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Correlates of health care utilization among HIV-seropositive injection drug usersY. Mizuno a , J. D. Wilkinson b , S. Santibanez a , C. Dawson Rose c , A. Knowlton d , K.Handley e , M. N. Gourevitch f & Inspire Team aa Centers for Disease Control and Prevention,, Atlantab University of Miami, Baltimore, San Franciscoc University of California, Baltimore, San Franciscod Johns Hopkins University, Baltimoree Health Resources and Services Administration, Rockville, MDf New York University, New YorkPublished online: 08 Dec 2010.

To cite this article: Y. Mizuno , J. D. Wilkinson , S. Santibanez , C. Dawson Rose , A. Knowlton , K. Handley , M. N.Gourevitch & Inspire Team (2006) Correlates of health care utilization among HIV-seropositive injection drug users, AIDSCare: Psychological and Socio-medical Aspects of AIDS/HIV, 18:5, 417-425, DOI: 10.1080/09540120500162247

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Correlates of health care utilization among HIV-seropositive injectiondrug users

Y. MIZUNO1, J. D. WILKINSON2, S. SANTIBANEZ1, C. DAWSON ROSE3,

A. KNOWLTON4, K. HANDLEY5, M. N. GOUREVITCH6, & INSPIRE TEAM

1Centers for Disease Control and Prevention, Atlanta, 2University of Miami, 3University of California, San Francisco, 4Johns

Hopkins University, Baltimore, 5Health Resources and Services Administration, Rockville, MD, and 6New York University,

New York

AbstractThis study sought to identify correlates of poor health care utilization among HIV-positive injection drug users (IDUs) usingAndersen’s behavioural health model. We used baseline data from INSPIRE, a study of HIV-positive IDUs (n�/1161) toidentify predisposing, enabling, and need factors related to poor utilization (defined as fewer than two outpatient visits in thepast six months, or identification of emergency room (ER) as the usual place for care). Using bivariate and multivariatemodels, we found a number of enabling factors that could facilitate the use of health care services such as having healthinsurance, having seen a case manager, and better engagement with health care providers. These enabling factors could bemodified through interventions targeting HIV-positive IDUs. In addition, health insurance and case management appear tobe important factors to address because they contributed in making other factors (e.g. lower education, lack of stablehousing) non-significant barriers to outpatient care utilization. In the future, these findings may be used to inform thedevelopment of interventions that maximize use of scarce HIV resources and improve health care utilization among HIV-positive IDUs.

Introduction

Injection drug users (IDUs) represent more than one

third of persons living with AIDS in the United

States (Centers for Disease Control and Prevention

[CDC] 2003). Studies of HIV-positive IDUs de-

scribe this population as socially and economically

disadvantaged with many health and psychosocial

problems (Knowlton et al., 2001; Masson et al.,

2004; Mizuno et al., 2003; Purcell et al., 2004).

High rates of substance abuse, mental illness,

marginal employment, and inadequate housing

may make it particularly difficult for HIV-positive

IDUs to access and utilize health care (Purcell et al.,

2004). Studies have found high levels of suboptimal

health care utilization (e.g. outpatient service use

and emergency room (ER) use) reported among

HIV-positive IDUs (Andersen et al., 2000; Knowl-

ton et al., 2001; Shapiro et al., 1999).

In order to improve HIV-positive IDUs’ health

care utilization, it is important to identify barriers

and facilitators, particularly those that are modifiable

by intervention. Knowlton et al. (2001) examined

this issue among a community sample of HIV-

positive IDUs recruited from Baltimore, USA, and

identified drug treatment and case management as

significant facilitators of medical service access and

utilization. Masson and colleagues (2004) found that

homelessness was a significant barrier to medical

service utilization among a clinic sample of HIV-

positive substance abusers. The present paper ex-

pands these studies by using baseline data collected

in a large multi-site behavioural intervention study of

HIV-positive IDUs. Use of these multi-site data is

advantageous because of the large sample size

(n�/1161), and also because measures include

psychosocial variables specific to this population

and which were not examined in prior studies.

We utilized the behavioural health model (e.g.

Andersen, 1995) as a conceptual framework to guide

our analyses. This model suggests that a person’s use

of health care services is determined by three

domains � predisposing, enabling, and need factors.

