Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Causal inference in occupational epidemiology: accounting for the healthy worker effect by using structural nested models.

ABSTRACT: In a recent issue of the Journal, Kirkeleit et al. (Am J Epidemiol. 2013;177(11):1218-1224) provided empirical evidence for the potential of the healthy worker effect in a large cohort of Norwegian workers across a range of occupations. In this commentary, we provide some historical context, define the healthy worker effect by using causal diagrams, and use simulated data to illustrate how structural nested models can be used to estimate exposure effects while accounting for the healthy worker survivor effect in 4 simple steps. We provide technical details and annotated SAS software (SAS Institute, Inc., Cary, North Carolina) code corresponding to the example analysis in the Web Appendices, available at http://aje.oxfordjournals.org/.

SUBMITTER: Naimi AI

PROVIDER: S-EPMC3858107 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Causal inference in occupational epidemiology: accounting for the healthy worker effect by using structural nested models.

Naimi Ashley I AI Richardson David B DB Cole Stephen R SR

American journal of epidemiology 20130927 12

In a recent issue of the Journal, Kirkeleit et al. (Am J Epidemiol. 2013;177(11):1218-1224) provided empirical evidence for the potential of the healthy worker effect in a large cohort of Norwegian workers across a range of occupations. In this commentary, we provide some historical context, define the healthy worker effect by using causal diagrams, and use simulated data to illustrate how structural nested models can be used to estimate exposure effects while accounting for the healthy worker s ...[more]

PMID: 24077092

Similar Datasets

Complex systems models for causal inference in social epidemiology.

Project description:Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

| S-EPMC8849440 | biostudies-literature

Estimating the effect of cumulative occupational asbestos exposure on time to lung cancer mortality: using structural nested failure-time models to account for healthy-worker survivor bias.

Project description:BackgroundPrevious estimates of the effect of occupational asbestos on lung cancer mortality have been obtained by using methods that are subject to the healthy-worker survivor bias. G-estimation of a structural nested model provides consistent exposure effect estimates under this bias.MethodsWe estimated the effect of cumulative asbestos exposure on lung cancer mortality in a cohort comprising 2564 textile factory workers who were followed from January 1940 to December 2001.ResultsAt entry, median age was 23 years, with 42% of the cohort being women and 20% nonwhite. During the follow-up period, 15% of person-years were classified as occurring while employed and 13% as occupationally exposed to asbestos. For a 100 fiber-year/ml increase in cumulative asbestos, a Weibull model adjusting for sex, race, birth year, baseline exposure, and age at study entry yielded a survival time ratio of 0.88 (95% confidence interval = 0.83 to 0.93). Further adjustment for work status yielded no practical change. The corresponding survival time ratio obtained using g-estimation of a structural nested model was 0.57 (0.33 to 0.96).ConclusionsAccounting for the healthy-worker survivor bias resulted in a 35% stronger effect estimate. However, this estimate was considerably less precise. When healthy-worker survivor bias is suspected, methods that account for it should be used.

| S-EPMC4457343 | biostudies-literature

Instrumental variable estimation of causal odds ratios using structural nested mean models.

Project description:We consider estimating causal odds ratios using an instrumental variable under a logistic structural nested mean model (LSNMM). Current methods for LSNMMs either rely heavily on possible "uncongenial" modeling assumptions or involve intricate numerical challenges, which have impeded their use. In this article, we present an alternative method that ensures a congenial parametrization, circumvents computational complexity of existing methods, and is easy to implement. We illustrate the proposed method to (1) estimate the causal effect of years of education on earnings using data from the NLSYM and (2) assess the impact of moving families from high to low-poverty neighborhoods had on lifetime major depressive disorder among adolescents in the "Moving to Opportunity (MTO) for Fair Housing Demonstration Project" from the Department of Housing and Urban Development.

| S-EPMC5862265 | biostudies-literature

Are network-based interventions a useful antiobesity strategy? An application of simulation models for causal inference in epidemiology.

