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Identifying cohabiting couples in administrative data: evidence from Medicare address data

Abstract

Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011–2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims. Address information represents a promising strategy for identifying cohabiting couples in administrative data including healthcare claims and other data types.

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Funding

We acknowledge funding from the National Institute on Aging (R21AG053698) and the Social Security Administration (Retirement Research Consortium through the University of Michigan Retirement Research Center Award RRC08098401-10). The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff of the Board of Governors of the Federal Reserve System, the National Institute on Aging or the Social Security Administration.

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Correspondence to Lauren Hersch Nicholas.

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Appendices

Appendix 1: Address cleaning

We geocoded the addresses in the Vital Status file using ArcGIS in order to obtain cleaned versions of the imputed addresses as well as each beneficiary’s census block. We concatenated apartment numbers and PO box numbers to each beneficiary’s cleaned address if available. The spelling sensitivity default in ArcGIS controls the amount of variation the geocoder will allow when identifying addresses in the reference data (Wilson et al. 2008). In other words, it standardizes directional terms such as “St.” and “Street” and names such as “Universe’ and “University,” which helped us avoid undercounting addresses that contained spelling errors or abbreviated words (Wilson et al. 2008, ESRI).

Appendix 2: Assortative mating

Among the identified couples 97% are different sex, 96% are of the same race, and 82% are within five years of each other. Among those who did not meet the couple definition (Not-Identified), only 78% were different sex, 53% were of the same race, and 51% were within five years of each other. Randomly assigning beneficiaries to partners in their census block suggests that our couple identification strategy is stronger than chance alone given that 83% of these randomly identified couples are of the same race, only 50% are opposite sex and 49% are within 5 years of each other, deviating severely from what we hypothesize a couple to resemble (Tables

Table 3 Assortative mating characteristics-all couples

3,

Table 4 Assortative mating characteristics-white younger spouse

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Table 5 Assortative mating characteristics-black younger spouse

5,

Table 6 Assortative mating characteristics-other/unknown younger spouse

6).

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Matta, S., Hsu, J.W., Iwashyna, T.J. et al. Identifying cohabiting couples in administrative data: evidence from Medicare address data. Health Serv Outcomes Res Method 21, 238–247 (2021). https://doi.org/10.1007/s10742-020-00229-1

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Keywords

  • Marriage
  • Cohabitation
  • Address
  • Couples