Factors Affecting Emergency Department Use by a Chronically Homeless Population
Abstract
Objective:
Homeless adults make extensive use of emergency department (ED) services. This study examined factors associated with moderate and high ED use in a cohort of chronically homeless individuals.
Methods:
A cross-sectional analysis identified factors related to ED use in a cohort of 755 individuals at 11 sites at entry into the Collaborative Initiative to Help End Chronic Homelessness (CICH). Bivariate analyses identified sociodemographic, housing status, health status, and service-related factors associated with moderate and high ED use. Independent risk factors were then identified by using a multivariate multinomial model. Hierarchical regression was used to compare the strengths of association between ED use and blocks of factors composed of sociodemographic, housing, health, and service-related characteristics.
Results:
In a three-month period, 30% of participants visited the ED one or two times (moderate ED use) and 12% visited three or more times (high-ED use). ED use was most strongly associated with poor health and utilization of other non-ED services and to a lesser extent with housing status.
Conclusions:
Increased ED utilization was associated with both general medical and psychiatric morbidity and greater use of non-ED services. Thus ED use was related to high need and acuity and was not ameliorated by use of other services. Housing instability and homelessness contributed less robustly to increased ED use. More coordinated services may better address the complex medical, housing, and psychosocial needs of chronically homeless individuals.
Utilization of emergency department (ED) services is a concern of growing importance because of increased ED overcrowding and worries that high ED use reflects inadequate treatment and access to primary care and social services (1). Homeless individuals have been shown to be among the highest users of ED services (2–8) and are more likely than others to be frequent ED users (2,4,5). Although a wide range of factors has been found to be associated with high ED use in general homeless populations, less is known about factors correlated with high ED use in chronically homeless populations—individuals with extended periods of homelessness or frequent homelessness—and the relative contributions of such factors.
Factors associated with high ED use are diverse; however, it is increasingly apparent that the high rates of general medical and mental health problems (3,9–13) in homeless populations are significant drivers of ED use (4,8,14–20). Homeless adults have increased rates of social isolation, unstable housing, hunger, safety concerns, and legal problems—all of which have been identified as associated with ED use (18,21,22). Homeless adults are less likely to have health insurance (21), and many have limited access to ambulatory services (6,11,23,24). There is evidence that lack of insurance and lack of ready access to ambulatory services are associated with increased ED use (21,25–27). However, other studies have found that frequent ED use is associated with having health insurance and with extensive use of other services (1,4,15–17,28,29). Thus, although it is apparent that poor health is associated with high ED use in homeless populations, the relationship between ED use and access to other services that might improve health status remains unclear.
The Collaborative Initiative to Help End Chronic Homelessness (CICH) was a multisite demonstration program that provided chronically homeless adults with permanent housing, case management, primary care, and addiction and mental health services at 11 U.S. sites (30,31). A prior analysis of CICH data found that having health insurance was associated with seeking medical help in a primary care setting as opposed to an ED (27). However, the broad range of factors associated with the amount and intensity of ED use was not investigated.
In this study, we sought to better understand factors associated with ED use by chronically homeless CICH participants before they received enriched CICH services. Guided by prior studies of ED use among homeless adults, we conceptually organized possible factors as being related to sociodemographic characteristics and psychosocial stressors (indicators of low socioeconomic status, social isolation, and legal problems), lack of housing, poor health status (general medical, psychiatric, and substance related), and poor access to other services. We then attempted to identify independent correlates of increased ED use and weighed the relative contributions of these four broad classes of factors.
Methods
Source of Data
CICH was a multisite demonstration program of assistance for chronically homeless adults funded jointly by three federal departments—the Department of Housing and Urban Development, the Department of Health and Human Services, and the Department of Veterans Affairs (VA)—and implemented in 11 localities: Chattanooga, Tennessee; Chicago; Columbus, Ohio; Denver; Fort Lauderdale, Florida; Los Angeles; Martinez, California; New York City; Philadelphia; Portland, Oregon; and San Francisco. Each site was responsible for development and implementation of outreach efforts to contact chronically homeless adults and provide comprehensive housing, case management, primary care, and addiction and mental health services. The primary entry criterion was chronic homelessness, defined as either having been homeless continuously for more than one year or having had four or more separate episodes of homelessness in the prior three years. There were no clinical exclusion or inclusion criteria. Written informed consent was provided by each participant, and the study was approved by the institutional review boards at the 11 individual sites and the coordinating site at the VA Northeast Program Evaluation Center in Connecticut. Baseline data used in the study reported here were collected between February 2004 and April 2006.
Data Collection
CICH staff were trained in a two-day workshop in which all procedures and measures were reviewed. Assessments were performed through face-to-face interviews.
Measures
ED use.
