Alive or dead: Validity of the Social Security Administration Death Master File after 2011
Open accessAbstract
Objective: To determine the reliability of the Social Security Death Master File (DMF) after the November 2011 changes limiting the inclusion of state records. Data Sources: Secondary data from the DMF, New York State (NYS) and New Jersey (NJ) Vital Statistics (VS), and institutional data warehouse. Study Design: Retrospective study. Two cohorts: discharge date before November 1, 2011, (pre‐2011) or after (post‐2011). Death in‐hospital used as gold standard. NYS VS used for out‐of‐hospital death. Sensitivity, specificity, Cohen's Kappa, and 1‐year survival calculated. Data Collection Methods: Patients matched to DMF using Social Security Number, or date of birth and Soundex algorithm. Patients matched to NY and NJ VS using probabilistic linking. Principal Findings: 97 069 patients January 2007‐March 2016: 39 075 pre‐2011; 57 994 post‐2011. 3777 (3.9 percent) died in‐hospital. DMF sensitivity for in‐hospital death 88.9 percent (κ = 0.93) pre‐2011 vs 14.8 percent (κ = 0.25) post‐2011. DMF sensitivity for NY deaths 74.6 percent (κ = 0.71) pre‐2011 vs 26.6 percent (κ = 0.33) post‐2011. DMF sensitivity for NJ deaths 62.6 percent (κ = 0.64) pre‐2011 vs 10.8 percent (κ = 0.15) post‐2011. DMF sensitivity for out‐of‐hospital death 71.4 percent pre‐2011 (κ = 0.58) vs 28.9 percent post‐2011 (κ = 0.34). Post‐2011, 1‐year survival using DMF data was overestimated at 95.8 percent, vs 86.1 percent using NYS VS. Conclusions: The DMF is no longer a reliable source of death data. Researchers using the DMF may underestimate mortality.
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Due to data quality concerns regarding the Social Security Death Master File, the authors did not attempt to link patients to the SSDMF for determining out-of-hospital mortality.
“Due to data quality concerns regarding the Social Security Death Master File (SSDMF)13, we did not attempt to link patients to the SSDMF for determining out-of-hospital mortality.”
From MIMIC-IV, a freely accessible electronic health record dataset (2023) · cites this paper as [13]
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Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01899-x · Cited by 3,401 · see citing works
“Due to data quality concerns regarding the Social Security Death Master File (SSDMF) 13 , we did not attempt to link patients to the SSDMF for determining out-of-hospital mortality.”
Background
- Authors
- Levin, M. A., Lin, H., Prabhakar, G., McCormick, P. J., & Egorova, N. N.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Texas Tech University Health Sciences Center (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Levin, Matthew A.
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2018-12-05
- Volume
- 54
- Issue
- 1
- Pages
- 24-33
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- PMC
- PMC6338290
- OpenAlex
- W2904250440
- Cited by
- 46
- References
- 13
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 5 · 2023: 2 · 2022: 6 · 2021: 8 · 2020: 7 · 2019: 4 · 2018: 1
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Update notices
- erratum (2019-05-08)
- Open access
- bronze
- doi.org
- Locations
- Health Services Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R25CA020449)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- mortality statistics, vital statistics, in-hospital mortality, out-of-hospital death, one-year survival, data linkage, New York State, New Jersey
- MeSH terms
- Death Certificates, Female, Forms and Records Control, Humans, Male, New Jersey, New York, Registries, Retrospective Studies, Social Security, United States, Vital Statistics, Databases, Factual, United States Social Security Administration, Validation Studies as Topic
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