Context Studies of selected populations suggest that not all
persons infected with human immunodeficiency virus (HIV) receive
adequate care.
Objective To examine variations in the care received by a national
sample representative of the adult US population infected with HIV.
Design Cohort study that consisted of 3 interviews from January
1996 to January 1998 conducted by the HIV Cost and Services Utilization
Consortium.
Patients and Setting Multistage probability sample of 2864
respondents (68% of those targeted for sampling), who represent the
231,400 persons at least 18 years old, with known HIV infection
receiving medical care in the 48 contiguous United States in early 1996
in facilities other than emergency departments, the military, or
prisons. The first follow-up consisted of 2466 respondents and the
second had 2267 (65% of all surviving sampled subjects).
Main Outcome Measures Service utilization (<2 ambulatory visits,
at least 1 emergency department visit that did not lead to
hospitalization, at least 1 hospitalization) and medication utilization
(receipt of antiretroviral therapy and prophylaxis against
Pneumocystis carinii pneumonia).
Results Inadequate HIV care was commonly reported at the time of
interviews conducted from early 1996 to early 1997 but declined to
varying degrees by late 1997. Twenty-three percent of patients
initially and 15% of patients subsequently had emergency department
visits that did not lead to hospitalization, 30% initially and 26%
subsequently of those who had CD4 cell counts below
0.20,×109/L did not receive P
carinii pneumonia prophylaxis, and 41% initially and 15%
subsequently of those who had CD4 cell counts below
0.50×109/L did not receive antiretroviral
therapy (protease inhibitor or nonnucleoside reverse transcriptase
inhibitor). Inferior patterns of care were seen for many of these
measures in blacks and Latinos compared with whites, the uninsured and
Medicaid-insured compared with the privately insured, women compared
with men, and other risk and/or exposure groups compared with men who
had sex with men even after CD4 cell count adjustment. With
multivariate adjustment, many differences remained statistically
significant. Even by early 1998, fewer blacks, women, and uninsured and
Medicaid-insured persons had started taking antiretroviral medication
(CD4 cell count adjusted P values <.001 to <.005).
Conclusions Access to care improved from 1996 to 1998 but
remained suboptimal. Blacks, Latinos, women, the uninsured, and
Medicaid-insured all had less desirable patterns of care. Strategies to
ensure optimal care for patients with HIV requires identifying the
causes of deficiency and addressing these important shortcomings in
care.
The US health care
system is going through many changes that raise concerns about adequacy
of care, including new constraints on public programs1 and
changes in the insurance options available to the privately
insured.2-4 Ascertaining the impact of the emerging
organization of services on those most likely to benefit from care is
important but difficult. Neither national surveys of households or
medical providers nor most studies of specific diseases provide
sufficient information. The former studies do not include enough
patients with specific conditions of interest to analyze issues of
particular concern in the care of those conditions and do not collect
enough disease-specific information.5 The latter studies
usually cannot be broadly generalized, either because they are local or
use some form of purposive sampling to assemble a nonrepresentative
cohort. Consequently, evidence of disparities in care at 1 or a few
institutions may challenge assumptions about adequacy of care to
certain sectors of the population, but nationally representative data
are needed to determine if broader attention is merited.
We conducted a comprehensive study of health care for a national
probability sample of US adults with 1 important chronic disease, human
immunodeficiency virus (HIV) infection.6 Adequate treatment
for this disease is important because of the clear benefit
of therapy and because HIV-infected persons are
a paradigmatic example of patients who need complex
treatment.7 Studies with limited or uncertain
generalizability have raised concerns about the possibility of
inequities in realized access or receipt of needed HIV
care.8-12 In this article, we examine variations in the
care received by a national sample representative of the adult
HIV-infected population receiving regular medical care in the 48
contiguous United States from early 1996 to early 1998.13
The HIV Cost and Services Utilization Study (HCSUS) used multistage
national probability sampling to select the study
cohort.6,13 The reference population was persons at least
18 years old with known HIV infection who made at least 1 visit for
regular care in the contiguous United States to other than a military,
prison, or emergency department (ED) facility between January 5 and
February 29, 1996, except for 1 city where sampling began and ended 2
months later.
