Associations between area-level unemployment, body mass index, and risk factors for cardiovascular disease in an urban area

INTRODUCTION: Il existe peu d’évidences sur l’association entre le taux de chômage dans le milieu résidentiel (CR) et le risque de maladies cardiovasculaires parmi les résidents de milieux urbains. De plus, on ne sait pas si ce lien diffère entre les deux sexes. Cette thèse a pour objectif de déterminer la direction et la taille de l’association entre le CR et le risque de maladies cardiovasculaires, et d’examiner si cette association varie en fonction du sexe. MÉTHODES: Un sous-échantillon de 342 participants de l’Étude sur les habitudes de vie et la santé dans les quartiers montréalais a rapporté ses habitudes de vie et sa situation socio-économique. Des mesures biologiques et anthropométriques ont été recueillies par une infirmière. Le CR a été opérationnalisé en fonction d’une zone-tampon d’un rayon de 250 m centrée sur la résidence de chacun des participants à l’aide d’un Système d’Information Géographique (SIG). Des équations d’estimation généralisées ont été utilisées afin d’estimer l’association entre le CR et l’Indice de Masse Corporelle (IMC) et un score cumulatif de Risque Cardio-métabolique (RC) représentant la présence de valeurs élevées de cholestérol total, de triglycérides, de lipoprotéines de haute densité et d’hémoglobine glyquée. RÉSULTATS: Après ajustement pour l’âge, le sexe, le tabagisme, les comportements de santé et le statut socio-économique, le fait de vivre dans un endroit classé dans le 3e ou 4e quartile de CR était associé avec un IMC plus élevé (beta pour Q4 = 2.1 kg/m2, IC 95%: 1.02-3.20; beta pour Q3 = 1.5 kg/m2, IC 95%: 0.55-2.47) et un taux plus élevé de risque cardiovasculaires Risque Relatif [RR pour Q4 = 1.82 (IC 95 %: 1.35-2.44); RR pour Q3 = 1.66 (IC 95%: 1.33-2.06)] par rapport au 1er quartile. L'interaction entre le sexe et le CR révèle une différence absolue d’IMC de 1.99 kg/m2 (IC 95%: 0.00-4.01) et un risque supérieur (RR=1.39; IC 95%: 1.06-1.81) chez les femmes par rapport aux hommes. CONCLUSIONS: Le taux de chômage dans le milieux résidentiel est associé à un plus grand risque de maladies cardiovasculaires, mais cette association est plus prononcée chez les femmes.INTRODUCTION: Little is known about whether area-level unemployment is independently associated with individual-level Cardiovascular Disease (CVD) in an urban setting. Furthermore, it is unclear whether this relationship differs by sex. This thesis examined the direction and magnitude of the association between area-level unemployment (ALU) and Body Mass Index (BMI) and a marker for CVD risk, and whether this association differs by sex. METHODS: A sample of 342 individuals from the Montreal Neighbourhood Survey of Lifestyle and Health (MNSLH) self-reported behavioural and socioeconomic information. A registered nurse collected biochemical and anthropometric data. ALU was operationalised within a 250 m radius buffer centered on individual residence using a Geographic Information System (GIS). Generalized Estimating Equations were used to determine if body mass index (BMI), and a cumulative score for total cardiometabolic risk (TCR) representing elevated values for total cholesterol, triglycerides, high-density lipoprotein cholesterol, and glycosylated hemoglobin, were associated with ALU. RESULTS: After adjustment for age, gender, smoking status, behavioural, and socioeconomic covariates, living in an area in the upper ALU quartiles was associated with an elevated BMI [Q4 beta = 2.1 kg/m2 (95% CI: 1.02-3.20)] and greater TCR [Q4 RR = 1.82 (95 % CI: 1.35-2.44); Q3 RR = 1.66 (95% CI: 1.33-2.06)] relative to the 1st quartile. Sex-by-ALU interaction revealed a 1.99 kg/m2 (95% CI: 0.00-4.01) difference in BMI and 1.39-fold (95% CI: 1.06-1.81) greater TCR Score for women compared to men. CONCLUSIONS: Area-level unemployment is associated with greater CVD risk in men and women but associations are stronger among women

