The Association between Neighborhood Poverty and HIV Diagnoses among Males and Females in New York City, 2010–2011

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Abstract

<div class="section"> <a class="named-anchor" id="d9345465e146">  </a> <h5 class="section-title" id="d9345465e147">Objective</h5> <p id="d9345465e149">We assessed the association of neighborhood poverty with HIV diagnosis rates for males and females in New York City. </p> </div><div class="section"> <a class="named-anchor" id="d9345465e151">  </a> <h5 class="section-title" id="d9345465e152">Methods</h5> <p id="d9345465e154">We calculated annual HIV diagnosis rates by ZIP Code, sex, and neighborhood poverty level using 2010–2011 New York City (NYC) HIV surveillance data and data from the U.S. Census 2010 and American Community Survey 2007–2011. Neighborhood poverty levels were percentage of residents in a ZIP Code with incomes below the federal poverty threshold, categorized as 0%–<10% (low poverty), 10%–<20% (medium poverty), 20%–<30% (high poverty), and 30%–100% (very high poverty). We used sex-stratified negative binomial regression models to measure the association between neighborhood-level poverty and HIV diagnosis rates, controlling for neighborhood-level education, race/ethnicity, age, and percentage of men who have sex with men. </p> </div><div class="section"> <a class="named-anchor" id="d9345465e156">  </a> <h5 class="section-title" id="d9345465e157">Results</h5> <p id="d9345465e159">In 2010–2011, 6,184 people were newly diagnosed with HIV. Median diagnosis rates per 100,000 population increased by neighborhood poverty level overall (13.7, 34.3, 50.6, and 75.6 for low-, medium-, high-, and very high-poverty ZIP Codes, respectively), for males, and for females. In regression models, higher neighborhood poverty remained associated with higher diagnosis rates among males (adjusted rate ratio [ARR] = 1.63, 95% confidence interval [CI] 1.34, 1.97) and females (ARR=2.14, 95% CI 1.46, 3.14) for very high- vs. low-poverty ZIP Codes. </p> </div><div class="section"> <a class="named-anchor" id="d9345465e161">  </a> <h5 class="section-title" id="d9345465e162">Conclusion</h5> <p id="d9345465e164">Living in very high- vs. low-poverty urban neighborhoods was associated with increased HIV diagnosis rates. After controlling for other factors, the association between poverty and diagnosis rates was stronger among females than among males. Alleviating poverty may help decrease HIV-related disparities. </p> </div>

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David Friedman, Joanne Mantell, K Blankenship … (2005)

Structural interventions refer to public health interventions that promote health by altering the structural context within which health is produced and reproduced. They draw on concepts from multiple disciplines, including public health, psychiatry, and psychology, in which attention to interventions is common, and sociology and political economy, where structure is a familiar, if contested, concept. This has meant that even as discussions of structural interventions bring together researchers from various fields, they can get stalled in debates over definitions. In this paper, we seek to move these discussions forward by highlighting a number of critical issues raised by structural interventions, and the subsequent implications of these for research.

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HIV treatment adherence, drug resistance, virologic failure: evolving concepts.

Jean Nachega, Vincent C. Marconi, Gert U Van Zyl … (2011)

Poor adherence to combined antiretroviral therapy (cART) has been shown to be a major determinant of virologic failure, emergence of drug resistant virus, disease progression, hospitalizations, mortality, and health care costs. While high adherence levels can be achieved in both resource-rich and resource-limited settings following initiation of cART, long-term adherence remains a challenge regardless of available resources. Barriers to optimal adherence may originate from individual (biological, socio-cultural, behavioral), pharmacological, and societal factors. Although patients and providers should continuously strive for maximum adherence to cART, there is accumulating evidence that each class of antiretroviral therapy has specific adherence-drug resistance relationship characteristics allowing certain regimens more flexibility than others. There is not a universally accepted measure for cART adherence, since each method has distinct advantages and disadvantages including cost, complexity, accuracy, precision, intrusiveness and bias. Development of a real-time cART adherence monitoring tool will enable the development of novel, pre-emptive adherence-improving strategies. The application of these strategies may ultimately prove to be the most cost-effective method to reduce morbidity and mortality for the individual and decrease the likelihood of HIV transmission and emergence of resistance in the community.

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Neighborhood social environment and risk of death: multilevel evidence from the Alameda County Study.

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Recent reports suggest the importance of associations between residential area characteristics and health status, but most research uses only census data to measure these characteristics. The current research examined the effect of overall neighborhood social environment on 11-year risk of death. On the basis of data, the authors developed a three-component neighborhood social environment scale: 1) commercial stores; 2) population socioeconomic status; and 3) environment/housing. Data from the 1983 wave of the Alameda County Study (n = 1,129) and deaths over 11 years were analyzed with two-level logistic regression models. Age- and sex-adjusted risk of death was higher for residents in low social environment neighborhoods (odds ratio = 1.58, 95% confidence interval 1.15-2.18). Mortality risks were significantly higher in neighborhoods with a low social environment, even after account was taken of individual income level, education, race/ethnicity, perceived health status, smoking status, body mass index, and alcohol consumption. When each component of the neighborhood social environment characteristics score was examined separately, each was found to be associated with higher risk for mortality, independent of individual risk factors. These findings demonstrate the role of area characteristics as a health risk factor and point to the need for more focused attention to the meaning and measurement of neighborhood quality.