Authors: Jeff Blossom*, Harvard Center for Geographic Analysis, SV Subramanian, Harvard University
Topics: Health and Medical, Geographic Information Science and Systems
Keywords: apportioning, crosswalk, COVID-19, health
Session Type: Virtual Paper
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 36
Presentation File: No File Uploaded
For elected officials to best represent constituencies they serve, information must be presented and analyzed for the entire geographic extent of their constituency. Health indicators are often collected and reported at census geographies, or other units that do not conform to constituency boundaries. To address this lack of health data being reported at policy relevant geographies, the Harvard Center for Population and Development Studies Geographic Insights team (GeoInsights) has been applying areal and population apportioning methods to create “crosswalks” between geographies at which health data is reported and policy relevant constituency boundaries.
Specifically, this presentation will showcase the methods used and findings for apportioning health data in India and the United States. In India, data on hundreds of key developmental indicators such as child malnutrition that formulate policies and interventions are routinely available for the administrative units of districts but not for the 543 parliamentary constituencies (PC) or 4,121 assembly constituencies (AC) that have elected officials in national and state government, respectively. In the United States the Centers for Disease Control reports totals for opioid use, COVID-19, and more at the county geographic level. U.S. congressional districts (CD) contain similar population sizes and each district has an elected member in the U.S. House of Representatives. PCs and ACs in India and CDs in the U.S. represent policy-relevant boundaries and are politically important levels at which program funding is determined and health crises are monitored. This presentation will highlight GeoInsight’s overall initiatives on apportioning health data into policy relevant boundaries.