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How Can Quantitative Analysis Be Used to Improve Occupational Health without Reinforcing Social Inequalities? An
Valérie Lederer1, Karen Messing2, Hélène Sultan-Taïeb3
1Department of Industrial Relations, Université du Québec en Outaouais, Gatineau, QC J8X 3X7, Canada.
Summary
Conventional statistical methods struggle to identify occupational health risks when considering sociodemographic factors. Emerging analyses like multilevel modeling are better suited for understanding complex interactions and guiding prevention strategies.
Area of Science:
- Occupational Health
- Biostatistics
- Sociology of Health
Background:
- Sex and gender significantly influence occupational health outcomes, but conventional statistical methods face challenges in analyzing these complex interactions.
- Sociodemographic variables like race, class, and age further interact with sex and gender, complicating the identification of workplace health determinants.
Approach:
- A critical review of statistical tools used in occupational health over the past two decades was conducted.
- The review screened literature from 1980-2022, focusing on both commonly used and emerging statistical methodologies.
- The analysis assessed tools based on their ability to incorporate sociodemographic variables, reflect intersectional health approaches, and suit available research data.
Key Points:
- Standard regression analyses adjusted for confounders and stratification are insufficient for revealing sociodemographic mechanisms in occupational health.
- These conventional methods can obscure critical occupational risks by failing to account for interacting variables.
- Emerging methods such as multilevel analysis (e.g., MAIHDA), decision trees, cluster analysis, and latent variable analysis offer more robust approaches.
Conclusions:
- Multilevel, decision tree, cluster, and latent analyses are recommended for identifying occupational health determinants and informing prevention strategies.
- Researchers should adopt statistical methods that effectively capture the intricate connections between sociodemographic factors and occupational health outcomes.
- Utilizing advanced analytical techniques is crucial for a comprehensive understanding of workplace health disparities.
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