• Loading...

    DSHS HIV/STD Program

    Post Office Box 149347, MC 1873
    Austin, Texas 78714

    Phone: (512) 533-3000

    E-mail the HIV/STD Program

    E-mail data requests to HIV/STD Program - This email can be used to request data and statistics on HIV, TB, and STDs in Texas. It cannot be used to get treatment or infection history for individuals, or to request information on programs and services. Please do not include any personal, identifying health information in your email such as HIV status, Date of Birth, Social Security Number, etc.

    For treatment/testing history, please contact your local Health Department.

    For information on HIV testing and services available to Persons Living with HIV and AIDS, please contact your local HIV services organization.

Unmet Need Estimates for HIV-Related Medical Care in Texas

Loading...

Once diagnosed, a person with HIV needs life-long, regularly scheduled medical care [1]. This allows the patient and care provider to monitoring disease status, make decisions about when to start or adjust complex antiretroviral treatment, and managing other health conditions that may be affected by HIV infection. Continuous medical visits, lab screens, and medication adherence are associated with decreases in deaths and a slower onset of AIDS. In fact, average life expectancy after HIV diagnosis increased from 10.5 years in 1996 to 22.5 years in 2005, and much of the improvements in survival among HIV patients in industrialized countries can be attributed to the introduction of effective treatment in 1996.

Despite these improvements, the number of new diagnoses of HIV has not decreased in Texas. While there are many complex reasons for this, two issues stand out: there are too many people with HIV in Texas with undiagnosed or late diagnosed infections, and there are too many people with HIV who are not receiving treatment for their infections. Persons with undiagnosed or untreated infections have higher viral loads, and these higher viral loads increase the chances of further transmission. While primary preventive actions are important (that is, preventing someone from getting HIV), improvements in diagnosis and participation in care provide the “breathing space” for primary prevention to work at a population level.

This indicator shows a series of snapshot estimates of the persons in Texas with diagnosed HIV infections who are not receiving treatment for their HIV. A person living with HIV is said to have unmet need for medical care if there is no evidence of a CD4 count, a viral load test, antiretroviral therapy or an outpatient/ambulatory medical care visit during a defined 12 month period.

Of course, this is not a complete picture of a complex issue. The staff at DSHS who work on the Texas Unmet Project have looked at unmet need in several ways, including:

  1. estimating the number and proportion of persons living with HIV in Texas who know their status and are not in medical care (2007-2010 unmet need);
  2. estimating the number and proportion of persons living with HIV in Texas retained in care consecutively since 2007 or since their entry into care (if entered after 2007);
  3. estimating number of newly HIV diagnosed individuals in Texas linked to medical care within three months of their HIV diagnosis; and
  4. estimating the number and proportion of persons living with HIV getting two visits for routine HIV medical care at least three months apart (outpatient/ambulatory medical visits, CD4 labs, viral load labs).

This indicator looks at only the first issue; separate reports on the other objectives are available in the 2011 HIV/STD Epidemiologic Profile.

This estimate starts with all reported cases of HIV infection in Texas who were diagnosed and alive as of the end of 2010, and then looks across a variety of data sources for evidence of medical visits, monitoring labs, or prescriptions for ARV. The details of this analysis can be found in the Epi Profile (PDF : 3,348 kb).

Changes in Unmet Need: 2007-2010

Although the number of persons living with HIV/AIDS increased by 16% over the 2007-2010 period, the number of persons with unmet need stayed stable, with about 21,000 to 23,000 persons with unmet need each year. Increases in the total number of living cases, but flat numbers out of care mean that the proportion of people with unmet need dropped by about five percentage points from 38% in 2007 to 33% in 2010.

San Antonio, Austin, and Fort Worth showed large decreases in the numbers of persons with unmet need, as shown in Table 1, below. San Antonio showed a 14% decrease in the number of persons with unmet need. The number of persons in Austin and Forth Worth with unmet need decreased by 4%-5%. For all three areas, these decreases were observed during a time when the number of PLWH increased by 14-18% in each area.

A note of caution: estimates of unmet need calculated in this way are very sensitive to improvements in data availability and quality and not completely to improvements in care.

Table 1

Unmet Need Table 1
Table 1 (PDF : 11 kb)


Detailed Breakdown of Unmet Need in Texas, 2010

For the year 2010, there is no evidence of HIV-specific medical care for a third (33%) of all PLWH in Texas (21,553 people).

Estimates of unmet need for 2010 vary by geographic area; for this analysis, we focus on the five Ryan White eligible metropolitan area/transitional grant areas (EMA/TGA). It also varies race/ethnicity, sex, age, and mode of transmission). It also varies by length of time since first HIV diagnosis, whether or not a person had a late HIV diagnosis, 2010 HIV/STD co-morbidity and a history of suffering a HIV/TB co-morbid condition. Only some of these differences are outlined here, and interested readers should go to the HIV/STD Epidemiologic Profile for more discussion.