Predisposing factors are those that exist prior to the

onset of illness (Mechanic, 1979) and are considered

to be indicators of a person’s general propensities to

use health care. These factors include sociodemo-

graphic variables such as age, gender, race/ethnicity,

Correspondence: Y. Mizuno, Prevention Research Branch, Division of HIV/AIDS Prevention, National Center for HIV/STD/TB

Prevention, Centers for Disease Control and Prevention, Atlanta, GA. Address for correspondence: Yuko Mizuno, Ph.D., 1600 Clifton

Road, NE Mail Stop E37, Atlanta, GA 30333, USA. Tel: �/1 (404)639-1925. Fax: �/1 (404)639-1950. E-mail: ymizuno@cdc.gov

AIDS Care, July 2006; 18(5): 417�425

ISSN 0954-0121 print/ISSN 1360-0451 online # 2006 Taylor & Francis

DOI: 10.1080/09540120500162247

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and education. Enabling factors are personal or

community resources that would facilitate the use

of health care services and include variables such as

income, health insurance, stable housing, and com-

munity characteristics that might be represented by

the city of residence. The present analyses also

included enabling factors that are pertinent to

HIV-positive IDUs such as receiving case manage-

ment and methadone maintenance treatment. We

also included psychosocial enabling factors such as

perceived social support and perception of the

quality of engagement with care providers. Finally,

need factors are a person’s objective and subjective

evaluations of his/her need to receive health care.

Again, we included variables that are pertinent to the

study population such as CD4 count and the

intensity of injection drug use, as well as more

commonly used measures of need factors such as

self-perceived health status and presence of depres-

sive symptoms. According to Mechanic (1979),

many earlier studies found need factors to be

accounting for most of the variance in service

utilization.

Using the conceptual framework of the

behavioural health model, we sought to identify

significant correlates of health care utilization among

HIV-positive IDUs. Most generally, we expected

bivariate associations between health care utilization

and the variables measuring each of the three factor

domains (predisposing, enabling and need factors).

But we also wanted to assess relative importance of

these various domains and further, relative impor-

tance of variables within domains. Thus we asked

specific research questions as follows: (1) whether

the associations between need factors and health

care utilization remained regardless of predisposing

and enabling factors*for example, did HIV-positive

IDUs who were more immuno-compromised utilize

care more often regardless of their general propen-

sities toward care and the level of resources that they

had?; (2) whether any of the associations between

predisposing factors and health care utilization

persisted after any of the enabling factors were taken

into account; and (3) whether any of the enabling

factors ‘explained’ any of the associations between

health care utilization and other enabling factors by

making these associations non-significant? These last

two questions will help us identify enabling factors

that may be most important to address � the

enabling factors that would make any of the general

propensities toward care or lack of any other

enabling factors less of a barrier to health care

utilization. Answers to these questions might further

inform the field about the components of interven-

tions necessary to improve health care utilization by

HIV-positive IDUs.

Methods

Data

We analysed baseline data collected from 1161 HIV-

positive IDUs who participated in Intervention for

Seropositive Injectors*Research and Evaluation

(INSPIRE). INSPIRE was a randomized controlled

trial of behavioural interventions conducted in four

cities in the US (Baltimore, Miami, New York, and

San Francisco) from 2001 through 2004. A detailed

description of INSPIRE and its methodology has

been reported elsewhere (Purcell et al., 2004). The

CDC IRB and human subject review boards of the

research sites approved the study protocol.

Recruitment procedure

Participants were recruited using active and passive

strategies in a variety of HIV care and community

venues including AIDS service organizations, med-

ical clinics, methadone clinics, as well as street-based

settings. Flyers distributed included information

about the study and a toll-free number for further

enquiries. In order to eliminate the need for poten-

tial participants to disclose HIV status in outreach

venues, potential participants were told: ‘If this card

does not apply to you, please give it to someone you

know.’ As a result, some potential participants were

reached through word-of-mouth.

Potential participants were informed of the study

and screened for eligibility either at the recruitment

venue or by telephone. Eligibility criteria included:

(1) 18 years of age or older; (2) injection drug use in

the past 12 months; (3) sex with an opposite-sex

partner in the past three months; (4) self identifica-

tion as HIV seropositive; (5) being willing to provide

oral fluids to have HIV serostatus confirmed; (6)

agreeing to a blood draw for CD4 and viral load

testing; (7) not being currently enrolled in an inter-

vention study conducted by one of the Principal

Investigators or never having been enrolled in the

INSPIRE pilot study; (8) living within the study area;

(9) being able to communicate in a group in English

(although the survey can occur in Spanish); and (10)

being available to attend the first intervention session.

Eligible participants were invited to join the study and

scheduled for the baseline assessment appointment.

Interview procedures

At baseline, participants were administered an

audio-computer assisted self interview (A-CASI) to

answer questions regarding sexual and drug using

behaviours, utilization of health care, and adherence

to HIV medications. Participants also provided an

oral fluid sample for confirmatory HIV-antibody

testing (OraSure, OraSure Technologies, Inc., Beth-

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lehem, PA, USA) and a blood specimen for CD4

count and viral load. HIV confirmation testing was

performed at local laboratories, and immunoassays

at the CDC laboratories. Participants were reim-

bursed $30 for their time and effort for the baseline

appointment.

Measures

Dependent variables

Based on the literature and availability of measures

in our dataset, we used the following operational

definitions of poor health care utilization as our

outcomes.

1. Fewer than two outpatient visits in the past six

months . Participants were asked ‘In the past six

months, how many times have you seen a

healthcare provider or gone to a clinic other

than the emergency room for medical care for

yourself for any reason?’ Using ‘two visits per

six months’ as a threshold for optimal care

(Shapiro et al., 1999), we created a dichoto-

mous variable indicating whether or not a

participant had fewer than two outpatient visits

in the past six months (if yes coded 1; if no

coded 0).

2. Identifying emergency room (ER) as the usual

source of medical care. Participants were asked

‘Where do you usually go if you are sick or need

medical care?’ and were given six response

options including: (1) hospital clinic; (2) com-

munity clinic; (3) doctor’s office or private

clinic; (4) methadone clinic or drug treatment

centre; (5) emergency room; and (6) other.

Again we created a dichotomous variable in-

dicating whether or not a participant identified

the ER as the usual source for care (if yes coded

1; if no coded 0).

Independent variables

Predisposing factors. Predisposing factors included

age (in years), sex (male, female), race/ethnicity

(Non-Hispanic white, Non-Hispanic black, Hispa-

nic, other), and education level (high school or

more�/1 vs. less than high school�/0).

Enabling factors

� Income: Annual income reported was dichot-

omized into $10,000 or more (coded 1) and less

than $10,000 (coded 0).

� Health insurance coverage: Participants were

asked, ‘What kind of health insurance do you

currently have?’ We categorized participants

into those who reported having any kind of

health insurance (Medicaid, Medicare, private

insurance, and/or VA) (coded 1) or those who

reported none (coded 0).

� Housing status: Participants were asked, ‘Do

you currently have a place where you stay five to

seven days a week?’ with response options yes

(coded 1) and no (coded 0).

� City of residence: Participants were categorized

into four groups based on the city of residence

(Baltimore, Miami, New York, and San Fran-

cisco).

� Seen a case manager: Participants were asked

‘In the past six months, have you seen a case

manager, social worker, or counsellor to help

you get medical care, insurance, food stamps,

housing assistance, and the like?’ with response

options yes (coded 1) and no (coded 0).

� Currently in a methadone maintenance pro-

gramme: Participants were asked ‘Are you

currently in a methadone maintenance pro-

gramme?’ with response options yes (coded 1)

and no (coded 0).

� Social support: Perceived social support was

measured by a 5-item scale adapted from the

social support scale developed by Barrera

(1980). Examples of questions included ‘If

you wanted to talk to someone about things

that are very personal and private or if a

situation came up where you needed some

advise, is there anyone you could talk to?’ ‘If

you needed to borrow 25 dollars or something

valuable, is there anyone you know who would

lend or give you 25 dollars, or something that

was valuable?’ Responses were scored from 1

(definitely not) to 5 (definitely yes). Cronbach’s

alpha was 0.87.

� Perception of engagement with health care

providers: Participants who reported HIV care

visit in the past two years were asked about their

perception of their engagement with their

health care provider using the 13-item Engage-

ment with Healthcare Provider Scale (Bakken

et al., 2000). This measure was developed

specifically to assess HIV-positive patients’

relationships with their health care providers,

and was found to be significantly associated

with HIV medication adherence in the original

study. Examples of questions included ‘My

healthcare provider or doctor listens to me’,

‘cares about me’, ‘respects me’. Responses were

scored from 1 (always) to 4 (never). For

consistency with the other enabling measures,

this scale was reverse scored so that a higher

score indicated better communication. Cron-

bach’s alpha was 0.95.