Clients reported the number of days of receipt of services for general medical, psychiatric, or substance use problems in an ED during the 90 days prior to program entry and were classified into three groups based on total ED use: non-ED users, moderate ED users (one or two days), and high ED users (more than two days).
Sociodemographic measures.
Interviews documented age, race-ethnicity, gender, marital status, education, employment, income, residential status, and legal history. Residential status was measured as the number of days out of the prior 90 that the person was living in a shelter, outdoors, in an abandoned building, or in a car. Clients were asked how many different places they had lived.
Social support.
From a list of ten classes of people, clients reported on whom they could rely for help in three situations: a $100 loan, transportation to an appointment, and suicidal thoughts. Responses produced an aggregate social support scale; possible scores ranged from 0 to 10, with higher scores indicating greater support (18,32).
Community integration.
Clients were asked whether they participated in 16 common community activities during the prior two weeks, which produced a scale with possible scores ranging from 0 to 16, with higher scores indicating greater integration (33).
Physical health status.
The presence of 27 general medical problems involving a range of body systems was evaluated by self-report. The 12-item Medical Outcomes Study Short Form (SF-12) physical component score was used to assess physical functioning and related quality of life (34). Possible scores range from 0 to 100, with higher scores indicating increased functioning.
Mental health status.
Participants reported whether they had ever been told they had each of the following psychiatric diagnoses: schizophrenia, another psychotic disorder, major depression, bipolar disorder, a personality disorder, posttraumatic stress disorder (PTSD), an adjustment reaction, or an anxiety disorder. The SF-12 mental health component score (34) was used to assess mental health–related quality of life. Scores range from 0 to 100, with higher scores indicating increased functioning.
Substance use.
Items from the Addiction Severity Index (ASI) (35) were used to assess current alcohol and drug use. Possible scores range from 0 to 1, with higher scores indicating more severe use.
Health care and social services access and utilization.
Participants reported the number of days on which they received outpatient or inpatient general medical, mental health, or substance use treatment in the previous 90 days. They also reported the number of outpatient providers (case managers, clinicians, treaters, or agency staff) with who they had met with during the prior three months. Participants reported whether they had been insured through Medicaid, Medicare, VA, state or local sources, private insurance, or other sources or whether they had no insurance. Medicaid, Medicare, and state or local insurances were combined into a single measure of publicly funded health insurance.
Clients also reported whether they received seven possible services related to employment, housing, income benefits, legal assistance, education, crisis care, or child care services. The sum total of services received during the prior 90 days was used to assess the degree of social services utilization.
Subjective service coordination was measured by using answers to five questions regarding the client’s perception of coordination of services (36). Possible scores ranged from 0 to 2, with higher scores indicating greater coordination.
Statistical Analysis
Statistical analyses were performed with SAS 9.3 or 9.4. Bivariate analyses of non-ED, moderate ED, and high ED users were conducted by using analysis of variance (ANOVA) and chi-square tests. If ANOVA or chi-square tests were significant (p<.05), pairwise comparisons were made using t tests or dichotomous chi-square tests, respectively, and the Hochberg adjustment for multiple comparisons was applied (37).
Measures that were significant in bivariate analyses or that were conceptually important were entered into a multinomial logistic regression model to identify independent factors associated with moderate and high ED use compared with non-ED use. Multinomial regression was chosen because bivariate analysis demonstrated that the three categories of ED users did not meet the proportional odds assumption.
Hierarchical multivariate regression was performed to better understand the contributions of the significant factors identified in the multinomial model. Factors were grouped into four blocks: sociodemographic characteristics, housing status, health status, and service use. Within each block, statistically significant measures (p<.05) were retained. Program site (not considered a characteristic of participants) was added first. The four blocks were then added sequentially into the multinomial model. The relative strengths of association for each block were evaluated by using the Cox-Snell pseudo-R2 statistic (38), with larger increases in R2 indicating greater strengths of association between ED use and a block of measures. Because health status and service use are highly related, this analysis was performed twice, reversing the orders in which the blocks were entered, in order to determine whether either might contribute more variance.
Bivariate analyses included the entire baseline cohort (N=755). Of these 755 participants, 5% (N=37) had missing data in at least one measure included in multivariate analyses, which were limited to participants with complete data (N=718). ED use by the 718 participants without missing data (29% [N=211] moderate use and 12% [N=88] high use) was similar to that of the entire cohort (30% [N=225] moderate use and 12% [N=92] high use).
Results
The mean±SD age of CICH participants was 45.4±8.7 years. Of the 755 participants, 572 (76%) were male and 465 (62%) were from racial-ethnic minority groups. Only 123 (16%) had been recently employed, and 322 (43%) reported prior legal convictions. Most clients reported having problems related to physical health (N=491, 65%), mental health (N=577, 76%), alcohol use (N=395, 52%), or drug use (N=391, 52%). Most (N=438, 58%) did not use the ED, and 225 (30%) had one or two visits to an ED and 92 (12%) had three or more visits to an ED during the prior three months.
