- Email: [email protected]
Supply-Side Differences Only Modestly Associated With Inpatient Hospitalizations Among Medicare Beneficiaries in the Last Six Months of Life
Accepted Manuscript Supply-Side Differences Only Modestly Associated with Inpatient Hospitalizations among Medicare Beneficiaries in the Last 6 Months of Life Elizabeth Crouch, PhD, Janice C. Probst, PhD, Kevin J. Bennett, PhD, James W. Hardin, PhD PII:
S0885-3924(17)30302-0
DOI:
10.1016/j.jpainsymman.2017.06.002
Reference:
JPS 9464
To appear in:
Journal of Pain and Symptom Management
Received Date: 17 March 2017 Revised Date:
8 May 2017
Accepted Date: 7 June 2017
Please cite this article as: Crouch E, Probst JC, Bennett KJ, Hardin JW, Supply-Side Differences Only Modestly Associated with Inpatient Hospitalizations among Medicare Beneficiaries in the Last 6 Months of Life, Journal of Pain and Symptom Management (2017), doi: 10.1016/j.jpainsymman.2017.06.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Supply-Side Differences Only Modestly Associated with Inpatient Hospitalizations among Medicare Beneficiaries in the Last 6 Months of Life
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Elizabeth Crouch, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center and Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-576-6055. Fax: 803-251-6399. [email protected]
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Janice C Probst, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center, Department of Health Services Policy and Management and Center for Research in Nutrition and Health Disparities, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-576-5959. Fax: 803-251-6399. [email protected]
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Kevin J Bennett, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center, Arnold School of Public Health and Department of Family and Preventive Medicine, School of Medicine, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-5766042. Fax: 803-251-6399. [email protected] James W Hardin, PhD, 915 Greene Street, Room 503G, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29208. Phone: 803-777-3191. Fax: 803-777-2524. [email protected] Corresponding author:
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Elizabeth Crouch, PhD 220 Stoneridge Drive, Suite 204 Columbia, SC 29210 Phone: (803) 576-6055 Fax: (803) 251-6399 [email protected]
Tables: 4; Figures: 0; References: 36; Word count: 2,443
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Running title: hospitalizations and end of life
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ACCEPTED MANUSCRIPT Hospitalizations and end of life
ABSTRACT Context: Inpatient hospitalizations are a driver of expenditures at the end of life and are a useful
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proxy for the intensity of care at that time. Objectives: Our study profiled rural and urban Medicare decedents to examine whether they differed in rates of inpatient hospital admissions in the last 6 months of life.
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Methods: Using a sample of 35,831 beneficiaries from the 2013 Medicare Research Identifiable Files, we examined inpatient hospital utilization patterns for a full 6 months before death.
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Supply-side variables included the number of hospital beds, certified skilled nursing facility beds, and hospice beds per 1,000 residents, plus primary care provider/population ratios. Patient characteristics included age, sex, race/ethnicity, dual eligibility status, region, and chronic conditions.
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Results: In both adjusted and unadjusted analysis, rural versus urban residence was not associated with an increased risk for hospitalization at the end of life among Medicare beneficiaries, nor was there a relationship between the supply of hospital, skilled nursing, and
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hospice services and the rate of hospitalization. Within rural residents alone, modest effects were found for facility supply. Rural residents in a county without a hospital were slightly less likely
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than other rural decedents to have been hospitalized during their last 6 months of life but were no less likely to have utilized skilled nursing facilities or hospice. Conclusions: The absence of major disparities in utilization suggests that end-of-life care
is reasonably equitable for rural Medicare beneficiaries.
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ACCEPTED MANUSCRIPT Hospitalizations and end of life
Keywords: end of life; rural health; Medicare
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Running title: hospitalizations and end of life
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ACCEPTED MANUSCRIPT Hospitalizations and end of life
Introduction Expenditures during end-of-life care comprise a substantial portion of Medicare
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expenditures (1). In 2014, 4 of 5 people who died in the United States were Medicare beneficiaries (1). Thus, the utilization of end-of-life care and subsequent costs are of importance to policymakers and administrators at the Centers for Medicare and Medicaid Services.
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The variety and intensity of services during the last year of life have been widely studied, demonstrating a heterogeneous distribution of service utilization across characteristics such as
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gender, race, and age (2-7). For example, in the last year of life, women use more inpatient services than men (8,9), and African-American beneficiaries are more likely to use inpatient hospitals than whites and other races (10). Also, older cohorts use acute care less often than younger cohorts (8-10).
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Previous research has also found wide regional variation in utilization of services at the end of life (11,12). Regions with higher rates of service utilization at the end of life have not been found to have better outcomes or quality of care, even after adjusting for health differences
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(13). Furthermore, patient preferences are not likely the cause of geographic variations in end-oflife care (14). Instead, the use of services has been associated with regional supply factors, such
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as the supply of providers and other health care resources (15-20). As well, physician behaviors have been shown to be a leading factor in geographic variation in healthcare use (21-22). Rural Medicare beneficiaries experience lower access to many services, including
hospice (23). Compared to urban residents, rural residents had lower intensive care unit stays, lower end of life care expenditures, and were less likely to use hospice and more likely to use skilled nursing facilities and die in the hospital (11, 5, 23). In 2014, 25% of hospices were
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located in rural areas (23). With the spread of rural hospital closures since 2010, rural beneficiaries may face additional barriers to inpatient services compared to urban beneficiaries (24). Thus, the intensity of care at the end of life for Medicare beneficiaries may vary across
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rurality. Because inpatient hospitalizations are often a major driver of expenditures, particularly at the end of life (25), hospitalization is a useful proxy for the intensity of care at the end of life (26). The purpose of this study was to profile rural and urban Medicare decedents and examine
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of life and further examine within rural decedents.
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whether rural-urban differences exist in rates of inpatient hospital admissions in the last 6 months
Methods
Data were obtained from a 5% sample of the 2013 Medicare Research Identifiable Files (n=2,972,192) using the following data files: beneficiary master summary, carrier claims,
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Medpar, home health claims, hospice claims, and outpatient claims. The analyses were restricted to Medicare fee-for-service beneficiaries who were continuously enrolled in Medicare Part A and Part B during 2013 (n=120,068), who were covered by Medicare for at least 6 months prior to
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death (n=118,930), and who died between July 1, 2013 and December 31, 2013 (n=57,111). Beneficiaries who died before July 1, 2013 were excluded so that we could study utilization for a
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full 6 months before death. Beneficiaries who were enrolled in an HMO were excluded (n=41,860), as well as those with missing information for residence, race/ethnicity, age, or sex (n=40,574). Finally, the study population was restricted to beneficiaries who had utilized at least one type of service (inpatient, outpatient, hospice, home health, skilled nursing facility, ambulance, or physician) during the year and who were 65 years of age or older. The final sample size was 35,831 beneficiaries.
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The dependent variable, inpatient utilization, was defined as whether the beneficiary had any inpatient hospitalizations during the last 6 months of life. In addition, to examine the intensity of the use of inpatient services, we calculated the per-beneficiary count of inpatient
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visits during the last 6 months of life.
The independent variable was whether the beneficiary resided in a rural or urban area, with the geographic analysis based on county of residence. Counties were characterized on level
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of rurality using Urban Influence Codes (UICs): metropolitan (UICs 1, 2), micropolitan (UICs 3,
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5, 8), small adjacent (UICs 4, 6, 7), and remote rural (UICs 9, 10, 11, 12) counties. Metropolitan counties were classified as urban. Micropolitan, small adjacent, and remote rural counties were classified as rural.
