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Health insurance was the strongest predictor of healthy aging

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Research area:finance-marketsactuarial-science-risk-modeling

What the study found

The study found that an XGBoost model could predict healthy aging in adults aged 50 and older better than logistic regression and a multi-layer perceptron. The authors also report that health insurance type was the most predictive feature in their analysis.

Why the authors say this matters

The authors conclude that their findings underscore the significant role of health insurance in contributing to healthy aging. They present this as relevant to understanding how social determinants of health, meaning social and economic conditions that shape health, relate to healthy aging.

What the researchers tested

The researchers used data from the All of Us Research Program registered tier dataset v7 in a retrospective cohort study. They included participants aged 50 and older who answered at least one social determinants of health survey question and had electronic health record data, and they trained logistic regression, a multi-layer perceptron, and XGBoost models to predict a composite healthy aging outcome based on comorbidities, cognitive conditions, and mobility function.

What worked and what didn't

The best-performing model was XGBoost with random oversampling, with an AUROC of 0.793 and an F1 score of 0.697. The same model also showed similar positive and negative predictive values across race and sex groups, and feature importance ranked health insurance type above employment status, substance use, and health insurance coverage. The abstract says XGBoost outperformed logistic regression and the multi-layer perceptron, but it does not provide detailed comparative scores for those models.

What to keep in mind

This summary is limited to what is stated in the abstract. The abstract does not describe external validation, causal inference, or limitations of the dataset beyond the study design and included population.

Key points

  • XGBoost was the strongest model for predicting healthy aging in this cohort.
  • Health insurance type was the top-ranked predictive feature.
  • The study included 99,935 participants aged 50 and older.
  • Healthy aging was defined using comorbidities, cognitive conditions, and mobility function.
  • The authors report similar predictive values across race and sex groups for the best model.

Disclosure

Research title:
Health insurance was the strongest predictor of healthy aging
Authors:
Wei‐Han Chen, Yao-An Lee, Huilin Tang, Chenyu Li, You Lü, Yu Huang, Rui Yin, Melissa J. Armstrong, Yang Yang, Gregor Stiglic, Jiang Bian, Jingchuan Serena Guo
Institutions:
Indiana University – Purdue University Indianapolis, Indiana University – Purdue University Indianapolis, Indiana University Health, Purdue University in Indianapolis, Regenstrief Institute, Regenstrief Institute, Regenstrief Institute, University of Edinburgh, University of Florida, University of Florida, University of Florida, University of Florida, University of Florida, University of Florida Health, University of Florida Health, University of Indianapolis, University of Maribor, University of Pittsburgh, University of Pittsburgh, Vibrant Data (United States)
Publication date:
2026-03-06
DOI:
10.1371/journal.pone.0342292
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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