Trends and Factors Associated With Extremity Fractures and Post-Acute Care Utilization of Nevada Older Adults: Insights of Age-Friendly State Planning in U.S.

Abstract

Along with the trend of a steady utilization decline in the U.S. nursing home beds, post-acute care (PAC) utilization at the skilled nursing facilities has declined. This study was a cross-sectional, retrospective review of hospital discharge-based claim data. We evaluate the factors associated with utilizing post-acute care at rehabilitation facilities among those with extremity fractures in the state of Nevada. All Nevada hospital discharges of aged ≥65 years with extremity fractures between 2018 and 2021 were divided to post-acute care locations by (1) rehabilitation facilities (skilled nursing facility and inpatient rehabilitation facility) and (2) homes (with and without services). PAC utilization at facilities declined from 55.1% in 2018 to 49.7% in 2021 (P < .001). In response, PAC utilization at homes continuously upwards, particularly, homes with services from 18.8% in 2018 to 24.5% in 2021 (P < .001). Older age, female, lower extremity fractures, comorbidities, and Medicare beneficiaries were associated with higher probabilities of utilizing post-acute rehabilitation facilities. Racial minorities, COVID-19 pandemic, upper extremities, Medicaid beneficiaries, rural hospitals, and prolonged hospital length of stay were associated with lower probabilities of PAC utilization at facilities. Caregiver burdens and workforce training is urgently warranted to respond to this utilization shift. Effective geriatrics workforce training might advance care efficiency of older adults with extremity fractures and guide to the insights of establishing the age-friendly state of Nevada in response to this utilization shift trends.

Keywords

Age-friendly health system extremities fractures health disparity older adults post-acute care rehabilitation

How does your research contribute to the field?

In a provider shortage state of Nevada, health disparities of utilizing post-acute rehabilitation services widened after the COVID-19 pandemic. Socially disadvantaged racial and ethnic minorities, Medicaid beneficiaries, the people living with dementia and rural residents are at risk of underutilizing resources and increasing caregiver burdens.

What are your research’s implications toward theory, practice, or policy?

Effective geriatrics workforce training might advance care efficiency of older adults with extremity fractures and highlight the insights of establishing the age-friendly state of Nevada.

Introduction

As post-acute care (PAC) locations, about 1 in 4 discharges was allocated to either skilled nursing facilities (SNFs) or inpatient rehabilitation facilities (IRFs) in 2019.^1,2 Along with the trend of a steady utilization decline in the number of US nursing home beds over the past decade, PAC utilization at the SNFs has declined.^3,4 COVID-19 has brought about many dramatic changes in people’s lifestyles and the governments have adopted policies of social isolation that may limit the population’s access to the health care, especially for elderly patients with fractures.⁵ Therefore, the COVID-19 pandemic has exacerbated the utilization decline at the SNFs.⁶

Health disparities exist among the socially disadvantaged populations, such as racial minorities, Medicaid beneficiaries, and rural residents, those who have limited access to and PAC use at the SNFs.^2,7 There are concerns about health disparities in the quality of PAC for people with extremity fractures who are at risk of developing complications and disability.⁸ For example, the people living with Alzheimer’s Disease and Related Disorders (ADRD) have complex PAC utilization needs as they recover from the extremity fractures due to difficulties in following rehabilitation and precaution instructions related to their communication challenges and behavioral challenges.^8,9The influx of older adults into Nevada persistently inclines over the past decade. Between 2010 and 2020, the state of Nevada ranked the second highest rate of growth in the population aged 65 and over (59%), after Alaska (73%). The state of Nevada as a statewide provider shortage area, has been ranked at the lowest number of primary care providers per capita despite population growth.¹⁰ In response, this enormous healthcare demand-supply gap results in worse effectiveness and efficiency of care for older adults. We aim to examine the trends and factors associated with the PAC utilization due to extremity fractures in the state of Nevada. Thus, our examination of the trends and factors associated with the PAC utilization due to extremity fractures provides the insights of planning the establishment of an age-friendly state from workforce education and policy-making perspectives.