An Introduction to G Methods

Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidemiologists has been hampered by limitations in understanding both conceptual and technical details. We present a simple worked example that illustrates basic concepts, while minimizing technical complications

Counterfactual Theory in Social Epidemiology: Reconciling Analysis and Action for the Social Determinants of Health

Abstract There is a strong and growing interest in applying formal methods for causal inference with observational data in social epidemiology. A number of challenges in defining, identifying, and estimating counterfactual-based causal effects have been especially problematic in social epidemiology, particularly for commonly used exposures such as race, education, occupation, or socioeconomic position. The purpose of this article is to revisit these challenges in light of the conceptual and analytic advancements in causal inference over the last two decades. We focus on a central assumption for causal inference known as the stable unit treatment value assumption, which can be divided into two component assumptions: counterfactual consistency and the absence of interference. We give simple hypothetical examples to illustrate how and why these assumptions are often violated in research on the social determinants of health (e.g., education, race/ethnicity, socioeconomic position) and provide strategies that can be used to sidestep these assumptions. In particular, we note that a recently proposed mediation analysis strategy can be used to explore questions about health disparities in a more formal causal inference framework. We emphasize that a central obstacle to estimating causal effects variables such as race, education (e.g., high school versus no high school), or occupation is the need to identify an intervention (possibly hypothetical) that will lead to changes in the exposure of interest

The Metropolis algorithm: A useful tool for epidemiologists

The Metropolis algorithm is a Markov chain Monte Carlo (MCMC) algorithm used to simulate from parameter distributions of interest, such as generalized linear model parameters. The "Metropolis step" is a keystone concept that underlies classical and modern MCMC methods and facilitates simple analysis of complex statistical models. Beyond Bayesian analysis, MCMC is useful for generating uncertainty intervals, even under the common scenario in causal inference in which the target parameter is not directly estimated by a single, fitted statistical model. We demonstrate, with a worked example, pseudo-code, and R code, the basic mechanics of the Metropolis algorithm. We use the Metropolis algorithm to estimate the odds ratio and risk difference contrasting the risk of childhood leukemia among those exposed to high versus low level magnetic fields. This approach can be used for inference from Bayesian and frequentist paradigms and, in small samples, offers advantages over large-sample methods like the bootstrap.Comment: 26 pages, 3 figure

Analysis of Occupational Asbestos Exposure and Lung Cancer Mortality Using the G Formula

We employed the parametric G formula to analyze lung cancer mortality in a cohort of textile manufacturing workers who were occupationally exposed to asbestos in South Carolina. A total of 3,002 adults with a median age of 24 years at enrollment (58% male, 81% Caucasian) were followed for 117,471 person-years between 1940 and 2001, and 195 lung cancer deaths were observed. Chrysotile asbestos exposure was measured in fiber-years per milliliter of air, and annual occupational exposures were estimated on the basis of detailed work histories. Sixteen percent of person-years involved exposure to asbestos, with a median exposure of 3.30 fiber-years/mL among those exposed. Lung cancer mortality by age 90 years under the observed asbestos exposure was 9.44%. In comparison with observed asbestos exposure, if the facility had operated under the current Occupational Safety and Health Administration asbestos exposure standard of <0.1 fibers/mL, we estimate that the cohort would have experienced 24% less lung cancer mortality by age 90 years (mortality ratio = 0.76, 95% confidence interval: 0.62, 0.94). A further reduction in asbestos exposure to a standard of <0.05 fibers/mL was estimated to have resulted in a minimal additional reduction in lung cancer mortality by age 90 years (mortality ratio = 0.75, 95% confidence interval: 0.61, 0.92)

A Comparison of Methods to Estimate the Hazard Ratio Under Conditions of Time-varying Confounding and Nonpositivity

In occupational epidemiologic studies, the healthy-worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable, and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assess the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulate data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity

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

BACKGROUND: Previous 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. METHODS: We 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. RESULTS: At 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). CONCLUSIONS: Accounting 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

The Parametric g-Formula for Time-to-event Data: Intuition and a Worked Example

The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied