Mortality attributable to sugar sweetened beverages consumption in Mexico: an update

BACKGROUND

In 2010, sugar sweetened beverages (SSBs) were estimated to cause 12% of all diabetes, cardiovascular disease (CVD) and obesity-related cancer deaths in Mexico. Using new risk estimates for SSBs consumption, we aimed to update the fraction of Mexican mortality attributable to SSBs, and provide subnational estimates by region, age, and sex.

METHODS

We used an established comparative risk assessment framework. All-cause mortality estimates were calculated from a recent pooled cohort analysis. Age- and sex-specific relative risks for SSBs-disease relationships were obtained from updated meta-analyses. Demographics and nationally representative estimates of SSBs intake were derived from the National Health and Nutrition Survey 2012; and mortality rates, from the National Institute of Statistics and Geography. Attributable mortality was calculated by estimating the population attributable fraction of each disease, with uncertainty in data inputs propagated through Monte Carlo probabilistic sensitivity analyses.

RESULTS

In Mexican adults 20 years and older, 6.9% (95%UI: 5.4-8.5) of all cause-mortality was attributable to SSBs, representing 40,842 excess deaths/year (95%UI: 31,950-50,138). Furthermore, 19% of diabetes, CVD and obesity-related cancer mortality was attributable to SSBs (95%UI: 11.0-26.5), representing 37,000 excess deaths/year (95%UI 21,240-51,045). Of these, 35.6% were diabetes-related (95%UI 16.4-52.0). Proportional burden was highest in the South (22.8%), followed by the Center (18.0%) and North (17.4%). Men aged 45-64-years in the Center region had highest proportional mortality (37.2%), followed by 20-44-year-old men living in the South (35.7%) and both men and women aged 20-44 living in the Center (34.4%).

CONCLUSIONS

Utilizing current evidence linking SSBs to cardiometabolic disease and obesity-related cancers, earlier estimates of Mexican mortality attributable to SSBs could have been underestimated. Mexico urgently needs stronger policies to reduce SSBs consumption and reduce these burdens.

The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study

BACKGROUND

Frailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Middle-aged individuals are also understudied for the association between FI and mortality. Furthermore, the population mortality impact of frailty remains understudied.

METHODS

We estimated the predictive value of FI for all-cause and cause-specific mortality, taking into account familial factors, and tested whether the associations are time-dependent. We also assessed the proportion of all-cause and cause-specific deaths that are attributable to increased levels of frailty. We analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20 years' mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes, and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.

RESULTS

Increased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality, with the corresponding hazard ratios of 1.28 (1.24, 1.32), 1.31 (1.23, 1.40), and 1.23 (1.11, 1.38) associated with a 10% increase in FI in male single responders, and 1.21 (1.18, 1.25), 1.27 (1.15, 1.34), and 1.26 (1.15, 1.39) in female single responders. No significant associations were observed for cancer mortality. No attenuation of the mortality associations in unrelated individuals was observed when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. Before the age of 80, the proportions of deaths attributable to FI levels > 0.21 were 18.4% of all-cause deaths, 25.4% of CVD deaths, and 20.4% of respiratory-related deaths in men and 19.2% of all-cause deaths, 27.8% of CVD deaths, and 28.5% of respiratory-related deaths in women. After the age of 80, the attributable proportions decreased, most notably for all-cause and CVD mortality.

CONCLUSIONS

Increased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI presents a relatively greater risk factor at midlife than in old age. Increased FI has a significant population mortality impact that is greatest through midlife until the age of 80.

On the relationship between the heritability and the attributable fraction

Heritability is the most commonly used measure of genetic contribution to disease outcomes. Being the fraction of the variance of latent trait liability attributable to genetic factors, heritability of binary traits is a difficult technical concept that is sometimes misinterpreted as the more-easily understandable concept of attributable fraction. In this paper we use the liability threshold model to describe the analytical relationship between heritability and attributable fraction. Towards this end, we consider a hypothetical intervention that is aimed to reduce the genetic risk of the disease for a specified target group of the population. We show how the relation between the heritability and the attributable fraction depends on the disease prevalence, the intervention effect and the size of the target group. We use two real examples to illustrate the practical implications of our theoretical results.

Decomposition of the population attributable fraction for two exposures

PURPOSE

The population attributable fraction (AF) is frequently used to quantify disease burden attributable to exposures. AF is interpreted as the fractional reduction of disease events that would occur if exposures were eliminated. This article aims to provide a decomposition of the overall AF for two exposures into AFs for each of two exposures and AF for their interaction, using potential outcomes framework.

METHODS

We provide the decomposition formula with and without confounders. We discuss an estimation method using standard regression models. We also show that these AFs without confounders can be effectively visualized.

