Dynamics of health behaviours and socioeconomic differences in mortality in the USA

Mehta, Neil K.; House, James S.; Elliott, Michael R.

doi:10.1136/jech-2014-204248

Cited by 25 publications

(29 citation statements)

References 40 publications

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“…Moreover, these studies suggest a tendency that the contribution of health behaviours is larger when longitudinal data of health behaviours are used 8 11 13. We have argued that several mechanisms may be responsible for differences between time-fixed and time-varying models and that the extent to which they do so may vary by health behaviour.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the role of physical activity in the explanation of socioeconomic inequalities in mortality has been shown to increase when multiple measurements of physical activity are used, even in the presence of a declining socioeconomic gradient in physical activity 8. Also, while inequalities in smoking increased over time, some studies have found no difference in the contribution of smoking between time-fixed and time-varying models 8 11. We argue that several mechanisms may cause differences in results between time-fixed and time-varying models, and that the extent to which they do so may vary by mediator.…”

Section: Introductionmentioning

confidence: 99%

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Contribution of time-varying measures of health behaviours to socioeconomic inequalities in mortality: how to understand the underlying mechanisms?

Groeniger¹,

Lenthe

2016

J Epidemiol Community Health

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A higher prevalence of unhealthy behaviours in lower socioeconomic groups contributes to socioeconomic inequalities in mortality. Recent cohort studies suggest that the contribution of health behaviours to socioeconomic inequalities in mortality is larger when measured repeatedly over time ('time-varying') instead of once only ('time-fixed'). Explanations for a larger contribution of health behaviours, however, are hardly discussed in the current literature, and appear to be more complex than a widening of inequalities in health behaviours over time alone. We describe the use of time-varying health behaviours to examine socioeconomic inequalities in mortality, systematically listing underlying mechanisms that may cause differences between time-varying and time-fixed models, and show that these mechanisms may be specific for each health behaviour. The use of time-varying health behaviours advances our understanding of the explanation of socioeconomic inequalities in mortality, but underlying mechanisms must be carefully examined.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contribution of time-varying measures of health behaviours to socioeconomic inequalities in mortality: how to understand the underlying mechanisms?

Groeniger¹,

Lenthe

2016

J Epidemiol Community Health

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“…25,27 This interpretation is supported by studies showing that health behavior has a larger role in explaining educational disparities compared with income disparities. 28 …”

Section: Discussionmentioning

confidence: 99%

Sociodemographic Patterns of Chronic Disease: How the Mid-South Region Compares to the Rest of the Country

Oates

Jackson

Partridge

et al. 2017

American Journal of Preventive Medicine

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Introduction States in the Mid-South region are among the least healthy in the nation. This descriptive study examines sociodemographic differences in the distribution of chronic diseases and health-related behaviors in the Mid-South versus the rest of the U.S., identifying subgroups at increased risk of chronic disease. Methods Data were obtained from the 2013 Behavioral Risk Factor Surveillance System; analyses were completed in January 2016. Twelve chronic health conditions were assessed: obesity, diabetes, high blood pressure, coronary heart disease, myocardial infarction, stroke, chronic kidney disease, cancer, arthritis, asthma, chronic obstructive pulmonary disease, and depression. