Applying Machine Learning Approach to Explore Childhood Circumstances and Self-Rated Health in Old Age - China and the US, 2020-2021

Aug 6, 2026

The study examines how childhood circumstances shape self-rated health in older age, using machine learning to quantify individual and collective contributions. Data from the Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS) include 2,434 U.S. participants and 5,612 Chinese participants aged 60+. Conditional inference trees and forests estimate inequality of opportunity, compare with conventional Roemer-style methods, and identify childhood health, financial status, and regional differences as key determinants.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Methods and Applications

Study design and data sources

Analytical approach

Findings and determinants

Discussion and implications

UC Davis UC Davis Previously Published Works Title Applying Machine Learning Approach to Explore Childhood Circumstances and SelfRated Health in Old Age - China and the US, 2020-2021. Permalink [https://escholarship.org/uc/item/98j703xc](https://escholarship.org/uc/item/98j703xc) Journal China CDC Weekly, 6(11) Authors Huo, Shutong Feng, Derek Gill, Thomas et al. Publication Date 2024-03-15 DOI 10.46234/ccdcw2024.043 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California China CDC Weekly Methods and Applications Applying Machine Learning Approach to Explore Childhood Circumstances and Self-Rated Health in Old Age —China and the US, 2020–2021 Shutong Huo1; Derek Feng2; Thomas M. Gill3; Xi Chen4,5,\# ABSTRACT Introduction: Childhood circumstances impact senior health, prompting the introduction of machine learning methods to assess their individual and collective contributions to senior health. Methods: Using health and retirement study (HRS) and China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,434 American and 5,612 Chinese participants aged 60 and above. Conditional inference trees and forests were employed to estimate the influence of childhood circumstances on self-rated health (SRH) . Results: The conventional method estimated higher inequality of opportunity (IOP) values in both China (0.039, accounting for 22.67% of the total Ginicoefficient 0.172) and the US (0.067, accounting for 35.08% of the total Gini coefficient 0. 191) . In contrast, the conditional inference tree yielded lower estimates (China: 0.022, accounting for 12.79% of 0.172; US: 0.044, accounting for 23.04% of 0.191), as did the forest (China: 0.035, accounting for 20.35% of 0.172; US: 0.054, accounting for 28.27% of 0.191) . Childhood health, financial status, and regional differences were key determinants of senior health. The conditional inference forest consistently outperformed others in predictive accuracy, as demonstrated by lower out-of-sample mean squared error (MSE) . Discussion: The findings emphasize the need for early-life interventions to promote health equity in aging populations. Machine learning showcases the potential in identifying contributing factors. INTRODUCTION The global phenomenon of rapid population aging, coupled with the growing health burden among older adults, highlights the importance of investigating the long-term effects of early life stages on the aging process ( 1) . Previous research in the fields of economics and epidemiology has consistently shown that childhood circumstances have a significant impact on later-life health outcomes. This suggests that childhood is a crucial period for implementing interventions aimed at reducing health disparities (2) . These circumstances encompass a wide range of factors, including parental influences (3), family socioeconomic status (SES) (4), as well as community and environmental factors such as rural/urban status (5) and natural surroundings (6) . Both early-life and later-life factors contribute to health outcomes in older age. However, childhood circumstances, particularly those that are beyond an individual’s control, are considered to be the most unacceptable and illegitimate sources of health inequality in older age (7–8) . This type of inequality, attributed to childhood circumstances, is commonly referred to as inequality of opportunity (IOP) . The focus on reducing IOP arises from a wide-ranging political and social discussion aimed at creating equal opportunities during the early stages of life and addressing the unfair health inequalities identified by the World Health Organization Commission on Social Determinants of Health (9) . Despite the considerable amount of research conducted on the impact of childhood circumstances on health outcomes, there are still methodological challenges that need to be addressed. These challe

FAQs

What is the main goal of the study?

To explore how childhood circumstances influence self-rated health in old age and to measure the inequality of opportunity created by factors beyond an individual’s control.

Which datasets are used for the analysis?

The analysis uses the Health and Retirement Study (HRS) for the U.S. and the China Health and Retirement Longitudinal Study (CHARLS) for China, covering the 2020–2021 HRS wave and 2020 CHARLS wave.

How do the machine learning models perform compared with the conventional method?

Conditional inference trees and forests provide different, generally lower inequality estimates than the conventional parametric approach and the conditional inference forest shows better predictive accuracy via lower out-of-sample mean squared error (MSE).

What factors are identified as key determinants of older adults’ health?

Childhood health, financial status, and regional differences are highlighted as important determinants of self-rated health.

Aug 6, 2026

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