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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Acad Emerg Med. Author manuscript; available in PMC 2014 January 1.
Published in final edited form as:
PMCID: PMC3623800
NIHMSID: NIHMS422795

Frequent Emergency Department Use among Released Prisoners with HIV: Characterization Including a Novel Multimorbidity Index

Jaimie P. Meyer, MD, Jingjun Qiu, MD, MS, Nadine E. Chen, MD, MPH, Gregory L. Larkin, MD, MSPH, MS, and Frederick L. Altice, MD, MA

Abstract

Objectives

To characterize the medical, social, and psychiatric correlates of frequent emergency department (ED) use among released prisoners with human immunodeficiency virus (HIV).

Methods

Data on all ED visits by 151 released prisoners with HIV on antiretroviral therapy were prospectively collected for 12 months. Correlates of frequent ED use, defined as having two or more ED visits post-release, were described using univariate and multivariate models, and generated medical, psychiatric, and social multi-morbidity indices.

Results

Forty-four (29%) of the 151 participants were defined as frequent ED users, accounting for 81% of the 227 ED visits. Frequent ED users were more likely than infrequent or non-users to be female; have chronic medical illnesses that included seizures, asthma, and migraines; and have worse physical health-related quality of life. In multivariate Poisson regression models, frequent ED use was associated with lower physical health-related quality of life (odds ratio [OR] 0.95, p = 0.02), and having not had pre-release discharge planning (OR 3.16, p = 0.04). Frequent ED use was positively correlated with increasing psychiatric multi-morbidity index values.

Conclusions

Among released prisoners with HIV, frequent ED use is driven primarily by extensive co-morbid medical and psychiatric illness. Frequent ED users were also less likely to have received pre-release discharge planning, suggesting missed opportunities for seamless linkages to care.

INTRODUCTION

In recent years, political discourse has centered on cost-cutting to improve efficiency in U.S. health care systems. The Affordable Care Act, passed by Congress in March 2010 and deemed constitutional by the Supreme Court in 2012, has significant implications for U.S. emergency departments (EDs) and the delivery of emergency services. As the only guaranteed haven for health care in America, EDs will be increasingly challenged to balance the mandate of patient safety with ED oversubscription and the attendant financial disincentives attached to ED recidivism.1,2 Until health care reform is fully realized, the effect of recidivism on ED operations is an important area for exploration and discussion.

Frequent ED users, as variously defined, are recognized in both emergency medicine literature and popular media as disproportionately expensive consumers of health care.3,4 In Oregon, for example, 50% of Medicaid dollars spent on emergency services were attributable to just 3% of enrollees who made multiple ED visits.5 Frequent users have been stigmatized as manipulative patients with repetitive non-urgent visits that contribute significantly to hospital costs and ED provider burnout.6 More recent systematic evaluations, however, document frequent ED users as being a heterogeneous group who defy these stereotypes: compared to infrequent or non-users, they are more likely to have a primary health care provider and medical insurance, be non-Hispanic white, and experience more severe medical and psychiatric co-morbidities.3,79 Rather than visits simply being “inappropriate,” a label perpetuated by medical insurance companies to justify denied reimbursement, frequent ED visits may be an important way by which clinically ill patients access resources they otherwise cannot in community settings. Some authors and policy makers have thus called for an end to the term “inappropriate” ED use.10,11

Most previous research on frequent ED users has either focused on all comers to a single ED, or on ED patients with a defining chronic illness, like migraines or asthma, which is associated with repeat visits.3 Existing literature on frequent ED users has often relied on self-reported measures of ED use,12 or has minimally examined individual-level socio-behavioral characteristics that may contribute to health care use.8 This article addresses these shortcomings with a richer database and a longitudinal design, which allows for a more robust understanding of ED recidivism. Furthermore, we describe an important subset of frequent ED users, specifically HIV-infected prisoners transitioning to the community, who have not yet been described in the ED health services literature.

Released prisoners constitute an important subgroup of frequent visitors to the ED. Indeed, many EDs remain the provider of first and last resort for released prisoners. Released prisoners overall experience disproportionately high rates of substance use disorders, mental disorders, and social instability, including homelessness, that likely contribute to their health care use.13,14 Within the first two weeks of being released from correctional settings, former prisoners experience markedly high levels of age-matched morbidity and mortality, often related to drug overdose and complications from HIV and liver disease.1517 Moreover, during this destabilizing period immediately following prison release, released prisoners with HIV are at high risk of discontinuous primary care and lapses in antiretroviral therapy (ART) adherence,1822 so that the ED becomes their only point of contact with health care systems.2326 Disenfranchisement with primary care may translate into reliance on ED services to meet extensive medical, psychiatric, and social needs because this out-of-care group is also more likely to be homeless, lack health insurance, and have significant underlying psychiatric co-morbidity.14,27

To the best of our knowledge, this is the first study of ED use by newly released prisoners, and we aimed to characterize and describe correlates of frequent ED use using both individual descriptors and multimorbidity indices. Multimorbidity indices are becoming increasingly recognized in the epidemiological literature as a useful and convenient measure of aggregate health burden, but have not previously been applied to frequent users of ED services.28 We hypothesized that frequent ED use would be associated with social or psychiatric instability rather than severity of medical illness. This unique cohort and methodology provides insights into frequent ED use more generally, and for released prisoners with chronic illness, in particular. Furthermore, by identifying an important patient population at risk for current and continued frequent ED use, we have addressed a major research agenda proposed by Pines et al. in Academic Emergency Medicine.9

METHODS

Study Design

The study’s analytical plan draws on the Behavioral Health Model29,30 that has been adapted for vulnerable populations.31 This conceptual model proposes that health behaviors are driven by individual predisposing factors, enabling resources, and needs characteristics of populations.

In this prospective longitudinal study of ED use among released prisoners with HIV, all participants provided written informed consent to have their health care records reviewed at all designated ED sites. Ethical approval for all procedures was obtained from the Yale University School of Medicine institutional review board, and the Connecticut Department of Correction Research Advisory Committee.

Study Setting and Population

The 151 study participants were HIV-infected, released prisoners on ART who were enrolled in an ongoing randomized clinical trial of directly administered antiretroviral therapy (DAART) versus self-administered antiretroviral therapy (SAT) between 2006 and 2010 (clinicaltrials.gov registry #NCT00786396).32 The study design and baseline characteristics have previously been described.33 Eligibility criteria included: age ≥ 18 years, laboratory-confirmed HIV infection, current receipt of ART, and planned return to New Haven or Hartford after release from prison. Subjects were recruited from Connecticut prisons within 90 days of release; individuals who otherwise met the inclusion criteria, but did not receive pre-release planning and were within 30 days of release, were also enrolled. After informed consent, all study participants were followed prospectively for 12 months after date of release, during which they received comprehensive case management services, adherence support with DAART (if randomized to this arm), assistance with enrollment in medical and medication entitlements, and prescribed buprenorphine if they qualified for and desired opioid substitution therapy. Subjects were also interviewed and assessed for HIV-1 RNA levels and CD4 counts quarterly. Buprenorphine procedures and preliminary outcomes of those meeting criteria for opioid dependence have previously been described.34,35

Study Protocol

Enrolled participants who completed a baseline assessment and consented to release of medical information were asked to designate all health care facilities at which they had ever received or intended to use medical care. Medical chart reviews were performed according to this designation, including both EDs in New Haven and all three in Hartford, insuring complete coverage of each urban catchment area so we could document the most extensive possible spectrum of ED utilization. The Veteran’s Administration (VA) and psychiatric hospitals were not accessed for chart review (although all psychiatric units within the reviewed EDs were included and are a conduit to psychiatric inpatient care). None of the participants designated the VA hospital as a site of prior medical care. Charts were prospectively reviewed for up to one year from the date of prison release. ED visit data was compiled for each participant, regardless of how long he or she was retained in the parent study. No one withdrew their consent for participation in the study.

The primary outcome of interest was quantity of ED visits. Using standardized chart review instruments, ED medical records were extracted for: time and status of triage and discharge, patient-cited reason(s) for visit (i.e., “chief complaint”), provider diagnoses and associated diagnostic codes, results of urine toxicology tests, whether the ED visit resulted in a hospitalization (including psychiatric hospitalization), disposition from the ED, and discharge medications. An ED visit was considered as being for substance misuse if it was coded by an ED provider as being for acute intoxication or overdose, regardless of the type of substance used, or if the patient’s chief complaint was recorded as “requesting detox.” Unless otherwise noted, all other measurements were collected as part of the baseline assessment.

Baseline demographic parameters were assessed as part of the previously validated36 Addiction Severity Index (ASI),37 including age, sex, education, self-reported race/ethnicity, and marital status. Anticipated housing status was categorized based on the question: “How would you describe your expected living situation on the day of release?” Subjects were considered homeless if they answered “in a shelter” or “street or other public place;” temporarily housed if they reported “with a family member or friend temporarily,“ “haven’t decided yet,” “short term boarding,” or “drug treatment program;” and permanently housed if they answered “with a family member or friend permanently,” or “my own place.” History of abuse was measured on the ASI by self-report of any emotional, physical, or sexual abuse in one’s lifetime. These questions have been shown to be a highly sensitive screen for subjects with histories of substance use and post-traumatic stress disorders (sensitivity= 0.91).38 Abuse was coded as a dichotomous variable indicating lifetime history of any physical or sexual abuse versus none.

Study involvement was described in terms of 1) pre-release discharge planning from the parent study (participants recruited in the 30 day “post-release” period did not receive pre-release discharge planning); 2) randomization status in the parent study (DAART versus SAT); 3) site of residence after release (New Haven versus Hartford); and 4) mean number of months retained in the parent study, up to six.

Physical and mental health was described in terms of the following:

Surrogates of HIV biological severity

Absolute CD4 count and quantitative HIV-1 RNA levels were measured at baseline and at 12-week follow-up. CD4 count (cells/μL) was described both continuously and dichotomously (< 200 versus ≥ 200); viral suppression was dichotomously defined as having HIV-1 viral load < 400 copies/ml, or not.

Substance use disorders

Alcohol use disorders were measured using the Alcohol Use Disorders Identification Test (AUDIT) (Cronbach’s α = 0.83, range 0.75 to 0.097), with hazardous drinking defined as a score ≥ 8 for men and ≥ 4 for women.39,40 Lifetime alcohol and drug use was measured by the ASI, and severity was assessed using previously validated continuous alcohol and drug composite sub-scores that ranged from 0 to 1 with larger values reflecting greater severity.37 If the subject initiated and was retained on buprenorphine at six months, this was recorded as a dichotomous variable. Self-reported history of ever using injection drugs was also recorded as a dichotomous variable.