Focusing on groups with the largest numbers with unmet need will help address the overall problem of unmet need, but if the proportion of unmet need in one group is much higher than other groups, it raises questions about barriers to care that are unique to that group.

Keeping in mind that there were 21,553 people with unmet need, the largest groups out of care were:

  • Males: 17,233 people
  • People living with HIV (not AIDS): 11,939 people
  • Men who have sex with men (MSM): 11,079 people
  • People in the Houston EMA: 7,129
  • People ages 35-44 (6,488 people) and ages 45-54 (6,462 people)
  • Black Men: 6,376 people

Some groups with large numbers of people out of care (namely, people living with HIV, Black Men, MSM, and people in the Houston EMA) are also profiled in the next section, which discusses groups with disproportionate shares of unmet need. Groups that have both a large number and a large proportion of people out of care should receive priority attention when creating strategies for increasing participation in treatment.

Demographic group estimates of unmet need

Table 2 shows the number of PLWH in each of the EMA/TGAs with unmet need in 2010. Keeping in mind that the overall state rate of unmet need was 33%, some groups had notably higher rates, including:

  • People living with HIV (not AIDS) (41% or 11,939 people)
  • Blacks (36% or 8,975 people), which is mostly attributed to unmet need in Black men in all major exposure categories (36-47%)
  • People ages 25-34 (37% or 4,347 people)
  • People in the IDU category (41% or 3,654 people)
  • People in the MSM/IDU category (39% or 1,626 people)
  • People with a timely HIV diagnosis (not a late diagnosis) (35% or 16,100)

Table 2

Unmet Need Table 2
Table 2 (PDF : 14 kb)

Regional estimates of unmet need by demographic group

  • Patterns of unmet need also differ across areas of Texas, and these can also be seen in Table 2. For example, Blacks and Hispanics in the Houston area have higher rates of unmet need, as do IDU diagnosed in the Dallas and Houston areas.

Estimates of unmet need by diagnosis (HIV or AIDS)

Table 3 reports the number and proportion of unmet need separately for HIV (not AIDS) cases and AIDS cases. In doing so, the following groups have higher than average proportions of people not in care:

  • Blacks living with HIV (47% or 5,662 people)
  • Males living with HIV (42% or 9,130 people)
  • People living with HIV ages 25-34 (46% or 3,391 people)
  • IDU and MSM/IDU living with HIV (53% and 50% or 1,997 and 729 people)

Table 3

Unmet Need Table 3
Table 3 (PDF : 14 kb)

It is evident that the proportion of people living with HIV (PLWH) with unmet need is greater than the proportion of unmet need among people living with AIDS (PLWA). Some of this difference may be attributable to the interaction of the case definitions for AIDS and met need: a large proportion of AIDS cases meet the case criteria for AIDS as a result of CD4 testing, which is also an indicator of met need. Nevertheless, Whites have roughly similar proportions of unmet need regardless of disease status, while the average state and regional proportions of Black PLWH with unmet need are, without exception, higher than the proportion of Black PLWHA with unmet. Overall, a greater proportion of all Black PLWHA are out of care when compared with White PLWHA, but the greatest disparity is seen among Blacks with HIV compared with Whites with HIV (47% versus 33% have unmet need). In other words, whereas one out of every three White males with HIV was out of care in 2010, nearly one out of every two Black males with HIV was out of care in 2010.

Building on the disparity seen above, we can see that in some areas, the proportion of Blacks with HIV with unmet need is nearly twice that of Blacks diagnosed with AIDS. Driven by the Houston EMA, TDCJ, and elsewhere in the state not part of an EMA/TGA, the statewide average for Hispanic PLWH with unmet need (41%) is above the state average.

Estimates of unmet need by race/ethnicity and sex

Table 4 compares the proportion of need met by race/ethnicity and gender among people living with HIV/AIDS. The average proportion of unmet need for males is similar to the state average (34% versus 33%), though this conceals the fact that Black men have higher than the average proportions of unmet need everywhere but the Austin TGA. Not only did Black males have the largest proportion of unmet need (39%) they also have the most absolute numbers of people with an unmet need for medical care out of all races/ethnicities and both genders (6,376 people out of care). Hispanic men also have higher than average proportions of unmet need in several regions: the Houston EMA, the TDCJ, and in other regions in Texas. These two populations (Hispanic and Black males) have a marked disparity in proportion of unmet need.

Table 4

Unmet Need Table 4
Table 4 (PDF : 11 kb)

Generally speaking, women have a smaller proportion of unmet need when compared with men (see Table 3). Although the proportion of Black females out of care was the same as the state average for women (30%), they comprised the majority (60% or 2,599 people) of all women not in care in the state of Texas. This is an example of how absolute numbers of groups can be important, even when the proportion of interest among different groups is the same. The large numbers of Black females not in care are concerning and follow the trend observed among Black males.