RESULTS

By a numerical comparison, we show that our decomposition is different from a previous decomposition, which does not have a causal interpretation if confounding exists. We illustrate the proposed decomposition using a large prospective cohort study data.

CONCLUSIONS

When the primary exposure cannot be modifiable, the interventional interpretation of AF is difficult. Even then, if there exists an interaction between the exposure and another modifiable exposure, our decomposition can show what extent of the effect of the primary exposure can be eliminated by intervening on the modifiable exposure.

Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data

The between-within frailty model has been proposed as a viable analysis tool for clustered survival time outcomes. Previous research has shown that this model gives consistent estimates of the exposure–outcome hazard ratio in the presence of unmeasured cluster-constant confounding, which the ordinary frailty model does not, and that estimates obtained from the between-within frailty model are often more efficient than estimates obtained from the stratified Cox proportional hazards model. In this paper, we derive novel estimation techniques for regression standardization with between-within frailty models. We also show how between-within frailty models can be used to estimate the attributable fraction function, which is a generalization of the attributable fraction for survival time outcomes. We illustrate the proposed methods by analyzing a large cohort on preterm birth and attention deficit hyperactivity disorder. To facilitate use of the proposed methods, we provide R code for all analyses.

Measurement Error and Environmental Epidemiology: a Policy Perspective

PURPOSE OF REVIEW

Measurement error threatens public health by producing bias in estimates of the population impact of environmental exposures. Quantitative methods to account for measurement bias can improve public health decision making.

RECENT FINDINGS

We summarize traditional and emerging methods to improve inference under a standard perspective, in which the investigator estimates an exposure-response function, and a policy perspective, in which the investigator directly estimates population impact of a proposed intervention. Under a policy perspective, the analyst must be sensitive to errors in measurement of factors that modify the effect of exposure on outcome, must consider whether policies operate on the true or measured exposures, and may increasingly need to account for potentially dependent measurement error of two or more exposures affected by the same policy or intervention. Incorporating approaches to account for measurement error into such a policy perspective will increase the impact of environmental epidemiology.

An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of -1.57% (95% confidence interval: -3.08, -0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions.

Using a Marginal Structural Model to Design a Theory-Based Mass Media Campaign

Background

The essential first step in the development of mass media health campaigns is to identify specific beliefs of the target audience. The challenge is to prioritize suitable beliefs derived from behavioral theory. The purpose of this study was to identify suitable beliefs to target in a mass media campaign to change behavior using a new method to estimate the possible effect size of a small set of beliefs.

Methods

Data were drawn from the 2010 Japanese Young Female Smoker Survey (n = 500), conducted by the Japanese Ministry of Health, Labor and Welfare. Survey measures included intention to quit smoking, psychological beliefs (attitude, norms, and perceived control) based on the theory of planned behavior and socioeconomic status (age, education, household income, and marital status). To identify suitable candidate beliefs for a mass media health campaign, we estimated the possible effect size required to change the intention to quit smoking among the population of young Japanese women using the population attributable fraction from a marginal structural model.

Results

Thirteen percent of study participants intended to quit smoking. The marginal structural model estimated a population attributable fraction of 47 psychological beliefs (21 attitudes, 6 norms, and 19 perceived controls) after controlling for socioeconomic status. The belief, “I could quit smoking if my husband or significant other recommended it” suggested a promising target for a mass media campaign (population attributable fraction = 0.12, 95% CI = 0.02–0.23). Messages targeting this belief could possibly improve intention rates by up to 12% among this population. The analysis also suggested the potential for regulatory action.

Conclusions

This study proposed a method by which campaign planners can develop theory-based mass communication strategies to change health behaviors at the population level. This method might contribute to improving the quality of future mass health communication strategies and further research is needed.

From exposures to population interventions: pregnancy and response to HIV therapy

Many epidemiologic studies identify contrasts between an "always-exposed" population and a "never-exposed" population. Such "exposure effects" are perhaps most valuable in discussing individual lifestyle changes, or in clinical care; they may be less valuable in estimating the potential effects of realistic public health interventions. Various methods, among them population attributable fractions and generalized impact fractions, attempt to obtain more policy-relevant estimates of "population intervention" effects, but such methods remain rare in the epidemiologic literature. Here, we describe the use of the parametric g-formula as a tool for the estimation of population intervention effects in longitudinal data. Our discussion is motivated by a previous study of the effect of incident pregnancy on time to virological failure among human immunodeficiency virus-positive women initiating antiretroviral therapy in South Africa between 2004 and 2011. We show that 1) interventional estimates of effect can be estimated in longitudinal data using the parametric g-formula and 2) exposure effects and population interventional effects can have dramatically different interpretations and magnitudes in real-world data. Epidemiologists should consider estimating interventional effects in addition to exposure effects; doing so would allow the results of epidemiologic studies to be more immediately relevant to policy-makers and to implementation science efforts.