Control variables were chosen based on the Andersen Behavior Model of predisposing
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characteristics (demographic, social structure, and health beliefs), enabling resources (personal/family and community), and need (both perceived and evaluated) leading to the use of health services (27,28). For our model, demographic variables included are age and sex. Age was
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included in models via a variable summarizing age using 4 categories: 65 to 74, 75 to 84, 85 to 94, and 95 and above. Social structure variables consisted of race/ethnicity and dual eligibility.
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Race was summarized by 4 categories: non-Hispanic African American, Hispanic, non-Hispanic white, and non-Hispanic other. Dual eligibility was defined as having been eligible for Medicaid enrollment for any time period between 1 and 12 months. Although we lacked actual income data, dual eligibility is a known proxy for low-income beneficiaries (29). Community enabling resources were region of the country and supply-side variables, the latter including the number of hospital beds, certified skilled nursing facility (SNF) beds, and hospice beds per 1,000 residents. These ratios were divided into quartiles over all U.S. counties 6
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for analysis, except for hospital and hospice, for which zero (no hospital or hospice in the county) was arbitrarily set as the lowest quartile. These were derived from the Area Health Resource File. Region was grouped into 4 categories by Census region: Northeast, Midwest,
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South, and West.
Finally, to measure evaluated need, we included chronic conditions more likely to require additional service use. Chronic condition variables were created based on treatment of the
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chronic condition using claims-based algorithms constructed by ResDAC
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[https://www.resdac.org/cms-data/files/ccsf]. The chronic condition algorithm criteria were calculated using the 2013 beneficiaries. The 27 conditions constructed by ResDAC were collapsed down into smaller categories, not included if not significantly associated with rurality, or not included if the sample was less than 50 people. Thus, the following conditions were included: Alzheimer’s disease and dementia, asthma, heart disease, heart failure, chronic kidney
osteoporosis, and stroke.
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disease, cancer, chronic obstructive pulmonary disease (COPD), hyperlipidemia, hypertension,
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Standard statistical analysis procedures were used to estimate frequencies and proportions for categorical variables. Bivariate analyses were carried out to detect statistical significance in
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variables and across rurality using chi-square tests with α = 0.05. Ordinary logistic regression was used to examine associations of independent variables with the likelihood of at least 1 inpatient hospitalization in the last 6 months of life. A zero-inflated negative binomial regression model was used to measure the intensity of use of hospital services at the end of life, predicting the counts of hospitalizations. A zero-inflated negative binomial regression models the associations of independent variables with the count of inpatient visits; this model assumes that zero inpatient visits results from a mixture of patients from a binary process that produces some 7
ACCEPTED MANUSCRIPT Hospitalizations and end of life
patients who have zero inpatient visits and patients who have inpatient visits that can be modeled by a count process (which might have zero visits). Models were run for all beneficiaries, and then subset to just rural beneficiaries. The model was further restricted to rural residents to
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determine if, within rural, there would be differences in facility level effects. The significance of the overdispersion parameter in the counting process part of the negative binomial model indicates that the equidispersion assumption of the Poisson model should be rejected.
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All analyses were conducted with statistical software (SAS, version 9.3; SAS Institute
Results Population and supply characteristics
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Inc.). The study was approved by the [name concealed for review] institutional review board.
In the total sample of 35,831 beneficiaries, the majority were female (60.2%), above the
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age of 74 (78.7%,), and lived in an urban location (77.7%; Table 1). Seventy percent (70.3%) of the sample were recipients of Medicare only. Compared to urban beneficiaries, a larger proportion of rural beneficiaries were non-Hispanic white (90.5% versus 83.5%, p<0.01). Rural
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beneficiaries were also more likely to be from the South (43.1% versus 38.1%, p<0.01) or Midwest (34.9% versus 22.3%, p<0.01) than urban beneficiaries. Rural beneficiaries were more
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likely to have been eligible for Medicaid for at least part of the year of death (34.0% versus 28.5%). Although there were minor differences in the prevalence of specific diagnoses, the relative order of conditions (with hypertension the most prevalent and asthma the least prevalent) was similar across both groups. Rural decedents were disproportionately located in counties that lacked a hospital (7.3% versus 2.6% for urban) or a hospice facility (41.3% versus 8.9%) and in counties with the lowest 8
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quartile nationally for primary care physician/population ratios (14.9% versus 3.1% for urban; Table 2). The majority of individuals living in a county without a hospital (1,313 decedents) also lacked access to in-county hospice (1,121 decedents, 85.4%). However, rural beneficiaries were
population (29.4% versus 2.5% for urban; Table 1).
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Hospitalization in last 6 months of life
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more likely than their urban peers to live in counties with the highest proportion of SNF beds to
Nearly 2/3 (65.4%) of all deceased beneficiaries had at least 1 inpatient hospitalization in
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the 6 months before death. In unadjusted analysis, rural residents were no more likely than urban residents to have had at least 1 inpatient stay in the last 6 months of life (Table 2). Rural residence was not associated with the probability of any hospitalization during the last 6 months of life in bivariate analysis (Table 2). The likelihood of any hospitalization was highest among
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decedents in the 75-84 age group and declined with older age. Men were more likely to be hospitalized than women (Table 2).
A zero-inflated negative binomial model was used to model the number of inpatient
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visits, holding all other variables constant (Table 3). This count model is a way to examine the intensity of service use of hospitals during the last six months of life. This model adjusted for
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region, chronic conditions, and primary care providers per 1,000. Residence was not significantly associated with the rate of hospital visits. Similarly, no supply-side variables were significantly related to the probability of hospitalization in the overall population. Older beneficiaries were less likely to utilize inpatient services than younger beneficiaries, with the expected number of inpatient visits for an 85-94 year old being 0.94 (exp (-0.061)) times the expected number of inpatient visits for a 65-74 year old. The expected number of inpatient visits for a female was 1.01 (exp (0.009)) times the expected number of inpatient visits for a male. Racial/ethnic 9
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minorities were more likely to have inpatient visits than non-Hispanic whites: non-Hispanic African Americans had 1.04 (exp (0.047)) times the expected number of inpatient visits than non-Hispanic whites. Dual-eligible beneficiaries were less likely than Medicare-only
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beneficiaries to utilize inpatient services; dual-eligible beneficiaries had 0.95 times the expected number of inpatient visits than Medicare-only beneficiaries. Of note, no supply-side variables were significantly related to the probability of hospitalization in the overall population.
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The ZINB analyses shown in Table 3 were repeated, restricted to rural decedents only
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(Table 4) and adjusting for region, chronic conditions, and primary care providers per 1,000. Within this group, findings for the relationship between personal characteristics and local health care resources were consistent with those of the total population of decedents. However, countylevel health care resources, which were not significantly related to care among the total population of decedents, were associated with the likelihood of hospitalization among rural
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residents. Beneficiaries living in counties without a hospital were less likely to have been hospitalized than those in the highest quartile for bed/population ratios (0.933, (exp(-0.069)), although the number of hospitalizations given at least 1 admission was not affected. Rural
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residents in counties in the second highest quartile of SNF beds were slightly less likely than
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those in the highest quartile to be hospitalized (0.957, (exp(-0.044)), whereas those in the lowest quartile for SNF beds had a greater number of hospitalizations, given any hospitalization (1.700, exp (0.531)). Although hospice availability was not associated with the likelihood of hospitalization among rural residents, it was linked to the number of hospitalizations, given a first hospitalization. Compared to residents in the highest quartile for number of hospice organizations per person, rural residents in the lowest quartile had fewer hospitalizations (0.551, exp (-0.597)), and those in the second quartile had more (1.676, exp (0.517)).