Methods

Data Source and Study Participants

This study was a cross-sectional, retrospective review of hospital discharge-based claim data. The State Inpatient Database (SID), a publicly available dataset, was used. SID contains more than 95% hospital discharge information of all community hospitals in the participating states. The SID was originally developed for the Healthcare Cost and Utilization Project (HCUP) by Agency for Healthcare Research and Quality (AHRQ).¹¹ The SID includes de-identified patient-level information of demographics, diagnostic and procedure codes, discharge location and hospital utilization (length of stay, LOS). The Nevada SID files were constructed under the authority of the Nevada Division of Healthcare Financing and Policy (DHCFP). We identified all hospital discharges for aged 65 years and older with extremity fractures between January 2018 and December 2021. Fracture locations were classified into 7 groups according to anatomic considerations: (1) shoulder, humerus shaft (2) elbow (3) radius/ulna shaft, wrist (4) carpal bones, hand (5) hip, femur shaft (6) knee, patella (7) tibia/fibula shaft, ankle, foot. We identified extremity fractures using the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) as elsewhere in Supplemental Table 1.¹² The Institutional Review Board (IRB) at the University of Nevada, Las Vegas found that the data included in the analysis is de-identified thereby safeguarding privacy and confidentiality concerns and the current study to be exempt (IRB no. 1098939-3).

Measured Outcomes and Variables

Measured outcomes were PAC locations after extremity fractures. We summarized PAC locations by (1) rehabilitation facilities (SNF and IRF) and (2) homes (with and without home health services) in each year of the study. Both SNF and IRF services were provided at the facilities and the difference between SNF and IRF is minimum therapy intensity (service hours per week).^13,14 We excluded less than 2% of hospital discharges including other hospital discharges such as psychiatric hospitals and left against medical advice. Long-term care hospitals (LTCH) were excluded because the goal of care at LTCH is prioritized to life-sustaining skilled nursing, that is, ventilation support and tracheostomy care. We calculated the trends of PAC utilization at rehabilitation facilities over total PAC discharges. We measured patient-level characteristics; demographics (age, years old: 65-74, 75-84, 85 and older), gender, races: non-Hispanic White, Black, Hispanic, Asian/Hawaiian/Pacific Islander (AHPI), other races), clinical factors (comorbidities—obesity, cardiovascular disease [CVD], dementia, substance use disorder, renal disease; and fracture locations), utilization factors (pay sources: Medicare, Medicaid, private insurance, uninsured, other pay sources; hospital locations—rural and metro/urban areas; hospital LOS). Characteristics were selected from the ICD-10-CM codes which were used elsewhere.^15
-18 “RACE” variable contained a uniform coding for race and ethnicity across the SID State of Nevada dataset.¹¹ Choice of above comorbidities was relevant to previous literature of older adults’ common diagnoses related to either extremity fractures or discharge to PAC at the facilities.^{7
-9,12,15
-18} Pre COVID years [2018, 2019] and COVID pandemic years [year 2020 and year 2021] were chosen for examining the trends of PAC location at the facilities.

Statistical Analysis

A bivariate analysis with Pearson’s chi-squared test for categorical variables and t-test for continuous variables was used to evaluate time trends. Dependent variable was PAC at the facilities (SNF and IRF); independent variables were trends (pre COVID years [2018, 2019] and COVID pandemic years [year 2020 and year 2021]); covariates were demographics, clinical factors, and utilization factors. Carpal bone and hand category was the referent of fracture sites. Estimation was calculated using odds ratios (ORs) and their corresponding 95% confidence intervals (CIs).¹⁹ Analyses were adjusted for all covariates and COVID pandemic years (2020 and 2021) using multivariate logistic regressions of PAC utilization at rehabilitation facilities. Sensitivity analysis was performed by separate regression models (PAC at the facilities vs home with home health; PAC at the facilities vs home without home health). Two-sided, P < .05 was considered statistically significant. Analysis was performed using the SAS software, version 9.4 (SAS Institute, Cary, NC).

Results

Descriptive analysis of demographics, clinical factors, and utilization factors by study year are demonstrated in Table 1. In terms of race, a downtrend was observed in non-Hispanic White and an uptrend was noted in AHPI and other races . There was a persistent uptrend of obesity (2018: 6.8%, 2019: 8.2%, 2020: 9.5%, 2021: 11.0%, P < .0001). In terms of pay source, Medicare was predominant (86.0%). Decrease in private insurance (P = .0053) and increase in other pay sources (P < .001) were noted. There was a persistent utilization decline in rural hospitals (2018: 22.3%, 2019: 22.4%, 2020: 17.8%, 2021: 17.1%, P < .0001). LOS increased (P = .0002).

Table 1.

Descriptive Analysis of Demographics, Clinical Factors, and Utilization Factors by Study Year.

Variables	2018 (N = 6031)	2019 (N = 6366)	2020 (N = 5963)	2021 (N = 6172)	P-value
Demographics
Age, mean ± SD	78.2 ± 7.9	78.3 ± 7.8	78.4 ± 7.7	78.2 ± 7.7	.578
Female, N(%)	4102(68.0)	4133(64.9)	3912(65.6)	4005(64.9)	.051
Races, N(%)
Non-Hispanic White	5026 (83.3)	5261 (82.6)	4862 (81.5)	4936 (80.0)	<.0001
Black	193 (3.2)	229 (3.6)	215 (3.6)	240 (3.9)	.1451
Hispanic	288 (4.78)	289 (4.54)	255 (4.28)	298 (4.83)	.7163
AHPI	206 (3.4)	229 (3.6)	239 (4.0)	273 (4.4)	.0039
Other races	318 (5.3)	358 (5.6)	392 (6,6)	425 (6.9)	<.0001
Clinical factors (Comorbidities)
Obesity, N(%)	407 (6.8)	520 (8.2)	564 (9.5)	677 (11.0)	<.0001
CVD, N(%)	358 (5.9)	391 (6.1)	361 (6.1)	394 (6.4)	.5571
ADRD, N(%)	1085 (18.0)	1089 (17.1)	1105 (18.5)	1031 (16.7)	.8928
Substance use disorder, N (%)	1066 (17.7)	1176 (18.5)	1014 (17.0)	1077 (17.5)	.0795
Renal disease, N(%)	1007 (16.7)	1182 (18.6)	1119 (18.8)	1138 (18.4)	.0556
Clinical factors (Fracture locations, N(%))
Shoulder/Humerus	926 (15.34)	1001(15.7)	901(15.1)	999 (16.2)	.8072
Elbow	188 (3.1)	223 (3.5)	183 (3.1)	232 (3.8)	.6499
Radius, Ulnar/Wrist	337 (5.6)	356 (5.6)	345 (5.8)	362 (5.9)	.6489
Carpal bones, Hand	168 (2.8)	209 (3.3)	147 (2.5)	175 (2.8)	.0681
Hip, Femur	3563 (59.1)	3746 (58.8)	3640 (61.0)	3598 (54.4)	.5234
Knee, Patella	540 (9.0)	557 (8.7)	525 (8.8)	546 (8.9)	.9490
Tibia, Fibula, Ankle, Foot	823 (13.6)	876 (13.8)	801 (13.4)	875 (14.2)	.4307
Utilization factors
Pay sources, N (%)
Medicare	5197 (86.2)	5461 (85.8)	5151 (86.4)	5287 (85.7)	.9223
Medicaid	43 (0.7)	53 (0.8)	45 (0.8)	43 (0.7)	.6552
Private insurance	564 (9.4)	523 (8.2)	469 (7.9)	476 (7.7)	.0053
Uninsured	36 (0.6)	50 (0.8)	30 (0.5)	39 (0.6)	.2163
Other	191 (3.2)	279 (4.4)	268 (4.5)	327 (5.3)	<.0001
Hospital locations, rural area N (%)	1346 (22.3)	1425 (22.4)	1063 (17.8)	1055 (17.1)	<.0001
Length of stay days, mean ± SD	7.3 ± 9.4	7.5 ± 10.7	7.9 ± 9.6	7.8 ± 8.9	.0002
PAC locations, N(%)
Rehabilitation facilities	3321(55.1)	3537 (55.6)	2997 (50.3)	3067 (49.7)	<.0001
Skilled nursing facility	2389 (39.6)	2550 (40.1)	2084 (35.0)	2119 (34.3)	<.0001
Inpatient rehabilitation facility	929 (15.4)	983 (15.4)	905 (15.2)	944 (15.3)	.2324
Home without service	916 (15.2)	972 (15.3)	866 (14.5)	839 (13.6)	.0090
Home with service	1135 (18.8)	1157 (18.2)	1427 (23.9)	1513 (24.5)	<.0001

ADRD = Alzheimer’s disease and related disorders; AHPI = Asian/Hawaiian/Pacific/Islander; CVD = cerebrovascular disease; PAC = post-acute care; SD = standard deviation.

PAC location trends are presented in Figure 1. PAC at rehabilitation facilities was downtrend, particularly, PAC at the SNFs (2018: 39.6% vs 2021: 34.3%, P < .0001). PAC at home with or without services was uptrend, particularly, home with services (2018:18.8 % vs 2021:24.5%, P < .0001).

Figure 1.

Percentage of post-acute care locations by study year.

Figure 2 presents prediction of PAC at rehabilitation facilities by demographics, clinical factors and utilization factors. The ORs of PAC utilization at rehabilitation facilities were significantly higher in women, with increasing age, comorbidities, and lower extremity fractures. Compared to those in non-Hispanic White, those of racial minorities were lower. Those with post-COVID years (0.791, 0.744-0.841), Medicaid beneficiaries (0.526, 0.350-0.790), uninsured individuals (0.330, 0.204-0.535), rural hospitals (0.815, 0.756-0.880), and those with longer LOS (0.963, 0.959-0.968) were associated with lower probabilities of PAC at rehabilitation facilities. Sensitivity analysis results were unchanged from the above regression analysis results.