Health care utilization among injection drug users 419

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Need factors

� CD4 count: CD4� lymphocyte count was

measured from the blood sample provided by

participants. We dichotomized the measure into

less than 200 (�/1) and 200 or greater (�/0)

CD4�/T cells/mm3.

� Injection drug use intensity: Participants were

asked, ‘How many times have you injected in

the past three months?’ In order to assist

participants in coming up with the number,

examples were provided. ‘If you inject four

times everyday, this means you have injected

360 times in the past three months. Or if you

inject four times a week . . .’ As a measure of

drug use intensity, this variable was further

dichotomized into ‘injected 180 times or more

in the past three months (two times or more

everyday in the past three months)’ (�/1)

‘injected less than 180 times’ (�/0).

� Self-perceived health status: Participants’ self-

perceived health status was measured by the 6-

item Physical Functioning subscale of the

Medical Outcome Study (MOS) (Stewart &

Kamberg, 1992). Examples of questions in-

cluded ‘How much does your health limit the

kinds or amounts of vigorous activities you can

do, like lifting heavy objects, running or parti-

cipating in strenuous sports, for example, bas-

ket ball?’ ‘How much does your health limit

walking uphill or climbing a few flights of

stairs?’ Responses were scored from 0 (not

limited at all) to 2 (limited a lot). Cronbach’s

alpha was 0.87.

� Depressive symptoms: Depressive symptoms

were measured by the 7-item depression sub-

scale of the Brief Symptom Inventory (BSI;

Derogatis & Spencer, 1982). Examples of

questions included ‘In the past week,

how much have you been bothered by thoughts

of ending your life’, ‘feeling lonely’, or

‘feeling blue’. Reponses were scored from 1

(not at all) to 5 (extremely). Cronbach’s alpha

was 0.88.

Analytic strategy

Bivariate analyses were conducted to examine the

associations between the two outcome measures of

poor health care utilization (i.e. (1) reported fewer

than 2 outpatient visits in the past six months and

(2) reported the ER as the usual place for care) and

each of the predisposing, enabling, and need factors

described above. For categorical variables, chi-

square analyses of independence were conducted

and for continuous variables t-tests were conducted.

Bivariate correlates that were associated with the

outcomes (p B/0.1) were then included in multi-

variate models (logistic regression) predicting poor

health care utilization. We first entered need factors,

added predisposing factors, and then enabling fac-

tors to the models. The purpose of this modeling was

to examine (1) whether the associations between the

outcomes and need factors remained regardless of

predisposing and enabling factors; and (2) whether

the associations between the outcomes and predis-

posing factors persisted after any of the enabling

factors were taken into account. (3) We further

estimated various models to identify enabling factors

that might have explained the associations between

other enabling factors and the outcomes.

Results

Sample characteristics

Characteristics of the 1161 HIV�/ IDUs with

respect to variables used in this paper are summar-

ized in Table I. The mean age was 42 years (SD�/

6.6, range 22�60 years). More than 60% were male

and black. More than 50% (n�/647) were high

school graduates or above but just over 10% (n�/

154) reported annual income $10,000 or higher.

More than 70% (n�/813) reported that they cur-

rently had health insurance (Medicaid or Medicare

(n�/744); private insurance (n�/68)) and most

participants (90%; n�/1037) reported that they

had a regular place to stay. More than three-quarters

(n�/885) reported seeing a case manager, social

worker, or counsellor in the past six months and

32% (n�/373) reported current enrolment in a

methadone maintenance programme. Approxi-

mately one-third (n�/323) had CD4 count less

than 200 and 17% (n�/192) reported injecting drugs

180 times or more in the past three months.

Utilization of health care

With respect to the frequency of outpatient visits in

the past six months, 22% (n�/253) of the sample

reported fewer than two visits in the past six months.

Regarding usual place for health care, 23% (n�/269)

reported the ER as the usual place for care. Other

reported usual places of care were hospital clinic

(36%; n�/420) doctor’s office (19%; n�/220) and

community clinic (14%, n�/165). The two outcome

variables were significantly, but not strongly asso-

ciated (X2�/29.27, df�/1, p B/0.001), that is, only

8% (n�/91) of participants reported that they had

fewer than two outpatient visits in the past six

months and also reported the ER as the usual place

for care.