In the first stage of sampling, we randomly selected 28 metropolitan
statistical areas and 24 clusters of rural counties that together
contained nearly 70% of all acquired immunodeficiency syndrome (AIDS)
cases in the United States.14,15 In the second stage, we
randomly selected 58 institutional or individual physicians known to
care for patients with HIV infection (known providers) in urban areas
and 28 in rural areas, who had been identified by local physicians or
public health officials. Using data from the American Medical
Association Master File, we randomly sampled approximately 4000
physicians in relevant specialties among whom 87 physicians (other
providers) in urban areas and 23 in rural areas had confirmed in a
screening survey that they cared for eligible patients.
In the first stage, we set sampling rates proportionate to caseload
based on data obtained from the Centers for Disease Control and
Prevention and local health departments. In the second stage, sampling
rates were proportionate to caseload reported by the providers. In the
third stage, we set sampling rates to equalize probabilities within
subgroups while increasing the overall sampling rate for women and
members of private staff-model health maintenance organizations
(HMOs).6 In the third stage of sampling, subjects were
randomly selected with the appropriate predetermined probability from
anonymous lists of those who received outpatient or inpatient care from
participating providers during January and February 1996. We removed
duplicate patient codes from the patient lists and adjusted for the
fact that a patient might see more than 1 provider using multiplicity
weights as described below.
After the replacement of a single urban provider with an equivalent one
in the same community, we obtained agreement to participate from 100%
of known urban providers and 79% in rural areas, 70% of other urban
providers and 83% in rural areas, and 84% of selected patients. The
baseline coverage rate, or the ratio of the population directly
represented to the population that would have been represented if we
had complete cooperation at all stages, was 68% for long-form
interviews and 87% for long, short, and proxy interviews and forms
completed by providers when no interview was possible (nonresponse
forms). The short-form and proxy interviews were administered when
subjects were too ill or otherwise unable to complete the full
interview themselves. Nonresponse data included vital status, date of
death if applicable, sex, age, race or ethnicity, insurance status,
risk-exposure group, lowest and most recent CD4 cell counts, date of
CD4 cell count, any AIDS-defining illness, and any inpatient stays in
the last 6 months.
We performed 3 rounds of interviews: baseline, first follow-up, and
second follow-up. Respondents who did not complete a long-form
interview in the previous survey wave were not approached for the
subsequent wave. Centrally trained personnel from the National Opinion
Research Center, Chicago, Ill, used computer-assisted personal
interviewing to conduct in-person interviews.16 We
approached anonymously selected subjects for interview only after
providers or their agents obtained permission. The institutional
review board of RAND, Santa Monica, Calif, and, if available, a local
review board reviewed all forms and materials.
In the analyses described below, our baseline sample consisted of 2864
long-form respondents interviewed between January 1996 and April 1997
(71% of all sampled subjects). The first follow-up sample consisted of
2466 respondents interviewed between December 1996 and July 1997 (69%
of all surviving sampled subjects); the second follow-up sample
consisted of 2267 respondents interviewed between August 1997 and
January 1998 (65% of all surviving sampled subjects).
Using all available data, we constructed a baseline analytic weight for
each respondent to adjust the sample to represent the entire reference
population, not just the proportion directly represented by the sample.
Each weight, which can be interpreted as the number of persons
represented by that respondent, is the product of a sampling weight,
which adjusts for differential sampling probabilities; a multiplicity
weight, which adjusts for patients who could have entered the sample
through visits to multiple providers; and a nonresponse weight, which
adjusts for differences in rates of cooperation.6,17,18 The
sum of the baseline weights is an estimate of the size of the target
population represented by HCSUS. The first follow-up weight is the
product of the baseline analytic weight and an attrition weight, which
accounts for the attrition of baseline respondents who were not
successfully interviewed at first follow-up; the second follow-up
weight takes into account attrition of first follow-up respondents not
successfully
interviewed at second follow-up. Respondents who
died prior to a wave are not considered eligible for that wave.
The analyses presented herein are based primarily on 3 cycles of
90-minute long-form interviews. At each wave, we asked all subjects
about use of medical services and HIV medications during the preceding
interval (6 months at baseline or since the last interview). We also
inquired about their clinical status including their latest and lowest
CD4 cell counts. We asked respondents who did not recall the exact
value of their CD4 cell counts to provide a range.
