1. National Cancer Institute U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Post-Katrina Population Estimates Dave Stinchcomb 1, Barry Miller 1, Vivien Chen 2 1 National Cancer Institute 2 Louisiana Tumor Registry June 7, 2007

2. Introduction In 2005, hurricanes Katrina and Rita caused major population shifts –From impacted areas to other nearby areas –As services were restored, some returned to rebuild, others did not Result: significant population shifts over time throughout the region

3. Population Impact of 2005 Hurricanes % Shift in Population as of January 1, 2006 Source: US Census Special Population Estimates released May 25, 2006

4. The Problem Cancer surveillance depends on accurate population estimates –Estimating completeness –Calculating rates Populations normally change gradually –For each year and each geographic area: We assume constant growth throughout the year We use the July 1 estimate as the average for the year For these hurricane impacted areas, we need time-series population estimates

5. Methods Study area: the State of Louisiana in collaboration with the Louisiana Tumor Registry Develop time-series population estimates –Parish (county) level –State as a whole Based on a conceptual model: –Evacuation and destination parishes –Base population, true population, cancer population

6. Conceptual Model – Step 1 Evacuation Parishes: Destination Parishes:

7. The Cancer Population But cancer registration is based on self- reported “permanent” residence For hurricane displaced cancer cases: –Initially report their original parish as their residence –Eventually, they might report their new parish if they decide they are not planning to return So the true population is not what we need –Not important where people are physically Define “cancer population”: –What they will report as their residence if diagnosed with cancer

8. Conceptual Model – Cancer Population Evacuation Parishes: Destination Parishes:

9. Model Support Conceptual model based in part on Hurricane Andrew survey data from Dade County, Florida (1992): [Smith & McCarty 1996]

10. Model Calibration Data Parish population estimates: Dates residents allowed to return to New Orleans by ZIP code:

11. Data Sources

12. Preliminary Results Estimates by parish and by month –True population and cancer population –Total person-months & person-years –State-wide totals

13. Demographic Shifts Demographics of displacement not uniform Sample of LPHI results for Orleans Parish: More men, fewer young people and fewer Blacks in New Orleans after the hurricane

14. Cancer Diagnosis Seasonality Cancer diagnosis is seasonal Rates drop off in November and December Need to combine with population shifts May need to do this differentially by cancer site

15. Other Future Work Application to completeness and rates –Reflection of uncertainty in rate confidence intervals Parametric curve fitting Sensitivity analysis

16. Conclusions Conceptual model useful for fitting data from disparate sources, different geographies, and different time periods. The “cancer population” – an estimate of the people who consider a place their permanent home – is needed for cancer surveillance. Method can be extended: –Other states impacted by Katrina and Rita –Other disaster-related population shifts with significant loss of housing stock Why is this important? –Completeness estimates: closed hospitals, lost records, interstate reporting –Cancer rate calculations: impact of hurricanes on local cancer rates

17. Thank You Dave Stinchcomb StinchcD@mail.nih.gov 301-594-7251