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Mapping Your World

Mapping Your World. Michael Reibel, PhD Professor, Department of Geography and Anthropology California State Polytechnic University. Deep Demographics: Understanding Local Variation in Rates of Potential Donor Willingness. Michael Reibel, PhD Donation perspectives meeting

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Mapping Your World

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  1. Mapping Your World Michael Reibel, PhD Professor, Department of Geography and Anthropology California State Polytechnic University

  2. Deep Demographics:Understanding Local Variation in Rates of Potential Donor Willingness Michael Reibel, PhD Donation perspectives meeting St. Louis - October 16, 2013 mreibel@csupomona.edu

  3. Background • The Deceased Donor Potential Study (DDPS) – a national study commissioned by HRSA and administered by UNOS • Used raw mortality data adjusted by diagnostic codes to estimate medically suitable potential donor population • Linked these estimates to OPTN donor files for multivariate analysis • Used factor analysis and statistical modeling to analyze complex neighborhood scale social environments

  4. Key DDPS Findings • As expected, the main sources of variation were age and cause of death. BUT: • Evidence was also found for socio-economic (income and education), geographic and local social environment effects: • Logistical obstacles to organ recovery, especially in rural areas • Higher rates, controlling for age, in areas with colleges and military installations • Lower rates in ethnic and immigrant enclaves (controlling for individual identity)

  5. Case Study: OneLegacy • Examine neighborhood level patterns of variation in willingness to donate and to register • Two baseline populations used for analysis of the OneLegacy DSA: Hospital referrals of medically suitable patients (“authorization rates”) and rates of donor registry participation (“registration rates”) • Goal: to draw conclusions re: neighborhood effects on donor willingness to inform the design of targeted outreach and publicity efforts to promote donations

  6. Methods, OL Case Study • Used Geographic Information Systems (GIS) software to automatically geocode street addresses • Geocoded addresses can be quickly assigned and re-assigned to superimposed zones (e.g zip codes and Health Service Areas) • Used analytical cartographic technics to interpret data patterns aggregated to zip and HAS zones • Used cluster analysis to define complex types of social environments in study area > mapped and analyzed • Estimated statistical models of neighborhood effects on willingness to donate and to register (not reported here)

  7. Part 1: Preliminary Analysis • To provide baseline understanding of the detailed, neighborhood level social environments that comprise the OneLegacy DSA

  8. Part 2: Donor Willingness Rate Analysis • To discover raw local variation in donor willingness in the OneLegacy DSA

  9. Fig. 2

  10. Part 3: Complex Analysis • To provide a deeper look into complex neighborhood social environments • Social environments are a function of local age/family structure, Race/ethnic structure, and aggregate local socio-economic status (SES). • Local SES is measured primarily by local median income and local patterns of educational attainment

  11. Social Environment Clusters, OneLegacy DSA

  12. Conclusions 1: Similarities Between Authorization & Registration Patterns • Broad socio-economic and racial/ethnic generalizations are consistent across the two Populations: • Affluent, well-educated neighborhoods and areas near military installations have higher rates of both authorization and registration • Asian enclaves (including, in some cases, Middle Eastern enclaves) tend to have lower rates of both authorization and registration

  13. Conclusions 2: Differences in Authorization & Registration Patterns • Hispanic and black neighborhoods are less consistent • Both sorts of areas tend strongly to have low registration rates across the DSA • Authorization rates seem to depend largely on their metropolitan geography: • Hispanic and black neighborhoods in the urban core have moderate authorization rates, while • Peripheral Hispanic areas have low authorization rates that more closely mirror their registration levels. • This may reflect either lower acculturation levels (more recent immigrants) in peripheral areas or higher levels of urban violence– need further study to determine

  14. Conclusions 3: Socio-Economics & Culture Drive Variation in White Neighborhoods • Higher status areas have a higher willingness to donate, except: • Some higher status areas have only average rates of registration that might reflect sizable ethnic minorities: • Jews in the South San Fernando Valley and Beverly Hills, • Native born Hispanics in the San Gabriel Foothills • Asians (incl. Middle Easterners) in the Chino Hills area

  15. Next Steps/Follow Up • Examine patterns of variation in willingness to donate between local neighborhoods with similar distinctive ethnic characteristics: • We know variation between Anglo areas is a function of affluence and ethnicity(both “white ethnics” and non-Anglos in Anglo areas), but • What about variation between Chinese areas, and between Latino areas? Suspect acculturation (time/generations since immigration) and affluence determine

  16. Break

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