Identifying New York City Neighborhoods at Risk of Being Overlooked for Interventions
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Identifying New York City Neighborhoods at Risk of Being Overlooked for Interventions

Filetype[PDF-432.70 KB]


  • English
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    Details:

    • Alternative Title:
      Prev Chronic Dis
    • Personal Author:
    • Description:
      Public health agencies are often faced with difficult decisions about where and how to allocate funding and resources. This question of resource allocation is central to public health policy; however, decisions related to resource allocation are sometimes made via informal or subjective approaches. We walk readers through a process of identifying needs across different neighborhoods in New York City (NYC) by examining community district-level health outcomes using data from published Community Health Profile reports released by the NYC Department of Health and Mental Hygiene (DOHMH) in 2015. In NYC, community districts are represented by community boards that provide a forum for addressing the needs of the community, making them a useful geographic unit for examining health information and turning data into action. We examined prevalence estimates and 95% confidence intervals of health indicators in each community district to identify significant disparities and calculated relative disparities in rates or prevalence to understand the relative magnitude of each disparity. Lastly, we demonstrate an application of this approach by identifying a cluster of neighborhoods with a high chance of being overlooked for public health interventions by conventional methods because of the relative number of disparities that exist in these neighborhoods. We present information on the disparity profile (number of disparities and relative disparity) for each neighborhood within the cluster and discuss potential public health implications. This approach can be applied to other jurisdictions to inform public health planning and resource allocation.
    • Subjects:
    • Source:
      Prev Chronic Dis. 2020; 17
    • Pubmed ID:
      32324532
    • Pubmed Central ID:
      PMC7207047
    • Document Type:
      Journal Article
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