Incidence of acute myocardial infarction in the workforce: Findings from the Occupational Disease Surveillance System.

BACKGROUND: Increased risks of acute myocardial infarction (AMI) may be attributable to the workplace, however, associations are not well-established. Using the Occupational Disease Surveillance System (ODSS), we sought to estimate associations between occupation and industry of employment and AMI risk among workers in Ontario, Canada. METHODS: The study population was derived by linking provincial accepted lost-time workers' compensation claims data, to inpatient hospitalization records. Workers aged 15-65 years with an accepted non-AMI compensation claim were followed for an AMI event between 2007 and 2016. Adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each industry and occupation group, compared to all other workers in the cohort. Sex-stratified analyses were also performed. RESULTS: In all, 24,514 incident cases of AMI were identified among 1,502,072 Ontario workers. Increased incidence rates of AMI were found across forestry (HR 1.37, 95% CI 1.19-1.58) and wood processing (HR 1.50, 1.27-1.77) job-titles. Elevated rates were also detected within industries and occupations both broadly related to mining and quarrying (HR 1.52, 1.17-1.97), trucking (HR 1.32, 1.27-1.38), construction (HR 1.32, 1.14-1.54), and the manufacturing and processing of metal (HR 1.41, 1.19-1.68), textile (HR 1.41, 1.07-1.88), non-metallic mineral (HR 1.30, 0.82-2.07), and rubber and plastic (HR 1.42, 1.27-1.60) products. Female food service workers also had elevated AMI rates (HR 1.36, 1.23-1.51). CONCLUSION: This study found occupational variation in AMI incidence. Future studies should examine work-related hazards possibly contributing to such excess risks, like noise, vibration, occupational physical activity, shift work, and chemical and particulate exposures.

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method.

Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set-a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.