420 Y. Mizuno et al.

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Bivariate analyses

Fewer than two outpatient visits in the past 6 months.

For the outcome ‘reporting fewer than two out-

patient visits in the past six months’, younger age

(p B/0.05) and less than high school education

(p B/0.01) were significant predisposing factors.

Among enabling factors, lower annual income (less

than $10,000) (p B/0.01), not having health insur-

ance (p B/0.01), not having a regular place to stay

(p B/0.01), city of residence (Miami) (p B/0.01), and

not seeing a case manager (p B/0.01) were signifi-

cantly associated with reporting poor outpatient care

utilization. Having a CD4 count 200 or above was a

borderline significant (p�/.08) need factor predict-

ing poor outpatient care utilization.

Reporting the ER as the usual place for care . For the

outcome ‘reporting the ER as the usual place

for care’, less than high school education was a

significant predisposing factor (p B/0.01). Among

enabling factors, lower annual income (p B/0.01),

not having health insurance (p B/0.01), city of

residence (not living in San Francisco), not seeing

a case manager (p B/0.01), and perception of poor

engagement with providers (p B/0.01) were signifi-

cantly associated with reporting the ER as the usual

place for care. Injection drug use intensity was a

borderline significant need factor predicting the

outcome (p�/.06).

Multivariate analyses

Fewer than two outpatient visits in the past six months.

Table II summarizes the results of a series of logistic

regression models predicting fewer than two out-

patient visits in the past six months. Model 1

included CD4 count, the sole need factor from the

bivariate analysis; Model 2 added the two significant

predisposing factors (age and education); and Model

3 added the five significant enabling factors (income,

health insurance, place to stay, city, and case

management).

Model 2 indicates that in the presence of predis-

posing factors, low CD4 count (B/200) was a

significant need factor associated with a nearly

30% reduction in the odds of reporting fewer than

two outpatient visits in the past six months. Among

predisposing factors, education remained significant;

higher education was associated with a 36% reduc-

tion in the odds of reporting poor outpatient care

utilization. Age became borderline significant

(p B/0.1).

In Model 3, the association of CD4 and age

achieved borderline significance (p B/0.1). Health

insurance, city, and case management were signifi-

cant enabling factors. The odds of reporting poor

outpatient care utilization was almost twice as likely

among participants living in Miami as those in three

other cities. Having health insurance and having seen

a case manager were both significantly associated

with reduction in the odds of reporting this outcome.

Education, a significant predisposing factor in the

bivariate analysis and in Model 2, was no longer

Table I. Characteristics of participants (n�/1161)*.

Variables

Categorical variables N %

Sex

Male 732 63.0

Female 429 37.0

Race/ethnicity

Non-Hispanic white 112 9.6

Non-Hispanic black 772 63.0

Hispanic 201 17.3

Non-Hispanic other 85 7.3

Education

Less than high school 508 44.0

High school or higher 647 56.0

Annual income

Less than $10,000 965 86.2

$10,000 or higher 154 13.8

Currently have health insurance

Yes 813 76.4

No 251 23.6

Currently have regular place to stay

Yes 1037 89.9

No 117 10.1

City

Baltimore 313 27.0

Miami 298 25.7

New York 271 23.3

San Francisco 279 24.0

Have seen a case manager in the past six months

Yes 885 76.7

No 269 23.3

Currently in a methadone maintenance programme

Yes 373 32.2

No 786 67.8

CD4

Less than 200 323 28.9

200 or higher 794 71.1

Injection drug use intensity

Less than 180 times in the past six

months

959 83.3

180 times or more in the past six

months

192 16.7

Continuous variables M (SD) Range

Age 42.13 (6.64) 22�60

Social support 3.97 (0.91) 1�5

Engagement with provider 3.54 (0.59) 1�4

Perceived health status 0.85 (0.53) 0�2

Depressive symptoms 2.09 (0.90) 1�5

Note: *Some of the variables have missing data thus the total does

not necessarily add up to 1161.

Health care utilization among injection drug users 421

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significant after enabling factors were taken into

account.

