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Swann Arp Adams, PhD Cancer Prevention and Control Program University of South Carolina

The Impact of Federally Qualified Health Centers on Cancer Mortality-to-Incidence Ratios: An Ecological Analysis. Swann Arp Adams, PhD Cancer Prevention and Control Program University of South Carolina. FQHC Data Sub-Committee. Progress last 6 months.

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Swann Arp Adams, PhD Cancer Prevention and Control Program University of South Carolina

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  1. The Impact of Federally Qualified Health Centers on Cancer Mortality-to-Incidence Ratios: An Ecological Analysis Swann Arp Adams, PhD Cancer Prevention and Control Program University of South Carolina FQHC Data Sub-Committee

  2. Progress last 6 months • Write up a paper about Breast, Cervical, Colon, and Prostate cancer and its relationship with FQHCs density • Data • Age-adjusted Breast, Cervical, Colon, and Prostate cancer mortality (2003-2007) and incidence (2004-2008) for each county from the Centers for Disease Control and Prevention’s National Program of Cancer Registries (NPCR) and National Cancer Institute’s Surveillance, Epidemiology, and End Results Registries (SEER) • Number of FQHCs in 2000, Health Professional Shortage Area (HPSA) designation in 2007, and urban/rural classification in 2003 from 2011-2012 Area Resource File • Issues • Definition of FQHCs density: Number of FQHCs in the county vs.. Number of FQHCs/10,000 population in the county • Time lag

  3. Issue 1: Density vs. Absolute Number • “Reverse” results Example: prostate cancer MIR # FQHCs/10,000 population # FQHCs

  4. Issue 1: Density vs. Absolute Number • Possible explanation • FQHCs are approved and build considering population size of the region. Duplicated consideration with population density may cause distorted results. • Doubt • Interpretability : does it make sense to differentiate between 0.01 FQHC /10,000 population 0.03 FQHC/10,000 population? i.e.) Quartile break up for prostate cancer Q1=0 FQHC Q2= 0<FQHC/10,000 pop ≤ 0.096 Q3= 0.096<FQHC/10,000 pop ≤ 0.271 Q4=0.271 + FQHC/10,000 pop

  5. Issue 2: Time lag • Time lag between FQHCs data and cancer mortality and incidence data • Cancer mortality: 2003-2007 • Cancer incidence: 2004-2008 • FQHCs data: 2011 (initial analysis); 2000 (final analysis) • Solution • Sensitivity analysis: there is no difference if we use another period of cancer mortality and/or incidence data • Trend of MIRs by FQHCs density does not change regardless reference time period for the FQHC information

  6. Final findings

  7. Breast cancer at the county level, by County-Level “Quartile” of FQHCs Concentration

  8. Cervical cancer at the county level, by County-Level “Quartile” of FQHCs Concentration

  9. Colon cancer at the county level, by County-Level Quartile of FQHCs Concentration

  10. Prostate cancer at the county level, by County-Level Quartile of FQHCs Concentration

  11. Breast cancer MIRstratified by HPSA designation p=0.192 p=0.002 p=0.083 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  12. Cervical cancer MIRstratified by HPSA designation p=0.320 p=0.684 p=0.341 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  13. Colon cancer MIRstratified by HPSA designation p=0.781 p=0.029 p=0.752 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  14. Prostate cancer MIRstratified by HPSA designation p=0.949 p=0.002 p=0.056 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  15. Breast cancer MIRstratified by urban/rural p=0.426 p=0.016 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  16. Cervical cancer MIRstratified by urban/rural p=0.614 p=0.233 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  17. Colon cancer MIRstratified by urban/rural p=0.426 p=0.016 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  18. Prostate cancer MIRstratified by urban/rural p=0.175 p=0.011 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  19. Breast cancer MIR stratified by race p<0.001 p=0.021 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  20. Cervical cancer MIR stratified by race p=0.778 p=0.374 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  21. Colon cancer MIR stratified by race p<0.001 p=0.264 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  22. Prostate cancer MIR stratified by race p=0.017 p<0.001 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

  23. Conclusion • The overall trend of MIRs is same for all cancers (Breast, Cervical, Conlon, and Prostate): with higher FQHCs concentration, the lower the MIR. • Blacks, rural, and HPSA have higher MIR for all four cancers than Whites, urban, and non-HPSA. • FQHCs may play a role in reducing cancer MIR.

  24. Interaction with FQHC partners • Partnership: getting practical advice that cannot be achieved by just sitting in the desk. • Allows for context • Shapes directions so that both academic and care provider goals are met • Help to disseminate research results to FQHCs

  25. National implication • Develop strategies to reduce cancer MIRs in regions • Where there are no or few FQHCs • designated as Health Professional Shortage Areas • Focus on reducing cancer MIRs disparities between • White and Black • Urban and Rural • Planning to submit the paper to • American Journal of Preventive Medicine

  26. Future work • Focus on minority health • A lack of data for minority population: Hispanic, Asian, etc. • Focus on rural health • FQHCs’ role for primary care • Cancer screening • Cancer prevention • Cancer registry • Data pooling • Data availability and accessibility • Development various GIS applications for cancer research

  27. FQHC data members

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