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Discussion In both adjusted and unadjusted analysis, rural versus urban residence was not associated
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with an increased risk for hospitalization at the end of life among Medicare beneficiaries. Similarly, we did not find a relationship between the supply of hospital, skilled nursing, and hospice services and the rate of hospitalization during the last 6 months of life across the entire population of decedents. However, when the analysis was restricted to rural residents alone,
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modest effects were found for facility supply. Rural residents in a county without a hospital were
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slightly less likely than other rural decedents to have been hospitalized during their last 6 months of life but were equally likely to have utilized skilled nursing facilities or hospice. Although residence alone was not related to inpatient utilization after adjusting for demographic factors, rural residents are demographically different from urban residents. Rural
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residents tend to be older, poorer, and have higher or lower non-white populations than urban residents, depending on the geographic region (30). Our findings confirm prior work that found older decedents are less likely to use inpatient services, and racial/ethnic minorities are more
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likely to utilize inpatient services (8-10). Although we lacked actual income data, dual eligibility is a known proxy for low-income status (29), which we found to be negatively associated with
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inpatient utilization.
There is very limited research on rural-urban differences at the end of life, particularly
during the last six months of life. Other research examining rural-urban differences in end-of-life care tended to focus on specific populations such as cancer patients or beneficiaries receiving nursing home care (30,31). This was the first study to specifically examine rural-urban differences in end-of-life care for a sample of Medicare beneficiaries focused only on inpatient hospitalizations for beneficiaries with all chronic conditions and all settings. 11
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Our conclusions are limited by our data source. First, we had to used backward cohort creation: only used 1 year of Medicare claims data and were thus restricted to a 6-month window before death. Analysis across multiple years, allowing for a 1-year look-back or more, might
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have shown different results. Second, our analysis was restricted to Medicare beneficiaries covered by fee-for-service plans and may not reflect the experience of patients enrolled in
Medicare Advantage or other managed care arrangements. Third, our research draws on billing
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data, which captures only limited clinical information and cannot shed light on personal
preferences or cultural norms at the end of life. Medicare claims have very little demographic
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data. Fourth, the measures of inpatient utilization employed are broad—admission counts. Analysis examining features such as intensive care use might have yielded different results. Finally, our final analyzed cohort is decedents which limits our sample size considerably from
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our 5% Medicare sample which contained both living and deceased beneficiaries.
Medicare beneficiaries residing in rural areas have less access to health care services,
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along with shortages of health care workers and lower access to home health and long-term care options (33-35). This access issue is particularly relevant during the last 6 months of life.
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Hospice use in rural communities is much lower, with the majority of hospice services located in urban areas (36). Home health and hospice providers face many logistical issues in rural areas, including travel challenges and worker shortages (37). In rural areas, informal caregiving may substitute for health care utilization at the end of life because of health care access barriers (38). Despite these challenges, we did not find major differences between rural and urban beneficiaries in the use of inpatient services at the end of life. Therefore, future research may include a subgroup analysis for those in hospice/not in hospice during the last six months of life and those 12
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who were in a skilled nursing facility or long term care facility. With the exception of rural counties lacking any hospital, rural and urban beneficiaries were equally likely to receive inpatient care. This may be due to the provision of universal health care coverage through
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Medicare, which may provide an “equalizing” effect. The absence of major disparities in
utilization, despite major differences in resource availability, suggests that end-of-life care is
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reasonably equitable for rural Medicare beneficiaries.
Disclosures and Acknowledgements
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This work was funded by a grant from the Federal Office of Rural Health Policy.
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Table 1: Demographic Characteristics of Medicare Beneficiaries Who Died between July 1, 2013 and December 31, 2013, by residence
(N=7,987)
Urban
(N=27,844) (N=35,831)
%
%
Zero Visits
35.5
34.4
1-2 Visits
48.9
49.1
>2 Visits
15.6
16.4
At Least 1 Visit, Total
64.5
65.6
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Age
%
34.7 49.1 16.2
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Inpatient Hospitalization
Beneficiary Characteristics
All
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Rural
65.4
22.8a
20.9
21.3
34.9a
32.8
33.3
35.7a
38.9
38.2
6.6a
7.4
7.2
59.3
60.4
60.2
40.7
39.6
39.8
Non-Hispanic White
90.5a
83.5
85.1
Non-Hispanic African American
5.9a
8.7
8.1
Hispanic
1.9a
4.7
4.1
Other
1.8a
3.1
2.8
Northeast
9.7a
21.9
19.2
Midwest
34.9a
22.3
25.1
South
43.1a
38.1
39.2
West
12.3a
17.7
16.5
65 to 74 75 to 84 85 to 94 >95
TE D
Sex Female Male
AC C
EP
Race
Region
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Dual Eligible Between 1 and 12 Months
34.0a
28.5
29.7
No Months
66.0a
71.5
70.3
Hypertension
77.0a
78.4
Heart Disease (Atrial Fibrillation or Ischemic Heart Disease)
59.7a
61.6
Heart Failure
51.2a
49.6
Chronic Kidney Disease
46.6a
50.0
Hyperlipidemia
44.4a
49.0
48.0
Alzheimer’s Disease, Related Disorders, or Senile Dementia
19.7a
22.9
22.2
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Diagnoses 78.1 61.2 50.0
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SC
49.20
36.0a
32.2
33.1
19.7a
21.4
21.0
11.9a
13.3
13.0
Stroke/Transient Ischemic Attack
13.2a
15.0
14.6
Asthma
6.7a
7.7
7.5
4.3a
5.8
5.4
1-3 Chronic Conditions
20.8a
17.9
18.6
4 or More Chronic Conditions
74.9a
76.4
76.0
Q1 (No Hospital)
7.3
2.6
3.7
Q2 (<1.886)
30.5
20.4
22.7
Q3 (<3.642)
32.3
48.4
44.8
Q4 (≥3.642)
29.9
28.6
28.9
Chronic Obstructive Pulmonary Disease Cancer (Colorectal, Endometrial, Breast, Lung, Prostate)
Condition Tally
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Zero Chronic Conditions
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Osteoporosis
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Community Characteristics
Hospital Beds in County/1,000 residentsa
Skilled Nursing Facility Beds in
ACCEPTED MANUSCRIPT
Countya 11.5
32.1
27.5
Q2 (<6.6067)
26.1
44.7
40.5
Q3 (<10.1701)
33.0
20.8
23.5
Q4 (≥10.1701)
29.4
2.5
Q1 (No Hospice)a
41.3
8.9
Q2 (<0.016)
10.8
65.6
Q3 (<0.035)
26.8
21.8
22.9
Q4 (≥0.035)
21.1
3.7
7.6
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Indicates significantly different from urban beneficiaries, p<0.05.
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a
8.5
16.1
53.4
SC
Hospice in County
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Q1 (<3.8165)
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Table 2: Factors Associated with Use of Inpatient Services at Least Once in the Last 6 Months of Life (Bivariate Analysis and Unadjusted Logistic Regression)
≥1 inpatient visit
P value
%
1 or more inpatient visitsa
SC
0.070 64.5
0.95 (0.91-1.00)
Urban
65.6
Reference
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Rural
Age
P values
OR (95% CI)
Beneficiary Characteristic Residence
Unadjusted Odds Ratio
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Percentage
0.070
<0.0001
65 to 74
67.0
75 to 84
69.7
85 to 94
63.7
>95
49.3
1.13 (1.06-1.20)
<0.001
0.86 (0.81-0.92)
<0.001
0.48 (0.44-0.52)
<0.001
0.003
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Sex
Reference
Female Male Race
64.7
Reference
66.3
1.07 (1.02-1.12)
0.003
<0.001
64.5
Reference
Non-Hispanic African American
71.9
1.41 (1.29-1.53)
<0.0001
Hispanic
69.0
1.22 (1.09-1.37)
0.3993
Other
1.11 (0.97-1.27)
0.2854
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Non-Hispanic White
66.9
No Months
67.2
Reference
Between 1 and 12 Months
61.1
0.77 (0.73-0.81)
Dual Eligible
<0.001
Community Characteristics Hospital Beds in
0.4919
<0.0001
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County/1,000 residents 3.7
1.03 (0.92-1.17)
0.6677
Q2 (<1.886)
22.5
0.99 (0.93-1.06)
0.3691
Q3 (<3.642)
45.1
1.03 (0.98-1.09)
0.4242
Q4 (≥3.642)
28.7
Reference
Skilled Nursing Facility Beds in County
<0.001 26.7
0.92 (0.85-0.97)
0.0030
Q2 (<6.6067)
41.0
1.04 (0.96-1.13)
0.0360
Q3 (<10.1701)
23.9
1.05 (0.96-1.14)
0.0324
Q4 (≥10.1701)
8.5
Reference
M AN U
SC
Q1 (<3.8165)
Hospice in County
0.0775
16.3
Q2 (<0.016)
53.5
Q3 (<0.035)
22.5
Q4 (≥0.035)
7.6
TE D
Q1 (No Hospice)
1.04 (0.94-1.14)
0.0830
0.99 (0.92-1.08)
0.9270
0.95 (0.86-1.04)
0.0213
Reference
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Compared to no inpatient hospitalization the last 6 months of life.
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a
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Q1 (No Hospital)
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Table 3: Factors Associated with the Number of Inpatient Visits During the Last 6 Months of Life (Logistic Regression; Zero inflated negative binomial), 2013 (n=35,831)a
Rural (ref: Urban)
P value
0.378
<0.0001
-0.004 (0.013)
0.9662
Beneficiary Characteristics Age Reference 0.106 (0.020)
85 to 94
-0.061 (0.022)
>95
-0.222 (0.045)
Male Female Race Non-Hispanic White Non-Hispanic African American
0.009 (0.011)
0.9427
0.0297
<0.001
-0.039 (0.067)
0.5641
<0.001
0.175 (0.113)
0.1205
Reference
0.010
Reference
0.137 (0.042)
-0.005 (0.069)
0.0007
-0.155 (0.071)
Reference
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Sex
<0.001
EP
75 to 84
-8.252 (2.424)
P value
Reference
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65 to 74
Coefficient (SE)
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Constant
given at least 1 (Count Analysis)
SC
Coefficient (SE)
Number of hospitalizations,
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Probability of at least 1 hospitalization
-0.029 (0.046)
0.488
Reference 0.0011
-0.293 (0.154)
0.06
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Other
0.045 (0.06)
0.4645
0.004 (0.169)
0.9797
-0.118(0.029)
0.6834
-0.089 (0.197)
0.6503
Dual Eligible No Months Between 1 and 12 Months
Reference
Reference
-0.047 (0.012)
0.0002
0.375 (0.052)
<0.0001
SC
Supply Characteristics
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Hospital Beds in County/1,000 residents -0.0581 (0.0232)
0.102
-0.355(0.224)
0.1130
Q2 (<1.886)
-0.0058 (0.012)
0.617
-0.072 (0.104)
0.4887
Q3 (<3.642)
0.0463 (0.011)
0.182
0.134 (0.096)
0.1643
Q4 (≥3.642)
Reference
0.014 (0.0127)
Q2 (<6.6067) Q3 (<10.1701) Q4 (≥10.1701) Hospice in County Q1 (No Hospice)
0.244
0.151 (0.107)
0.1586
-0.0088 (0.009)
0.332
-0.062 (0.078)
0.429
0.365
-0.037 (0.086)
0.6644
0.012 (0.013)
0.345
EP
Q1 (<3.8165)
Reference
AC C
Skilled Nursing Facility Beds in County
TE D
Q1 (No Hospital)
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Hispanic
-0.0093 (0.010) Reference
Reference
-0.140 (0.112)
0.2098
ACCEPTED MANUSCRIPT
Q2 (<0.016)
-0.0103 (0.0106)
0.512
0.08 (0.090)
0.3945
Q3 (<0.035)
-0.036 (0.046)
0.440
0.138 (0.089)
0.1203
Q4 (≥0.035)
Reference
RI PT
Reference
a
AC C
EP
TE D
M AN U
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Coefficient estimates with standard errors reported in parentheses. Adjusting for region, chronic conditions, and primary care providers per 1,000. Note: no supply-side variables were significant.
ACCEPTED MANUSCRIPT
Table 4: Factors Associated with the Number of Inpatient Visits during the Last 6 Months of Life, Rural Residents Only (n = 7,987), 2013 (Zero-Inflated Negative Binomial)a
P value
0.371
<0.0001
given at least 1 (Count analysis) Coefficient (SE)
-16.234
P value <0.0001
SC
Constant
Coefficient (SE)
Hospitalization count,
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Probability of at least 1 hospitalization
Beneficiary Characteristics
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Age Reference
75 to 84
0.105
<0.001
-0.109
0.531
85 to 94
-0.060
<0.001
0.160
0.345
>95
-0.221
<0.001
0.069
0.810
Sex
Race Non-Hispanic White Non-Hispanic African American Hispanic Other
0.009
EP
Female
Reference
0.435
Reference
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Male
TE D
65 to 74
Reference
Reference -0.078
0.458
Reference
0.134
0.00
0.823
0.824
0.043
0.003
1.810
0.625
-0.115
0.916
-3.758
0.734
ACCEPTED MANUSCRIPT
Dual Eligible Reference
-0.045
<0.001
Supply Characteristics Hospital Beds in County/1,000 residents Q1 (No hospital)
0.047
Q2 (<1.886)
0.020
0.323
Q3 (<3.642)
0.026
0.187
Q4 (≥3.642)
Reference
M AN U
-0.069
Q2 (<6.6067)
-0.044
Q3 (<10.1701)
0.016
Q4 (≥10.1701)
Reference
Q1 (No Hospice) Q2 (<0.016) Q3 (<0.035) Q4 (≥0.035)
AC C
Hospice in County
0.008
-0.129
0.714
-0.098
0.601
-0.010
0.958
Reference
0.213
0.531
0.031
0.030
-0.191
0.315
0.393
-0.117
0.490
EP
0.038
TE D
Skilled Nursing Facility Beds in County Q1 (<3.8165)
0.294
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Between 1 and 12 Months
Reference
SC
No Months
Reference
0.001 (0.018)
0.943
-0.597
0.001
0.010
0.723
0.517
0.020
-0.026
0.206
0.220
0.203
Reference
Reference
ACCEPTED MANUSCRIPT
a
AC C
EP
TE D
M AN U
SC
RI PT
Zero-inflated negative binomial regression. Coefficient estimates with standard errors reported in parentheses. Adjusting for region, chronic conditions, and primary care providers per 1,000.
