Kupperman index-based analysis of factors influencing perimenopausal symptoms and establishment of risk nomogram in female petrochemical workers
- VernacularTitle:基于Kupperman指数的石化女工围绝经期综合征的影响因素分析和风险列线图的建立
- Author:
Lu ZHAO
1
;
Wenlan YU
2
;
Hui LI
3
;
Shulin WANG
4
;
Zhenxia KOU
3
Author Information
- Publication Type:Selectedarticle
- Keywords: occupational health; perimenopausal symptom; Kupperman index; influencing factor; nomogram model
- From: Journal of Environmental and Occupational Medicine 2022;39(4):404-409
- CountryChina
- Language:Chinese
- Abstract: Background Women face more reproductive health problems in their whole life cycle. Occupational exposure to harmful factors in the petrochemical industry may have a synergistic effect on women’s existing health problems. Objective To analyze the influencing factors of perimenopausal syndrome (PMS) in female workers in petrochemical industry, and establish a nomogram model of the risk of PMS in female workers, so as to provide a easy and quick health monitoring and evaluation method for female workers. Methods A total of 2653 perimenopausal female workers aged 45-55 years old were selected from a petrochemical enterprise. A questionnaire survey was conducted to collect information on demographic characteristics, occupational characteristics, psychological status, and reproductive health information. The prevalence of PMS of female workers was evaluated by the Kupperman Index Scale, the physical fatigue and mental fatigue were evaluated by the Fatigue Scale. A linear graph prediction model was established by multiple logistic regression. A nomogram was presented and C-index was used to verify the differentiation of the model. Then Bootstrap method was used for internal validation. Results Among the 2653 female worker, a total of 1306 cases (49.2%) presented PMS with a Kupperman score ≥7. The main symptoms were fatigue (79.95%), irritability (71.32%), and insomnia (66.79%). Significant differences in PMS prevalence were found among female workers of different age, body mass index, and working posture groups (P < 0.05). The participants with alcohol drinking, maternal premature or late menopause, hypertension, lack of physical exercise, heavy lifting, sick leave in the last 6 months, combined occupational exposures to dust, chemicals, noise [> 80 dB(A)], or electromagnetic field, and not wearing protective masks, gloves or protective earplugs reported higher prevalence rates of PMS (P < 0.05). The prevalence rate of PMS in female workers with sleep duration ≤ 6 h was higher than that with > 6 h (P < 0.05), and higher in female workers with physical and mental fatigue than in those without (P < 0.05). The results of logistic regression analysis showed that those with maternal premature or late menopause (OR=1.572, 95%CI: 1.320−1.872), hypertension (OR=1.579, 95%CI: 1.127−2.213), alcohol drinking (OR=1.286, 95%CI: 1.080−1.532), no physical exercise (OR=1.598, 95%CI: 1.330−1.920), sleep duration ≤ 6 h (OR=1.853, 95%CI: 1.518−2.263), sick leave in recent 6 months (OR=1.614, 95%CI: 1.226−2.123), physical fatigue (OR=2.384, 95%CI: 1.887−3.012), mental fatigue (OR=5.649, 95%CI: 4.382−7.283), combined exposure to occupational harmful factors (OR=1.329, 95%CI: 1.108−1.593), long-time sitting (OR=2.014, 95%CI: 1.271−3.190), and heavy lifting (OR=1.505, 95%CI: 1.178−1.923) showed a higher risk of reporting PMS (P<0.05). The C-index from the ROC curve of the nomogram model was 0.748 (95%CI: 0.729−0.766). The results of Bootstrap validation showed that the standard curve and the predicted curve almost overlapped, and the absolute error was 0.008, indicating that the model fitness was good. Conclusion PMS in female petrochemical workers may occur due to long-term exposures to multiple factors. The established nomogram model has good predictive ability and could be applied to monitor and evaluate female reproductive health in petroleum industry.
