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Residential Movement Between Time of Cancer Diagnosis & Death

Residential Movement Between Time of Cancer Diagnosis & Death. Recinda Sherman, MPH, CTR Florida Cancer Data System. Place is important. Epidemiologic triad Technological advancements Desktop GIS, geocoding, mapping Spatial statistic, hypothesis testing Geographic patterns

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Residential Movement Between Time of Cancer Diagnosis & Death

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  1. Residential Movement Between Time of Cancer Diagnosis & Death Recinda Sherman, MPH, CTR Florida Cancer Data System

  2. Place is important • Epidemiologic triad • Technological advancements • Desktop GIS, geocoding, mapping • Spatial statistic, hypothesis testing • Geographic patterns • Disease and risk not randomly distributed • Important for prevention strategies • Causality

  3. Exposure measurements • Lack biologic measurements; proxies • Model incomplete risk • Residential risk versus work or school • Cross-sectional residential history • Lack of residential history • Lag time between exposure and diagnosis • Migration • Fallacy • Denominator Issues

  4. Breast cancer screening Incidence risk Mortality risk • Community A • Low • Community B • Low • Community C • High • Community A • Low • Community B • High • Community C • Low

  5. Residential mobility • Incidence and mortality data presented same years • Women diagnosed with breast cancer • Women dying from breast cancer • Migration in (and die)? • Migration out (with diagnosis)?

  6. Research questions • Do cancer patients get diagnosed and die at same residence? • First primary reported to Florida • Death certificate issued by Florida • If the residence is different, is the community similar? • SES • Rurality

  7. Methods • Address at diagnosis versus death • Cancer cases linked with Vital Stats • Probabilistic matching; SSN, name, dob • 1995-2006, 1st primary, no DCOs • Valid dx and death year, sex, age • Geocoded by FCDS and Vital Stats • Unit of analysis • Census Tract

  8. Results – Selection Bias • 446,722 cases for analysis • 44% of total first primary cases • Significantly (p < 0.001) • Older (7 years) • Sicker • More late stage (26%), more subsequent primaries (3%) • More Males (6%) • More Blacks (1%) • Less Hispanics (2%)

  9. Results: Overall Migration

  10. More Likely Poverty Neighborhood at dx OR 1.1; 1.6 Rural Community at dx OR 1.3; 1.9 Longer survival OR 1.2 per year Non-white OR 1.2 Dying in Nursing Home/Hospice OR 1.1 Less Likely Increasing Age at dx 0.99 per year Increasing Stage OR 0.91; 0.72 Married at death OR 0.45 Female OR 0.93 Second Primary OR 0.97 Results: Tract Movement Predictors

  11. More Likely Longer survival OR 1.2 per year Non-white OR 1.7 Dying in Nursing Home/Hospice OR 1.1 Less Likely Increasing Age at dx 0.99 per year Increasing Stage OR 0.89; 0.75 Married at death OR 0.42 Female OR 0.93 Second Primary Not a predictor Results: Poverty Movement Predictors 11

  12. More Likely Longer survival OR 1.1 per year Non-white OR 0.75 Dying in Nursing Home/Hospice OR 1.2 Less Likely Increasing Age at dx 0.98 per year Increasing Stage OR 0.89; 0.83 Married at death OR 0.52 Female OR 0.76 Second Primary Not a predictor Results: Urban/Rural Movement Predictors 12

  13. Vast majority do not move Women, married, White, and sicker advanced disease, older age Misclassification bias Reduce effect size Unlikely to produce erroneous results Target Based on physical community and demographic profile Implications

  14. Future directions • Do people change residences after a cancer diagnosis? • Who do not die • Linkage: DMV, Voter Reg, Medical billing • Proprietary residential history databanks • Who die outside Florida • Linkage: state/county from SSDI • State data exchange

  15. Acknowledgements Gary Levin Brad Wohler • We acknowledge the CDC for financial support under cooperative agreement U58/DP000844 • Contents are responsibility of authors and do not represent views of CDC, FL DOH, or FCDS

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