We further estimated a series of additional logistic

regression models (results not shown) to identify the

enabling factors that would make education a non-

significant correlate of outpatient care utilization. In

these analyses, education was no longer associated

with this outcome when a combination of ‘income,

city, and health insurance’ or ‘income, city, and case

management’ or ‘income, health insurance, and case

management’ was included in the model. Health

insurance, city of residence, and case management

were consistently significant enabling factors in these

models.

Similarly, we conducted additional analyses

(results not shown) to identify enabling factors that

would make other enabling factors non-significant

correlates of outpatient care utilization. We found

that the association between income and the

outcome became non-significant when health insur-

ance and city were taken into account. Also, the

association between housing and the outcome

became non-significant when a combination of

‘health insurance, city, and case management’ or

‘income, city, and case management’ was included in

the model.

Reporting the ER as the usual place for care . Table III

summarizes the results of a series of logistic regres-

sion models with the outcome ‘reporting the ER as

the usual place for care’. We found in Model 2 that

drug use intensity was not a significant correlate of

reporting the ER as the usual place for care in the

presence of the education variable. Education was a

significant predisposing factor in Model 2 showing

that higher the educational level, less likely that a

participant reported the ER as the usual place for

care. Unlike the outpatient utilization outcome, this

Table II. Logistic regression models: correlates of ‘fewer than two outpatient visits in the past six months’ by need, predisposing, and

enabling factors.

Odds Ratios (95% CI)

Model 1 Model 2 Model 3

Need Factor

CD4

B/200 0.75�/(0.53�1.03) 0.71*(0.51�0.98) 0.71�/(0.49�1.03)

Predisposing Factors

Age � 0.98�/(0.95�1.00) 0.98�/(0.95�1.00)

Education

High school or more � 0.64** (0.48�0.86) 0.82 (0.59�1.14)

Enabling Factors

Income � � 0.64

$10,000 or more (0.36�1.11)

Currently have health insurance � � 0.58** (0.40�0.84)

Currently have place to stay � � 0.67 (0.41�1.08)

City

Miami (vs. other cities) � � 1.86** (1.30�2.66)

Have seen a case manager � � 0.31** (0.22�0.44)

Sample size 1106 1103 993

Notes:�/ p B/.1; *p B/.05; **p B/.01.

Table III. Logistic regression models: correlates of ‘identifying the

emergency room the usual place for care’ by need, predisposing,

and enabling factors.

Odds Ratios (95% CI)

Model 1 Model 2 Model 3

Need Factor

Injection drug

use intensity

1.41�/ 1.32 1.12

�/�/180 times (0.99�1.99) (0.92�1.89) (0.70�1.78)

Predisposing Factor

Education

High school � 0.52** 0.66*

or more (0.39�0.69) (0.46�0.93)

Enabling Factors

Income

$10,000 � � 0.49*

or more (0.25�0.93)

Currently have

health insurance

� � 0.73 (0.48�1.11)

City

San Francisco � � 0.23**

(vs. other cities) (0.12�0.42)

Have seen a case

manager

� � 0.66�/

(0.43�1.00)

Engagement

with provider

� � 0.74* (0.55�0.98)

Sample Size 1147 1143 865

Notes :�/ p B/0.1; *p B/.05; **p B/.01.

422 Y. Mizuno et al.

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pattern still held even after taking into account

enabling factors (Model 3).

Income, city, and perception of engagement with

providers were significant enabling factors. Living in

San Francisco as opposed to the other cities (Balti-

more, Miami, and New York) reduced the odds of

reporting the ER as the usual source for care by

almost 80%. Also, higher income and better per-

ceived engagement with providers both reduced the

odds of reporting this outcome. Case management

approached significance (p B/0.1), and health insur-

ance was not a significant correlate in this model.

Additional analyses (results not shown) found that

the association between health insurance and the

outcome was no longer significant when income and

engagement with providers were taken into account.

