Brendan T. Smith

Associations Between Childhood Socioeconomic Position and Adulthood Obesity

Childhood socioeconomic position (SEP) is inversely associated with cardiovascular disease and all-cause mortality. Obesity in adulthood may be a biologic mechanism. Objectives were to systematically review literature published between 1998 and 2008 that examined associations of childhood SEP with adulthood obesity. Five databases (Cochrane Library, MEDLINE, EMBASE, PsycINFO, Web of Science) were searched for studies from any country, in any language. Forty-eight publications based on 30 studies were identified. In age-adjusted analyses, inverse associations were found between childhood SEP and adulthood obesity in 70% (14 of 20) of studies in females and 27% (4 of 15) in males. In studies of females showing inverse associations between childhood SEP and adulthood obesity, typical effect sizes in age-adjusted analyses for the difference in body mass index between the highest and lowest SEP were 1.0-2.0 kg/m(2); for males, effect sizes were typically 0.2-0.5 kg/m(2). Analyses adjusted for age and adult SEP showed inverse associations in 47% (8 of 17) of studies in females and 14% (2 of 14) of studies in males. When other covariates were additionally adjusted for, inverse associations were found in 4 of 12 studies in females and 2 of 8 studies in males; effect sizes were typically reduced compared with analyses adjusted for age only. In summary, the findings suggest that childhood SEP is inversely related to adulthood obesity in females and not associated in males after adjustment for age. Adulthood SEP and other obesity risk factors may be the mechanisms responsible for the observed associations between childhood SEP and adulthood obesity.

Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions

Reducing health inequalities has become a major public health priority internationally. However, how best to achieve this goal is not well understood. Population health intervention research has the potential to address some of this knowledge gap. This review argues that simulation studies can produce unique evidence to build the population health intervention research evidence base on reducing social inequalities in health. To this effect, the advantages of using simulation models over other population health intervention research methods are discussed. Key questions regarding the potential challenges of developing simulation models to investigate population health intervention research on reducing social inequalities in health and the types of population health intervention research questions that can be answered using this methodology are reviewed. We use the example of social inequalities in coronary heart disease to illustrate how simulation models can elucidate the effectiveness of a number of ‘what-if’ counterfactual population health interventions on reducing social inequalities in coronary heart disease. Simulation models are a flexible, cost-effective, evidence-based research method with the capacity to inform public health policy-makers regarding the implementation of population health interventions to reduce social inequalities in health.

Estimating the direct and indirect pathways between education and diabetes incidence among Canadian men and women: a mediation analysis.

PURPOSE To estimate the direct and indirect pathways between education and diabetes. METHODS We examined the relative contribution of eight different pathways between education and diabetes incidence over a 9-year period in Ontario, Canada. Our data source was respondents (35-60 years of age) to the 2000-2001 Canadian Community Health Survey individually linked with physician and hospital administrative data. Our sample contained 11,899 participants with no previous diagnosis of diabetes. The direct and indirect effects of education level on incident diabetes were estimated using Aalen additive hazard models. RESULTS Not having completed secondary education was associated with 120 extra diabetes cases per 10,000 men per year and 43 additional diabetes cases per 10,000 women per year, compared with having Bachelors education or higher. Body mass index accounted for 13 of the 120 extra diabetes cases among men, and 24 of the 43 additional diabetes cases for women. CONCLUSIONS Of the mediating pathways examined in this paper, body mass index was the pathway through which the largest number of diabetes cases was mediated among men and women. A substantial number of excess diabetes cases among respondents with lower education levels, in particular among men, were not mediated through any of the eight pathways examined.

The Fundamental Role of Socioeconomic Resources for Health and Health Behaviors

Socioeconomic resources – those of individuals, communities, and societies – are perhaps the most fundamental determinant of nearly every health and health behavioral outcome in every society and at every point in history, including the present day. With little exception, fewer socioeconomic resources are associated with lower health status and worsened health behaviors, a phenomenon that is often termed the “socioeconomic gradient in health.” In this chapter, we (a) review key insights that have emerged from the large body of literature in this area, and that provide strong and consistent support for the rather lofty assertion about the critical role of socioeconomic resources, (b) present an emergent conceptual model that synthesizes this literature, and (c) develop two case studies in which we trace the mechanisms that produce a socioeconomic health gradient in various societies, and the mechanisms that can “flatten” the gradient, and thereby produce greater health equity.