Current mental illness

Depressive symptoms at baseline were measured using the 16-item Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) (Cronbach’s α = 0.86, range 0.73 to 0.92).41 Depression severity scores were continuous from 0 to 27 and were also classified dichotomously as major depression (score > 11) versus no or mild depression (score ≤ 11). Self-report of ever having attempted suicide was recorded as a dichotomous variable.

Other health-related factors

Social support was measured continuously using the validated 31-item Social Support Scale (range 0 to 100 with higher scores reflecting greater social support).42 Trust in physician was measured continuously using the 11-item Trust in Physician scale (Cronbach’s α = 0.90).43 Each item is scored on a five-point Likert scale, and a summary measure is equal to the un-weighted mean of the responses (range 0 to 100 with higher scores reflecting higher levels of trust).44 HIV-associated health-related quality of life (HRQoL) was measured using the 36-item Medical Outcomes Study Short Form (SF-36) (Cronbach’s α > 0.80);45 physical and mental health-related quality of life sub-scores were calculated separately using norm-based scoring (mean = 50, standard deviation ± 10) with higher scores representing perception of better health. HIV-related stigma was measured using the Berger HIV Stigma Scale, a 40-item assessment in which each item is rated on a four-point Likert type scale and then summed for a total score. Higher scores represent higher levels of perceived HIV-related stigma and the scale has been shown to be reliable and valid in diverse populations with HIV (Cronbach’s α = 0.96).46

Medical history

Previous chronic illnesses were measured as part of the baseline interview, in which participants were asked, “Have you ever been told by a health care provider that you currently have any of the following”: seizures, hypertension (high blood pressure), asthma, emphysema, tuberculosis, pneumonia, diabetes, hepatitis, cirrhosis (liver problems), abnormal Pap smear (women only), cancer, arthritis/bone pain, sickle cell, migraine headaches, depression, anxiety disorder, schizophrenia, post-traumatic stress disorder (PTSD), eating disorders, thyroid problems, or dyslipidemia (high cholesterol). Though not all-inclusive, this comprehensive list of comorbidities was derived from an extensive review of medical and psychiatric comorbidity among HIV-infected people who use drugs.47 These comorbidities were also chosen because participants were more likely aware of medical illnesses from which they have had symptoms or are receiving medical treatment (e.g. hypertension as compared to coronary artery disease). Responses were coded dichotomously for each and are presented in the “Results” section if the prevalence of the illness within the total cohort was > 5%.

Use of other health care services was derived from the follow-up interview at three months. HIV clinic utilization was determined from the question: “In the last 30 days or since the last interview, did you see or talk with an HIV provider or go to an HIV clinic?” and responses were coded dichotomously. Supervised withdrawal from alcohol or drugs was coded dichotomously from the question, “In the past three months, have you been to a detox center?”

Multimorbidity Indices

In recognition that a number of factors may contribute to frequent ED use, multimorbidity indices portray a broader picture of overall health, taking into account the compound burden of multiple concurrent illnesses. To portray aggregate health burden, multimorbidity indices were generated for this population by taking into account the number and/or severity of medical, psychiatric, and social issues identified on the baseline assessment. Based on previously published literature, selection criteria for included conditions was based on: 1) prolonged duration (chronicity), 2) need for continuous treatment, 3) severe impact on those affected, and 4) high prevalence in this patient population.28 Medical indices thus included markers of HIV biologic severity (baseline CD4<200, baseline HIV viral suppression), self-reported presence of co-morbid conditions (seizures, hypertension, asthma or emphysema, diabetes, cirrhosis, migraine headaches, arthritis), and overall perception of physical health (lowest quartile physical HRQoL score). Psychiatric indices took into account current and prior substance use disorders (hazardous drinking, highest quartile ASI score for alcohol and for drugs, injection drug use), current and prior mental illnesses (major depression, suicide attempt, anxiety, schizophrenia), and self-perception of mental health (lowest quartile mental HRQoL score). Social indices included variables related to housing stability (homelessness), lifetime histories of physical or sexual abuse, social support (lowest quartile social support score), relationship status (not in a relationship), trust in physician (lowest quartile Trust in Physician score), stigma (highest quartile HIV stigma score), and ART adherence support (not randomized to DAART). The final composite score was calculated by the sum of total “yes” responses.

Data Analysis

Descriptive statistics were applied to the total cohort and subsets stratified by number of ED visits, using chi-square tests for categorical variables and Student’s t-tests for continuous variables. Frequent users were defined has having two or more ED visits in the year following prison-release. This definition was based on the distribution of ED use frequency in our sample and is within the range of what has been previously reported in the literature on frequent ED users.3 To determine correlates of being a frequent user, univariate analyses were performed first using all possible covariates. Significant associations with p < 0.2 from the univariate model were then included in a multivariate model that used Poisson regression to address non-Gaussian distribution of ED count data. Exposure time, defined as up to 12 months from study enrollment, was included as an offset variable. Multiple combinations of variables were evaluated for interaction or co-linearity, including depression and mental HRQoL, baseline and 12-week viral suppression, alcohol severity score and hazardous drinking; none were found to be statistically significant. Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to describe goodness of fit for final models.48,49 Odds ratios (OR) with 95% confidence intervals (CIs) are reported, and p-values < 0.05 are considered to indicate a statistically significant difference. After medical, psychiatric, and social multimorbidity indices were calculated, scatter plots with fitted regression lines were used to plot number of ED visits against each index separately. All analyses were performed using statistical software STATA version 9.0 (StataCorp, College Station, TX).