We imputed missing values for essential covariates using a standard
"hot-deck" strategy19 with further details given by
Duan et al.17 Briefly, for each variable being imputed, we
classified all respondents into imputation classes based on observed
values for other variables. Then, for each respondent missing a value
for the variable being imputed, we randomly selected a donor value from
those respondents not missing a value in the same imputation class. At
baseline, we had to impute 4.9% of CD4 cell count values, less than
4% for income, less than 2% for insurance status, 1.4% of values for
ambulatory visits, and less than 0.5% of missing values in other
essential variables. The follow-up data had comparable amounts of
missing values, which were imputed using the same approach as at
baseline.
Information on the use of protease inhibitors (PIs) and nonnucleoside
reverse transcriptase inhibitors (NNRTIs) was obtained from
baseline, first follow-up, and second follow-up interviews. To obtain
comprehensive information on their use through January 1998, an
additional, brief interview was completed with most subjects in early
1998. We asked respondents for the date on which they began use. Thus,
for any particular month, we could determine the proportion who had
used the medications by that month. We imputed the date of probable
first use for the 10% of respondents who had not used these
medications at baseline and did not complete a follow-up
interview.17
Conceptual Model and
Measures of Utilization
Our model of utilization or access to needed HIV care assumes
that a need for care exists and that a less-than-optimal pattern of
care is reflected in infrequent office visits among persons known to
have HIV disease, in the use of EDs when there is no need for
hospitalization, in increased hospitalizations, and in failure to take
appropriate anti-HIV medications.
We constructed 3 measures of service and 3 measures of
pharmaceutical utilization to gain insight into the adequacy of the
access to needed care. The measures of service utilization included
having, in the 6 months prior to the interview: (1) fewer than 2
ambulatory visits, (2) at least 1 ED visit that did not lead to a
hospitalization, and (3) at least 1 hospitalization. Two visits were
selected as an initial threshold for adequate care because 1 visit
every 3 months is about the upper limit of an acceptable interval for
monitoring disease events and response to treatment in most patients.
While some hospitalizations are probably inevitable as HIV disease
progresses, higher hospitalization rates clearly result from failure to
receive indicated outpatient therapy. Ambulatory care that is
sufficiently frequent, high in continuity, and adequate in quality
should prevent some complications and decrease the rate of
hospitalization, even in the advanced stages of the disease. Persons
with good continuity of ambulatory care should, likewise, be better
able to avoid using the ED in nonemergent situations.
The measures of medication utilization included having taken: (1)
prophylaxis against Pneumocystis carinii pneumonia (PCP) in
the 6 months preceding the interview for those subjects whose lowest
CD4 cell count was less than 0.20 × 109/L, the accepted
threshold for this treatment; (2) at least 1 antiretroviral medication
at any time prior to the baseline interview or second follow-up
interview, among subjects with a CD4 cell count of less than 0.50 ×
109/L (Table 1, Table 2, and Table
3); and (3) either PI or NNRTI therapy by a specified date (December 1996
for the initial analyses presented in Table 1 and Table 3, and January
1998 for the analyses presented in Table 2), also among subjects with
CD4 cell counts of less than 0.50 × 109/L. We asked about
lifetime use of medications in these last 2 categories because of the
possibility that subjects might have discontinued therapy prior to the
time covered by the interview. We conducted additional analyses of
these 2 variables that included all subjects irrespective of CD4 cell
count. We constructed the variable indicating use of newer
antiretroviral therapies from baseline, first follow-up, and second
follow-up interview data as previously noted.17 To gain
insight into the stability of differences in the use of these
medications across population subgroups, we also examined patterns of
dissemination of these therapies from January 1996 to January 1998,
thereby encompassing a period during which there was growing awareness
and wide publicity of the effectiveness of these
treatments.20
For the baseline sample and using the baseline long-form data, we
estimated the weighted proportions and odds ratios for the utilization
measures across categories defined by lowest CD4 cell count (Table 1). For each measure of utilization, we then adjusted for possible
confounding via weighted logistic regression. We adjusted all SEs and
statistical tests to compensate for the complex sampling design and
differential weighting using linearization methods available in the
SUDAAN (Research Triangle Institute, Research Triangle Park, NC) and
Stata software packages (Stata Corp, College Station,
Tex).21
We estimated 2 main effects models with different covariates to
determine which covariates were most important in explaining
differences among subgroups in the access measures. Model A includes
CD4 cell count; model B adds age, sex, race or ethnicity, exposure
group, insurance coverage,
education, and region to model A. (Results are
shown as statistical significance in Table 1 and Table 2 both with and
without adjustment for CD4 cell count [model A]. Table 3 presents the
results of the model B analyses.)