Project description:Recent research suggests that social networks may present an avenue for intervention against obesity. By using a simulation model in which artificial individuals were nested in a social network, we assessed whether interventions targeting highly networked individuals could help reduce population obesity. We compared the effects of targeting antiobesity interventions at the most connected individuals in a network with those targeting individuals at random. We tested 2 interventions, the first "preventing" obesity among 10% of the population at simulation outset and the second "treating" obesity among 10% of the obese population yearly, each in 2 separate simulations. One simulation featured a literature-based parameter for the network spread of obesity, and the other featured an artificially high parameter. Interventions that targeted highly networked individuals did not outperform at-random interventions in simulations featuring the literature-based parameter. However, in simulations featuring the artificially high parameter, the targeted prevention intervention outperformed the at-random intervention, whereas the treatment intervention implemented at random outperformed the targeted treatment intervention. Results were qualitatively similar across network topologies and intervention scales. Although descriptive studies suggest that social networks influence the spread of obesity, policies targeting well-connected individuals in social networks may not improve obesity reduction. We highlight and discuss the potential applications of counterfactual simulations in epidemiology.

| S-EPMC3712657 | biostudies-literature

Using <i>n</i>-Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials.

Project description:Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may not. Such instances of partial nesting requires a more flexible framework for estimating treatment effects so that the model coefficients are correctly estimated. Although several recommendations have been offered to the field on handling partially nested data, few are comprehensive in their treatment of manifest and latent variables in the context of partial nesting, full nesting, and cross-classification. The present study introduces n-level structural equation modeling (SEM) as a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of n-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using n-level SEM framework is demonstrated through five examples for single outcome manifest variables, as in the traditional multilevel model, as well as latent applications as in multilevel SEM.

| S-EPMC6777070 | biostudies-literature

A structural approach to address the healthy-worker survivor effect in occupational cohorts: an application in the trucking industry cohort.

Project description:Occupational cohort studies are often challenged by the Healthy Worker Survivor Effect, which may bias standard methods of analysis. G-estimation of structural failure time models is an approach for reducing this type of bias. Accelerated failure time models have recently been applied in an occupational cohort but cumulative failure time models have not.We used g-estimation of a cumulative failure time model to assess the effect of working as a long-haul driver on ischaemic heart disease mortality in a cohort of 30 448 men employed in the unionised US trucking industry in 1985. Exposure was defined by job title and based on work records. We also applied g-estimation of an accelerated failure time model as a sensitivity analysis and approximated HRs from both models to compare them.The risk ratio (RR) obtained from the cumulative failure time model, comparing the observed risk under no intervention to the risk had nobody ever been exposed as a long-haul driver, was 1.09 (95% CI 1.02 to 1.16). The RR comparing the risk had everyone been exposed as long-haul driver for 8 years to the risk had nobody ever been exposed was 1.20 (95% CI 1.04 to 1.46). After HR approximations, accelerated failure time model results were similar.The cumulative failure time model can effectively control time-varying confounding by Healthy Worker Survivor Effect, and provides an easily interpretable effect estimate. RRs estimated from the cumulative failure time model indicate an elevated ischaemic heart disease mortality risk for long-haul drivers in the US trucking industry.

| S-EPMC4051133 | biostudies-literature

Healthy worker survivor analysis in an occupational cohort study of Dutch agricultural workers.

Project description:ObjectivesHigh microbial exposures in farmers and agricultural workers are associated with less atopy. Although it has been speculated that healthy worker survival could be an explanation, this has not been studied so far. Therefore, we investigated the presence of healthy worker survival in a five-year follow-up study of an occupational cohort of Dutch farmers and agricultural industry (company) workers.MethodsWe compared baseline demographic characteristics, respiratory health, atopy and endotoxin exposure of 259 workers followed up with 124 workers lost to follow-up. Additionally, baseline health status of 31 participants who had changed to lower exposure jobs at follow-up was compared to those with similar or higher exposure jobs at follow-up.ResultsIn general, no major healthy worker survival effect was found. Nonetheless, small differences were observed between subjects included in follow-up and those lost to follow-up. Those lost to follow-up were older, had a lower peak expiratory flow, and were less often raised on a farm. Company workers lost to follow-up with a farm childhood had more often self-reported allergy, but this was not observed for subjects with atopic sensitization or other respiratory symptoms. No differences were found for any of the studied characteristics in participants with lower exposure at follow-up compared to participants with similar or higher exposure at follow-up.ConclusionsNo major healthy worker survival is present in this organic dust exposed cohort. Differences between participants lost to follow-up and participants included in follow-up with regard to health characteristics are small and unlikely to explain the previously reported inverse associations between endotoxin exposure and atopy.

| S-EPMC4608974 | biostudies-literature

Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Project description:Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.

| S-EPMC6522350 | biostudies-literature

Formalizing the role of agent-based modeling in causal inference and epidemiology.

Project description:Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry.

| S-EPMC4351348 | biostudies-literature

Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?

Project description: Not available

| S-EPMC5841837 | biostudies-literature