Dataset Information

Modeling obesity histories in cohort analyses of health and mortality.

ABSTRACT: 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.

SUBMITTER: Preston SH

PROVIDER: S-EPMC3870658 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Modeling obesity histories in cohort analyses of health and mortality.

Preston Samuel H SH Mehta Neil K NK Stokes Andrew A

Epidemiology (Cambridge, Mass.) 20130101 1

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 ne ...[more]

PMID: 23211348

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Project description:Given the lack of complete vital registration data in most developing countries, for many countries it is not possible to accurately estimate under-five mortality rates from vital registration systems. Heavy reliance is often placed on direct and indirect methods for analyzing data collected from birth histories to estimate under-five mortality rates. Yet few systematic comparisons of these methods have been undertaken. This paper investigates whether analysts should use both direct and indirect estimates from full birth histories, and under what circumstances indirect estimates derived from summary birth histories should be used.Usings Demographic and Health Surveys data from West Africa, East Africa, Latin America, and South/Southeast Asia, I quantify the differences between direct and indirect estimates of under-five mortality rates, analyze data quality issues, note the relative effects of these issues, and test whether these issues explain the observed differences. I find that indirect estimates are generally consistent with direct estimates, after adjustment for fertility change and birth transference, but don't add substantial additional insight beyond direct estimates. However, choice of direct or indirect method was found to be important in terms of both the adjustment for data errors and the assumptions made about fertility.Although adjusted indirect estimates are generally consistent with adjusted direct estimates, some notable inconsistencies were observed for countries that had experienced either a political or economic crisis or stalled health transition in their recent past. This result suggests that when a population has experienced a smooth mortality decline or only short periods of excess mortality, both adjusted methods perform equally well. However, the observed inconsistencies identified suggest that the indirect method is particularly prone to bias resulting from violations of its strong assumptions about recent mortality and fertility. Hence, indirect estimates of under-five mortality rates from summary birth histories should be used only for populations that have experienced either smooth mortality declines or only short periods of excess mortality in their recent past. Please see later in the article for the Editors' Summary.

Project description:Objectives To investigate the associations of physical activity level with COVID-19 mortality risk across body mass index (BMI) categories, and to determine whether any protective association of a higher physical activity level in individuals with obesity may be explained by favourable levels of cardiometabolic and inflammatory biomarkers. Design Prospective cohort study (baseline data collected between 2006 and 2010). Physical activity level was assessed using the International Physical Activity Questionnaire (high: ≥3000 Metabolic Equivalent of Task (MET)-min/week, moderate: ≥600 MET-min/week, low: not meeting either criteria), and biochemical assays were conducted on blood samples to provide biomarker data. Setting UK Biobank. Main outcome measures Logistic regressions adjusted for potential confounders were performed to determine the associations of exposure variables with COVID-19 mortality risk. Mortality from COVID-19 was ascertained by death certificates through linkage with National Health Service (NHS) Digital. Results Within the 259 397 included participants, 397 COVID-19 deaths occurred between 16 March 2020 and 27 February 2021. Compared with highly active individuals with a normal BMI (reference group), the ORs (95% CIs) for COVID-19 mortality were 1.61 (0.98 to 2.64) for highly active individuals with obesity, 2.85 (1.78 to 4.57) for lowly active individuals with obesity and 1.94 (1.04 to 3.61) for lowly active individuals with a normal BMI. Of the included biomarkers, neutrophil count and monocyte count were significantly positively associated with COVID-19 mortality risk. In a subanalysis restricted to individuals with obesity, adjusting for these biomarkers attenuated the higher COVID-19 mortality risk in lowly versus highly active individuals with obesity by 10%. Conclusions This study provides novel evidence suggesting that a high physical activity level may attenuate the COVID-19 mortality risk associated with obesity. Although the protective association may be partly explained by lower neutrophil and monocyte counts, it still remains largely unexplained by the biomarkers included in this analysis.

Dataset Information

Modeling obesity histories in cohort analyses of health and mortality.

Publications

Modeling obesity histories in cohort analyses of health and mortality.

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