To assess the sensitivity of findings to model specifications, we also
conducted a number of exploratory
analyses. In addition to the main analysis of 2 or
more ambulatory visits in the preceding 6 months, we examined
variations in receipt of 3 or more and 4 or more visits. The pattern of
variations in disparities were very similar with these other measures.
We stratified the highly associated sex and risk variables in a single,
7-category variable for alternative analyses (male injection drug
users, female injection drug users, men having sex with men, male
heterosexuals, female heterosexuals, male other, or female other, with
the hierarchy among women being injection drug users, heterosexuals,
and others), again with little or no impact on the analyses. We
examined variation in the rate
of hospitalization within CD4 cell count strata
because of the strong effect of disease progression. Finally, we
examined the patterns of PI and NNRTI use by additional specific dates
during 1996. (Results of the regressions discussed in the text that are
not presented in the tables are available from the authors on request.)
To assess change over time, we estimated the proportions for the
utilization measures across subgroups at the second follow-up. To
separate the effects of time vs attrition, we focused on the second
follow-up sample only. As a result, our reference population for this
analysis is the baseline reference population who survived until the
second follow-up. We estimated this population's proportions at second
follow-up, and conducted tests of these proportions adjusted for CD4
cell count. We also analyzed the percentage change since baseline in
utilization proportions for this population, and we discuss some of
these findings in the text.
Patterns of Care in 1996
and Early 1997
The HCSUS data are weighted to represent 231,400 adults who
received care in the 48 contiguous United States outside of EDs,
prisons, or the active military during a 2-month period in early
1996.13 While a majority of these persons represented by
the HCSUS met the standards represented by our 6 utilization measures
during 1996 and 29% were in compliance with all of them, many did not.
Overall, 15% of the represented population had fewer than 2 ambulatory
visits in the previous 6 months, 23% had at least 1 ED visit that did
not lead directly to hospitalization, and 19% were hospitalized at
least once during the same period (Table 1). Forty-one percent of those
with CD4 cell counts of less than 0.50 × 109/L had not
received PI or NNRTI therapy by the end of 1996, and 30% of persons
with CD4 cell counts of less than 0.20 × 109/L had not
taken medications to prevent PCP in the 6 months prior to their
interview, but only 8% had never taken any antiretroviral therapy by
the time of their initial interview.
Use of needed care differed by CD4 cell count, age, sex, race or
ethnicity, HIV risk–exposure group, insurance, socioeconomic status,
and region (Table 1). The CD4 cell count was associated with care for
all 6 measures tested, with a lower CD4 cell count being associated
with more use of medications, hospitalization, and ambulatory care. The
relationship between age and use of needed care was mixed; HIV-infected
persons who were at least 50 years old were less likely to use EDs but
were more likely not to take indicated PCP prophylaxis. Women were more
likely than men to use the ED and to be hospitalized and were more
likely not to have taken indicated PCP prophylaxis or to have started
PI or NNRTI therapy by the end of 1996. Race or ethnicity was
associated with the adequacy of care for 5 of 6 measures. Considering
individual comparisons, care received by blacks and Latinos was
significantly less optimal than that received by whites on 6 and 4 of 6
measures, respectively.
Exposure route was associated with care received on 4 of 6 measures.
Those having either injection drug use or heterosexual contact as their
risk factor had less favorable patterns of use than men who had sex
with men (Table 1). The care received by both uninsured patients and
those with Medicaid insurance was unfavorable compared with that
received by the privately insured on 5 of 6 access measures. Medicare
beneficiaries, most of whom had also had Medicaid, had high rates of ED
and hospital use, similar to subjects who had Medicaid only. However,
persons with Medicare coverage were more likely to have started PI or
NNRTI therapy and were less likely to be deficient in PCP prophylaxis
compared with those with Medicaid coverage alone. Those enrolled in
HMOs had a pattern of care very similar to others with private
insurance, except that they were more likely to have received
appropriate PCP prophylaxis. Those with the most education had a more
desirable pattern of care than others for 4 of 6 measures, including PI
and NNRTI therapy and PCP prophylaxis. The pattern of care varied by
income in a similar way, with the lowest income group having the least
favorable rates for most measures (data not shown). Finally, care
varied by region for 4 of 6 measures. Patients in the Northeast were
more likely to use an ED, be hospitalized, or miss indicated PCP
prophylaxis, while those in the South were more likely to have not used
a PI or NNRTI.
