Texas Department of State Health Services HIV/STD Conference 2014

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Authors of the Poster Presentation Goes Here
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Problem
The state of Texas had the fourth highest occurrence of
tuberculosis (TB) in 2010 in addition to the fourth highest new
diagnoses of the human immunodeficiency virus (HIV).[1]
The Texas Department of State Health Services (DSHS) identified
HIV-infected persons as a high risk population for TB. In 2010, an
estimated 13.7% of TB cases were co-infected with HIV in Harris
County.[2]
The primary action of TB control programs is verification and
treatment of TB via direct observed therapy coupled with contact
investigation. However, recent research revealed that cohorts of
TB infected individuals in Houston lacked direct personal contact;
transmission was correlated to places of high crowding.[3]
More investigation is needed about the correlation of HIV/TB at
the aggregate level and potential community risk factors.
Description
A population based ecological study was conducted to identify
areas with a high number of TB and HIV new diagnoses in Harris
County, Texas from 2009-2010 at the census tract level.
Data Sources:
• HIV New Diagnoses - HIV Surveillance Program at the Houston
Department of Health and Human Services (HDHHS)
• TB New Diagnoses - TB Infection Control Program at DSHS
• Social Determinants of Health (SDH)– 2010 U.S. Census Data;
census tracts were designed to be homogenous across demographic
variables and ideal for research purposes
HIV cases were defined as the residential address of new
diagnoses of HIV within Harris County from 1 January 2009
through 31 December 2010 and geocoded prior to study initiation.
TB cases were defined as the residential address of new
diagnoses of TB within Harris County from 1 January 2009
through 31 December 2010 and geocoded under this study.
Kernel density was performed using ArcGIS 10.1 (output cell size
of 500; search radius of 20,000; area units of feet2); kernel density
estimation calculates the magnitude per unit area from point
features.
A co-morbidity variable was created that identified census tracts
with above average rates of HIV (> 6.81 per 10000 persons) and
TB (> 1.87 per 10000 persons). Logistic regression was
performed to analyze population-level predictor variables of
HIV/TB using SAS 9.3 software.
Data collection and management were supported by a cooperative agreement with the CDC for supplemental funding under the opportunity number CDC-RFA-PS08-8020301SUPP10. We would like to acknowledge Raouf Arafat, MD, MPH, the Director of the Office of Surveillance and Public Health Preparedness and Overall Responsible Party for HIV Surveillance at HDHHS; Salma Khuwaja, MD, MPH, DrPH, HIV Division Manager of the Bureau of Epidemiology at HDHHS; Marlene McNeese, Bureau
Chief, of the Bureau of HIV/STD and Viral Hepatitis Prevention at HDHHS. Further gratitude for DSHS staff members Maria Rodriguez and Sharon K. Melville, MD, MPH for allowing this opportunity to be fulfilled and to Dejian Lai, PhD, of the University of Texas School of Public Health at Houston for statistical guidance. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the project supporters.
Methods
Harris County was composed of 786 census tracts with a total population of 4.09 million in 2010.[4] For 2009 through 2010, there
was a rate of 1.87 TB cases per 10,0000 person and a rate of 6.81 HIV cases per 10,000 persons.
Participant characteristics for TB positive individuals within Harris County prior to geocoding exclusion (N=755, Hispanic=43.84%)
had an average age of 41.52 (+ 20.26) years with 43.05% born in the United States.
The following independent variables were selected based on literature evidence and extracted from the U.S. Census Bureau:
average household size (housing), percentage of residents living below poverty (poverty), percentage of Black residents (Black),
percentage of Asian residents (Asian), percentage of Hispanic residents (Hispanic), percentage of foreign born residents (foreign
born), percentage of male residents (male), and percentage of working aged adults (15-55 years of age) per census tract.
• Average household size was dichotomized to >3 persons per household unit or < 3 persons due to the limited range of
values and to create a more suitable proxy measure of household crowding
Logistic regression was conducted independently for HIV and TB rates. Significant variables from each analysis were included as
predictor variables for co-morbidity of HIV/TB at the census tract level.
Correlation of TB and HIV in Harris County, Texas from 2009 through 2010 Using
Geographic Information Systems
Kellie L Watkins MS1, Biru Yang PhD2, Katherine Ngo MPH³, Marcia Wolverton MPH2, Lu-Yu Hwang MD1
1 The University of Texas Health Science Center, Epidemiology, Human Genetics, and Environmental Sciences; 2 Houston Department of Health and Human Services, Bureau of Epidemiology;
3 Baylor College of Medicine
Results
A total of 735 TB case records had viable addresses and were
geocoded with an average match score of 93.75; maximum of
100 and a minimum of 73.45.
The Kernel density maps indicate neighborhoods with high
densities of HIV and TB rates that should be targeted for
specialized community prevention efforts (Figures 1 & 2).
Census tracts with a high percentage of poverty, Black, foreign
born, and working aged adults were more likely to have above
average rates of HIV new diagnoses (Table1).
• However, census tracts with a higher percentage of Asian
residents were less likely to have above average rates of HIV
Census tracts with a higher percentage of poverty, Black, Asian,
and Hispanic residents were more likely to have above average
rates of TB new diagnoses (Table 1).
Implications
Co-morbidity analysis suggested that census tracts with a higher
percentage of poverty, Black, foreign born, and working aged
adults were at greater risk of high rates of HIV/TB (Table 2).
• Percentage of Asian residents was borderline significant and
could be considered a protective factor against high rates of
HIV and HIV/TB co-morbidity although it was a significant risk
factor for high rates of TB at the census tract level
Joint TB and HIV testing efforts and/or educational campaigns
should be targeted towards areas with a high percentage of
poverty, Black, foreign born, and working aged residents.
Limitations:
1.Ecological Fallacy – analyses at the aggregate level should
not be applied to individuals
2.Housing size was not an adequate measure of household
crowding; a more suitable variable is needed
3.Race/ethnicities might not be mutually exclusive
4.Multicollinearity remained high even after eliminating
unnecessary independent variables
Future analyses should employ mixed effects regression models
for clustered data to assess the risk of TB at the individual level.
References
[1] Centers for Disease Control and Prevention: the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention .(2010). Texas – 2010 Profile. Available at:
http://www.cdc.gov/nchhstp/stateprofiles/pdf/Texas_profile.pdf. Accessed August 8, 2014.
[2] Harris County HCPHES Public Health & Environmental Services. (2011). Biennial Report: Health People, Health Communities…A Health Harris County. Available at:
http://www.hcphes.org/HCPHESReports/BiennialReport2010-11.pdf. Accessed August 8, 2014.
[3] Klovdahl AS, Graviss EA, Yaganehdoost A, et al. Networks and tuberculosis, an undetected community outbreak involving public places. Social Science and Medicine 2001; 52(5); 681-
694.
[4] Harris (county), Texas. 2010 Population Estimate. United States Census Bureau. Available at: http://quickfacts.census.gov/qfd/states/48/48201.html. Accessed August 8, 2014.
** Statistically significant coefficients (P-value<0.05) highlighted in red
* Borderline significance (0.05<P-value<0.06)
** Statistically significant coefficients (P-value<0.05) highlighted in red
* Borderline significance (0.05<P-value<0.06)
OR = Odds Ratio
CI = Confidence Interval
Table 1. Regression Results for HIV Rates and TB Rates Table 2. Regression Results for HIV/TB Co-Morbidity
Figure 1. Kernel Density of TB New Diagnoses Figure 2. Kernel Density of HIV New Diagnoses