S0885-3924(17)30302-0
DOI:
10.1016/j.jpainsymman.2017.06.002
Reference:
JPS 9464
To appear in:
Journal of Pain and Symptom Management
Received Date: 17 March 2017 Revised Date:
8 May 2017
Accepted Date: 7 June 2017
Please cite this article as: Crouch E, Probst JC, Bennett KJ, Hardin JW, Supply-Side Differences Only Modestly Associated with Inpatient Hospitalizations among Medicare Beneficiaries in the Last 6 Months of Life, Journal of Pain and Symptom Management (2017), doi: 10.1016/j.jpainsymman.2017.06.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Supply-Side Differences Only Modestly Associated with Inpatient Hospitalizations among Medicare Beneficiaries in the Last 6 Months of Life
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Elizabeth Crouch, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center and Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-576-6055. Fax: 803-251-6399. [email protected]
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Janice C Probst, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center, Department of Health Services Policy and Management and Center for Research in Nutrition and Health Disparities, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-576-5959. Fax: 803-251-6399. [email protected]
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Kevin J Bennett, PhD, 220 Stoneridge Drive, Suite 204, South Carolina Rural Health Research Center, Arnold School of Public Health and Department of Family and Preventive Medicine, School of Medicine, University of South Carolina, Columbia, SC, USA 29210. Phone: 803-5766042. Fax: 803-251-6399. [email protected] James W Hardin, PhD, 915 Greene Street, Room 503G, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA 29208. Phone: 803-777-3191. Fax: 803-777-2524. [email protected] Corresponding author:
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Elizabeth Crouch, PhD 220 Stoneridge Drive, Suite 204 Columbia, SC 29210 Phone: (803) 576-6055 Fax: (803) 251-6399 [email protected]
Tables: 4; Figures: 0; References: 36; Word count: 2,443
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Running title: hospitalizations and end of life
1
ACCEPTED MANUSCRIPT Hospitalizations and end of life
ABSTRACT Context: Inpatient hospitalizations are a driver of expenditures at the end of life and are a useful
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proxy for the intensity of care at that time. Objectives: Our study profiled rural and urban Medicare decedents to examine whether they differed in rates of inpatient hospital admissions in the last 6 months of life.
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Methods: Using a sample of 35,831 beneficiaries from the 2013 Medicare Research Identifiable Files, we examined inpatient hospital utilization patterns for a full 6 months before death.
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Supply-side variables included the number of hospital beds, certified skilled nursing facility beds, and hospice beds per 1,000 residents, plus primary care provider/population ratios. Patient characteristics included age, sex, race/ethnicity, dual eligibility status, region, and chronic conditions.
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Results: In both adjusted and unadjusted analysis, rural versus urban residence was not associated with an increased risk for hospitalization at the end of life among Medicare beneficiaries, nor was there a relationship between the supply of hospital, skilled nursing, and
EP
hospice services and the rate of hospitalization. Within rural residents alone, modest effects were found for facility supply. Rural residents in a county without a hospital were slightly less likely
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than other rural decedents to have been hospitalized during their last 6 months of life but were no less likely to have utilized skilled nursing facilities or hospice. Conclusions: The absence of major disparities in utilization suggests that end-of-life care
is reasonably equitable for rural Medicare beneficiaries.
2
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Keywords: end of life; rural health; Medicare
AC C
EP
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M AN U
SC
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Running title: hospitalizations and end of life
3
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Introduction Expenditures during end-of-life care comprise a substantial portion of Medicare
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expenditures (1). In 2014, 4 of 5 people who died in the United States were Medicare beneficiaries (1). Thus, the utilization of end-of-life care and subsequent costs are of importance to policymakers and administrators at the Centers for Medicare and Medicaid Services.
SC
The variety and intensity of services during the last year of life have been widely studied, demonstrating a heterogeneous distribution of service utilization across characteristics such as
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gender, race, and age (2-7). For example, in the last year of life, women use more inpatient services than men (8,9), and African-American beneficiaries are more likely to use inpatient hospitals than whites and other races (10). Also, older cohorts use acute care less often than younger cohorts (8-10).
TE D
Previous research has also found wide regional variation in utilization of services at the end of life (11,12). Regions with higher rates of service utilization at the end of life have not been found to have better outcomes or quality of care, even after adjusting for health differences
EP
(13). Furthermore, patient preferences are not likely the cause of geographic variations in end-oflife care (14). Instead, the use of services has been associated with regional supply factors, such
AC C
as the supply of providers and other health care resources (15-20). As well, physician behaviors have been shown to be a leading factor in geographic variation in healthcare use (21-22). Rural Medicare beneficiaries experience lower access to many services, including
hospice (23). Compared to urban residents, rural residents had lower intensive care unit stays, lower end of life care expenditures, and were less likely to use hospice and more likely to use skilled nursing facilities and die in the hospital (11, 5, 23). In 2014, 25% of hospices were
4
ACCEPTED MANUSCRIPT Hospitalizations and end of life
located in rural areas (23). With the spread of rural hospital closures since 2010, rural beneficiaries may face additional barriers to inpatient services compared to urban beneficiaries (24). Thus, the intensity of care at the end of life for Medicare beneficiaries may vary across
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rurality. Because inpatient hospitalizations are often a major driver of expenditures, particularly at the end of life (25), hospitalization is a useful proxy for the intensity of care at the end of life (26). The purpose of this study was to profile rural and urban Medicare decedents and examine
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of life and further examine within rural decedents.
SC
whether rural-urban differences exist in rates of inpatient hospital admissions in the last 6 months
Methods
Data were obtained from a 5% sample of the 2013 Medicare Research Identifiable Files (n=2,972,192) using the following data files: beneficiary master summary, carrier claims,
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Medpar, home health claims, hospice claims, and outpatient claims. The analyses were restricted to Medicare fee-for-service beneficiaries who were continuously enrolled in Medicare Part A and Part B during 2013 (n=120,068), who were covered by Medicare for at least 6 months prior to
EP
death (n=118,930), and who died between July 1, 2013 and December 31, 2013 (n=57,111). Beneficiaries who died before July 1, 2013 were excluded so that we could study utilization for a
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full 6 months before death. Beneficiaries who were enrolled in an HMO were excluded (n=41,860), as well as those with missing information for residence, race/ethnicity, age, or sex (n=40,574). Finally, the study population was restricted to beneficiaries who had utilized at least one type of service (inpatient, outpatient, hospice, home health, skilled nursing facility, ambulance, or physician) during the year and who were 65 years of age or older. The final sample size was 35,831 beneficiaries.
5
ACCEPTED MANUSCRIPT Hospitalizations and end of life
The dependent variable, inpatient utilization, was defined as whether the beneficiary had any inpatient hospitalizations during the last 6 months of life. In addition, to examine the intensity of the use of inpatient services, we calculated the per-beneficiary count of inpatient
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visits during the last 6 months of life.
The independent variable was whether the beneficiary resided in a rural or urban area, with the geographic analysis based on county of residence. Counties were characterized on level
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of rurality using Urban Influence Codes (UICs): metropolitan (UICs 1, 2), micropolitan (UICs 3,
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5, 8), small adjacent (UICs 4, 6, 7), and remote rural (UICs 9, 10, 11, 12) counties. Metropolitan counties were classified as urban. Micropolitan, small adjacent, and remote rural counties were classified as rural.
Control variables were chosen based on the Andersen Behavior Model of predisposing
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characteristics (demographic, social structure, and health beliefs), enabling resources (personal/family and community), and need (both perceived and evaluated) leading to the use of health services (27,28). For our model, demographic variables included are age and sex. Age was
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included in models via a variable summarizing age using 4 categories: 65 to 74, 75 to 84, 85 to 94, and 95 and above. Social structure variables consisted of race/ethnicity and dual eligibility.
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Race was summarized by 4 categories: non-Hispanic African American, Hispanic, non-Hispanic white, and non-Hispanic other. Dual eligibility was defined as having been eligible for Medicaid enrollment for any time period between 1 and 12 months. Although we lacked actual income data, dual eligibility is a known proxy for low-income beneficiaries (29). Community enabling resources were region of the country and supply-side variables, the latter including the number of hospital beds, certified skilled nursing facility (SNF) beds, and hospice beds per 1,000 residents. These ratios were divided into quartiles over all U.S. counties 6
ACCEPTED MANUSCRIPT Hospitalizations and end of life
for analysis, except for hospital and hospice, for which zero (no hospital or hospice in the county) was arbitrarily set as the lowest quartile. These were derived from the Area Health Resource File. Region was grouped into 4 categories by Census region: Northeast, Midwest,
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South, and West.
Finally, to measure evaluated need, we included chronic conditions more likely to require additional service use. Chronic condition variables were created based on treatment of the
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chronic condition using claims-based algorithms constructed by ResDAC
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[https://www.resdac.org/cms-data/files/ccsf]. The chronic condition algorithm criteria were calculated using the 2013 beneficiaries. The 27 conditions constructed by ResDAC were collapsed down into smaller categories, not included if not significantly associated with rurality, or not included if the sample was less than 50 people. Thus, the following conditions were included: Alzheimer’s disease and dementia, asthma, heart disease, heart failure, chronic kidney
osteoporosis, and stroke.
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disease, cancer, chronic obstructive pulmonary disease (COPD), hyperlipidemia, hypertension,
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Standard statistical analysis procedures were used to estimate frequencies and proportions for categorical variables. Bivariate analyses were carried out to detect statistical significance in
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variables and across rurality using chi-square tests with α = 0.05. Ordinary logistic regression was used to examine associations of independent variables with the likelihood of at least 1 inpatient hospitalization in the last 6 months of life. A zero-inflated negative binomial regression model was used to measure the intensity of use of hospital services at the end of life, predicting the counts of hospitalizations. A zero-inflated negative binomial regression models the associations of independent variables with the count of inpatient visits; this model assumes that zero inpatient visits results from a mixture of patients from a binary process that produces some 7
ACCEPTED MANUSCRIPT Hospitalizations and end of life
patients who have zero inpatient visits and patients who have inpatient visits that can be modeled by a count process (which might have zero visits). Models were run for all beneficiaries, and then subset to just rural beneficiaries. The model was further restricted to rural residents to
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determine if, within rural, there would be differences in facility level effects. The significance of the overdispersion parameter in the counting process part of the negative binomial model indicates that the equidispersion assumption of the Poisson model should be rejected.
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All analyses were conducted with statistical software (SAS, version 9.3; SAS Institute
Results Population and supply characteristics
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Inc.). The study was approved by the [name concealed for review] institutional review board.
In the total sample of 35,831 beneficiaries, the majority were female (60.2%), above the
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age of 74 (78.7%,), and lived in an urban location (77.7%; Table 1). Seventy percent (70.3%) of the sample were recipients of Medicare only. Compared to urban beneficiaries, a larger proportion of rural beneficiaries were non-Hispanic white (90.5% versus 83.5%, p<0.01). Rural
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beneficiaries were also more likely to be from the South (43.1% versus 38.1%, p<0.01) or Midwest (34.9% versus 22.3%, p<0.01) than urban beneficiaries. Rural beneficiaries were more
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likely to have been eligible for Medicaid for at least part of the year of death (34.0% versus 28.5%). Although there were minor differences in the prevalence of specific diagnoses, the relative order of conditions (with hypertension the most prevalent and asthma the least prevalent) was similar across both groups. Rural decedents were disproportionately located in counties that lacked a hospital (7.3% versus 2.6% for urban) or a hospice facility (41.3% versus 8.9%) and in counties with the lowest 8
ACCEPTED MANUSCRIPT Hospitalizations and end of life
quartile nationally for primary care physician/population ratios (14.9% versus 3.1% for urban; Table 2). The majority of individuals living in a county without a hospital (1,313 decedents) also lacked access to in-county hospice (1,121 decedents, 85.4%). However, rural beneficiaries were
population (29.4% versus 2.5% for urban; Table 1).
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Hospitalization in last 6 months of life
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more likely than their urban peers to live in counties with the highest proportion of SNF beds to
Nearly 2/3 (65.4%) of all deceased beneficiaries had at least 1 inpatient hospitalization in
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the 6 months before death. In unadjusted analysis, rural residents were no more likely than urban residents to have had at least 1 inpatient stay in the last 6 months of life (Table 2). Rural residence was not associated with the probability of any hospitalization during the last 6 months of life in bivariate analysis (Table 2). The likelihood of any hospitalization was highest among
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decedents in the 75-84 age group and declined with older age. Men were more likely to be hospitalized than women (Table 2).
A zero-inflated negative binomial model was used to model the number of inpatient
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visits, holding all other variables constant (Table 3). This count model is a way to examine the intensity of service use of hospitals during the last six months of life. This model adjusted for
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region, chronic conditions, and primary care providers per 1,000. Residence was not significantly associated with the rate of hospital visits. Similarly, no supply-side variables were significantly related to the probability of hospitalization in the overall population. Older beneficiaries were less likely to utilize inpatient services than younger beneficiaries, with the expected number of inpatient visits for an 85-94 year old being 0.94 (exp (-0.061)) times the expected number of inpatient visits for a 65-74 year old. The expected number of inpatient visits for a female was 1.01 (exp (0.009)) times the expected number of inpatient visits for a male. Racial/ethnic 9
ACCEPTED MANUSCRIPT Hospitalizations and end of life
minorities were more likely to have inpatient visits than non-Hispanic whites: non-Hispanic African Americans had 1.04 (exp (0.047)) times the expected number of inpatient visits than non-Hispanic whites. Dual-eligible beneficiaries were less likely than Medicare-only
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beneficiaries to utilize inpatient services; dual-eligible beneficiaries had 0.95 times the expected number of inpatient visits than Medicare-only beneficiaries. Of note, no supply-side variables were significantly related to the probability of hospitalization in the overall population.
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The ZINB analyses shown in Table 3 were repeated, restricted to rural decedents only
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(Table 4) and adjusting for region, chronic conditions, and primary care providers per 1,000. Within this group, findings for the relationship between personal characteristics and local health care resources were consistent with those of the total population of decedents. However, countylevel health care resources, which were not significantly related to care among the total population of decedents, were associated with the likelihood of hospitalization among rural
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residents. Beneficiaries living in counties without a hospital were less likely to have been hospitalized than those in the highest quartile for bed/population ratios (0.933, (exp(-0.069)), although the number of hospitalizations given at least 1 admission was not affected. Rural
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residents in counties in the second highest quartile of SNF beds were slightly less likely than
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those in the highest quartile to be hospitalized (0.957, (exp(-0.044)), whereas those in the lowest quartile for SNF beds had a greater number of hospitalizations, given any hospitalization (1.700, exp (0.531)). Although hospice availability was not associated with the likelihood of hospitalization among rural residents, it was linked to the number of hospitalizations, given a first hospitalization. Compared to residents in the highest quartile for number of hospice organizations per person, rural residents in the lowest quartile had fewer hospitalizations (0.551, exp (-0.597)), and those in the second quartile had more (1.676, exp (0.517)).