We reassessed compliance with the standards during the latter half of
1997 and early 1998, after a median of 15.1 months of follow-up (Table
2). At the second follow-up interview, 16% of patients represented had
fewer than 2 visits per 6 months, 16% had an ED visit without
hospitalization within the prior 6 months, and 14% were hospitalized
at least once during the same period. In addition, 15% had not
received indicated PI or NNRTI therapy by January 1998, 3% had never
taken any antiretroviral therapy, and 26% had not taken indicated PCP
prophylaxis. Overall, the percentage who were in compliance with all 6
indicators had increased from 29% to 47% and the percentage who were
noncompliant with 2 or more measures had fallen from 34% to 17%.
Relative to baseline values, there was virtually no change in the
proportion meeting the standard of 2 outpatient visits in 6 months (Table 2). Interestingly, there was an increase of 26% in the
proportion not having at least 3 ambulatory visits, perhaps reflecting
improving health in the population (data not shown). For the other 5
indicators, there were declines in the proportions of subjects not
meeting the standards in the indicators. Of note, the improvement in
proportion of persons with CD4 cell counts not receiving PCP
prophylaxis (from 30%-26%) was much more modest than the decline in
the proportion not having taken
PI and NNRTI therapy (41%-15%). At each point
in time, almost one quarter of persons who had initiated PI and NNRTI
therapy did not receive indicated PCP prophylaxis. Significant
predictors of improvement were CD4 cell count and race for 3 indicators
each, and exposure and region for 1 indicator each (data not shown).
Despite these improvements, there continued to be significant
differences in compliance by CD4 cell count for 5 of 6 measures, by
exposure group, insurance type, and region for 4 each, and by sex and
race or ethnicity for 3 each (Table 2).
Although the unadjusted values given accurately describe variations in
the care actually received by HIV-infected US adults, we constructed
multiple logistic models to test the independent fixed effects of the
factors listed above. We evaluated the 6 measures with respect to 7
demographic characteristics, for a total of 42 assessments. The CD4
cell count adjustment had little impact: in the initial period, of 30
unadjusted significant overall effects within the 7 demographic
variable categories (sex, age, race or ethnicity, insurance status,
exposure group, education, and region) in our 6 measures (out of a
possible 42), 28 effects remained significant with CD4 cell count
adjustment (Table 1). Twenty-two of 42 unadjusted effects were
significant in the second period examined, and 20 of these remained
significant with CD4 cell count adjustment (Table 2). Although some
overall effects within demographic variable categories lost
significance, in models that included all 7 categories of variables
listed above plus CD4 cell count, 15 of 42 possible effects (other than
by CD4 cell count) remained significant in the initial period (Table
3). Eleven of 18 possible effects were significant in the multivariate
models, for insurance, race or ethnicity, and age (including 5 of the 6
access indicators for insurance, 3 of the 6 indicators for race or
ethnicity, and 3 of the 6 indicators for age), compared with 14
significant effects for these 3 variables with CD4 cell count
adjustment alone. On the other hand, only 4 of 24 possible effects were
significant in the multivariate models for sex, education, exposure
group, and region compared with 15 effects with CD4 cell count
adjustment alone.
Even with the differential improvement rates, significant differences
in the presence of deficiencies persisted for 20 of 48 possible
predictors at follow-up in the full multiple regression model. These
included race or ethnicity for 1 of 6 indicators; age for 2; sex,
insurance, and exposure group for 3 each; region for 4; and CD4 cell
count for 5 (data not shown). While there was less evidence of an
independent overall effect of race or ethnicity at follow-up, important
CD4 cell count–adjusted differences remained between whites and blacks
in PI and NNRTI use and between whites and Latinos in the pattern of
ambulatory visits (Table 2).
PI and NRRTI Use Over Time
To explore further the differences in access to a key component
of care over time, we examined the proportion of the HIV-infected adult
population that had ever used PI or NNRTI over a 2-year period.