Bivariate Analysis
A range of factors related to psychosocial stressors and housing instability (Table 1), poor health (Table 2), and high service use (Table 3) were associated with increased ED utilization. ED use was associated with a greater number of places lived in the past 90 days, a lower rate of employment, and receipt of public support income (Table 1). Except for site location, no significant differences in demographic factors, levels of social support, or community integration were found.
| Characteristic | No use (N=438) | Moderate use (N=225)a | High use (N=92)b | Test statistic | df | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Sociodemographic | ||||||||
| Age (M±SD) | 45.7±8.6 | 45.4±8.7 | 44.6±9.4 | F=.6 | 2, 752 | |||
| Male | 345 | 79 | 160 | 71 | 67 | 73 | χ2=5.2 | 2 |
| Race-ethnicity | χ2=7.1 | 6 | ||||||
| White | 151 | 35 | 95 | 42 | 37 | 41 | ||
| Black | 222 | 51 | 103 | 46 | 41 | 46 | ||
| Hispanic | 37 | 9 | 11 | 5 | 8 | 9 | ||
| Other | 24 | 6 | 15 | 7 | 4 | 4 | ||
| Location | χ2=52.7*** | 20 | ||||||
| Chattanooga | 23 | 5 | 21 | 9 | 9 | 10 | ||
| Chicago | 41 | 9 | 19 | 8 | 4 | 4 | ||
| Columbus | 54 | 12 | 19 | 8 | 7 | 8 | ||
| Denver | 55 | 13 | 35 | 16 | 7 | 8 | ||
| Ft. Lauderdalec | 37 | 8 | 13 | 6 | 3 | 3 | ||
| Los Angeles | 50 | 11 | 9 | 4 | 5 | 5 | ||
| Martinez | 31 | 7 | 15 | 7 | 8 | 9 | ||
| New York Cityc | 39 | 9 | 9 | 4 | 4 | 4 | ||
| Philadelphia | 31 | 7 | 24 | 11 | 14 | 15 | ||
| Portland | 33 | 8 | 25 | 11 | 14 | 15 | ||
| San Francisco | 44 | 10 | 36 | 16 | 17 | 19 | ||
| Psychosocial | ||||||||
| Never married | 207 | 47 | 104 | 46 | 46 | 50 | ||
| Employedd,e | 83 | 19 | 34 | 15 | 6 | 7 | χ2=8.9* | 2 |
| Disability or public support income (M±SD $)c,d,e | 311.0±320 | 300.9±305.6 | 417.4±326.7 | F=4.9** | 2, 752 | |||
| Convicted of felonyf | 189 | 43 | 87 | 39 | 46 | 50 | χ2=3.6 | 2 |
| Housingg | ||||||||
| Days homeless (M±SD) | 56.2±38.2 | 56.2±35.8 | 53.5±34.7 | F=.2 | 2, 752 | |||
| Places lived (M±SD)c,e | 1.9±1.2 | 2.4±1.7 | 2.6±1.6 | F=15.0*** | 2, 752 | |||
a
1 or 2 visits
b
>2 visits
c
Significant difference between no use and moderate use (p<.05)
d
Past 30 days
e
Significant difference between no use and high use (p<.05)
f
Lifetime
g
Past 90 days
*
p<.05, **p<.01, ***p<.001
| Characteristic | No use (N=438) | Moderate use (N=225)a | High use (N=92)b | Test statistic | df | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Physical health | ||||||||
| General medical problems (M±SD)c,d,e,f | 3.6±2.9 | 4.9±3.3 | 6.0±3.8 | F=28.2*** | 2, 752 | |||
| SF-12 physical health score (M±SD)d,e,f,g | 46.4±9.7 | 44.3±10.4 | 40.0±10.3 | F=16.2*** | 2, 752 | |||
| Mental health | ||||||||
| Mental health problems (M±SD)d,e,h | 1.9±1.7 | 2.3±1.7 | 2.4±1.7 | F=7.2*** | 2, 749 | |||
| Schizophrenia | 114 | 26 | 53 | 24 | 29 | 32 | χ2=2.3 | 2 |
| Bipolar disorder | 137 | 32 | 81 | 36 | 36 | 40 | χ2=2.6 | 2 |
| Depression | 228 | 53 | 137 | 61 | 56 | 62 | χ2=5.6 | 2 |
| Other psychotic disorder | 54 | 13 | 40 | 18 | 13 | 15 | χ2=4.0 | 2 |
| Adjustment disorder | 18 | 4 | 16 | 7 | 4 | 5 | χ2=2.9 | 2 |
| Personality disorder | 41 | 10 | 27 | 12 | 12 | 13 | χ2=1.6 | 2 |
| PTSDd,e | 83 | 20 | 71 | 32 | 24 | 27 | χ2=13.2** | 2 |
| Anxiety disorderd,e | 117 | 27 | 80 | 36 | 37 | 41 | χ2=9.5** | 2 |
| Co-occurring mental and substance use disordersd,e | 212 | 50 | 118 | 55 | 65 | 72 | χ2=15.1*** | 2 |
| SF-12 mental health score (M±SD)i | 38.6±8.0 | 39.0±8.4 | 39.8±8.9 | F=.8 | 2, 752 | |||
| Substance use | ||||||||
| ASI alcohol use (M±SD)d,e,j | .1±.2 | .2±.2 | .2±.3 | F=5.9** | 2, 752 | |||
| ASI drug use (M±SD)d,j | .05±.1 | .05±.1 | .08±.1 | F=5.7** | 2, 752 | |||
| Current smoker | 346 | 79 | 180 | 80 | 81 | 88 | χ2=4.0 | 2 |
a
1 or 2 visits
b
>2 visits
c
Possible scores range from 0 to 27, with higher scores indicating more medical problems.