RESULTS

Participant Characteristics

As has been described previously,32,33 154 released prisoners with HIV were enrolled in an ongoing randomized clinical trial of DAART versus SAT. Three participants either declined release of medical information or did not complete a full baseline assessment, leaving 151 evaluable subjects for whom ED records were prospectively reviewed.

Table 1 describes characteristics of all 151 subjects and subsets stratified by number of ED visits. Overall, the demographic profile of the total cohort reflected the statewide correctional population in Connecticut,50 in which the typical individual was a middle-aged, unmarried, non-Hispanic African American man facing homelessness or temporary housing upon release from prison. Nearly 80% of participants had HIV viral suppression by the time of release, despite high prevalence of other chronic illnesses like hypertension, asthma, viral hepatitis, depression, and anxiety. Drug and alcohol use disorders were also common in this sample, reflected in relatively high mean lifetime ASI severity scores for drug and alcohol. At enrollment, 94 participants met criteria for opioid dependence and 50 initiated buprenorphine.

Table 1
Characteristics of Subjects, Stratified by Number of Emergency Department Visits

Forty-four of 151 subjects (29%) visited the ED twice or more during the year following prison-release, and were considered frequent ED users. Frequent ED users were more likely than infrequent or non-users to be female and have medical histories of seizures, asthma, tuberculosis, pneumonia, and migraine headaches. Frequent ED users were also less likely to have received pre-release discharge planning, although there was no association between receipt of pre-release discharge planning and measure of physical HRQoL (OR 0.98, 95% CI = 0.95 to 1.02), mental HRQoL (OR 0.99, 95% CI = 0.96 to 1.02), or addiction severity (data not shown.) Participants who did not receive pre-release discharge planning were more likely than those who did to be non-Hispanic white (p = 0.02) and have a lower mean baseline CD4 count (319, SD ± 22.9 versus 403, SD ± 241; p = 0.01). Despite achieving parity with other participants in terms of baseline viral suppression, only half of frequent ED users maintained viral suppression by 12-week follow-up, suggesting reduced short-term persistence on ART and/or interval development of genotypic drug resistance.51

ED and other Health Care Use

There were 227 total ED visits made by 151 released prisoners with HIV (1.50 ED visits per person year) who were enrolled in the parent study in the 12 months after release from prison (Table 2). Nearly one-fourth of these visits resulted in hospital admission and almost the same proportion were considered incomplete, because the patient either left the ED before being seen by a medical provider or left against medical advice. The majority of visits occurred during standard hours of typical primary care clinic operation (9 am to 5 pm), even though 68% of subjects self-reported having established at least minimal care in a primary HIV clinic by three months post-release. In contrast to this pattern of ED use, frequent ED users’ visits were more likely to take place during the off-hours of 5 pm to 12 am, and 12 am to 9 am. Frequent ED users’ reason for ED visit differed significantly from infrequent or non-users: substance misuse diagnosis was more common among frequent ED users (43% vs. 8%; p < 0.001).

Table 2
Healthcare Utilization, Stratified by Number of Emergency Department Visits

Correlates of Frequent ED Use

Using multivariate Poisson regression models, correlates of being a frequent ED user included not receiving pre-release discharge planning, and having poorer baseline physical HRQoL (Table 3). Specifically, subjects recruited after release from prison and who had not received pre-release discharge planning were over three times more likely to frequently use the ED compared to those who were assessed and had adequate time to plan for post-release activities during the period of incarceration.

Table 3
Correlates of Having Two or More ED Visits (n=151)

When we plotted number of ED visits against calculated medical and social multimorbidity indices, the relationships were not found to be significant (data not shown.) As shown in Figure 1, however, we did find a positive relationship between the number of ED visits and increasing levels of ‘psychiatric multimorbidity’ for frequent ED users.

Figure 1
Emergency Department Utilization by Psychiatric Multimorbidity Index, Stratified by Frequent ED Use

DISCUSSION

While studies by Hunt et al.8 and Byrne et al.52 have addressed frequent ED use as a general construct, to our knowledge this is the first study of frequent ED use by released prisoners, and specifically those with HIV. Released prisoners and those living with HIV are an important subpopulation at risk of high ED utilization. In describing frequent ED use by this subpopulation, we build on our previous findings that many people with HIV visit the ED once within 30 days of prison-release, often for untreated mental disorders and social access issues.53 Similarly, previous studies of other vulnerable populations have shown that frequent ED use represents a type of repetitive health-seeking behavior that is often reflective of severe underlying medical and psychiatric illness in the context of social instability. Hunt et al. used the Community Tracking Study Household Survey to show that frequent ED users typically were poor, yet over 80% possessed both insurance and a usual source of care; like our findings, Hunt et al. found frequent ED use to be independently associated with poor physical and mental health.8 In both studies, although far from being “inappropriate” or even unnecessary, frequent ED use by a small subset of individuals contributes significantly to ED use overall.

