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<p>Psychosocial adversity is a major determinant of health, but the mortality associations of its clinical documentation via ICD-10 Z55–Z65 codes remain unclear. We conducted a matched cohort study of UK Biobank participants with linked Hospital Episode Statistics and mortality records. Participants with ≥ 1 ICD-10 Z55–Z65 code recorded between 2010 and 2020 were propensity score–matched (1:1) to hospitalized participants without Z-codes. Mortality follow-up occurred from 2020 to 2023. Associations with all-cause mortality were estimated using Cox proportional hazards models, and cause-specific mortality using Fine–Gray subdistribution hazard models accounting for competing risks. The matched cohort included 13,604 participants (6802 with Z-codes and 6802 without). During a mean follow-up of 3.75&#xa0;years, mortality rates were 50.8 versus 22.9 deaths per 1000 person-years among participants with and without Z-codes, respectively. Z-code documentation was associated with higher all-cause mortality (HR = 2.02, 95% CI = 1.82, 2.23). Elevated risks were observed across major causes of death, with the strongest association for housing and economic hardship (Z59). Hospital documentation of psychosocial adversity (ICD-10 Z55–Z65 codes) was associated with substantially higher mortality and may contribute to population health surveillance when integrated into electronic health records.</p> Graphical abstract <p><b>Exposure identification</b></p> <p>• UK Biobank participants with ≥1 Z55–Z65 code.</p> <p>• Codes reflect social, economic, and psychosocial adversity.</p> <p><b>Study design</b></p> <p>• 1:1 propensity score (PS) matching with hospitalized controls, after alignment of admission dates.</p> <p>• Matched on baseline and admission age, sex, race/ethnicity, TDI, education and income.</p> <p>• Sensitivity analysis for 1:2, 1:3 PS matching and IPTW type of modeling.</p> <p><b>Analytic approach</b></p> <p>• Follow‑up: 2020–2023.</p> <p>• Cox &amp; Fine–Gray models.</p> <p>• Adjusted for all above factors along with LE8, self-rated health, comorbidities and medication use.</p> <p><b>Key findings</b></p> <p>• HR all‑cause mortality : HR=2.02; 95% CI: 1.82–2.23.</p> <p>• Elevated&#xa0; external, respiratory and other causes, as well as cancer and CVD mortality.&#xa0;</p> <p>• Housing/Economic adversity (Z59) was consistently the strongest predictor for all-cause mortality,&#xa0;especially in the 1:1 matched cohort.</p> <p></p>

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Psychosocial adversity captured by ICD-10 Z-codes and mortality risk: evidence from the UK Biobank

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • May A. Beydoun,
  • Hind A. Beydoun,
  • Jack Tsai,
  • Indira C. Turney,
  • Osorio Meirelles,
  • Tianyi Huang,
  • Nicole Noren Hooten,
  • Lenore J. Launer,
  • Michele K. Evans,
  • Alan B. Zonderman

摘要

Psychosocial adversity is a major determinant of health, but the mortality associations of its clinical documentation via ICD-10 Z55–Z65 codes remain unclear. We conducted a matched cohort study of UK Biobank participants with linked Hospital Episode Statistics and mortality records. Participants with ≥ 1 ICD-10 Z55–Z65 code recorded between 2010 and 2020 were propensity score–matched (1:1) to hospitalized participants without Z-codes. Mortality follow-up occurred from 2020 to 2023. Associations with all-cause mortality were estimated using Cox proportional hazards models, and cause-specific mortality using Fine–Gray subdistribution hazard models accounting for competing risks. The matched cohort included 13,604 participants (6802 with Z-codes and 6802 without). During a mean follow-up of 3.75 years, mortality rates were 50.8 versus 22.9 deaths per 1000 person-years among participants with and without Z-codes, respectively. Z-code documentation was associated with higher all-cause mortality (HR = 2.02, 95% CI = 1.82, 2.23). Elevated risks were observed across major causes of death, with the strongest association for housing and economic hardship (Z59). Hospital documentation of psychosocial adversity (ICD-10 Z55–Z65 codes) was associated with substantially higher mortality and may contribute to population health surveillance when integrated into electronic health records.

Graphical abstract

Exposure identification

• UK Biobank participants with ≥1 Z55–Z65 code.

• Codes reflect social, economic, and psychosocial adversity.

Study design

• 1:1 propensity score (PS) matching with hospitalized controls, after alignment of admission dates.

• Matched on baseline and admission age, sex, race/ethnicity, TDI, education and income.

• Sensitivity analysis for 1:2, 1:3 PS matching and IPTW type of modeling.

Analytic approach

• Follow‑up: 2020–2023.

• Cox & Fine–Gray models.

• Adjusted for all above factors along with LE8, self-rated health, comorbidities and medication use.

Key findings

• HR all‑cause mortality : HR=2.02; 95% CI: 1.82–2.23.

• Elevated  external, respiratory and other causes, as well as cancer and CVD mortality. 

• Housing/Economic adversity (Z59) was consistently the strongest predictor for all-cause mortality, especially in the 1:1 matched cohort.