Figure 2.

Predictors of post-acute care at rehabilitation facilities by post COVID years demographics, clinical factors, and utilization factors (a) Demographics, (b) Clinical factors, (c) Fracture locations, and (d) Utilization factors.

Discussion

This study has demonstrated declining trends of PAC utilization at rehabilitation facilities represent a significant shift to home with services among Nevada older adults with extremity fractures. We also explored the factors associated with PAC utilization at rehabilitation facilities and whether health disparities for economically disadvantaged populations. Our study findings confirmed the majority of fracture location was hip and femur (>50%) and unchanged proportion of fracture location over time. Our study findings noted a significant increase in obesity among several comorbid conditions. Several literatures have reported that COVID-induced lockdown has affected individuals’ eating patterns and reduced physical activity, leading to an increase in the obese population.²⁰ It is speculated that the increased prevalence of obesity since the COVID-19 pandemic as reported by the Center for Disease Control and Prevention.²⁰ Obesity is also known as increasing probability of fall risk and fractures among older adults.¹⁹ Obesity adds more comorbidities to older adults and increases complications when fracture occurs among older adults.²¹ There is an increasing balance and flexibility challenge among older adults with obesity.²¹

There was a remarkable utilization decline in PAC utilization at rehabilitation facilities in rural areas. Rural areas have fewer healthcare resources than urban areas.²² Rural population makes up 9.1% of the Nevada state’s population but lives across 86.9% of the state’s land mass.²² Rural residents remain at risk of disability and caregiver burdens when older adults with extremity fractures overflow from rehabilitation facilities to their own homes with limited access to healthcare and community resources.²³

Our study found that there was a trend of prolonged hospital LOS since COVID-19 pandemic and prolonged LOS limited transfer to rehabilitation facilities. These findings extend speculations that more medically complex cases tend to prolong hospital LOS and discharge to homes rather than facilities at PAC.²⁴ LOS is a key driver of determining hospital care efficiency since the implementation of diagnosis related group (DRG).²⁴

There is an opportunity for older adults with extremity fractures which are eligible for the alternative payment model, for example, bundled payment system - proven model of advancing care efficiency and health equity of hip and femur fracture care - most common (>50%) fracture cases in this study.²⁵ For example, DRG 480 to 482 inpatient urgent hip and femur fracture bundled payment program implemented in an urban health system noted that approximately $15 000 cost-saving effect occurred when home was chosen as PAC discharge place rather than facilities.²⁶ Inpatient hip and femur fracture care takes disadvantages of losing opportunities of pre-op medical optimization contrast to elective arthroplasty care.²⁶ The bundled payment system was built on traditional fee-for-service architecture and plays a bridge role to population-based payment model for post-fracture care. The bundle payment system for post-fracture care may provide more synergic effects of advancing quality of care and reducing unnecessary hospitalization by more coordinated communication between healthcare providers, particularly, for those with PAC facility under-utilizing and home-favoring racial minorities (Hispanics and Asians) in this study.²⁵ Incentive programs in the bundled payment system are key matters for increasing participating healthcare systems and providers for those with socially disadvantaged populations - racial minorities, rural residents, and Medicaid beneficiaries.²⁵

The PAC utilization decline trends at the SNFs after extremity fractures is noted in our study, particularly among socially disadvantaged populations—racial minorities, Medicaid beneficiaries, and rural residents may lead to the greater risk for adverse outcomes, caregiving burdens related hospitalizations and responsive increases in healthcare cost. From these findings, we highlight the priorities of educating a geriatrics workforce who serves socially disadvantaged populations by mitigating concentrated caregiving burdens. Evidence-based Age-Friendly Health System framework, 4Ms (What Matters, Mobility, Medication and Mentation), has been applied for training geriatrics workforce effectively and locally adaptable, especially, serving culturally and linguistically diverse older adults and their caregivers.^27,28 In addition, telehealth as a primary care tool combined with 4Ms-based care, has been delivered to the people living with dementia in a provider shortage area and achieved more efficient care coordination by avoiding unnecessary hospitalizations.^29,30 The study findings—PAC utilization decline trends at the SNFs and associated factors provide insights for planning Age-Friendly Health System in provider shortage states sharing similar demographics with state of Nevada (population range 3-5 million, larger surfaces of rural areas)—Alabama, Arkansas, Oklahoma and Utah.¹⁰ Planning strategies of establishing Age-Friendly Health State are prioritized to the workforce capacity enhancement and innovative access to care development (ie, telehealth) that is more practical to accomplish rather than structural investment (ie, increase of hospital beds).^27,314M-based telehealth care in provider shortage states might provide an improvement opportunity of relieving caregiver burdens of those with socially disadvantaged populations in lieu of a shift from SNFs and home with services. Another recommendation to support implementing incentives that better align payment with quality of care would encourage increased access to these socially disadvantaged populations, particularly Medicaid beneficiaries.^32,33 By enhancing home health workforce capability of managing complex older adults with extremity fractures, incentives of state Medicaid programs, the largest funding sources for long-term services and support (LTSS) in the U.S., may prevent and delay the progression of extremity fractures to disability for those selecting PAC locations at homes and communities.³⁴

