Using residential histories to estimate area-based poverty

1. Spatial Analytics Lab @ Temple Using residential histories to estimate area-based poverty Daniel Weise, PhD Candidate, Temple University Kevin Henry, PhD, Temple University Antoinette M. Stroup, PhD (Presenter) New Jersey State Cancer Registry Rutgers Cancer Institute of New Jersey 2019 NAACCR Annual Conference Vancouver, BC, Canada, June 12, 2019 An exploratory analysis of colon cancer survival in New Jersey

2. Residential Histories • Long history of the collection of residential histories by survey for case control studies to assess environmental exposures • Cohort studies have started to integrate and collect residential history data (e.g. California Teachers Study; The Multiethnic Cohort Study) • Jacquez et al. (2011); Wheeler and Wang (2015), Hurley et al. (2017), and NCI demonstrate the utility of  collecting residential histories from commercial databases • Population-based cancer surveillance systems in the United States and Canada currently only collect residence at time of diagnosis.

3. Cancer Disparities Research • Several registry-based  studies found associations between area-poverty based on residence at time of DX and cancer stage at diagnosis and survival  (Henry et al 2009, Warner & Gomez 2010, Feinglas et al 2015, Shariff-Marco et al 2015) • Most cancer disparities research is based on residential location at time of diagnosis. • Linking cancer registry data to residential histories will provide researchers new opportunities to integrate longitudinal and life course approaches when examining neighborhood-effects on cancer outcomes  

4. Presentation Outline • Part 1 • Describe the residential histories database that we linked NJ NHL and colon cases: LexisNexis • Summarize the linkage results and data quality issues • Creating residential histories • Part 2 • Evaluate differences in colon cancer survival, comparing estimates based on census tract poverty at diagnosis to estimates based on census tract poverty from residential histories. • Compare survival estimates based on residence at time of diagnosis to residence at date of last contact

5. Part I:LexisNexisResidential History Database

6. LexisNexis (LN), Inc. Risk Solutions • Collect and sell information that commercial organizations, government agencies and nonprofits use to profile individuals, businesses and assets with data and analytic products. • LN implements standard operating procedures to standardize and normalize data • LN leverages more than 10,000 data sources, • LN is equipped with encryption and secure file transfer protocols (SFTP)

7. Linkage Results Cases with 1+ usable CT: N=29,130 N=58 cases with no geographic information (unknown, PO Boxes, prison facilities) • Honest broker protocol • Dedicated, encrypted file transfer • LN returned a maximum number of 20 addresses through 2017

8. Key issues with LexisNexis • Missing dates and dates that fall after death or before birth. • Overlapping and gaps in residence start and end dates • Small number of nonresidential addresses (LN has implemented procedures for removing nonresidential addresses)

9. Creating Residential Histories

10. Validation *includes NHL and Colon cases diagnoses from 2006-2014

11. Part II:Survival AnalysisUsing Residential Histories

12. Objective • Evaluate differences in colon cancer survival, comparing estimates based on census tract poverty at diagnosis to estimates based on census tract poverty from residential histories.

13. Study Population • First and only primary colon cancer cases diagnosed at the regional stage in persons aged 21+ from 2006-2011 NJ Colon Cases (Seq 00) DX 2006-2011 (N=11,365) Regional Stage Cases (N=4,120) Exclude: death certificate or autopsy reported cases, survival time=0 Final Subset (N=4,049) Followed until 12/31/2016 Census tract poverty joined to each residential address

14. Definitions of Census Tract Poverty Definitions of Census Tract Poverty • Tract poverty at residential address at time of diagnosis • Average poverty over all addresses from 5 years prior to diagnosis until end of follow-up • Time-weighted average poverty based on all addresses from 5 years prior to diagnosis until end of follow-up • Time-varying poverty for addresses after diagnosis

15. Statistical Methods • Cox proportional hazard regression models were used to estimate the risk of colon cancer death by tract poverty (continuous measure). • Standard Cox models and time varying model (start, stop dates at residence) run using R SURVSIM, SURVIVAL as proposed by Zhang et al (2018) • Models adjusted for age, sex, and stage (regional stage subgroups), random effects tested

16. Results

17. Number of residential addresses (N=4,049) Percent of Cases Number of Residences

18. Census Tract Poverty Changes Change in Neighborhood Poverty between Diagnosis Address at date of Last Contact No Change Decrease Increase

19. Results

20. Compare survival estimates based on residence at time of DX to residence at date of last contact • Similar gender and age (<50 vs. 50+) distributions

21. Survival by Residential Location 5 yrSurv 82% 5 yrSurv 73% 5 yrSurv 72%

22. Conclusion Residential Poverty • Area-based SES changes over time • Place of diagnosis remained unchanged for 55% of patients • Consistency across measures – increasing poverty, increasing risk of colon cancer death • Slightl variation in risk estimates depending on approach Residence at DX vs DLC • Better survival among patients who are outside of NJ at end of follow-up

23. Limitations • Limited to regional stage colon cancer • Limited validation of residential histories, including time at each address

24. Recommendations Residential Histories • Linkage to residential histories is feasible • Registries might consider developing standards for maintaining addresses over time with time stamp and source data • Validation routine for retrospective data collection ($$$)

25. Next Steps • Compare LN addresses and derived residential histories with the NJSCR current/supplemental addresses information • Link to other databases to get more address date/time information for verifying and developing and residential histories (tuning algorithms) • Integrate geographic distance measures and other criteria into data cleaning and building residential histories • Application of Spatial Modeling Tools for cluster detection (BayesX) • Comparison place of diagnosis vs place of last contact

26. Thank you • Questions • Contact: Khenry1@temple.edu Co-Authors NJSCR/Rutgers CINJ Gerry Harris, PhD Temple University Slobadon Vucetic, PhD & Aniruddha Maiti Department of Computer Science Acknowledgement: This project was funded by NSF #1560888. NJSCR data were collected through funding from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute under contract HHSN261201300021I, the National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention under grant 5U58DP003931-02 as well as the State of New Jersey and the Rutgers Cancer Institute of New Jersey.