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SDI and Health: Regional Comparisons

SDI and Health: Regional Comparisons. Robert Graham Center Visiting Scholar Heather Bennett, MD July 20, 2012. Outline. Background and Rationale for the SDI Regional Overviews: Orange County, CA, Oakland, CA and Providence, RI Zooming in on SDI and Outcomes

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SDI and Health: Regional Comparisons

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  1. SDI and Health: Regional Comparisons Robert Graham Center Visiting Scholar Heather Bennett, MD July 20, 2012

  2. Outline • Background and Rationale for the SDI • Regional Overviews: Orange County, CA, Oakland, CA and Providence, RI • Zooming in on SDI and Outcomes • Next Steps: Detailed Analysis, Application to Health Policy and Resource Allocation • Discussion

  3. Social Deprivation Index • SDI created by RGC: Danielle Butler, Steve, Bob, and Andrew • Literature review and international examples used to determine measures; data based on national area-level data • Social deprivation measures identified using regression model and factor analysis American Community Survey (Census Bureau); CDC: Center for Disease Control, ARF: Area Resource File, ZCTA: Zip Code Tabulation Areas; PCSA: Primary Care Service Areas

  4. Social Deprivation Index • Poverty is the strongest single predictor of health care needs • Aggregated into this index, these measures provide a stronger predictor of health care needs • Pairwise correlations indicate that SDI is positively and significantly (p<.01) associated with each of the following health outcomes: mortality, low birth weight, diabetes prevalence, cost, and ambulatory care sensitive hospitalizations.

  5. Social Deprivation Index • Currently using for State of RI “Gap Analysis” of primary care need, demand and supply for the health commissioner • Policy application: SDI to identify areas that are more/less healthy than predicted given the level of deprivation

  6. SDI: Exploring withRegional Comparisons • Based on fascination with Gawande’s “Cost Conundrum” • Importance of pairing compelling quantitative data with descriptive analysis, to “tell a story” • Many levels of comparison possible

  7. Regional Overview: Orange County, CA

  8. Regional Overview: Orange County, CA U.S. Census Bureau, 2010 American Community Survey

  9. Regional Overview: Orange County, CA California Employment Development Department,  The Orange County Business Journal, and Almanac research

  10. Regional Overview: Oakland, CA

  11. Regional Overview: Oakland, CA U.S. Census Bureau, 2010 American Community Survey

  12. Regional Overview: Oakland, CA City of Oakland, California Comprehensive Annual Financial Report, for the Year ended June 30, 2010

  13. Regional Overview: Providence, RI

  14. Regional Overview: Providence, RI U.S. Census Bureau, 2010 American Community Survey

  15. Regional Overview: Providence, RI CAFR, RI. http://www.providenceri.gov/efile/294

  16. Regional SDI and Health: Methods

  17. Regional Comparisons

  18. Correlation Matrix and Scatterplot: Orange County

  19. Excuse me, I’m becoming a little verklempt.Talk amongst yourselves.I’ll give you a topic:Rhode Island. It is neither a road, nor is it an island. Discuss. Mike Myers as Linda Richman on “Coffee Talk” sketch, SNL

  20. Poverty in the USA

  21. Poverty: CA Bay Area and New England

  22. Poverty:Orange County, Oakland, Providence

  23. UDS Mapper, http://www.udsmapper.org, accessed May 23, 2012 Mapping Poverty:Population at or below 100% FPL

  24. A Broader Perspective: US data

  25. Diabetes Epidemic in the USA

  26. Diabetes: Orange County, Oakland, Providence

  27. Low Birth Weight:Orange County, Oakland, Providence

  28. Mortality: Orange County, Oakland, Providence

  29. “The Hispanic Paradox?”

  30. Hispanic population: Orange County, Oakland, Providence

  31. Hispanic population and Mortality: Orange County, Oakland, Providence

  32. Cost: Orange County, Oakland, Providence

  33. Hospitalizations: Orange County, Oakland, Providence

  34. Are these outcomes expected?SDI and Mortality

  35. Are these outcomes expected?SDI and Cost

  36. Mapping SDI and Predicted Outcomes • Example: Joe Average lives in ZCTA 12345. The SDI his ZCTA is 25, which is high. His health care cost this year was $1000. Based on his ZCTA, our model would predict his health care cost to be $2000 annually. In this example, Joe’s cost was $1000 less than predicted by his region’s SDI. Therefore, the difference between actual and predicted cost is negative $1000.

  37. HealthLandscape: Australia Tool for observed vs. predicted avoidable mortality in rural regions

  38. For researchers, maps do not necessarily answer questions.Instead, they create questions.~Anonymous Young Thinker

  39. Next Steps • Workforce: Correlate and map primary care supply, specialist supply, ratios • Medical infrastructure: CHCs, other safety-net resources, specialty groups, ACOs, hospitals • Regional ecology: green space, food security/supply, school performance, “social capital” • High utilizers and cost “Hot Spotters” (à la Jeff Brenner) • Local medical and political culture (à la AtulGawande)

  40. Thank You! Andrew BazemoreNewton Cheng Meiying HanCarrie FaheySean FinneganSteve PattersonBob PhillipsEntire RGC Staff!

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