Estimates of unmet need by exposure group, race/ethnicity, and sex

To further describe the populations without evidence of medical care in 2010, Table 5 combines HIV/AIDS cases but crosses mode of exposure by gender and race allowing for the comparison of unmet need between the racial/ethnic groups, males and females, and the different modes of exposure. This information is presented to spur further conversation at the community level.

Table 5

Unmet Need Table 5
Table 5 (PDF : 15 kb)


Third Level Supplement: Data Sources and Matching

Matching methods varied depending on the type of information that was available for matching. For data sets where names and other personal identifiers (e.g. date of birth) were available, Link King or other linking algorithms (ELR data only) were used for matching. When only unique record numbers or limited data elements were available (e.g. first and third initial of first and last name combined with date of birth) were available, exact matching using SAS 9.2 was used.

The following data sets were matched against HIV/AIDS cases in eHARS to determine if a client had a met medical need:

  • Electronic HIV/AIDS Reporting System (eHARS) -This is the data source that is used as the universe of HIV/AIDS cases for estimating unmet need, retention to care for PLWH, linkage to care for newly diagnosed individuals and continuity of care for outpatient/ambulatory medical care visits, CD4 labs, and viral load labs. 
  • Texas AIDS Drug Assistance Program (ADAP) or State Pharmacy Assistance Program (SPAP) – If ADAP/SPAP provided antiretroviral (ARV) medications for a client, then that person was considered to have met medical need for the year in which the medication was provided. Name based matching was performed to determine persons with a met medical need during 2010.
  • Electronic Lab Reporting (ELR) – The largest providers of laboratory services throughout the state report CD4 and viral load labs to DSHS. Name based matching of these reports was used to determine if individuals received a CD4 count or viral load test during 2010. Please note that most paper-based labs and labs reported directly to the Houston health jurisdiction were not available at the time these measures were developed and are not reflected in the estimates.
  • AIDS Regional Information and Evaluation System (ARIES) – Services provided to Ryan White eligible clients by funded service providers are reported in ARIES. If a client received a viral load, CD4 count, laboratory service, ARV medication, or an outpatient/ambulatory visit medical care during 2010, the client was reported as having a met medical need during that year. When available, name based matching was used to determine persons with a met medical need during 2010. When client names were not available, matching was based on a unique record number generated in ARIES and eHARS.
  • Medicaid/ Children's Health Insurance Program (CHIP) – If a client received a viral load, CD4 count, laboratory service, ARV medication, or an outpatient/ambulatory medical visit through Medicaid/CHIP during 2010, the client was reported as having a met medical need during that year. Name based matching was performed to determine persons with a met medical need during 2010. Please note that at the time of the project, the fourth quarter of the 2010 Medicaid/CHIP data was not available for release at the time these estimates were developed and are not reflected in the estimates.
  • Private Insurers – For this analysis, a few of the largest private providers in Texas extracted relevant procedures (CD4 counts, viral load measurements, ARV, or an outpatient/ambulatory medical visit) from their claims systems. Matching was based on available data elements such as first and third initial of first and last name, date of birth and sex.

Methods

The midyear 2010 eHARS dataset (6/30/2011) was used for the 2010 unmet need estimates, 2007-2009 unmet need updated estimates, retention to care estimates, linkage to care estimates, and the continuity of care measures (OAMC visit, CD4 labs, and viral load labs). Diagnosed HIV/AIDS cases that had been entered and were living on 12/31/2010 were included for the total population for unmet need in 2010. Using the datasets and matching methods described above, persons living with HIV were identified as having a met medical need if they received a relevant service through any of these data sources.

Limitations

The estimates of unmet need and all the measures computed using the 2010 data call for unmet need should be considered liberal estimates for a number of reasons: 

  1. They do not include all the HIV-related care provided by the VA, Medicare, and all private providers in the state. 
  2. Matches conducted between eHARS and some of the cases in ARIES and between eHARS and private payer data were based on a unique identifier or limited data elements rather than client name; this may underestimate the true number of clients with met need from these data sources. 
  3. There are persons reported in eHARS who have since moved out of the state (out-migrated cases) and since we do not have a systematic way of identifying and removing these out-migrated cases they remain in the denominator and inflate our unmet need estimate.

DSHS did not exclude TDCJ cases when estimating unmet need for 2010 and did not exclude TDCJ cases when recalculating unmet need estimates for previous years. They were also not excluded in the linkage to care analysis. When TDCJ cases are excluded, the statewide unmet need estimates increased by approximately a one or two percentage points. While including TDCJ cases in this year’s unmet need analysis did not impact overall estimates, it is evident that DSHS is not receiving all medical service data for cases diagnosed in TDCJ. In addition, University of Texas Medical Branch paper labs received by DSHS were not available at the time these measures were developed and are not reflected in the estimates.


Notes:

[1] HIV treatment guidelines www.aidsinfo.nih.gov/Guidelines/


Back to the dashboard


Last updated January 31, 2012