In a cohort with significant medical and psychiatric comorbidity, we found important sex differences, in that women were consistently more likely to be frequent ED users, compared to men. While mental health-related ED visits share a similar overall prevalence between the sexes, women are more regular users of health care services in general, with higher rates of psychosocial-related ED visits, in particular.54 Previous data suggest that women who interact with the criminal justice system differ from their male counterparts in that women experience a higher burden of medical, psychiatric, and substance use disorders.55 Following release from prison, women’s medical and psychiatric needs may be compounded by specific psychosocial stressors such as unequal relationship power dynamics, and challenges caring for children and reuniting with families.56,57 Women’s excess burden of illness and stress following prison-release is likely reflected in frequent ED use, although this remains an important area for future research.

For both men and women, the transition period from correctional settings to communities is an important window for intervening with an otherwise neglected population of people living with HIV.14,58 To maintain the benefits of prison- or jail-based ART, continuity of HIV care is critical.60 Unfortunately, care is often disrupted, resulting in decreased engagement in HIV primary care and reduced persistence on ART.18,20,21,6062 In the often chaotic lives of recently released prisoners, energy is diverted away from positive health promotion activities and towards meeting basic subsistence needs, and distractions from coping with the demands of drug or alcohol addiction. This element of chaos is reflected in frequent off-hour ED visits, often for issues related to substance misuse. Furthermore, the cumulative burden of psychiatric illness is represented by the psychiatric multimorbidity index, which was positively correlated with increased number of ED visits, especially for frequent ED users. This is especially critical in a population with few other positive resources and serious underlying medical and psychiatric illness.

For the population of released prisoners described here, frequent ED use is correlated with mental disorders and serious medical co-morbidities and is compounded by a lack of pre-release discharge planning during incarceration. Pre-release discharge planning has been associated with increased retention in HIV care among individuals with HIV released from jail across 10 diverse U.S. sites.63 In our study, while all participants who were recruited before release met with transitional case managers during incarceration and by our team the day of release, the group who did not receive pre-release discharge planning was released from prison directly to communities without any planning for continuity of care. For these individuals, an important opportunity for linkage to community-based care immediately after release was missed, but might have otherwise stabilized health conditions or reduced an over-reliance on ED services. Findings from our study complement those from a randomized controlled trial of HIV-infected prisoners in North Carolina, who received either pre-release discharge planning or strengths-based case management; linkage to care and ED use was similar between the two groups.64

Our study, through its naturalistic design, confirms that pre-release discharge planning reduces ED use and is the minimum required to address basic needs and effectively transition people with HIV to the community.14 Our findings also suggest that there may be gaps in community medical and mental health resources for newly released prisoners that translate directly into frequent ED use. Other proposed interventions aimed primarily at reducing frequent ED use have included expansion of case management services within EDs and ED diversion towards expanded primary care programs.65 For the most part, these interventions have had mixed results,10 perhaps because they miss critical contextual interactions between individuals and communities.9 For released prisoners with HIV and other marginalized patient populations, more meaningful structural interventions that increase availability of or facilitate entry into evidence-based substance abuse treatment, stabilize housing, and maintain continuity of care between prison and communities will secondarily reduce the need for ED services. While some components of these interventions were undertaken in the parent DAART study described here, our observational cohort study design prohibited us from evaluating the specific effect of the DAART intervention on reducing frequent ED use.

Other studies have shown that additional beneficial services for this population are short, brief interventions and referral to treatment, evidence-based interventions that reduce the negative consequences of alcohol and drug use.66 Support for substance abuse treatment as a means of reducing ED use is evidenced by a recent study of a similar urban population accessing mobile health care services, in which each month of retention on buprenorphine treatment was associated with a 17% reduction in expected ED use per person.67 These findings were not replicated here, perhaps due to the small number of participants who met criteria for opioid dependence and initiated buprenorphine. Our study suggests overall that there may be gaps in community medical and mental health resources for newly released prisoners that translate directly into frequent ED use.

LIMITATIONS

Despite every attempt to measure the full spectrum of ED use by subjects in the year following prison-release, visits may have been inadvertently missed if they occurred outside of designated study areas. Although other data from this university community suggest that ED use in the two designated hospitals is associated with the vast majority of all ED visits, we cannot fully ensure that it is the case here.66,68 We were also unable to control for re-incarceration during follow-up as a possible confounder to ED use. Subjects may have had somewhat restricted ED use during periods of re-incarceration, which is representative of the cyclical health care use common among those who interface with the criminal justice system. This secondary analysis was limited by the use of baseline and some 3-month data, which cannot account for changes in housing stability or physical/mental health over time, reflecting instead only the period immediately before or after prison-release. External validity of results may be challenged by regional variations in insurance coverage, drug use patterns, or support services available for HIV-infected released prisoners. Although the multimorbidity indices were designed to be comprehensive, items may have been missing from the baseline assessment from which the indices were derived. Of note, weighted index scores could not be calculated because limited data is available on the effect of multimorbidity on health care use in this patient population. We did approximate weight by including measures of both the presence and severity of various medical, psychiatric, and social issues in each respective index.

