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 FQHC Data Sub-Committee

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 ( ) and incidence ( ) 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 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

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

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 ≤ Q3= 0.096<FQHC/10,000 pop ≤ Q4= FQHC/10,000 pop Issue 1: Density vs. Absolute Number

Time lag between FQHCs data and cancer mortality and incidence data Cancer mortality: Cancer incidence: 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 Issue 2: Time lag

Breast cancer at the county level, by County-Level “Quartile” of FQHCs Concentration FQHCs# of countiesMean±SEp-value Q1: 0 FQHC1, ± Q2: 1 FQHC ±0.003 Q3: 2 FQHCs ±0.006 Q4: 3 FQHCs ±0.003

Cervical cancer at the county level, by County-Level “Quartile” of FQHCs Concentration FQHCs# of countiesMean ± SEp-value Q1: 0 FQHC ± Q2: 1 FQHC ±0.017 Q3: 2-4 FQHCs ±0.068 Q4: 5 FQHCs ±0.079

Colon cancer at the county level, by County-Level Quartile of FQHCs Concentration FQHCs# of countiesMean±SEp-value Q1: 0 FQHC1, ± Q2: 1 FQHC ±0.004 Q3: 2 FQHCs ±0.006 Q4: 3 FQHCs ±0.005

Prostate cancer at the county level, by County-Level Quartile of FQHCs Concentration FQHCs# of countiesMean±SEp-value Q1: 0 FQHC ±0.002<0.001 Q2: 1 FQHC ±0.004 Q3: 2 FQHCs ±0.005 Q4: 3 FQHCs ±0.003

Breast cancer MIR stratified by HPSA designation Q1 Q2 Q3 Q4 p=0.192p=0.002p=0.083

Cervical cancer MIR stratified by HPSA designation Q1 Q2 Q3 Q4 p=0.320p=0.684p=0.341

Colon cancer MIR stratified by HPSA designation Q1 Q2 Q3 Q4 p=0.781p=0.029p=0.752

Prostate cancer MIR stratified by HPSA designation Q1 Q2 Q3 Q4 p=0.949p=0.002p=0.056

Breast cancer MIR stratified by urban/rural Q1 Q2 Q3 Q4 p=0.016 p=0.426 Q1 Q2 Q3 Q4

Cervical cancer MIR stratified by urban/rural Q1 Q2 Q3 Q4 p=0.233 p=0.614 Q1 Q2 Q3 Q4

Colon cancer MIR stratified by urban/rural Q1 Q2 Q3 Q4 p=0.016 p=0.426 Q1 Q2 Q3 Q4

Prostate cancer MIR stratified by urban/rural Q1 Q2 Q3 Q4 p=0.011 p=0.175 Q1 Q2 Q3 Q4

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

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

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

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

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.

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

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

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

University of South Carolina Swann A. Adams, PhD Dayna Campbell Daniela Friedman, PhD James Hébert MSPH, ScD James Lyndon (Lyn) McCracken Vicki M. Young PhD Sudha Xirasagar MBBS, PhD NCI Russell E. Glasgow, PhD CDC Dave Butterworth University of Washington Mei Po Yip, PhD Harvard University Reginald Tucker-Seely, PhD University of Texas, Houston Glenna Dawson, MPH Guillermo Tortolero-Luna MD, PhD FQHC data members