d
Significant difference between no use and high use (p<.05)
e
Significant difference between no use and moderate use (p<.05)
f
Significant difference between moderate and high use (p<.05)
g
Possible scores range from 0 to 100, with higher scores indicating better health.
h
Possible scores range from 0 to 7, with higher scores indicating more mental health problems.
i
12-item Medical Outcomes Study Short Form. Possible scores range from 0 to 100, with higher scores indicating better functioning.
j
Addiction Severity Index. Possible scores range from 0 to 1, with higher scores indicating greater addiction severity.
**
p<.01, ***p<.001
| Characteristic | No use (N=438) | Moderate use (N=225)a | High use (N=92)b | Test statistic | df | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Social support (M±SD)c | 1.4±1.1 | 1.4±1.2 | 1.6±1.4 | F=2.0 | 2, 752 | |||
| Community integration (M±SD)d | 6.8±2.9 | 6.9±2.8 | 7.0±2.8 | F=.1 | 2, 752 | |||
| Health insurancee | ||||||||
| Medicaid, Medicare, local sourcef,g | 232 | 54 | 141 | 64 | 65 | 71 | χ2=12.7** | 2 |
| VA | 106 | 24 | 46 | 20 | 17 | 19 | χ2=2.2 | 2 |
| Private | 7 | 2 | 3 | 1 | 1 | 1 | χ2= .2 | 2 |
| Uninsuredg | 109 | 25 | 42 | 19 | 12 | 13 | χ2=7.9* | 2 |
| Inpatient days (M±SD)e,f,g,h | 2.7±12.5 | 7.4±15.3 | 10.9±18.9 | χ2=16.8*** | 2, 752 | |||
| Outpatient days (M±SD)e,g,h,i | 11.4±23.5 | 12.2±21.6 | 19.6±30.3 | χ2=4.5* | 2, 752 | |||
| Case managere | 381 | 87 | 208 | 92 | 80 | 88 | χ2=4.7 | 2 |
| Social services (M±SD)e,f,g,j | 1.6±.8 | 1.8±1.0 | 1.8±.9 | F=5.8** | 2, 752 | |||
| N of outpatient providers (M±SD)e,f,g,i,k | 3.8±4.2 | 4.9±5.1 | 6.3±6.4 | F=11.9*** | 2, 741 | |||
| Services coordination (M±SD)l | 1.2±.6 | 1.1±.6 | 1.0±.6 | F=3.7 | 2, 556 | |||
a
1 or 2 visits
b
>2 visits
c
Possible scores range from 0 to 10, with higher scores indicating greater support.
d
Possible scores range from 0 to 16, with higher scores indicating greater community integration.
e
Past 90 days
f
Significant difference between no use and high use (p<.05)
g
Significant difference between no use and moderate use (p<.05)
h
Sum of treatment days (includes medical, mental health, and substance abuse treatment)
i
Significant difference between moderate and high use (p<.05)
j
Possible scores range from 0 to 7, with higher scores indicating more social services.
k
Sum of different outpatient providers
l
Possible scores range from 0 to 2, with higher scores indicating greater perceived service coordination.
*
p<.05, **p<.01, ***p<.001
ED use was associated with a greater number of comorbid general medical and mental health problems and with having a comorbid substance use disorder (Table 2). High ED utilizers had significantly more general medical problems and poorer physical health than moderate ED users, who were significantly less healthy than people who did not use the ED. Moderate and high ED use were correlated with an increased number of mental health problems, particularly with higher rates of anxiety disorder and PTSD. The rate of co-occurring mental and substance use disorders was associated with increased ED use, as were the ASI indices of more severe alcohol and drug use.
