86.7	94.2	0.00
Percent of female residents	69.4	69.3	69.8	0.62
Percent of Medicaid residents	18.1	16.6	27.5	0.00
Provider characteristics
Provides hospice care	76.7	76.5	77.9	0.55
Ownership
Nonprofit	17.8	16.3	26.1	0.00
For-profit	82.2	83.7	73.9	0.00
Occupancy^a	74.8	75.2	72.5	0.03
Bed size
4–25 beds	60.3	61.4	54.5	0.01
26–50 beds	13.7	11.7	25.0	0.00
More than 50 beds	25.9	26.9	20.6	0.00
Number of residents
Mean (range)	24 (1–430)	25 (1–430)	22 (1–167)	0.00

Staff hours	Estimate (average hours)			p-value
RN hours per resident day	0.57	0.56	0.62	0.37
LPN hours per resident day	0.37	0.36	0.42	0.33
Aide hours per resident day	3.83	3.77	4.12	0.07
Social worker hours per resident day	0.03	0.03	0.04	0.33
Activities staff hours per resident day	0.41	0.42	0.35	0.09

Source: NCHS, National Post-acute and Long-term Care Study, 2020.

^aOccupancy rate is the ratio of the total number of residents to the total number of available beds.

Provider characteristics demonstrated some variation between metro and nonmetro settings, with a higher percentage of nonmetropolitan RCCs owned by nonprofit organizations (26.1%) compared to metro areas (16.3%). Differences in size were also seen: RCCs with fewer than 25 beds were more common in metro areas (61.4%) compared to nonmetro ones (54.5%). Metropolitan areas also had a higher percentage (26.9%) of the largest facilities (>50 beds) compared to nonmetro areas (20.6%). The majority of RCCs in metro (76.5%) and nonmetro (77.9%) settings provided hospice care. Occupancy rates were slightly higher in metro (75.2%) than nonmetro areas (72.5%). Occupancy was inversely related to bed size, and nonprofit providers had lower occupancy than for-profit providers (tabulations not shown). Staffing ratios were similar for metro and nonmetro settings across all service types, although nonmetro facilities appear to report higher hours per day on average for all staffing categories, but these differences were not statistically significant.

Selected Resident Outcomes by Location of Residential Care Community

Emergency Department Visits

When resident and provider characteristics were controlled for, residents in metro RCCs had similar rates of emergency department visits as residents in nonmetro RCCs (IRR = 1.02; 95% CI, .93–1.13) (Table 2 and 3 ). Residents in communities with more than 50 beds were more likely to have emergency department visits (IRR = 1.14; 95% CI, 1.02–1.26) compared to communities with 4–25 beds.

Overnight Hospitalization

One of eleven residents (8.6%) in residential care settings had been a resident at a hospital overnight or longer. A higher percentage of residents in metro than in nonmetro settings had been hospitalized (8.9% vs. 7.1%; p < .001) (Table 1). Even after controlling for resident and provider characteristics, residents in metro settings were more likely to have been hospitalized than those in nonmetro settings (IRR = 1.24; 95% CI, 1.09–1.42) (Table 3). LPN/LVN hours were related to overnight hospitalization (IRR = 1.15; 95% CI, 1.02–1.30).

Table 3.

Relationship of Provider Characteristics to Rate of Adverse Events Adjusted for Resident Population Characteristics

	Emergency department visits			Overnight hospital stays			Falls
	IRR^a	p-value	95% CI	IRR^a	p-value	95% CI	IRR^a	p-value	95% CI
Metro (ref: nonmetro)	1.02	0.65	.93–1.13	1.24	0.00	1.09–1.42	0.90	0.06	.82–1.00
Staff hours
RN hours per resident day	0.99	0.72	.96–1.03	0.99	0.71	.95–1.04	0.98	0.26	.95–1.01
LPN/LVN hours per resident day	1.04	0.38	.95–1.15	1.15	0.02	1.02–1.30	1.07	0.00	1.02–1.11
Activities staff hours per resident day	1.05	0.20	.97–1.14	1.10	0.10	.98–1.24	0.97	0.56	.88–1.07
Aide hours per resident day	1.01	0.12	1.00–1.03	0.99	0.58	.97–1.02	1.04	0.00	1.02–1.06
Social worker hours per resident day	0.87	0.50	.58–1.30	0.94	0.79	.61–1.45	0.94	0.69	.68–1.29
Hospice care (ref: not provided)	1.01	0.85	.92–1.10	1.00	1.00	.89–1.13	1.13	0.02	1.02–1.25
Occupancy rate	0.99	0.00	.99–1.00	0.99	0.00	.99–1.00	1.00	0.09	1.00–1.00
Nonprofit ownership (ref: for-profit)	0.96	0.42	.88–1.06	0.95	0.39	.84–1.07	1.05	0.39	.95–1.15
Bed size (ref: small 4–25 beds)
Medium (26–50 beds)	1.08	0.24	.95–1.22	1.05	0.50	.90–1.24	1.71	0.00	1.53–1.92
Large (more than 50 beds)	1.14	0.01	1.02–1.26	1.12	0.12	.97–1.28	2.16	0.00	1.93–2.41

Adjusted for resident characteristics (% of residents 85 and older, non-Hispanic White, female, Alzheimer’s disease, difficulty walking, using Medicaid).

