Andrew Stokes

Net Benefits: A Multicountry Analysis of Observational Data Examining Associations between Insecticide- Treated Mosquito Nets and Health Outcomes

Stephen Lim and colleagues report findings from a multi-country analysis of household survey data on the association between possession of insecticide-treated mosquito nets and child mortality and parasitemia. Scale-up of net coverage was associated with a substantial reduction in childhood mortality and in parasitemia prevalence.

Projecting the Effect of Changes in Smoking and Obesity on Future Life Expectancy in the United States

We estimate the effects of declining smoking and increasing obesity on mortality in the United States over the period 2010–2040. Data on cohort behavioral histories are integrated into these estimates. Future distributions of body mass indices are projected using transition matrices applied to the initial distribution in 2010. In addition to projections of current obesity, we project distributions of obesity when cohorts are age 25. To these distributions, we apply death rates by current and age-25 obesity status observed in the National Health and Nutrition Examination Survey, 1988–2006. Estimates of the effects of smoking changes are based on observed relations between cohort smoking patterns and cohort death rates from lung cancer. We find that changes in both smoking and obesity are expected to have large effects on U.S. mortality. For males, the reductions in smoking have larger effects than the rise in obesity throughout the projection period. By 2040, male life expectancy at age 40 is expected to have gained 0.83 years from the combined effects. Among women, however, the two sets of effects largely offset one another throughout the projection period, with a small gain of 0.09 years expected by 2040.

Modeling Obesity Histories in Cohort Analyses of Health and Mortality

There is great interest in understanding the role of weight dynamics over the life cycle in predicting the incidence of disease and death. Beginning with a Medline search, we identify, classify, and evaluate the major approaches that have been used to study these dynamics. We identify four types of models: additive models, duration-of-obesity models, additive-weight-change models, and interactive models. We develop a framework that integrates the major approaches and shows that they are often nested in one another, a property that facilitates statistical comparisons. Our criteria for evaluating models are two-fold: the model’s interpretability and its ability to account for observed variation in health outcomes. We apply two sets of nested models to data on adults age 50–74 years at baseline in two national probability samples drawn from National Health and Nutrition Examination Survey. One set of models treats obesity as a dichotomous variable and the other treats it as a continuous variable. In three of four applications, a fully interactive model does not add significant explanatory power to the simple additive model. In all four applications, little explanatory power is lost by simplifying the additive model to a duration model in which the coefficients of weight at different ages are set equal to one another. Other versions of a duration-of-obesity model also perform well, underscoring the importance of obesity at early adult ages for mortality at older ages.

Using maximum weight to redefine body mass index categories in studies of the mortality risks of obesity

BackgroundThe high prevalence of disease and associated weight loss at older ages limits the validity of prospective cohort studies examining the association between body mass index (BMI) and mortality.MethodsI examined mortality associated with excess weight using maximum BMI—a measure that is robust to confounding by illness-induced weight loss. Analyses were carried out on US never-smoking adults ages 50-84 using data from the National Health and Nutrition Examination Surveys (1988-1994 and 1999-2004) linked to the National Death Index through 2006. Cox models were used to estimate hazard ratios for mortality according to BMI at time of survey and at maximum.ResultsUsing maximum BMI, hazard ratios for overweight (BMI, 25.0-29.9 kg/m2), obese class 1 (BMI, 30.0-34.9 kg/m2) and obese class 2 (BMI, 35.0 kg/m2 and above) relative to normal weight (BMI, 18.5-24.9 kg/m2) were 1.28 (95% confidence interval [CI], 0.89-1.84), 1.67 (95% CI, 1.15-2.40), and 2.15 (95% CI, 1.47-3.14), respectively. The corresponding hazard ratios using BMI at time of survey were 0.98 (95% CI, 0.77-1.24), 1.18 (95% CI, 0.91-1.54), and 1.31 (95% CI, 0.95-1.81). The percentage of mortality attributable to overweight and obesity among never-smoking adults ages 50-84 was 33% when assessed using maximum BMI. The comparable figure obtained using BMI at time of survey was substantially smaller at 5%. The discrepancy in estimates is explained by the fact that when using BMI at time of survey, the normal category combines low-risk stable-weight individuals with high-risk individuals that have experienced weight loss. In contrast, only the low-risk stable-weight group is categorized as normal weight using maximum BMI.ConclusionsUse of maximum BMI reveals that estimates based on BMI at the time of survey may substantially underestimate the mortality burden associated with excess weight in the US.

The Dynamics of Diabetes Among Birth Cohorts in the U.S.

OBJECTIVE Using a nationally representative sample of the civilian noninstitutionalized U.S. population, we estimated trends in diabetes prevalence across cohorts born 1910–1989 and provide the first estimates of age-specific diabetes incidence using nationally representative, measured data. RESEARCH DESIGN AND METHODS Data were from 40,130 nonpregnant individuals aged 20–79 years who participated in the third National Health and Nutrition Examination Survey (NHANES III), 1988–1994, and the continuous 1999–2010 NHANES. We defined diabetes as HbA1c ≥6.5% (48 mmol/mol) or taking diabetes medication. We estimated age-specific diabetes prevalence for the 5-year age-groups 20–24 through 75–79 for cohorts born 1910–1919 through 1980–1989 and calendar periods 1988–1994, 1999–2002, 2003–2006, and 2007–2010. We modeled diabetes prevalence as a function of age, calendar year, and birth cohort, and used our cohort model to estimate age-specific diabetes incidence. RESULTS Age-adjusted diabetes prevalence rose by a factor of 4.9 between the birth cohorts of 1910–1919 and 1980–1989. Diabetes prevalence rose with age within each birth cohort. Models based on birth cohorts show a steeper age pattern of diabetes prevalence than those based on calendar years. Diabetes incidence peaks at 55–64 years of age. CONCLUSIONS Diabetes prevalence has risen across cohorts born through the 20th century. Changes across birth cohorts explain the majority of observed increases in prevalence over time. Incidence peaks between 55 and 64 years of age and then declines at older ages.