ED use was correlated with increased use of non-ED services, and high ED utilizers were less likely than non-ED and moderate ED users to be uninsured (Table 3). ED utilization was associated with more days admitted to inpatient treatment. Greater use of outpatient services and social services was also correlated with high ED use, with the number of outpatient providers increasing with ED use. Although overall outpatient service use was higher among moderate and high ED users, there was no significant association with subjective experience of service coordination.
Multivariate Analysis
Because many significant bivariate relationships were found, multivariate multinomial regression was used to identify independent correlates of greater ED use (Table 4). Among sociodemographic factors, only younger age was associated with high ED use. Among housing indicators, the number of places lived was associated with both moderate and high ED use, and the number of days homeless was associated with moderate ED use. ED visits were also associated with a number of indicators of poor health. Both moderate ED use and high ED use were strongly correlated with an increased number of reported general medical problems and with severity of alcohol abuse. High ED use was also associated with poor physical functioning as measured by the SF-12 index.
| Characteristic | Moderate use (N=211)a | High use (N=88)a | ||||
|---|---|---|---|---|---|---|
| ORb | 95% CI | χ2 | ORb | 95% CI | χ2 | |
| Sociodemographic | ||||||
| Racec | ||||||
| White | 1.07 | .68–1.69 | .09 | 1.04 | .54–2.00 | .01 |
| Hispanic | 1.16 | .52–2.61 | .14 | .53 | .14–1.99 | .89 |
| Other | .74 | .32–1.70 | .50 | .94 | .32–2.79 | .01 |
| Male | .84 | .54–1.33 | .53 | 1.28 | .66–2.47 | .53 |
| Aged | .94 | .74–1.20 | .24 | .68 | .48–.96 | 4.75* |
| Educatione | 1.05 | .97–1.14 | 1.46 | .91 | .82–1.01 | 2.94 |
| Felony convictionf | .78 | .53–1.16 | 1.52 | 1.10 | .63–1.91 | .11 |
| Employedg | 1.02 | .62–1.69 | .01 | .46 | .17–1.2 | 2.54 |
| Housing | ||||||
| N of places livedh | 1.32 | 1.11–1.57 | 10.13** | 1.47 | 1.20–1.81 | 13.68*** |
| Days homelessh | 1.01 | 1.00–1.01 | 5.32* | 1.01 | 1.00–1.02 | 2.84 |
| Health | ||||||
| General medical problemsi | 1.14 | 1.05–1.23 | 10.63** | 1.20 | 1.08–1.33 | 12.07*** |
| SF-12 physical health scorej | 1.00 | .98–1.03 | .08 | .95 | .92–.99 | 7.54** |
| Mental health problemsk | 1.12 | .98–1.28 | 2.91 | 1.01 | .83–1.23 | .02 |
| SF-12 mental health scorej | 1.01 | .98–1.04 | .35 | .99 | .95–1.03 | .15 |
| ASI alcohol usel | 3.25 | 1.22–8.63 | 5.58* | 4.08 | 1.12–14.89 | 4.54* |
| ASI drug usel | 1.13 | .11–11.22 | .01 | 5.91 | .37–94.96 | 1.57 |
| Supports and services | ||||||
| Social supportsm | .99 | .83–1.18 | .01 | 1.10 | .86–1.4 | .55 |
| Public insuranceh | 1.76 | 1.16–2.68 | 6.91** | 2.35 | 1.26–4.39 | 7.22** |
| Social servicesh,n | 1.27 | 1.03–1.58 | 4.87* | 1.41 | 1.04–1.92 | 4.88* |
| Outpatient daysh,o | 1.00 | .99–1.01 | .10 | 1.01 | 1–1.02 | 2.84 |
| Inpatient daysh,o | 1.02 | 1.01–1.04 | 8.56** | 1.02 | 1.01–1.04 | 8.11** |
a
Moderate use, 1 or 2 days. High use, >2 days. Persons with missing data were not included (N=718 observations).
b
The reference group for all comparisons was non-ED users.
c
Compared with black participants
d
OR standardized for each 10 years
e
Education expressed in years
f
Lifetime
g
Past 30 days
h
Past 90 days
i
Possible scores range from 0 to 27, with higher scores indicating more medical problems.
j
12-item Medical Outcomes Study Short Form. Possible scores range from 0 to 100, with higher scores indicating better health.
k
Possible scores range from 0 to 7, with higher scores indicating more mental health problems.
l
Addiction Severity Index. Possible scores range from 0 to 1, with higher scores indicating greater addiction severity.
m
Possible scores range from 0 to 10, with higher scores indicating greater support.
n
Possible scores range from 0 to 7, with higher scores indicating more social services.
o
Sum of medical, mental health, and substance use treatment days
*
p<.05, **p<.01, ***p<.001
ED use was associated with increased utilization of non-ED services. Both moderate and high ED use were strongly associated with receipt of Medicaid, Medicare, or local state insurance and were also associated with accessing more types of social services. Moderate and high ED use were also correlated with a greater number of days admitted to inpatient units.
