Factors associated with dyslipidemia among healthcare workers in a COVID-19-designated hospital in Hangzhou, Zhejiang, China: A retrospective cohort study from 2019 to 2022

PLoS One. 2025 Jun 30;20(6):e0323934. doi: 10.1371/journal.pone.0323934. eCollection 2025.

Abstract

Background: This study investigated dyslipidemia and its relative factors among Chinese healthcare workers from 2019 to 2022.

Method: This retrospective cohort study was conducted from 2019 to 2022. The endpoints were dyslipidemia or the end of follow-up. Univariate Cox proportional hazard regression and LASSO regression models were used to select variables, and a multivariate Cox proportional hazard regression model was constructed to explore factors associated with dyslipidemia.

Results: 67 (9.2%) medical staff members were diagnosed with dyslipidemia, 106 (14.5%) resigned from the hospital, and 558 (76.3%) kept normal lipid files. Compared with healthcare workers with previous working time <10 years, the hazard ratios (HRs) of those with 10-20 years and ≥ 20 years of working experience were 0.34 (0.18-0.64) (P = 0.001) and 0.47 (0.26-0.85) (P = 0.01); compared with 0-day frontline working time, the HR of those with ≥ 30 days frontline working time was 0.38 (0.19-0.75) (P = 0.005). The HRs of TG, HDL, LDL, TBIL and HB were 3.14 (1.65-6.01) (P < 0.001), 0.20 (0.06-0.65) (P = 0.008), 2.93 (1.70-5.05) (P < 0.001), 1.06 (1.02-1.10) (P = 0.002) and 0.98 (0.97-0.99) (P = 0.04), respectively.

Conclusion: Healthcare workers with high frontline working time and longer previous working time were less likely to have dyslipidemia, while healthcare workers with high levels of TG, LDL, HB, TBIL, and low levels of HDL were more likely to have dyslipidemia. Supporting healthcare workers should be a priority for policymakers and hospital administrators.

MeSH terms

  • Adult
  • COVID-19* / epidemiology
  • China / epidemiology
  • Dyslipidemias* / blood
  • Dyslipidemias* / epidemiology
  • Female
  • Health Personnel* / statistics & numerical data
  • Hospitals
  • Humans
  • Male
  • Middle Aged
  • Proportional Hazards Models
  • Retrospective Studies
  • Risk Factors
  • SARS-CoV-2 / isolation & purification