10
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Discussion In both adjusted and unadjusted analysis, rural versus urban residence was not associated
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with an increased risk for hospitalization at the end of life among Medicare beneficiaries. Similarly, we did not find a relationship between the supply of hospital, skilled nursing, and hospice services and the rate of hospitalization during the last 6 months of life across the entire population of decedents. However, when the analysis was restricted to rural residents alone,
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modest effects were found for facility supply. Rural residents in a county without a hospital were
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slightly less likely than other rural decedents to have been hospitalized during their last 6 months of life but were equally likely to have utilized skilled nursing facilities or hospice. Although residence alone was not related to inpatient utilization after adjusting for demographic factors, rural residents are demographically different from urban residents. Rural
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residents tend to be older, poorer, and have higher or lower non-white populations than urban residents, depending on the geographic region (30). Our findings confirm prior work that found older decedents are less likely to use inpatient services, and racial/ethnic minorities are more
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likely to utilize inpatient services (8-10). Although we lacked actual income data, dual eligibility is a known proxy for low-income status (29), which we found to be negatively associated with
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inpatient utilization.
There is very limited research on rural-urban differences at the end of life, particularly
during the last six months of life. Other research examining rural-urban differences in end-of-life care tended to focus on specific populations such as cancer patients or beneficiaries receiving nursing home care (30,31). This was the first study to specifically examine rural-urban differences in end-of-life care for a sample of Medicare beneficiaries focused only on inpatient hospitalizations for beneficiaries with all chronic conditions and all settings. 11
ACCEPTED MANUSCRIPT Hospitalizations and end of life
Our conclusions are limited by our data source. First, we had to used backward cohort creation: only used 1 year of Medicare claims data and were thus restricted to a 6-month window before death. Analysis across multiple years, allowing for a 1-year look-back or more, might
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have shown different results. Second, our analysis was restricted to Medicare beneficiaries covered by fee-for-service plans and may not reflect the experience of patients enrolled in
Medicare Advantage or other managed care arrangements. Third, our research draws on billing
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data, which captures only limited clinical information and cannot shed light on personal
preferences or cultural norms at the end of life. Medicare claims have very little demographic
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data. Fourth, the measures of inpatient utilization employed are broad—admission counts. Analysis examining features such as intensive care use might have yielded different results. Finally, our final analyzed cohort is decedents which limits our sample size considerably from
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our 5% Medicare sample which contained both living and deceased beneficiaries.
Medicare beneficiaries residing in rural areas have less access to health care services,
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along with shortages of health care workers and lower access to home health and long-term care options (33-35). This access issue is particularly relevant during the last 6 months of life.
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Hospice use in rural communities is much lower, with the majority of hospice services located in urban areas (36). Home health and hospice providers face many logistical issues in rural areas, including travel challenges and worker shortages (37). In rural areas, informal caregiving may substitute for health care utilization at the end of life because of health care access barriers (38). Despite these challenges, we did not find major differences between rural and urban beneficiaries in the use of inpatient services at the end of life. Therefore, future research may include a subgroup analysis for those in hospice/not in hospice during the last six months of life and those 12
ACCEPTED MANUSCRIPT Hospitalizations and end of life
who were in a skilled nursing facility or long term care facility. With the exception of rural counties lacking any hospital, rural and urban beneficiaries were equally likely to receive inpatient care. This may be due to the provision of universal health care coverage through
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Medicare, which may provide an “equalizing” effect. The absence of major disparities in
utilization, despite major differences in resource availability, suggests that end-of-life care is
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reasonably equitable for rural Medicare beneficiaries.
Disclosures and Acknowledgements
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This work was funded by a grant from the Federal Office of Rural Health Policy.
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Table 1: Demographic Characteristics of Medicare Beneficiaries Who Died between July 1, 2013 and December 31, 2013, by residence
(N=7,987)
Urban
(N=27,844) (N=35,831)
%
%
Zero Visits
35.5
34.4
1-2 Visits
48.9
49.1
>2 Visits
15.6
16.4
At Least 1 Visit, Total
64.5
65.6
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Age
%
34.7 49.1 16.2
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Inpatient Hospitalization
Beneficiary Characteristics
All
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Rural
65.4
22.8a
20.9
21.3
34.9a
32.8
33.3
35.7a
38.9
38.2
6.6a
7.4
7.2
59.3
60.4
60.2
40.7
39.6
39.8
Non-Hispanic White
90.5a
83.5
85.1
Non-Hispanic African American
5.9a
8.7
8.1
Hispanic
1.9a
4.7
4.1
Other
1.8a
3.1
2.8
Northeast
9.7a
21.9
19.2
Midwest
34.9a
22.3
25.1
South
43.1a
38.1
39.2
West
12.3a
17.7
16.5
65 to 74 75 to 84 85 to 94 >95
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Sex Female Male
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Race
Region
ACCEPTED MANUSCRIPT
Dual Eligible Between 1 and 12 Months
34.0a
28.5
29.7
No Months
66.0a
71.5
70.3
Hypertension
77.0a
78.4
Heart Disease (Atrial Fibrillation or Ischemic Heart Disease)
59.7a
61.6
Heart Failure
51.2a
49.6
Chronic Kidney Disease
46.6a
50.0
Hyperlipidemia
44.4a
49.0
48.0
Alzheimer’s Disease, Related Disorders, or Senile Dementia
19.7a
22.9
22.2
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Diagnoses 78.1 61.2 50.0
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SC
49.20
36.0a
32.2
33.1
19.7a
21.4
21.0
11.9a
13.3
13.0
Stroke/Transient Ischemic Attack
13.2a
15.0
14.6
Asthma
6.7a
7.7
7.5
4.3a
5.8
5.4
1-3 Chronic Conditions
20.8a
17.9
18.6
4 or More Chronic Conditions
74.9a
76.4
76.0
Q1 (No Hospital)
7.3
2.6
3.7
Q2 (<1.886)
30.5
20.4
22.7
Q3 (<3.642)
32.3
48.4
44.8
Q4 (≥3.642)
29.9
28.6
28.9
Chronic Obstructive Pulmonary Disease Cancer (Colorectal, Endometrial, Breast, Lung, Prostate)
Condition Tally
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Zero Chronic Conditions
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Osteoporosis
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Community Characteristics
Hospital Beds in County/1,000 residentsa
Skilled Nursing Facility Beds in
ACCEPTED MANUSCRIPT
Countya 11.5
32.1
27.5
Q2 (<6.6067)
26.1
44.7
40.5
Q3 (<10.1701)
33.0
20.8
23.5
Q4 (≥10.1701)
29.4
2.5
Q1 (No Hospice)a
41.3
8.9
Q2 (<0.016)
10.8
65.6
Q3 (<0.035)
26.8
21.8
22.9
Q4 (≥0.035)
21.1
3.7
7.6
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Indicates significantly different from urban beneficiaries, p<0.05.