To better evaluate the relative contributions of sociodemographic, housing, health, and service-related factors, hierarchical multivariate analysis was performed by using statistically significant factors identified in the multivariate model. Relatively substantial variance was explained by site (∆R2=.076), minimal variance by age (∆R2=.001), and moderate variance by housing status (places lived and days homeless, ∆R2=.034). Service-related factors (social services, inpatient days, and public insurance, ∆R2=.051) and health factors (general medical problems, SF-12 physical component score, and ASI alcohol use, ∆R2=.069) also contributed substantially. When the order of health and service blocks was reversed, the contribution of health status increased (∆R2=.082) and services (∆R2=.038) decreased, suggesting substantial shared variance and a stronger association with health factors.
Discussion
This cohort of chronically homeless adults used ED services at high rates, with nearly half reporting at least one ED visit in the prior 90 days and 12% reporting three or more visits. As in prior reports (4,8,14–20), poor health, including general medical, psychiatric, and addiction problems, was the strongest correlate of frequent ED use. Significant associations were also observed with extensive use of non-ED services and to a lesser extent with housing instability.
High ED use was correlated with poor health but not with decreased access to alternative health and social services. Individuals who used the ED were more likely to have insurance and to use more ambulatory health and social services. High ED users reported seeing on average six different outpatient providers and reported nearly 20 outpatient visits in three months—findings that highlight the difficulty of interpreting exceptionally high service use in the face of severe illness. A possible interpretation is that high ED users are “super users,” a term indicating indiscriminate and inappropriate service utilization. Alternatively, the strong association between ED use and both high morbidity and increased need for inpatient stabilization points toward severe illness and high acuity despite access to extensive outpatient services. Among high ED users, there was a trend toward a decreased sense of coordination between outpatient providers, suggesting that simply improving access to standard outpatient and social services, which generally do not include housing support, may not improve health outcomes or decrease the use of (or need for) emergency services without significant additional efforts at coordination.
These data pose the question of how to best structure outpatient services for severely ill patients who also have high degrees of housing and psychosocial instability. Engagement and care coordination through assertive community treatment teams have resulted in improved health outcomes and decreased use of acute services in severely mentally ill homeless populations (39). Also, same-day primary care in the VA system has been found to be associated with decreased use of EDs for problems that can be managed on an outpatient basis and that might not require acute services (40). Perhaps most striking is the growing number of studies that have found decreased use of acute services after entry into supportive housing with case management (41–43). In this study of chronically homeless adults, although health and service-related factors shared the most variance with ED use, housing-related factors also contributed. The correlation between housing instability and ED use is likely multifactorial. The likelihood that some ED visits might simply represent a search for shelter cannot be excluded. However, the need to look for housing could distract individuals from attending to health needs or prevent coordination of care by providers. In addition, severe illness could prevent homeless individuals from making effective efforts to secure housing.
More research is needed to better understand the relationships between housing, the use of acute services, and overall health status. This study focused on individuals before they received the coordinated services that were the focus of the CICH intervention. Prior studies during the follow-up period have found trends toward decreased overall health expenditure among CICH participants, suggesting that housing and improved service coordination may improve the effectiveness of services (44), but the specific impact on ED use after program entry has yet to be studied and will be the subject of a future report. Further longitudinal analysis of ED use by the CICH cohort will also allow investigation of how ED use is related to key health outcomes, such as mortality or future inpatient hospitalizations.
The study had some limitations. The importance of study location should not be underestimated but could not be thoroughly examined. Service environment accounted for significant variance in our model. Unfortunately, analysis of ED use at individual sites was not possible because there were too few participants to properly power such an investigation. Models were adjusted for site to minimize the idiosyncrasies between sites that might bias findings. Second, although selected from a broad diversity of sites, CICH participants may not be representative of the chronic homeless population. Furthermore, ED use in this sample and in a comparable sample of domiciled individuals cannot be directly compared because there was no domiciled control group. Multivariate analyses included only participants with complete data, which could have introduced selection bias. However, only a small number of participants were excluded (N=37, 5%), and ED use by those included in the multivariate analysis was similar to that of the entire CICH cohort. Finally, because this was a cross-sectional study, the causal effects of various factors on ED use over time could not be studied.