Discussion

Our sample of HIV-positive IDUs was predomi-

nantly persons of colour with more than 85%

reporting annual income of less than $10,000,

showing characteristics similar to those reported in

other studies of IDUs (e.g. Kotranski et al., 1998;

Knowlton et al., 2001; Mizuno et al., 2003). The

rates of poor health care utilization found in our

sample (more than 20% whether it was defined as

‘less than two outpatient care visits in the past six

months’ or as ‘identifying the ER as the usual source

for medical care’) were slightly higher than those

reported in a national study of HIV-positive persons

in care (Shapiro et al., 1999) or in a local sample

(Masson et al., 2004). This may be due to the fact

that our study participants were recruited from a

variety of community-based venues as opposed to

mainly from clinic venues. More than 70% of our

sample reported having health insurance (mostly

government-funded) which is comparable to results

from another study of HIV-positive IDUs (Knowlton

et al., 2001). More than three-quarters of our

sample reported having seen a case manager in the

prior six months which is substantially higher than

the 37% reported in the earlier Knowlton study with

a longer recall period (12 months).

Using Andersen’s behavioural health model as a

conceptual framework, we identified a number of

correlates of poor healthcare utilization. Most of the

significant correlates found in our multivariate

analyses were ‘enabling factors’ � personal or com-

munity resources that would facilitate the use of

health care services. Significant correlates of ‘fewer

than two outpatient visits in the past six months’

were all enabling factors: health insurance, city of

residence, and case management. Another study of

HIV-positive IDUs also found health insurance

status and case management experience to be

significant correlates of this outcome (Knowlton

et al., 2001). As Knowlton and her colleague noted,

case management may be an important aid for HIV-

positive IDUs to navigate through a complex health

care delivery system. The importance of case man-

agement in linking HIV-positive persons to care has

also been reported in ARTAS, a randomized con-

trolled trial of a linkage-to-care case management

model (Gardner et al., 2003). For the outcome

‘identifying ER as the usual source for care’,

significant correlates were education, a predisposing

factor, and enabling factors such as income, city, and

engagement with health care providers.

In addition to identifying significant correlates of

poor health care utilization, we asked several specific

research questions. First, we asked if participants

with greater medical needs utilized care regardless of

their general propensities toward care and the level

of resources that they had. Our results were mixed

depending on the outcome. There was a trend

indicating that participants with lower CD4 count

were more likely than those with higher CD4 count

to utilize outpatient care regardless of predisposing

and enabling factors. On the other hand, for the ER

outcome, the trend that drug use intensity (need)

predicting poor health care utilization become non-

significant when predisposing and/or enabling fac-

tors were taken into account. It is also noteworthy

that our analyses did not find significant need

factors; even in bivariate analyses we found only a

few borderline significant need factors. These find-

ings suggest that more sensitive measures of health

status might have been required.

We also asked whether any of the associations

between the predisposing factors and the outcomes

persisted after any of the enabling factors were taken

into account. This is an important question because

predisposing factors are often not modifiable but

enabling factors (e.g. health insurance, case manage-

ment) can be addressed by interventions. We found

that lower education (a predisposing factor) was no

longer a significant barrier to outpatient care utiliza-

tion when some combinations of enabling factors

such as health insurance, case management, income,

and city were taken into account. On the other hand,

the association between lower educational level and

the ER outcome remained significant even after

enabling factors were taken into account. This result

suggests that less educated IDUs are especially

important targets for intervention to decrease ERs

as usual sources for non-urgent care.

We also asked whether any enabling factors made

any other enabling factors non-significant correlates

of the outcomes. We found that lower income was no

longer a significant barrier to outpatient care utiliza-

tion when health insurance and city were taken into

account. Similarly, lack of stable housing was no

longer an issue for outpatient care utilization when

Health care utilization among injection drug users 423

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some combinations of health insurance, case man-

agement, income, and city were taken into account.

These findings suggest that in order to improve

health care utilization among HIV-positive IDUs,

not all the enabling factors may need to be addressed

if it is not feasible to do so. Again, interventionists

may focus on health insurance and case management

as relatively modifiable enabling factors that also

appear to have some roles in making other enabling

factors non-significant. These two factors are also

linked with each other, as case managers often work

with HIV-positive persons to help them obtain

publicly funded health insurance.

There are two other enabling factors that are

worthy of further discussion. First, we found city

of residence to be correlated with both outcomes.

Controlling for other factors, Miami residents were

significantly more likely to report poor outpatient

care utilization, and San Francisco residents were

significantly less likely to identify the ER as the usual

source for care. Our finding suggests that different

cities have different levels of medical infrastructure

and resources available for HIV-positive IDUs and

that some structural solutions may be needed to

address regional differences in health care utiliza-

tion. More research is needed to further investigate

the meaning of city differences in service use.