Professional Football Participation and Mortality

Professional Football Participation and Mortality To t h e Ed i t o r The retrospective cohort study by Dr Venkataramani and colleagues1 found a hazard ratio (HR) for mortality in career National Football League (NFL) players compared with NFL replacement players following NFL retirement of 1.38 (95% CI, 0.95-1.99); P = .09. First, the main conclusion of the authors and editorialists2 was that there was no statistically significant difference in mortality between the 2 groups. The American Statistical Association has warned against interpreting scientific significance using a statistical threshold because of the myriad factors influencing precision of estimates.3 In this case, the study was insufficiently powered to detect a relative mortality hazard less than 1.4.1 The result is an underestimate of a potentially clinically meaningful finding. To put this finding into context, this HR is similar to the association between physical inactivity (HR, 1.43 [95% CI,1.34-1.53]) or hypertension (HR, 1.38 [95% CI, 1.30-1.46]) and mortality.4 Second, we have concerns regarding the study design. Venkataramani and colleagues1 made use of a natural experiment, an often powerful design in observational studies, and compared career NFL players with NFL replacement players, the latter of whom played in the league for a limited time during a strike. The authors suggested this design better isolated the effect of playing in the NFL because replacement players are capable of playing professionally and thus are otherwise similar to career players. However, the NFL replacement players may be a diverse group. For many, their general mortality risk factors, such as income, diet, and exercise, may diverge considerably from those of career NFL players. On the other hand, some NFL replacement players may have more similar exposure to league-specific factors (eg, repeated head trauma) than the study suggested because of ongoing participation in football in other leagues. Thus, the study findings may be biased away from the null (in the case of general mortality risk factors) or toward the null (in the case of league-specific risk factors). Third, the larger issue is whether isolating the effect of playing in the NFL is a useful approach. Prevailing hypotheses about the effects of head trauma posit that it is the accumulation of repeated traumas that is most consequential for health.5 As such, the real question is whether increased longevity of playing American football, rather than playing in the NFL per se, is associated with mortality or other health outcomes.

Simulation modeling to enhance population health intervention research for chronic disease prevention

Population Health Intervention Research (PHIR) is an expanding field that explores the health effects of population-level interventions conducted within and outside of the health sector. Simulation modeling—the use of mathematical models to predict health outcomes in populations given a set of specified inputs—is a useful, yet underutilized tool for PHIR. It can be employed at several phases of the research process: (1) planning and designing PHIR studies; (2) implementation; and (3) knowledge translation of findings across settings and populations. Using the example of community-wide, built environment interventions for the prevention of type 2 diabetes, we demonstrate how simulation models can be a powerful technique for chronic disease prevention research within PHIR. With increasingly available data on chronic disease risk factors and outcomes, the use of simulation modeling in PHIR for chronic disease prevention is anticipated to grow. There is a continued need to ensure models are appropriately validated and researchers should be cautious in their interpretation of model outputs given the uncertainties that are inherent with simulation modeling approaches. However, given the complexity of disease pathways and methodological challenges of PHIR studies, simulation models can be a valuable tool for researchers studying population interventions that hold the potential to improve health and reduce health inequities.RésuméLa recherche interventionnelle en santé des populations (RISP), un domaine en expansion, explore les effets sur la santé des interventions en population menées à l’intérieur et à l’extérieur du secteur de la santé. La modélisation de simulation—le recours à des modèles mathématiques pour prédire les résultats de santé au sein de populations selon un jeu d’intrants spécifiés— est un outil sous-utilisé en RISP. Il peut être employé à plusieurs stades du processus de recherche : 1) la planification et la conception d’une étude de RISP; 2) sa mise en œuvre; et 3) l’application des constatations d’un milieu et d’une population à l’autre. En prenant l’exemple d’interventions de proximité sur l’environnement bâti qui visent à prévenir le diabète de type 2, nous démontrons que les modèles de simulation peuvent constituer une puissante technique pour étudier la prévention des maladies chroniques en RISP. Avec la disponibilité croissante de données sur les facteurs de risque et les issues des maladies chroniques, le recours à la modélisation de simulation en RISP pour la prévention des maladies chroniques devrait augmenter. Bien entendu, il faut s’assurer que les modèles sont dûment validés, et la prudence est de mise dans l’interprétation des extrants de ces modèles, étant donné l’incertitude inhérente des démarches de modélisation de simulation. Néanmoins, vu la complexité de la progression des maladies et les difficultés méthodologiques des études de RISP, les modèles de simulation peuvent être de précieux outils pour les chercheurs qui étudient les interventions en population susceptibles d’améliorer la santé et de réduire les inégalités de santé.