Conclusions
In this study of a cohort of chronically homeless individuals, substantial evidence was found that high ED use was most robustly associated with severe health problems and high need. It also appears that the standard outpatient services and non–housing-related social services accessed by these individuals were not sufficient to manage their clinical and social service needs. Simply increasing access to insurance or other services without concomitant efforts to coordinate and enrich care may not go far enough to improve outcomes and reduce suffering.
References
1.
LaCalle E, Rabin E: Frequent users of emergency departments: the myths, the data, and the policy implications. Annals of Emergency Medicine 56:42–48, 2010
2.
Mandelberg JH, Kuhn RE, Kohn MA: Epidemiologic analysis of an urban, public emergency department’s frequent users. Academic Emergency Medicine 7:637–646, 2000
3.
D’Amore J, Hung O, Chiang W, et al: The epidemiology of the homeless population and its impact on an urban emergency department. Academic Emergency Medicine 8:1051–1055, 2001
4.
Doran KM, Raven MC, Rosenheck RA: What drives frequent emergency department use in an integrated health system? National data from the Veterans Health Administration. Annals of Emergency Medicine 62:151–159, 2013
5.
Kushel MB, Perry S, Bangsberg D, et al: Emergency department use among the homeless and marginally housed: results from a community-based study. American Journal of Public Health 92:778–784, 2002
6.
Salit SA, Kuhn EM, Hartz AJ, et al: Hospitalization costs associated with homelessness in New York City. New England Journal of Medicine 338:1734–1740, 1998
7.
Hastings SN, Smith VA, Weinberger M, et al: Emergency department visits in Veterans Affairs medical facilities. American Journal of Managed Care 17:e215–e223, 2011
8.
Tsai J, Doran KM, Rosenheck RA: When health insurance is not a factor: national comparison of homeless and nonhomeless US veterans who use Veterans Affairs emergency departments. American Journal of Public Health 103(suppl 2):S225–S231, 2013
9.
Barrow SM, Herman DB, Córdova P, et al: Mortality among homeless shelter residents in New York City. American Journal of Public Health 89:529–534, 1999
10.
Nielsen SF, Hjorthøj CR, Erlangsen A, et al: Psychiatric disorders and mortality among people in homeless shelters in Denmark: a nationwide register-based cohort study. Lancet 377:2205–2214, 2011
11.
Folsom DP, Hawthorne W, Lindamer L, et al: Prevalence and risk factors for homelessness and utilization of mental health services among 10,340 patients with serious mental illness in a large public mental health system. American Journal of Psychiatry 162:370–376, 2005
12.
Susser E, Moore R, Link B: Risk factors for homelessness. Epidemiologic Reviews 15:546–556, 1993
13.
Hibbs JR, Benner L, Klugman L, et al: Mortality in a cohort of homeless adults in Philadelphia. New England Journal of Medicine 331:304–309, 1994
14.
Padgett DK, Struening EL, Andrews H, et al: Predictors of emergency room use by homeless adults in New York City: the influence of predisposing, enabling and need factors. Social Science and Medicine 41:547–556, 1995
15.
Sun BC, Burstin HR, Brennan TA: Predictors and outcomes of frequent emergency department users. Academic Emergency Medicine 10:320–328, 2003
16.
Hunt KA, Weber EJ, Showstack JA, et al: Characteristics of frequent users of emergency departments. Annals of Emergency Medicine 48:1–8, 2006
17.
Hansagi H, Olsson M, Sjöberg S, et al: Frequent use of the hospital emergency department is indicative of high use of other health care services. Annals of Emergency Medicine 37:561–567, 2001
18.
Chambers C, Chiu S, Katic M, et al: High utilizers of emergency health services in a population-based cohort of homeless adults. American Journal of Public Health 103(suppl 2):S302–S310, 2013
19.
Thakarar K, Morgan JR, Gaeta JM, et al: Predictors of frequent emergency room visits among a homeless population. PLoS One 10:e0124552, 2015
20.
Mackelprang JL, Qiu Q, Rivara FP: Predictors of emergency department visits and inpatient admissions among homeless and unstably housed adolescents and young adults. Medical Care 53:1010–1017, 2015
21.
Kushel MB, Gupta R, Gee L, et al: Housing instability and food insecurity as barriers to health care among low-income Americans. Journal of General Internal Medicine 21:71–77, 2006
22.
Rodriguez RM, Fortman J, Chee C, et al: Food, shelter and safety needs motivating homeless persons’ visits to an urban emergency department. Annals of Emergency Medicine 53:598–602, 2009
23.
Gelberg L, Gallagher TC, Andersen RM, et al: Competing priorities as a barrier to medical care among homeless adults in Los Angeles. American Journal of Public Health 87:217–220, 1997
24.