Another interesting finding is the association

between perception of engagement with providers

and the ER outcome. Our findings suggest that

regardless of the presence of other correlates, if

people perceive their providers to be more engaged

with them, the odds of identifying the ER as the

usual source for care is significantly reduced. This

finding identifies another potential intervention

opportunity; that of educating providers to improve

communication skills with their patients who are

HIV-positive IDUs. However, because our data are

cross sectional, we need to consider a possibility that

ER use predicts the perception of poor provider

engagement, rather than provider engagement pre-

dicting ER use. Data collected with a longitudinal

design should be able to address such a question. It

should also be noted that the measure of provider

engagement we used here only assesses patients’

perception of how their providers are communicat-

ing with them, and not a measure of how they are

communicating with their providers. A measure that

assesses the two-way nature of communication

would be useful to inform interventionists about

the kinds of communication skills needed to improve

health care utilization.

This study has the following limitations. We used

baseline data from a randomized controlled trial.

Thus, findings discussed here may not be general-

izable to the general population of HIV-positive

IDUs. Second, the data are cross sectional, thus we

were unable to establish causal inferences from this

analysis. Third, our measures of health care utiliza-

tion were based on self-report. Although the A-CASI

technology has been found to yield more valid

responses with respect to stigmatized behaviours

(Turner et al., 1998), we might have had more valid

data if more objective outcomes measures (e.g.

medical records) had been used. Finally, as dis-

cussed earlier, we would have benefited from more

sensitive measures of health status and other con-

structs (e.g. provider-patient communication).

In sum, we generated a relatively short list of

correlates of health care utilization among our

sample of HIV-positive IDUs. We did not find

many significant need or predisposing factors, but

we did find a number of important enabling factors

that could be modified through interventions. These

factors include getting health insurance, seeing a

case manager, and positively engaging with health

care providers. Provision of health insurance and

case management particularly warrants attention of

interventionists as these enabling factors may help

diminish the negative effects of predisposing factors

(e.g. education) which many behavioural interven-

tions do not or cannot address. In addition, these

enabling factors might make lack of other enabling

factors (e.g. housing) less of a barrier to HIV-positive

injectors’ health care utilization. In the future, these

findings may be used to inform the development of

interventions that maximize use of scarce HIV

resources and improve health care utilization among

HIV-positive injecting drug users.

Acknowledgements

The INSPIRE Team includes the following people:

Carl Latkin, Amy Knowlton, and Karin Tobin

(Baltimore); Lisa Metsch, Eduardo Valverde, James

Wilkinson, and Martina DeVarona (Miami); Mary

Latka, Dave Vlahov, Phillip Coffin, Marc Goure-

vitch, Julia Arnsten, and Robert Gern (New York);

Cynthia Gomez, Kelly Knight, Carol Dawson Rose,

Starley Shade, and Sonja Mackenzie (San Francis-

co); David Purcell, Yuko Mizuno, Scott Santibanez,

Richard Garfein, and Ann O’Leary (Centers for

Disease Control and Prevention [CDC]); Lois

Eldred, Kathleen Handley (Health Resources and

Services Administration). We would also like to

acknowledgements the following people for their

contributions to this research: Susan Sherman,

Roeina Marvin, Joanne Jenkins, Donny Gann, and

Tonya Johnson (Baltimore); Clyde McCoy, Rob

Malow, Wei Zhao, Lauren Gooden, Sam Comer-

ford, Virginia Locascio, Curtis Delford, Laurel Hall,

Henry Boza, Cheryl Riles (Miami); George Fesser,

Carol Gerran, Diane Thornton (New York); Caryn

Pelegrino, Barbara Garcia, Jeff Moore, Erin Rowley,

424 Y. Mizuno et al.

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Debra Allen, Dinah Iglesia-Usog, Gilda Mendez,

Paula Lum, and Greg Austin (San Francisco);

Gladys Ibanez, Hae-Young Kim, Toni McWhorter,

Jan Moore, Lynn Paxton, and John Williamson

(CDC); Lee Lam, Jeanne Urban, Stephen Soroka,

Zilma Rey, Astrid Ortiz, Sheila Bashirian, Marjorie

Hubbard, Karen Tao, Bharat Parekh, Thomas Spira

(CDC Laboratory).

Note

The findings and conclusions in this paper are those of the authors

and do not necessarily represent the views of the Centers for

Disease Control and Prevention.

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