Diabetes, employment and behavioural risk factors in China: Marginal structural models versus fixed effects models

Till Seuring, Pieter Serneels, Marc Suhrcke, Max O. Bachmann

Research output: Contribution to journalArticlepeer-review

Abstract

We use longitudinal data from the China Health and Nutrition Survey, coveringthe years 1997 to 2011, to estimate the eect of a diabetes diagnosis on an economicoutcome (employment probabilities) and behavioural risk factors (alcohol consumption,smoking cessation, body mass index (BMI), physical activity and hypertension) for menand women. We apply two complementary statistical techniques—marginal structuralmodels (MSMs) and fixed eects (FE) models—to deal with confounding. Both methodssuggest, despite their dierent underlying assumptions, similar patterns that indicateimportant dierences between men and women. Employment probabilities declinesubstantially after the diagnosis for women (-12.4 (MSM) and -15.5 (FE) percentagepoints), but do not change significantly for men. In particular, the MSM estimatesindicate an increase in hypertension (13 percentage points) and a decrease in physicalactivity for women, while men have small and statistically insignificant changes in theseoutcomes. For BMI, the MSM results indicate statistically significant changes for men(-.76), but not for women, while the FE estimates show similar reductions for men andwomen (-.80 and -.73 respectively). Men also reduce their alcohol consumption, but donot cease to smoke. For women these risk factors have a prevalence close to zero to beginwith, though women seem to still reduce alcohol consumption somewhat. These resultssuggest important gender dierences in the impact of diabetes in China. To narrowthese inequities policies supporting women to reduce diabetes related risk factors arelikely important.
Original languageEnglish
Article number100925
Pages (from-to)100925
JournalEconomics and Human Biology
Volume39
DOIs
Publication statusPublished - Dec 2020

Keywords

  • China
  • Diabetes
  • Employment
  • Behavioural risk factors
  • Marginal structural model

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