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a
8.5
16.1
53.4
SC
Hospice in County
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Q1 (<3.8165)
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Table 2: Factors Associated with Use of Inpatient Services at Least Once in the Last 6 Months of Life (Bivariate Analysis and Unadjusted Logistic Regression)
≥1 inpatient visit
P value
%
1 or more inpatient visitsa
SC
0.070 64.5
0.95 (0.91-1.00)
Urban
65.6
Reference
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Rural
Age
P values
OR (95% CI)
Beneficiary Characteristic Residence
Unadjusted Odds Ratio
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Percentage
0.070
<0.0001
65 to 74
67.0
75 to 84
69.7
85 to 94
63.7
>95
49.3
1.13 (1.06-1.20)
<0.001
0.86 (0.81-0.92)
<0.001
0.48 (0.44-0.52)
<0.001
0.003
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Sex
Reference
Female Male Race
64.7
Reference
66.3
1.07 (1.02-1.12)
0.003
<0.001
64.5
Reference
Non-Hispanic African American
71.9
1.41 (1.29-1.53)
<0.0001
Hispanic
69.0
1.22 (1.09-1.37)
0.3993
Other
1.11 (0.97-1.27)
0.2854
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Non-Hispanic White
66.9
No Months
67.2
Reference
Between 1 and 12 Months
61.1
0.77 (0.73-0.81)
Dual Eligible
<0.001
Community Characteristics Hospital Beds in
0.4919
<0.0001
ACCEPTED MANUSCRIPT
County/1,000 residents 3.7
1.03 (0.92-1.17)
0.6677
Q2 (<1.886)
22.5
0.99 (0.93-1.06)
0.3691
Q3 (<3.642)
45.1
1.03 (0.98-1.09)
0.4242
Q4 (≥3.642)
28.7
Reference
Skilled Nursing Facility Beds in County
<0.001 26.7
0.92 (0.85-0.97)
0.0030
Q2 (<6.6067)
41.0
1.04 (0.96-1.13)
0.0360
Q3 (<10.1701)
23.9
1.05 (0.96-1.14)
0.0324
Q4 (≥10.1701)
8.5
Reference
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SC
Q1 (<3.8165)
Hospice in County
0.0775
16.3
Q2 (<0.016)
53.5
Q3 (<0.035)
22.5
Q4 (≥0.035)
7.6
TE D
Q1 (No Hospice)
1.04 (0.94-1.14)
0.0830
0.99 (0.92-1.08)
0.9270
0.95 (0.86-1.04)
0.0213
Reference
EP
Compared to no inpatient hospitalization the last 6 months of life.
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a
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Q1 (No Hospital)
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Table 3: Factors Associated with the Number of Inpatient Visits During the Last 6 Months of Life (Logistic Regression; Zero inflated negative binomial), 2013 (n=35,831)a
Rural (ref: Urban)
P value
0.378
<0.0001
-0.004 (0.013)
0.9662
Beneficiary Characteristics Age Reference 0.106 (0.020)
85 to 94
-0.061 (0.022)
>95
-0.222 (0.045)
Male Female Race Non-Hispanic White Non-Hispanic African American
0.009 (0.011)
0.9427
0.0297
<0.001
-0.039 (0.067)
0.5641
<0.001
0.175 (0.113)
0.1205
Reference
0.010
Reference
0.137 (0.042)
-0.005 (0.069)
0.0007
-0.155 (0.071)
Reference
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Sex
<0.001
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75 to 84
-8.252 (2.424)
P value
Reference
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65 to 74
Coefficient (SE)
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Constant
given at least 1 (Count Analysis)
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Coefficient (SE)
Number of hospitalizations,
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Probability of at least 1 hospitalization
-0.029 (0.046)
0.488
Reference 0.0011
-0.293 (0.154)
0.06
ACCEPTED MANUSCRIPT
Other
0.045 (0.06)
0.4645
0.004 (0.169)
0.9797
-0.118(0.029)
0.6834
-0.089 (0.197)
0.6503
Dual Eligible No Months Between 1 and 12 Months
Reference
Reference
-0.047 (0.012)
0.0002
0.375 (0.052)
<0.0001
SC
Supply Characteristics
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Hospital Beds in County/1,000 residents -0.0581 (0.0232)
0.102
-0.355(0.224)
0.1130
Q2 (<1.886)
-0.0058 (0.012)
0.617
-0.072 (0.104)
0.4887
Q3 (<3.642)
0.0463 (0.011)
0.182
0.134 (0.096)
0.1643
Q4 (≥3.642)
Reference
0.014 (0.0127)
Q2 (<6.6067) Q3 (<10.1701) Q4 (≥10.1701) Hospice in County Q1 (No Hospice)
0.244
0.151 (0.107)
0.1586
-0.0088 (0.009)
0.332
-0.062 (0.078)
0.429
0.365
-0.037 (0.086)
0.6644
0.012 (0.013)
0.345
EP
Q1 (<3.8165)
Reference
AC C
Skilled Nursing Facility Beds in County
TE D
Q1 (No Hospital)
RI PT
Hispanic
-0.0093 (0.010) Reference
Reference
-0.140 (0.112)
0.2098
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Q2 (<0.016)
-0.0103 (0.0106)
0.512
0.08 (0.090)
0.3945
Q3 (<0.035)
-0.036 (0.046)
0.440
0.138 (0.089)
0.1203
Q4 (≥0.035)
Reference
RI PT
Reference
a
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Coefficient estimates with standard errors reported in parentheses. Adjusting for region, chronic conditions, and primary care providers per 1,000. Note: no supply-side variables were significant.
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Table 4: Factors Associated with the Number of Inpatient Visits during the Last 6 Months of Life, Rural Residents Only (n = 7,987), 2013 (Zero-Inflated Negative Binomial)a
P value
0.371
<0.0001
given at least 1 (Count analysis) Coefficient (SE)
-16.234
P value <0.0001
SC
Constant
Coefficient (SE)
Hospitalization count,
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Probability of at least 1 hospitalization
Beneficiary Characteristics
M AN U
Age Reference
75 to 84
0.105
<0.001
-0.109
0.531
85 to 94
-0.060
<0.001
0.160
0.345
>95
-0.221
<0.001
0.069
0.810
Sex
Race Non-Hispanic White Non-Hispanic African American Hispanic Other
0.009
EP
Female
Reference
0.435
Reference
AC C
Male
TE D
65 to 74
Reference
Reference -0.078
0.458
Reference
0.134
0.00
0.823
0.824
0.043
0.003
1.810
0.625
-0.115
0.916
-3.758
0.734
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Dual Eligible Reference
-0.045
<0.001
Supply Characteristics Hospital Beds in County/1,000 residents Q1 (No hospital)
0.047
Q2 (<1.886)
0.020
0.323
Q3 (<3.642)
0.026
0.187
Q4 (≥3.642)
Reference
M AN U
-0.069
Q2 (<6.6067)
-0.044
Q3 (<10.1701)
0.016
Q4 (≥10.1701)
Reference
Q1 (No Hospice) Q2 (<0.016) Q3 (<0.035) Q4 (≥0.035)
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Hospice in County
0.008
-0.129
0.714
-0.098
0.601
-0.010
0.958
Reference
0.213
0.531
0.031
0.030
-0.191
0.315
0.393
-0.117
0.490
EP
0.038
TE D
Skilled Nursing Facility Beds in County Q1 (<3.8165)
0.294
RI PT
Between 1 and 12 Months
Reference
SC
No Months
Reference
0.001 (0.018)
0.943
-0.597
0.001
0.010
0.723
0.517
0.020
-0.026
0.206
0.220
0.203
Reference
Reference
ACCEPTED MANUSCRIPT
a
AC C
EP
TE D
M AN U
SC
RI PT
Zero-inflated negative binomial regression. Coefficient estimates with standard errors reported in parentheses. Adjusting for region, chronic conditions, and primary care providers per 1,000.