Source: NCHS, National Post-acute and Long-term Care Study, 2020.

^aIncidence rate ratio is the exponential of the coefficients in negative binomial regression.

Falls

About 21.1% of residents in RCCs had falls, and this rate did not appear to differ across metro and nonmetro settings (Table 1). However, after adjusting for resident and provider characteristics, the risk of falls was marginally lower for residents in metro than in nonmetro settings, although not statistically significant (adjusted IRR = .90; 95% CI, .82–1.00; p = .06) (Table 2 and 3). Aide hours per resident day (IRR = 1.04; 95% CI, 1.02–1.06) and LPN/LVN hours per resident day (IRR = 1.07; 95% CI, 1.02–1.11) were related to falls. Compared to residential care communities with 4–25 beds, the risk of falls also was higher in communities with 26–50 beds (IRR = 1.71; 95% CI, 1.53–1.92) and more than 50 beds (IRR = 2.16; 95% CI, 1.93–2.41). Adding an interaction between bed size and location reduced the effect of bed size (results not shown), but residential care communities with more than 25 beds continued to have a significantly higher risk of falls.

For all three adverse event outcomes, additional models were run and included additional ADLS (difficulty eating, bathing, transferring, toileting, and dressing), diagnosis of depression, and incorporating state fixed effects. No differences were found in the outcomes of interest. Collapsing the bed size variable into 2 categories (4–25 beds and more than 25 beds) also did not change the outcomes of interest.

Discussion

Nationally in 2020, almost 9 in 10 RCCs were located in metropolitan areas. Previous research on metro versus nonmetro differences in long-term care have focused on nursing homes and relatively little is known about other residential care settings. Results from this study show that RCCs in nonmetro settings had resident populations with similar health risks but serve a higher percentage of non-Hispanic White residents and ones who used Medicaid to pay for services. Although the majority of RCCs in 2020 are under for-profit ownership, this level was lower in nonmetro areas than in metro settings, and the RCCs in nonmetro areas tended to be mid-sized, with a lower proportion of both large and small communities. These findings are largely consistent with findings from nursing homes and residential care (Lenardson et al., 2014; Sharma et al., 2022), but unlike earlier studies, we did not find differences in occupancy rates and in the age and walking difficulty of residents between metro and nonmetro settings. Occupancy rates are lower than in previous years, presumably reflecting changes seen across long-term care settings during the COVID-19 pandemic, which may have overtaken historic patterns of use across locations. We also have yet to see how differential mortality of residents in RCCs during the first part of the pandemic may have changed the profile of the resident populations in these settings.

To assess differences in the incidence of adverse events between metro and nonmetro settings, three outcomes (emergency department visits, overnight hospital stays, and falls) were examined. Overnight hospital stays were more common in metro (8.9%) than nonmetro settings (7.1%), and models adjusted for other provider and resident characteristics confirmed a significantly higher incidence of hospitalization in metro settings versus nonmetro settings. This finding is consistent with studies that have found that rural hospitals had lower rates of overnight admissions following emergency department visits and were more commonly “treat-and-release” visits compared to other settings (Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health & Human Services, 2021). It is possible that differences in the use of overnight hospital care may also reflect supply challenges in nonmetro areas. Even when hospitals are available, residents in rural areas may be more likely to seek care at a distant hospital “due to real or perceived capacity constraints or perceptions of quality of care at their local rural hospital” (Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health & Human Services, 2021).

In this study, there were no differences in emergency department visits by metro and nonmetro location, even after adjusting for resident and provider characteristics. In general, earlier studies have found a disproportionate increase of emergency department visits in rural areas reflecting a deteriorating primary health care infrastructure (Greenwood-Ericksen & Kocher, 2019). However, more recent studies examining emergency department visits during the COVID-19 pandemic indicate an overall decrease in the volume of emergency department visits (Hartnett et al., 2020) with volumes dropping more in rural areas (Keyes et al., 2021). In the current study, the general concern about being exposed to the COVID-19 virus in the emergency room, particularly for the vulnerable residential care population, may have limited emergency department visits in both metro and nonmetro locations.

Although unadjusted estimates showed almost identical rates of falls reported for residents in metro and nonmetro residential care communities, adjusted estimates suggest they occurred less frequently among residents in metro areas than nonmetro areas once resident populations and provider characteristics were controlled for, although this result was not statistically significant. These results are consistent with an earlier study about unintentional injury deaths which found that rural counties have experienced the largest increase in death rates due to unintentional falls and that large metro counties had the lowest rates of falls compared to other locations (Olaisen et al., 2019).