Overall, use of these drugs grew very rapidly throughout this period
among persons with a CD4 cell count lower than 0.50 ×
109/L, increasing from 17% in January 1996 to 59% in
January 1997, and to 85% in January 1998 (Figure
1). Generally, use varied inversely by CD4
cell count (Figure 1). In addition, there were clear and
substantial differences in cumulative use of
these medications by sex, race, and insurance with women, minorities,
and the underinsured being less likely to have received these drugs (Figure 1 and Figure 2). The 24%
difference in initial use of these medications in
late 1996 (P<.001, Table 1) between blacks and whites had
declined to 8% in early 1998, but remained significant, even with CD4
cell count adjustment (P=.016, Table 2). Similarly, the difference in treatment rates between those uninsured
and those privately insured declined from 26% (P <.001) to
12% (P=.016). The rate for Medicaid
beneficiaries was within 2% of the rate for the uninsured at the later
date and significantly different from that of the privately insured
(P<.001); both remained significantly different from those
who were privately insured, even with multivariate adjustment
(P=.026, Medicaid;
P=.048, no insurance). Finally, the difference
between men and women (12% in late 1996) declined very little and was
9% in early 1998 (P=.003, P<.001,
respectively, with CD4 cell count adjustment).
Even though the differences between certain groups abated over time,
the lag in dissemination to traditionally disadvantaged groups meant
that these groups waited much longer before having an opportunity to
try these agents. For example, on average men waited 11.2 months from
the beginning of 1996 while women waited 13.5 months
(P≤.001); blacks waited 13.5 months while whites waited 10.6
(P<.001); uninsured persons waited 13.9 months while
Medicaid beneficiaries waited 12.4; and those with private
fee-for-service insurance waited 9.4 (P≤.001;
P<.002, respectively). There were other contrasts of
interest: southerners waited 13.4 months compared with 10.6 to 10.8
months elsewhere (P=.03). Overall, in a
multiple regression model, CD4 cell count, sex, race, education,
insurance, and risk or exposure were the independent significant
predictors of delay in time to first use of a PI or NNRTI
(P<.05).
This first detailed examination of medical care for a
specific chronic condition in a nationally representative sample of
Americans has yielded a mixed picture. Realized access, or use of
needed care, is good for many HIV-infected patients. At the time of the
first study interview, about 80% met each of our individual standards
for continuity of outpatient services. About 90% of those with
indications had received antiretroviral therapy, and nearly 60% had
received PIs within a year of these agents' approval. However, 30% of
those with indications denied taking medications to treat or prevent
PCP. Moreover, less than 30% of patients were in compliance on all of
the measures for which they were eligible. Finally, the deficiencies in
HIV care were unevenly distributed across the population.
Disparities in HIV care cut across important population
categories, with disadvantaged populations having the least favorable
patterns of use. Compared with whites, use was less optimal for blacks
and Latinos for 5 and 3 measures, respectively, of the 6 measures
studied, in models that were statistically adjusted for CD4 cell count,
age, sex, and exposure group. Addition of insurance to the models
attenuated these effects for many of the measures, suggesting that
insurance contributes importantly to racial and ethnic differences in
receipt of care. Women and persons whose exposure group was not men
having sex with men fared worse on most measures; however, most of
these effects were
mitigated or eliminated by covariate adjustment,
suggesting that at least some of the less desirable pattern of care
experienced by women at that time was related to insurance coverage and
other demographic characteristics, notably race or ethnicity.
Those who lacked health insurance fared worse in terms of most
measures. Surprisingly, those with Medicaid also were deficient in many
of the categorical measures relative to private insurance. We found no
evidence of an unfavorable pattern of care among private HMO enrollees.
Along with Medicare beneficiaries (most of whom must be followed by the
health care system long enough to establish 2 years of disability),
they were most likely to receive indicated prophylaxis against PCP.
The results of the interview in late 1997 and early 1998 indicate
important changes in the pattern of care. The proportion of persons
taking PI or NNRTI therapy for the first time continued to rise until
near the end of that time, by which time 85% of all persons with CD4
cell counts of less than 0.50 × 109/L had tried these
agents. There continued to be differences in use across demographic
groups, but many were of lesser magnitude than those groups that had
been evident a year earlier. For example, the difference
between blacks and whites had decreased from 24% to 8% and the
disparities by insurance category declined by about half. On the other
hand, there was no further narrowing of the gap between men and women.
At the same time, some other access measures had improved.