Mortality and excess risk in US adults with pre-diabetes and diabetes: a comparison of two nationally representative cohorts, 1988–2006

BackgroundThere is strong evidence on the efficacy of behavioral modification and treatment for reducing diabetes incidence and diabetes-related morbidity and mortality in persons with pre-diabetes and diabetes. But the extent to which the evidence has translated into gains in health in these population sub-groups in the US is unclear. Monitoring national diabetes-related mortality levels over time is important for evaluating the effectiveness of the US health system response to diabetes.MethodsWe identified individuals with pre-diabetes and diabetes using Hemoglobin A1c. Two consecutive periods for investigating differences in mortality according to categories of glycemia were derived using nationally representative survey data on US adults ages 35–74 from subsequent rounds of the National Health and Nutrition Examination Survey (1988–1994 and 1999–2002). Age-standardized mortality rates were calculated for individuals with pre-diabetes and diabetes and proportional hazards models were used to assess change in the relative risks of dysglycemia (pre-diabetes and diabetes) adjusting for multiple confounding factors.ResultsAge-standardized mortality rates in individuals with pre-diabetes and diabetes showed no statistically significant change between 1988–2001 and 1999–2006. In individuals with pre-diabetes, mortality rates were 11.19 and 14.02 deaths per 1,000 person-years in the early and later period, respectively. The corresponding values for individuals with diabetes were 20.34 and 20.82 deaths per 1,000 person-years. In contrast, the absolute level of mortality in the normo-glycemic population declined significantly between 1988–2001 and 1999–2006 (7.81 to 6.04; p for differenceu2009<u20090.05). Adjusting for social and demographic variables, smoking and body mass index in a multivariate analysis, the hazard ratio of dysglycemia increased from 1.62 (95% CI: 1.36–1.93) in 1988–2001 to 2.36 (95% CI: 1.70–3.27) in 1999–2006 (p for differenceu2009<u20090.05).ConclusionsWe find no evidence of declines in excess mortality in persons with dysglycemia between 1988–2001 and 1999–2006, a result that was robust to adjustment for social and demographic variables, smoking and body mass index. In the context of long-term secular declines in mortality in the US population, our findings suggest that individuals with pre-diabetes and diabetes should be an important focus of future interventions aimed at improving population health in the US.

Factors responsible for mortality variation in the United States: A latent variable analysis

BACKGROUND Factors including smoking, drinking, substance abuse, obesity, and health care have all been shown to affect health and longevity. The relative importance of each of these factors is disputed in the literature, and has been assessed through a number of methods. OBJECTIVE This paper uses a novel approach to identify factors responsible for interstate mortality variation. It identifies factors through their imprint on mortality patterns and can therefore identify factors that are difficult or impossible to measure directly, such as sensitive health behaviors. METHODS The analysis calculates age-standardized death rates by cause of death from 2000-2009 for white men and women separately. Only premature deaths between ages 20-64 are included. Latent variables responsible for mortality variation are then identified through a factor analysis conducted on a death-rate-by-state matrix. These unobserved latent variables are inferred from observed mortality data and interpreted based on their correlations with individual causes of death. RESULTS Smoking and obesity, substance abuse, and rural/urban residence are the three factors that make the largest contributions to state-level mortality variation among males. The same factors are at work for women but are less vividly revealed. The identification of factors is supported by a review of epidemiologic studies and strengthened by correlations with observable behavioral variables. Results are not sensitive to the choice of factor-analytic method used. CONCLUSIONS The majority of interstate variation in mortality among white working-age adults in the United States is associated with a combination of smoking and obesity, substance abuse and rural/urban residence.

Re: “Obesity and US Mortality Risk Over the Adult Life Course”

Masters et al. (1) suggested that previous studies have misestimated the age pattern of the mortality risks of obesity. Although prior work has indicated that the mortality risks associated with obesity decline with age, Masters et al. argued that these risks rise with age. The authors reached their conclusion based on a model that included both age at survey and attained age over the follow-up period. They reported a positive interaction between attained age and obesity (i.e., the hazard ratios for obesity increased with increasing attained age). n nAn alternative explanation not considered by the authors is that the pattern of the obesity-mortality relationship observed as participants age over the follow-up period is attributable to interactions between obesity and time in study. When data are conditioned on age at survey, attained age is highly and positively correlated with time in study. Prior research using data from the National Health Interview Survey, the data used by Masters et al., showed that the mortality risks of obesity increase with increasing time in study (2). Other studies based on large prospective cohorts have also reported this finding (3, 4). A prominent explanation for this pattern is that reverse causal pathways (i.e., disease-induced weight loss) are stronger during the initial years of follow-up and subsequently weaken as individuals with pre-existing diseases die. These observations suggest that caution should be taken in drawing firm conclusions about the age pattern of the obesity-mortality relationship based on the authors’ approach.