There are no studies addressing how differences in care quality between rural and urban RCCs might affect fall risks, and studies in nursing home settings present mixed results (Quigley et al., 2021). Falls prevention programs in RCCs tend to be inconsistently applied, with little guidance from regulators and inconsistency even within states on policies (Coughlin et al., 2019). However, other studies have pointed to the disadvantages of rural nursing homes: lower staff ratios, fewer staff with nursing degrees, and fewer specialized services, like dementia units (Quigley et al., 2021).

In this study, the risk of falls increased with community bed size confirming earlier findings (Caffrey & Sengupta, 2018). Larger facilities that are serving more residents may be environmentally more challenging. The distance between resident units and congregate spaces, such as dining areas, may be greater and thus present more opportunities for falls. Research on fall prevention among older people in general has found that, in addition to individual health and functional conditions that make older adults vulnerable to falls, environmental factors like building codes, grab bars, handrails, and lighting, as well as appropriate staff training to identify risky behavior and people at risk of falls, can make a difference (HUD, 2017).

Residents in RCCs that reported a higher number of aide hours and LVN/LPN hours per resident day had a higher rate of falls. Earlier studies are inconsistent in findings about the relation between staffing and resident falls. Some studies have found that hospitals and nursing homes with higher certified nursing staff and higher RN to aide ratio have lower fall rates, and higher non-RN staffing was associated with a higher fall rate (Gray-Miceli et al., 2016; Lake et al., 2010; Leland et al., 2012; Shin & Hyun, 2015; Staggs et al., 2014). RCCs that reported higher number of LVN/LPN hours per resident day had higher rates of overnight hospitalization. Other studies have found that the decision to hospitalize nursing home residents is related to availability as well as the skill level of attending staff (Polniaszek et al., 2011). Since LPNs have limited training in identifying and managing health conditions, nursing home residents are more likely to be hospitalized when nursing homes make more use of LPNs (Carter & Porell, 2003).

There are some limitations to this study. Since the resident data are aggregated at the provider level, the unit of analysis is the residential care community. It is therefore not possible to link specific incidents to outcomes for individual residents. Using a resident measure at the gross provider level also does not allow for cross classification of multiple resident characteristics. The data are reported by residential care community administrators or directors and may have resulted in underreporting or other proxy-related biases. This is of particular concern with regard to staffing ratios which may not accurately reflect the distribution of staff across the facility. Data collection was conducted during a period when many long-term care facilities suffered staff shortages (Segelman et al., 2021; Shen et al., 2022; Telesford et al., 2023) which may have affected reported staffing levels. The use of a 90-day window for adverse events is broad and limits our ability to link short-term variation in care to specific outcomes, a particular concern in the post-pandemic era in which staffing shortages and fluctuations have been common. Another limitation is that we cannot measure the supply of hospitals in the local area of each of the facilities. Many rural hospitals are under severe financial stress, resulting in closures and longer distances rural residents need to travel for care (US GAO 2021).

In the post-acute and long-term care sector, there were historically few alternative options available for older adults in nonmetro areas other than nursing home facilities or living with family (Berry & Kirschner, 2013). While the majority of older adults remain in their own homes in the community, as they become older and experience increased co-morbidities, many now move into residential care settings at rates higher than nursing home admissions (Chyr et al., 2020). In recent years, nursing home closures around the United States have hit rural areas particularly hard (McSweeney-Field 2020; Sharma et al., 2021), and residential care communities may be caring for older adults with greater needs than in the past.

We anticipated that established disadvantages in the nonmetropolitan health sector would show similar patterns in terms of the risk factors for adverse events among residential care residents. Results from our models suggest instead that there are few provider characteristics which predict hospitalization and emergency department visits. These suggest that the use of acute care may depend on availability and access rather than facility resources. However, the incidence of falls among residents is more closely tied to features of the communities in which they live. Larger facilities with more hours of health aides all have a higher reported incidence of falls. Further investigation may help to understand whether these differences reflect differences in identification of falls, how they are reported, and their severity.

Findings from this study outline differences in adverse events in residential care communities located in metro and nonmetro settings. Using a large sample from the only national study of residential care communities, findings about metro–nonmetro differences confirm the disadvantages of rurality found in studies of other health care sectors. With the increased aging of the population and with more than 1 in 5 older Americans living in rural areas, the need for long-term care supports and services will continue to increase in these rural settings.

Supplemental Material

Supplemental Material - Metro–Nonmetro Differences in Adverse Events in Residential Care Communities: Results From the National Post-Acute and Long-Term Care Study

Supplemental Material for Metro–Nonmetro Differences in Adverse Events in Residential Care Communities: Results From the National Post-Acute and Long-Term Care Study by Manisha Sengupta and Emily M. Agree in Journal of Applied Gerontology.

Footnotes

Acknowledgments

The authors would like to acknowledge all the directors/administrators/owners of residential care communities that participated in the 2020 National Post-acute and Long-term Care Study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Disclaimer

The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Center for Health Statistics, Centers for Disease Control and Prevention.

Ethical Statement

ORCID iD

Manisha Sengupta

Supplemental Material

Supplemental material for this article is available online.

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