Hospitalization rates and ED use both declined overall, and the gaps
declined among subgroups. By early 1998, almost all subjects had taken
an antiretroviral medicine at some time if their CD4 cell counts were
less than 0.50 × 109/L. On the other hand, the proportion
of persons taking PCP prophylaxis had improved only very modestly, and
26% of those with an indication reported not receiving any in the
preceding 6 months.
Thus, there was a substantial improvement in access to care in
1997 and 1998 (with the proportion not deficient in any access measures
for which they were eligible increasing from 29% to 46%), but some
important deficiencies remained, and disparities by race, insurance
status, and sex persisted, although their magnitude had lessened.
Overall, the HCSUS data show that disenfranchised groups in the United
States are less likely to receive medications for HIV that are
potentially lifesaving, yet being in an advantaged group does not
guarantee access to ongoing effective care.
As noted by Bozzette et al,13 the study represents
only persons receiving ongoing care and used a sampling strategy that
is likely to underrepresent those with very poor access, those who are
less compliant, and those who are relatively healthy; however, it does
account for the 32% of nonrespondents to the full interview with proxy
and nonresponse data. The study uses self-report data. Patient reports
are subject to recall bias. To minimize this, subjects were shown
photographs of antiretroviral medications when questioned about their
use. Patient reports of CD4 cell count have been shown to be reliable
and valid in relation to critical thresholds at which the therapies
queried in this article are recommended (<0.20, or <0.50 ×
109/L).22 Data on use of newer medications
represent the practices through early 1998 for persons who received
ongoing care for HIV disease in early 1996 but not for persons who may
have entered care after the eligibility period for this study. It is
also possible that intergroup differences in use of services and
medications reflect, in part, patient choice and/or nonadherence rather
than social or system factors. The findings relating to regional
differences in hospitalization rates may reflect, in part, regional
differences in practice style; they are consistent with national data
on overall hospitalization rates.23 Finally, the 6 measures
of access are not measuring independent phenomena, and there is some
correlation among them.
Nonetheless, several conclusions can be drawn from the patterns of
deficiencies in care uncovered by this study. First, being uninsured
and having HIV is a serious problem and places the individual at risk
of not obtaining adequate care. Second, having Medicaid does not result
in as appropriate of a pattern of use for needed treatments as does
having private insurance. Third, blacks and Latinos under care for HIV
also have less favorable patterns of use of needed services than do
whites, patterns that cannot be explained by other characteristics of
these patients. Fourth, use varies by certain other characteristics,
including sex, exposure group, income, and education. While these
disparities diminished over time, they did not disappear. In the
initial period, patient insurance status, race or ethnicity, and age
were the most important explanatory variables; in the second period
studied, sex, exposure group, insurance, and region were each an
independent predictor of access for at least 3 of the 6 access
variables. Strategies to ensure optimal care for persons infected with
HIV in the US population need to take into account these patterns of
deficiency and the fact that no one characteristic of disenfranchised
populations is responsible for them.
What are the implications of these findings for the future of
care for HIV disease? Up-to-date treatment for those infected with HIV
offers the prospect of long-term survival, but such therapy is likely
to continue to evolve rapidly. Patients need to be in continuous
relationships with providers who can monitor their clinical status and
their compliance with therapy and who can assess their need for
modifications in their therapy if they become resistant to their
regimens. As newer therapies become available, members of the medical
community must be concerned that such treatments may not disseminate
therapy at similar rates throughout the populations who need them, as
the HCSUS data show occurred with the PI and NNRTI medications. A
lag in obtaining newer therapies may well place patients at risk of
death and other serious complications.
In the last 2 years, HIV has become a highly treatable
disease.7,20 For much longer, it has been clear that good
medical care can prolong life by preventing
complications and attacking the infecting organism. While access to HIV
care is good for many adults and improving for others, it is still not
nearly optimal. These data show a pattern of less access to
high-quality HIV care for disadvantaged groups across the United
States, and these data suggest the need for comprehensive efforts to
improve patient care. Such efforts should include steps to ensure that
lags in access to newer HIV treatments are not recapitulated with each
improvement in treatment.
There have been major advances in recent years in the care
of many other chronic diseases, including heart disease, diabetes
mellitus, and some cancers. Representative national data regarding care
for these conditions would help us to determine the extent to which
variations in care are contributing to inequities in the health care of
the US population. Such populations need to be monitored longitudinally
because of the likelihood that the patterns of any disparities in care
may well change over time as they have in the case of HIV
disease.24
1.Miller RH, Luft HS. Managed care plans:
characteristics, growth, and premium performance.