CONCLUSIONS

In this study, we explored correlates of frequent ED use by 151 released prisoners with HIV, using individual descriptors, multivariate Poisson regression models, and generated multimorbidity indices. Frequent ED users were more likely than infrequent or non-users to be female, have significant medical and psychiatric co-morbidities including substance misuse, and report worse physical health-related quality of life. They were no different, however, in terms of HIV biologic surrogates and use of other health care services either at baseline or in short-term follow-up. The cumulative burden of mental disorders experienced by frequent users was best captured in a psychiatric multimorbidity index, which was positively associated with ED utilization. Surprisingly, and in contrast to other published studies on frequent ED use, social multimorbidity indices were not correlated with number of ED visits. Perhaps most pertinent for health policy and intervention development, frequent ED users were less likely to have received pre-release discharge planning, suggesting missed opportunities for seamless linkages to care.

Acknowledgments

Funding: Primary funding for this research was provided by the National Institute on Drug Abuse (R01 DA017059, FLA). The authors would also like to acknowledge career development funding from the National Institutes of Allergy and Infectious Diseases (T32 AI007517 for JPM), the National Institute of Mental Health (T32 MH020031 for JPM), the National Institute on Drug Abuse (K24 DA017072 for FLA and K23 DA033858 for JPM) and the American Foundation for Suicide Prevention (Distinguished Investigator Award for GLL).

The authors would like to thank the study participants and Project CONNECT research assistants and substance abuse counselors for their tireless work with and insight about program participants.

Footnotes

The authors have no further disclosures or conflicts of interest to report.

