Association between area-level poverty and HIV diagnoses, and differences by sex, New York City 2010- 2011 Ellen Wiewel, HIV Epidemiology & Field Services Program, New York City Department of Health & Mental Hygiene APHA abstract #296498, Session 4331.0, “Structural and Institutional Issues in HIV/AIDS,” Tuesday, November 18, 2014
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Association between area- level poverty and HIV diagnoses, and differences by sex, New York City 2010-2011 Ellen Wiewel, HIV Epidemiology & Field Services.
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Association between area-level poverty and
HIV diagnoses, and differences by sex, New
York City 2010-2011Ellen Wiewel, HIV Epidemiology & Field Services Program, New
York City Department of Health & Mental Hygiene
APHA abstract #296498, Session 4331.0, “Structural and Institutional Issues in HIV/AIDS,” Tuesday, November 18, 2014
Presenter DisclosuresPresenter Disclosures
The following personal financial relationships with commercial interests relevant to this presentation existed
during the past 12 months:
Ellen Wiewel
No relationships to disclose
Place & social conditions Place & social conditions mattermatter
• Most investigations of determinants of HIV infection focus on individual traits (risk, race)
• Social conditions (e.g., neighborhood poverty) contribute to health disparities of many kinds, including HIV infection
Gap in research on HIV and Gap in research on HIV and neighborhood povertyneighborhood poverty
• Link between HIV and neighborhood poverty has not been explored by ZIP code in a US city and could inform local policy
• Are differences by neighborhood poverty level explained by differences in age and racial/ethnic composition?
ObjectivesObjectives
• Assess the association of neighborhood poverty with HIV diagnosis rates, after controlling for other neighborhood factors such as age and racial/ethnic composition
• Determine whether neighborhood poverty has different effects on HIV rates by sex
Methods (1)Methods (1)
• Ecological analysis
• Outcome: ZIP code-level HIV diagnosis rates among New York City residents 13+ years old, by sex
Methods (2)Methods (2)
• Sex-stratified negative binomial regression models measured effect of neighborhood-level poverty on diagnosis rates
• Covariates: neighborhood-level education, racial/ethnic composition, age distribution, and percent men who have sex with men
Data SourcesData Sources
Data source
NYC HIV surveillance
registry 2010-2011
NYC Community
Health Survey 2009-
2011
American Community
Survey 2007-2011
US Census 2010
Population All NYC HIV cases
Representative sample of NYC
adults
Representative sample of US
population
Every US resident
Measures HIV diagnoses Percent MSMPoverty
Education
SexRace/Ethnicity
Age
Text Explanation of Data Text Explanation of Data Sources TableSources Table
• Table of four data sources and the measures gleaned from each
• Data sources include NYC HIV surveillance registry, NYC Community Health Survey, American Community Survey, and US Census
*Controlling for racial/ethnic composition, age distribution, proportion MSM (for males), and education.
Males Females
Text Explanation of Text Explanation of Regression TableRegression Table
• Table of crude and adjusted rate ratios (RRs) from Poisson regression of ZIP code-level HIV diagnosis rates among males and females by neighborhood poverty level
• Crude and adjusted RRs increase with increasing poverty, and increase more among females than males
Summary of Findings (1)Summary of Findings (1)
• HIV diagnosis rates among males and females increased with increasing neighborhood poverty
• At all neighborhood poverty levels, higher diagnosis rates among males than females
Summary of Findings (2)Summary of Findings (2)
• Living in very-high- vs. low-poverty neighborhoods was associated with an increase in HIV diagnosis rates of 63% for males and 114% for females
• Relative difference in rates between neighborhoods with very high vs. low poverty particularly large for women
LimitationsLimitations
• ZIP code is larger area than is optimal for detection of disparities (Census tract and Census block)
• Diagnosis rates rather than incidence rates (only diagnoses are reportable)
• Explained neither how poverty influences HIV nor why relative impact of poverty is larger on females
StrengthsStrengths
• First ecological analysis of HIV diagnosis rates and poverty at the ZIP code level and in a US city
• One of few analyses to assess sex differences in the poverty-HIV relationship
ConclusionsConclusions
• Area-based poverty was associated with HIV diagnoses in NYC, supporting interventions making structural and social changes
• Other structural urban HIV prevention should focus on higher-poverty areas
• Prevention strategies addressing poverty are important for males and (especially) females
Next StepsNext Steps
• NYC health department already focuses HIV prevention, care, and treatment on higher-poverty areas
• Support other local place-based approaches to HIV prevention
• Future research: poverty’s influence on care continuum, e.g., linkage to care and viral suppression
AcknowledgmentsAcknowledgments
• Coauthors Angelica Bocour, Laura Kersanske, Sara Bodach, Qiang Xia, and Sarah Braunstein (all with the New York City Department of Health & Mental Hygiene at the time of this analysis)
• NYC Department of Health & CUNY SPH Dean Dr. Ayman El-Mohandes for travel stipends