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

Neighborhood Poverty Neighborhood Poverty DefinitionDefinition

• Percent in a ZIP with incomes below the federal poverty threshold

• Categories:

• 0-<10% (“low-poverty”)

• 10-<20% (“medium-poverty”)

• 20-<30% (“high-poverty”)

• 30-100% (“very-high-poverty ZIPs”)

Results (1)Results (1)

• 180 residential NYC ZIP codes included

• 6,184 persons newly diagnosed with HIV in NYC in 2010-2011: 4,754 males and 1,430 females

• Overall annual HIV diagnosis rate: 44.8 per 100,000

Results (2)Results (2)

• ZIP-level diagnosis rates in NYC ranged from 0.0 to 241.7 per 100,000 (median: 34.8 per 100,000)

• Median diagnosis rates by sex:

–56.2 per 100,000 males

–10.4 per 100,000 females

Figure 1b. Male HIV diagnosis rates Figure 1c. Female HIV diagnosis rates

Mapping Poverty Mapping Poverty and HIV in NYCand HIV in NYC

Text Explanation of MapsText Explanation of Maps

• Three NYC ZIP code maps showing poverty rates and male and female HIV diagnosis rates

• High- and very-high-poverty ZIP codes appear to have higher male and female HIV diagnosis rates

Box Plots of Annualized HIV Diagnosis Box Plots of Annualized HIV Diagnosis Rates per 100,000 Persons, by Rates per 100,000 Persons, by

Neighborhood Poverty, NYC 2010-2011Neighborhood Poverty, NYC 2010-2011

Males Females

Text Explanation of Box PlotsText Explanation of Box Plots

• Two box plots of male and female HIV diagnosis rates by NYC ZIP code

• Males and females have higher HIV diagnosis rates as neighborhood poverty increases

Regression of Neighborhood Poverty Regression of Neighborhood Poverty and HIV Diagnosis Rates Among and HIV Diagnosis Rates Among

Males and Females in 180 NYC ZIP Males and Females in 180 NYC ZIP Codes, 2010-2011Codes, 2010-2011

Poverty (% of population below the federal poverty threshold)

Crude rate ratio (95% CI) p

Adjusted rate

ratio (95% CI)* pCrude rate ratio

(95% CI) p

Adjusted rate

ratio (95% CI)* pLow-poverty (0-<10%) 1.00 - 1.00 - 1.00 - 1.00 -Medium-poverty (10-<20%) 1.43 (1.25 – 1.63) <.0001 1.25 (1.08 – 1.45) 0.002 2.33 (1.71 – 3.19) <.0001 1.61 (1.16 – 2.23) 0.004High-poverty (20-<30%) 2.09 (1.83 – 2.40) <.0001 1.46 (1.23 – 1.73) <.0001 3.57 (2.61 – 4.88) <.0001 1.62 (1.14 – 2.30) 0.007Very-high-poverty (30-100%) 2.42 (2.10 – 2.78) <.0001 1.63 (1.34 – 1.97) <.0001 6.52 (4.81 – 8.84) <.0001 2.14 (1.46 – 3.14) <.0001

*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

ContactContact

Ellen Wiewel

ewiewel@health.nyc.gov