Annu Rev Public
Health.1994;15:437-459.Google Scholar 2.Robinson JC, Casalino LP. The growth of medical groups
paid through capitation in California.
N Engl J Med.1995;333:1684-1687.Google Scholar 3.Welch WP. Growth in HMO share of the Medicare market,
1989-1994.
Health Aff (Millwood).1996;15:201-214.Google Scholar 4.Gold M, Sparer M, Chu K. Medicaid managed care: lessons
from five states.
Health Aff (Millwood).1996;15:153-166.Google Scholar 5.Shapiro MF, Berk ML, Berry SH.
et al. National
probability samples in studies of low-prevalence diseases, I:
perspectives and lessons from the HIV Cost and Services Utilization
Study.
Health Serv Res.In press.Google Scholar 6.Frankel MR, Shapiro MF, Duan N.
et al. National probability samples in studies of low-prevalence diseases, II:
designing and implementing the HIV cost and services utilization study
sample.
Health Serv Res.In press.Google Scholar 7.Carpenter CC, Fischl MA, Hammer SM.
et al. Antiretroviral therapy for HIV infection in 1997: updated
recommendations of the International AIDS Society-USA panel.
JAMA.1997;277:1962-1969.Google Scholar 8.Stone VE, Mauch MY, Steger K, Janas SF, Craven DE. Race,
gender, drug use, and participation in AIDS clinical trials: lessons
from a municipal hospital cohort.
J Gen Intern Med.1997;12:150-157.Google Scholar 9.Niemcryk SJ, Bedros A, Marconi KM, O'Neill JF. Consistency in maintaining contact with HIV-related service providers:
an analysis of the AIDS Cost and Services Utilization Study (ACSUS).
J Community Health.1998;23:137-152.Google Scholar 10.Fleishman JA, Hsia DC, Hellinger FJ. Correlates of
medical service utilization among people with HIV infection.
Health Serv Res.1994;29:527-548.Google Scholar 11.Moore RD, Stanton D, Gopalan R, Chaisson RE. Racial
differences in the use of drug therapy for HIV disease in an urban
community.
N Engl J Med.1994;330:763-768.Google Scholar 12.Chaisson RE, Keruly JC, Moore RD. Race, sex, drug use,
and progression of human immunodeficiency virus disease.
N Engl J
Med.1995;333:751-756.Google Scholar 13.Bozzette SA, Berry SH, Duan N.
et al. The care of
HIV-infected adults in the United States.
N Engl J Med.1998;339:1897-1904.Google Scholar 14.Kish L. Survey Sampling. New York, NY: John
Wiley & Sons Inc; 1965.
15.Lam NSN, Liu KB. Use of space-filling curves in
generating a national rural sampling frame for HIV/AIDS research.
The Professional Geographer.1996;48:321-332.Google Scholar 16.Berry SH, Brown JA, Athey L.
et al. HCSUS Baseline
Patient Questionnaire Documentation. Santa Monica, Calif: RAND;
1998. MR-1090-AHCPR.
17.Duan N, McCaffrey DF, Frankel MR.
et al. HCSUS
Baseline Methods Technical Report. Santa Monica, Calif: RAND; 1998.
MR-1060-AHCPR.
18.Sirken MG. Household surveys with multiplicity.
J
Am Stat Assoc.1970;65:257-266.Google Scholar 19.Brick J, Kalton G. Handling missing data in
survey research.
Stat Methods Med Res.1996;5:215-238.Google Scholar 20.Carpenter CC, Fischl MA, Hammer SM.
et al. Antiretroviral therapy for HIV infection in 1996: recommendations of an
international panel, International AIDS Society-USA.
JAMA.1996;276:146-154.Google Scholar 21.Kish L, Frankel MR. Inference from complex samples.
J R Stat Soc B.1974;36:1-37.Google Scholar 22.Cunningham WE, Rana HM, Shapiro MF, Hays RD. Reliability and validity of self-report CD4 counts in persons
hospitalized with HIV disease.
J Clin Epidemiol.1997;50:829-835.Google Scholar 23.Graves E, Gillum B. National Hospital Discharge Survey:
annual summary, 1994.
Vital Health Stat 13.1997;128:1-50.Google Scholar