A comprehensive review of the healthy worker effect in occupational epidemiological studies

The reduction of mortality and morbidity rates among occupational cohort studies may be attributed to the presence of the healthy worker effect (HWE). Occupational epidemiologic studies investigating worker’s health are prone to the risk of having the HWE phenomenon and this special form of bias has been debated over the years. Hence, it’s imperative to explore in-depth the magnitude and sources of HWE, and further, elucidate the factors that may affect HWE and strategies reducing HWE. The HWE should be considered as a mixed bias between selection and confounding bias. The validity threats due to the HWE among morbidity studies are the same as the mortality studies. The consequent reduction due to the HWE in the association between the exposure and outcome may lead to underestimating some harmful exposures in the workplace or occupational settings. Healthy hire effect and healthy worker survivor effect are the main sources of HWE. Several factors can increase or decrease the probability of HWE; therefore, the investigators should consider them among future occupational epidemiological studies. Many strategies can help in reducing the impact of HWE, but each strategy has its weaknesses and strengths. Not all strategies can be applied among all occupational epidemiological studies. Mathematical procedures still need further investigations to be validated. HWE is a consequence of inappropriate comparison groups in nature. The usage of the general population as a reference group is not an appropriate choice. By considering the HWE sources and factors and using appropriate strategies, the impact of HWE may be reduced.

Application of directed acyclic graphs in evidence-based medicine / 中国医师杂志

Evidence-based medicine (EBM) is a kind of clinic practice where clinicians use the best and the latest available evidence to diagnose and treat patients, and both evidence providers and users need to identify and control different kinds of biases in medical research.Directed acyclic graphsis is a tool to explore the causal relationship.The possible biases in the study can be revealed in a simple graphical language.The use of directed acyclic graphs could avoid the occurrence of bias and improve the quality of medical research and better guide clinical practice.

On Which Variable(s) Should We Condition to Remove Confounding Bias?.

Using causal diagrams and an axiomatization of causality, we examined the well-known claim that conditioning on confounders (“adjustment” for confounders) is sufficient to remove confounding bias. We show that this advice is poorly stated and is incomplete. To remove confounding bias, it is necessary to condition on three types of variables, none of which is a confounder. Conditioning on one of them, however, leads to an interesting form of colliding bias, which in turn, can be removed by conditioning on two other types of variables.

Introduction to an individual-based standardization method-propensity score weighting / 中华流行病学杂志

In this article,we presented the rationale and calculation procedures of a propensity score weighting method,with its application in epidemiological studies.The rationale for propensity score weighting method is similar to those for traditional standardization methods.Propensity score is used to estimate the weight for each individual.As the propensity score serves the function of observed covariates,the propensity score weighting can balance the distribution of the observed covariates between the comparison groups.There are two weighting methods according to the target standard populations:the Inverse probability of treatment weighting(IPTW)and the Standardized mortality ratio weighting(SMRW).Results of the example show that the distribution of the covariates tended to be consistent after weighting,and the IPTW and SMRW methods showed similar effect estimates.Propensity score weighting method can effectively balance the distribution of the confounding factors between the compared groups in non-randomized controlled trials.

Application of propensity score matching in the design of an epidemiological study / 中华流行病学杂志

In this article, we presented the rationale and calculation procedures of the propcnsity score matching (PSM), and its application in the designing stage of an cpidcrniological study. Based on existing observational data, PSM can be used to select one or more comparable controls for each subject in 'treatment' group according to the propensity scores estimated by 'treatment' variable and main covariates. The results of an example analysis showed that the bias for main confounders between the treated and control samples declined more than 55% after PMS. Conclusion: PSM can reduce most of the confounding bias of the observational study, and can obtain approximate study effect to the randomized controlled trials when used in the designing of thc cpidcmiological study.