Our study has the advantage of using SID data to obtain reliable information on a large number of patients from most of the scattered hospitals in Nevada. However, there are several significant limitations. First, this study only included discharge data, and no actual medical assessments were included. We were unable to accurately analyze other factors (including COVID test, delay to surgery, presence of comorbidities) associated with the utilization decline at the SNFs during COVID pandemic. Second, our cross-sectional study has the limitation that there is no evidence of a temporal relationship between exposure and outcome. Third, sample size calculation was not included in the study as all claim data of the State of Nevada Inpatient Database was analyzed.

Conclusions and Implications

Declining trends of PAC at rehabilitation facilities represent a significant shift to home with services among Nevada older adults with extremity fractures. Caregiver burdens and workforce training is urgently warranted to respond to this shift, particularly, socially disadvantaged populations, racial minorities, Medicaid beneficiaries, and rural residents in a statewide provider shortage area.

Supplemental Material

sj-docx-1-inq-10.1177_00469580241290318 – Supplemental material for Trends and Factors Associated With Extremity Fractures and Post-Acute Care Utilization of Nevada Older Adults: Insights of Age-Friendly State Planning in U.S.

Supplemental material, sj-docx-1-inq-10.1177_00469580241290318 for Trends and Factors Associated With Extremity Fractures and Post-Acute Care Utilization of Nevada Older Adults: Insights of Age-Friendly State Planning in U.S. by Soo Hwan Kang, Jay J. Shen, Yonsu Kim, Iulia Ioanitoaia-Chaudhry, Se Won Lee, Tae Ha Chung, Ian Choe, Connor Jeong, Songe Kwon, Daniel Lim, Yena Hwang, Leora Frimer and Ji Won Yoo in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

Sponsor did not play any role in the design, methods, subject recruitment, data collections, analysis and preparation of paper.

Author Contributions

Soo Hwan Kang: Conceptualization, Writing—original and revised draft. Jay J. Shen: Funding Acquisition, Methodology, Supervision, Analysis. Yonsu Kim: Methodology, Analysis, Writing—original draft. Iulia Ioanitoaia-Chaudhry: Writing—original and revised draft. Se Won Lee: Conceptualization, Writing—original draft. Tae Ha Chung: Project Administration, Writing—revised draft. Ian Choe: Writing—original and revised draft. Connor Jeong: Visualization, Project Administration, Writing—original draft. Songe Kwon: Visualization, Project Administration, Writing—original draft. Daniel Lim: Visualization, Project Administration. Yena Hwang: Writing—revised draft. Leora Frimer: Conceptualization. Ji Won Yoo: Funding Acquisition, Methodology, Supervision, Writing—original and revised draft. All authors reviewed and approved the submitted version.

Data Availability

Data is available for purchase from the Healthcare Cost and Utilization Project (HCUP) State Inpatient Database of Nevada between January 2018 and December 2021. Others can access the data by contacting HCUP through the HCUP Central Distributor () and purchasing the relevant years of data. This is how the authors accessed these data; the authors did not have any special access privileges others would not have.availability:

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the U.S. Department of Health and Human Services, Health Resources Services Administration, Bureau of Health Workforce (U1QHP33069).

Ethical Considerations

All data is de-identified patient-level (demographics, insurance, diagnostic and procedure codes, discharge location and hospital utilization) and hospital-level information (location). The Institutional Review Board at the University of Nevada, Las Vegas found that the data included in the analysis is de-identified thereby safeguarding privacy and confidentiality concerns and the current study to be exempt (IRB no. 1098939-3).

Consent

All data is de-identified and consent from the individual patient level is not applicable.

ORCID iDs

Ji Won Yoo

Jay J. Shen

Supplemental Material

Supplemental material for this article is available online.

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