Desai MM, Rosenheck RA, Kasprow WJ: Determinants of receipt of ambulatory medical care in a national sample of mentally ill homeless veterans. Medical Care 41:275–287, 2003
25.
Ku BS, Scott KC, Kertesz SG, et al: Factors associated with use of urban emergency departments by the US homeless population. Public Health Reports 125:398–405, 2010
26.
Oates G, Tadros A, Davis SM: A comparison of national emergency department use by homeless versus non-homeless people in the United States. Journal of Health Care for the Poor and Underserved 20:840–845, 2009
27.
Chwastiak L, Tsai J, Rosenheck R: Impact of health insurance status and a diagnosis of serious mental illness on whether chronically homeless individuals engage in primary care. American Journal of Public Health 102:e83–e89, 2012
28.
Kushel MB, Vittinghoff E, Haas JS: Factors associated with the health care utilization of homeless persons. JAMA 285:200–206, 2001
29.
Tsai J, Rosenheck RA: Risk factors for ED use among homeless veterans. American Journal of Emergency Medicine 31:855–858, 2013
30.
Notice of funding availability for the Collaborative Initiative to Help End Chronic Homelessness. Federal Register 68:8616, 2003
31.
Rickards LD, McGraw SA, Araki L, et al: Collaborative Initiative to Help End Chronic Homelessness: introduction. Journal of Behavioral Health Services and Research 37:149–166, 2010
32.
Vaux A, Athanassopulou M: Social support appraisals and network resources. Journal of Community Psychology 15:537–556, 1987
33.
Katz AH: Social adaptation in chronic illness: a study of hemophilia. American Journal of Public Health and the Nation’s Health 53:1666–1675, 1963
34.
Larson CO: Use of the SF-12 instrument for measuring the health of homeless persons. Health Services Research 37:733–750, 2002
35.
McLellan AT, Luborsky L, Woody GE, et al: An improved diagnostic evaluation instrument for substance abuse patients: the Addiction Severity Index. Journal of Nervous and Mental Disease 168:26–33, 1980
36.
Mares AS, Greenberg GA, Rosenheck RA: Client-level measures of services integration among chronically homeless adults. Community Mental Health Journal 44:367–376, 2008
37.
Hochberg Y: A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75:800–802, 1988
38.
Cox DR, Snell EJ: Analysis of Binary Data. New York, Chapman and Hall, 1989
39.
Lehman AF, Dixon LB, Kernan E, et al: A randomized trial of assertive community treatment for homeless persons with severe mental illness. Archives of General Psychiatry 54:1038–1043, 1997
40.
Yoon J, Cordasco KM, Chow A, et al: The relationship between same-day access and continuity in primary care and emergency department visits. PLoS One 10:e0135274, 2015
41.
Sadowski LS, Kee RA, VanderWeele TJ, et al: Effect of a housing and case management program on emergency department visits and hospitalizations among chronically ill homeless adults: a randomized trial. JAMA 301:1771–1778, 2009
42.
Gilmer TP, Stefancic A, Ettner SL, et al: Effect of full-service partnerships on homelessness, use and costs of mental health services, and quality of life among adults with serious mental illness. Archives of General Psychiatry 67:645–652, 2010
43.
Larimer ME, Malone DK, Garner MD, et al: Health care and public service use and costs before and after provision of housing for chronically homeless persons with severe alcohol problems. JAMA 301:1349–1357, 2009
44.
Tsai J, Mares AS, Rosenheck RA: A multi-site comparison of supported housing for chronically homeless adults: “Housing First” versus “residential treatment first”. Psychological Services 7:219–232, 2010
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Cover: pocket watch, by Robert et Courvoisier, 1800–1816. Silver, brass, ormuolu, enamel, and steel. Bequest of Henry Francis du Pont, Winterthur Museum, Winterthur, Del.
History
Received: 8 December 2015
Revision received: 4 April 2016
Accepted: 25 April 2016
Published online: 15 July 2016
Published in print: December 01, 2016
Authors
Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
U.S. Department of Housing and Urban Development10.13039/100000204
U.S. Department of Health and Human Services10.13039/100000016
U.S. Department of Veterans Affairs10.13039/100000738
National Institute of Mental Health10.13039/100000025: 5T32MH062994-13
The Collaborative Initiative to Help End Chronic Homelessness Funder's Group, representing the U.S. Department of Housing and Urban Development, the U.S. Department of Health and Human Services, and the Department of Veterans Affairs, provided support and guidance to this evaluation. This material is also based on work supported by the Office of Research and Development, Veterans Health Administration. Further financial support was provided by training grant 5T32MH062994-13 from the National Institute of Mental Health.Metrics & Citations
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