References

1. Kline JA, Walthall JD. Patient Protection and Affordable Care Act of 2010: summary, analysis, and opportunities for advocacy for the academic emergency physician. Acad Emerg Med. 2010;17:e69–74. [PMC free article] [PubMed]
2. The White House. [Accessed Oct 24, 2012];Health care reform in action. Available at: http://www.whitehouse.gov/healthreform.
3. LaCalle E, Rabin E. Frequent users of emergency departments: the myths, the data, and the policy implications. Ann Emerg Med. 2010;56:42–8. [PubMed]
4. Jamieson D. Health care’s frequent fliers: the treatment of Kenny Farnsworth. [Accessed Oct 24, 2012];The Washington Post. 2009 Nov 29; Available at: http://www.washingtonpost.com/wp-dyn/content/article/2009/11/20/AR2009112003175.html.
5. Handel DA, McConnell KJ, Wallace N, Gallia C. How much does emergency department use affect the cost of Medicaid programs? Ann Emerg Med. 2008;51:614–21. [PubMed]
6. Olejarz D. Frequent users wear down emergency department physicians. [Accessed Oct 24, 2012];Med News Today. 2011 Available at: http://www.medicalnewstoday.com/releases/227515.php.
7. Fuda KK, Immekus R. Frequent users of Massachusetts emergency departments: a statewide analysis. Ann Emerg Med. 2006;48:9–16. [PubMed]
8. Hunt KA, Weber EJ, Showstack JA, Colby DC, Callaham ML. Characteristics of frequent users of emergency departments. Ann Emerg Med. 2006;48:1–8. [PubMed]
9. Pines JM, Asplin BR, Kaji AH, et al. Frequent users of emergency department services: gaps in knowledge and a proposed research agenda. Acad Emerg Med. 2011;18:e64–9. [PubMed]
10. Bernstein SL. Frequent emergency department visitors: the end of inappropriateness. Ann Emerg Med. 2006;48:18–20. [PubMed]
11. Weber EJ. Defining frequent use: the numbers no longer count. Ann Emerg Med. 2012;60:33–4. [PubMed]
12. Leukefeld CG, Hiller ML, Webster JM, et al. A prospective examination of high-cost health services utilization among drug using prisoners reentering the community. J Behav Health Serv Res. 2006;33:73–85. [PubMed]
13. Meyer J, Chen N, Springer S. HIV treatment in the criminal justice system: critical knowledge and intervention gaps. AIDS Res Treatm. 2011;2011:e680617. [PMC free article] [PubMed]
14. Springer S, Spaulding A, Meyer J, Altice F. Public health implications for adequate transitional care for HIV-infected prisoners: five essential components. Clin Infect Dis. 2011;53(5):469–79. [PMC free article] [PubMed]
15. Fazel S, Baillargeon J. The health of prisoners. Lancet. 2011;277:9769, 956–65. [PubMed]
16. Leach D, Oliver P. Drug-related death following release from prison: a brief review of the literature with recommendations for practice. Curr Drug Abuse Rev. 2011;4(4):292–7. [PubMed]
17. Lim S, Seligson AL, Parvez FM, et al. Risks of drug-related death, suicide, and homicide during the immediate post-release period among people released from new york city jails, 2001–2005. Am J Epidemiol. 2012;175:519–26. [PubMed]
18. Springer S, Pesanti E, Hodges J, Macura T, Doros G, Altice F. Effectiveness of antiretroviral therapy among HIV-infected prisoners: reincarceration and the lack of sustained benefit after release to the community. Clin Infect Dis. 2004;38:1754–60. [PubMed]
19. Rosen DL, Schoenbach VJ, Wohl DA. All-cause and cause-specific mortality among men released from state prison, 1980–2005. Am J Public Health. 2008;98:2278–84. [PubMed]
20. Baillargeon J, Giordano T, Rich J, et al. Accessing antiretroviral therapy following release from prison. JAMA. 2009;301:848–57. [PMC free article] [PubMed]
21. Baillargeon J, Giordano T, Harzke A, Baillargeon G, Rich J, Paar D. Enrollment in outpatient care among newly released prison inmates with HIV infection. Public Health Rep. 2010;125(Suppl 1):64–71. [PMC free article] [PubMed]
22. Baillargeon J, Giordano T, Harzke A, et al. Predictors of reincarceration and disease progression among released HIV-infected inmates. AIDS Patient Care STDS. 2010;24:389–94. [PMC free article] [PubMed]
23. Masson C, Sorensen J, Phibbs C, Okin R. Predictors of medical service utilization among individuals with co-occurring HIV infection and substance abuse disorders. AIDS Care. 2004;16:744–55. [PubMed]
24. Kidder D, Wolitski R, Campsmith M, Nakamura G. Health status, health care use, medication use, and medication adherence among homeless and housed people living with HIV/AIDS. Am J Public Health. 2007;97:2238–45. [PubMed]
25. Cunningham WE, Sohler NL, Tobias C, et al. Health services utilization for people with HIV infection: comparison of a population targeted for outreach with the U.S. population in care. Med Care. 2006;44:1038–47. [PubMed]
26. Palepu A, Horton NJ, Tibbetts N, Dukes K, Meli S, Samet JH. Substance abuse treatment and emergency department utilization among a cohort of HIV-infected persons with alcohol problems. J Subst Abuse Treat. 2003;25:37–42. [PubMed]
27. Chen NE, Meyer JP, Avery AK, et al. Adherence to HIV treatment and care among previously homeless jail detainees. AIDS Behav. 2011 doi: 10.1007/s10461-011-0080-2. [PMC free article] [PubMed] [Cross Ref]
28. Diederichs C, Berger K, Bartels DB. The measurement of multiple chronic diseases--a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci. 2011;66:301–11. [PubMed]
29. Andersen R. Behavior Models of Families’ Use of Health Services. Chicago, IL: Center for Health Administration Studies, University of Chicago; 1968.
30. Aday LA, Awe WC. Health Services Utilization Model. In: Gochman DS, editor. Handbook of Health Behavior Research, vol 1. Personal and Social Determinants. New York, NY: Plenum Publishing Company; 1997.
31. Gelberg L, Andersen R, Leake B. The behavioral model for vulnerable populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34:1273–302. [PMC free article] [PubMed]
32. Altice F, Saber-Tehrani A, Qiu J, Herme M. DAART is superior to self-administered therapy among released HIV+ prisoners: results from a randomized controlled trial [Abstract]. 18th Conference on Retroviruses and Oportunistic Infections; Boston, MA. 2011.
33. Saber-Tehrani AS, Springer SA, Qiu J, Herme M, Wickersham J, Altice FL. Rationale, study design and sample characteristics of a randomized controlled trial of directly administered antiretroviral therapy for HIV-infected prisoners transitioning to the community - a potential conduit to improved HIV treatment outcomes. Contemp Clin Trials. 2011;33(2):436–44. [PMC free article] [PubMed]
34. Springer S, Chen S, Altice F. Improved HIV and substance abuse treatment outcomes for released HIV-infected prisoners: the impact of buprenorphine treatment. J Urban Health. 2010;87:592–602. [PMC free article] [PubMed]
