Mapping a Measure of HIV Acquisition Risk in San Diego County

Mapping a Measure of HIV Acquisition Risk in San Diego County Rebecca Horne, MPH California Department of Health Services, Office of AIDS Final Draft 4/3/2007 Please do not distribute without permission of presenter

Using GIS to Plan for a Population Based Health Study • National HIV Behavioral Surveillance among Heterosexuals at Risk of HIV Infection • Joint effort of CDC, CDHS Office of AIDS, San Ysidro Health Center and MAAC Project • San Diego MSA is one of 25 participating sites nation-wide Final Draft 4/3/2007 Please do not distribute without permission of presenter

Study Components • 45 minute questionnaire administered by a trained interviewer • HIV testing offered • Respondent Driven Sampling (RDS) • Goal of 750 interviews • Study measures - Sexual and drug use behaviors - HIV prevalence - HIV testing behaviors - Use of prevention services Final Draft 4/3/2007 Please do not distribute without permission of presenter

Defining Heterosexuals at Risk for HIV (the target population) • Physical or social connection to a geographic area (High Risk Area or “HRA”) with • High rates of heterosexually acquired HIV infection • High rates of poverty • And had sex with at least one opposite sex partner in the last year Final Draft 4/3/2007 Please do not distribute without permission of presenter

Importance of the HRA • Index based on HIV/AIDS rate and poverty rate for each ZIP Code in San Diego MSA • Compare HIV acquisition risk across MSA • Mapping HRAs with GIS identifies hot spots for risk and geographic concentrations Final Draft 4/3/2007 Please do not distribute without permission of presenter

After HRA Identification Study team planned accordingly to reach residents of high risk areas 1. Impacts criteria for Respondent Driven Sampling (RDS) - yielding higher numbers in target pop 2. Interview and HIV testing sites need to be conveniently centered in HRA clusters - reducing barriers and maximizing participation Final Draft 4/3/2007 Please do not distribute without permission of presenter

Mapping the HRAs Run CDC SAS Program Output 1. Standardized Index Values for Each ZIP Code 2. Spreadsheets and Graphs Collect and Format Data 1. HIV/AIDS 2. Poverty ArcGIS Select Index Cut Point for HRA Review SAS Output Map of HRAs at ZIP Code Level in San Diego MSA Final Draft 4/3/2007 Please do not distribute without permission of presenter

Data Sources HIV/AIDS Poverty California’s HIV/AIDS Reporting System (HARS) 2000 US Census Percentage of Persons Living in Poverty in Each ZIP Code Heterosexually Acquired Adult Cases in San Diego County Final Draft 4/3/2007 Please do not distribute without permission of presenter

Combining the Data • HRA index is a composite measure of HIV Risk • Need to merge the poverty and HIV/AIDS data for each zip code Final Draft 4/3/2007 Please do not distribute without permission of presenter

Example Merged Data Note: To protect confidentiality, data shown in this table are entirely fictitious Final Draft 4/3/2007 Please do not distribute without permission of presenter

Standardizing the Rates • Poverty rates are greater than HIV/AIDS rates, so need to standardize before calculating the HRA index • Next slides demonstrate how standardized morbidity ratios (SMRs) are created for HIV/AIDS and poverty Final Draft 4/3/2007 Please do not distribute without permission of presenter

SMRs • SMR for HIV AIDS in each ZIP Code= HIV AIDS rate in that ZIP Code/ HIV AIDS rate for entire San Diego MSA • SMR for Poverty in each ZIP Code= Poverty rate in that ZIP Code/ Poverty rate for entire San Diego MSA Final Draft 4/3/2007 Please do not distribute without permission of presenter

Calculating the HRA Index • HRA Index in each ZIP Code= SMR for HIV AIDS + SMR for Poverty • For national comparisons, HRA Index values were standardized to a max value of 1000 where • Standardized HRA Index for each ZIP Code= HRA Index for that ZIP Code * 1000/ the highest HRA Index Final Draft 4/3/2007 Please do not distribute without permission of presenter

Selecting the HRA Index Cut-Off • Reviewed output from SAS program - Considered effect of low population sizes on HIV AIDS rates (where index values could be over estimated) • Calculated cumulative proportion of cases - Looked for a logical cut-off • Considered geographic areas of interest, i.e. along the US and Mexico border • 30th highest HRA was selected as cut-off (out of 95 ZIP Codes) Final Draft 4/3/2007 Please do not distribute without permission of presenter

Results - HRA Index Values Final Draft 4/3/2007 Please do not distribute without permission of presenter

Mapping • ArcGIS 9 • Joined the two files on ZIP Code 1. Shape file of San Diego County includes ZIP Code 2. SAS output into Excel contains HRA index values for each ZIP Code • Choropleth map of HRA index values • Range from 0-1000, HRA cut-off was 223 Final Draft 4/3/2007 Please do not distribute without permission of presenter

Final Draft 4/3/2007 Please do not distribute without permission of presenter

Preliminary Data on Study Population • Study currently underway • Interviews began in November 2006 • Study expected to continue through October 2007 • 97.8% of study participants live in an HRA (using data through 3/4/2007) • 179 interviews completed at sites (as of 3/25/2007) Final Draft 4/3/2007 Please do not distribute without permission of presenter

Acknowledgements Special thanks to the following who provided assistance with HRA identification: Christopher Krawczyk, Ph.D; Assunta Ritieni, MHS; Paul Denning, MD, MPH; and Behavioral Surveillance Team, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention Final Draft 4/3/2007 Please do not distribute without permission of presenter

Questions? Final Draft 4/3/2007 Please do not distribute without permission of presenter

Mapping a Measure of HIV Acquisition Risk in San Diego County