35. Springer SA, Qiu J, Saber-Tehrani AS, Altice FL. Retention on buprenorphine is associated with high levels of maximal viral suppression among HIV-infected opioid dependent released prisoners. PLoS ONE. 2012;7(5):e38335. [PMC free article] [PubMed]
36. McLellan A, Luborsky L, Cacciola J, et al. New data from the Addiction Severity Index. Reliability and validity in three centers. J Nerv Ment Dis. 1985;173:412–23. [PubMed]
37. McLellan AT, Kushner H, Metzger D, et al. The fifth edition of the addiction severity index. J Subst Abuse Treat. 1992;9:199–213. [PubMed]
38. Najavits L, Weiss R, Reif S, et al. The Addiction Severity Index as a screen for trauma and posttraumatic stress disorder. J Studies Alcohol. 1998;59:56–62. [PubMed]
39. Reinert D, Allen J. The Alcohol Use Disorders Identification Test (AUDIT): a review of recent research. Alcohol Clin Exp Res. 2002;26:272–9. [PubMed]
40. Reinert D, Allen J. The alcohol use disorders identification test: an update of research findings. Alcohol Clin Exp Res. 2007;31:185–99. [PubMed]
41. Rush A, Trivedi M, Ibrahim H, et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54:573–83. [PubMed]
42. Huba G, Melchior L. Module 46: Social Support Form. Culver City, CA: The Measurement Group; 1996. Staff of the Measurement Group, and HRSA/HAB’s Special Projects of National Significance Cooperative Agreement Steering Committee.
43. Anderson L, Dedrick R. Development of the Trust in Physician scale: a measure to assess interpersonal trust in patient-physician relationships. Psychol Rep. 1990;67:1091–100. [PubMed]
44. Freburger JK, Callahan LF, Currey SS, Anderson LA. Use of the Trust in Physician Scale in patients with rheumatic disease: psychometric properties and correlates of trust in the rheumatologist. Arthritis Rheum. 2003;49:51–8. [PubMed]
45. Quality Metric. [Accessed Oct 19, 2012];SF-36v2 Health Survey. Available at: http://www.qualitymetric.com/WhatWeDo/GenericHealthSurveys/SF36v2HealthSurvey/tabid/185/Default.aspx.
46. Berger BE, Ferrans CE, Lashley FR. Measuring stigma in people with HIV: psychometric assessment of the HIV stigma scale. Res Nurs Health. 2001;24:518–29. [PubMed]
47. Altice F, Kamarulzaman A, Soriano V, Schechter M, Friedland G. Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. Lancet. 2010;376:367–87. [PubMed]
48. Akaike H. Information Theory as an Extension of the Maximum Likelihood Principle. In: Petrov BN, Csaki F, editors. Second International Symposium on Information Theory. Budapest, Hungary: Akademiai Kiado; 1973. pp. 267–81.
49. Raftery AE. Bayesian Model Selection in Social Research. In: Raftery AE, editor. Sociological Methodology. Oxford, UK: Blackwell; 1995. pp. 111–64.
50. State of Connecticut, Department of Correction. [Accessed Oct 19, 2012];Statistics. 2011 Jan 1; Available at: http://www.ct.gov/doc/cwp/view.asp?a=1505&q=471438.
51. Bae JW, Guyer W, Grimm K, Altice FL. Medication persistence in the treatment of HIV infection: a review of the literature and implications for future clinical care and research. AIDS. 2011;25:279–90. [PubMed]
52. Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003;41:309–18. [PubMed]
53. Meyer JP, Qiu J, Chen NE, Larkin GL, Altice FL. Emergency department use by released prisoners with HIV: an observational longitudinal study. PLoS ONE. 2012;7(8):e42416. [PMC free article] [PubMed]
54. Larkin GL, Claassen CA, Emond JA, Pelletier AJ, Camargo CA., Jr Trends in U.S. emergency department visits for mental health conditions, 1992 to 2001. Psychiatr Serv. 2005;56:671–7. [PubMed]
55. Binswanger IA, Merrill JO, Krueger PM, White MC, Booth RE, Elmore JG. Gender differences in chronic medical, psychiatric, and substance-dependence disorders among jail inmates. Am J Public Health. 2010;100:476–82. [PMC free article] [PubMed]
56. Richie BE, Freudenberg N, Page J. Reintegrating women leaving jail into urban communities: a description of a model program. J Urban Health. 2001;78:290–303. [PMC free article] [PubMed]
57. Institute on Women and Criminal Justice. [Accessed Oct 19, 2012];Quick Facts: Women and Criminal Justice. 2009 Available at: http://wpaonline.org/pdf/Quick%20Facts%20Women%20and%20CJ%202009.pdf.
58. Spaulding A, Arriola K, Ramos K, et al. Enhancing linkages to HIV primary care in jail settings: report on a consultants’ meeting. J Correctional Health Care. 2007;13:93–128.
59. Draine J, Ahuja D, Altice FL, et al. Strategies to enhance linkages between care for HIV/AIDS in jail and community settings. AIDS Care. 2011;23:366–77. [PubMed]
60. Fontana L, Beckerman A. Recently released with HIV/AIDS: primary care treatment needs and experiences. J Health Care Poor Underserved. 2007;18:699–714. [PubMed]
61. Harzke A, Ross M, Scott D. Predictors of post-release primary care utilization among HIV-positive prison inmates: a pilot study. AIDS Care. 2006;18:290–301. [PubMed]
62. Stephenson B, Wohl D, Golin C, Tien H, Stewart P, Kaplan A. Effect of release from prison and re-incarceration on the viral loads of HIV-infected individuals. Public Health Rep. 2005;120:84–8. [PMC free article] [PubMed]
63. Althoff A, Zelenev A, Meyer J, et al. Correlates of retention in HIV care after release from jail: results from a multisite study. AIDS Behav. In Press. [PMC free article] [PubMed]
64. Wohl D, Scheyett A, Golin C, et al. Intensive case management before and after prison release is no more effective than comprehensive pre-release discharge planning in linking HIV-infected prisoners to care: a randomized trial. AIDS Behav. 2011;15(2):356–64. [PMC free article] [PubMed]
65. Althaus F, Paroz S, Hugli O, et al. Effectiveness of interventions targeting frequent users of emergency departments: a systematic review. Ann Emerg Med. 2011;58:41–52. [PubMed]
66. Academic ED SBIRT Research Collaborative. The impact of screening, brief intervention and referral for treatment in emergency department patients’ alcohol use: a 3-, 6- and 12-month follow-up. Alcohol Alcohol. 2010;45:514–9. [PMC free article] [PubMed]
67. Schwarz R, Zelenev A, Bruce RD, Altice FL. Retention on buprenorphine treatment reduces emergency department utilization, but not hospitalization, among treatment-seeking patients with opioid dependence. J Subst Abuse Treat. 2012 [PMC free article] [PubMed]
68. Pollack HA, Khoshnood K, Blankenship KM, Altice FL. The impact of needle exchange-based health services on emergency department use. J Gen Intern Med. 2002;17:341–8. [PMC free article] [PubMed]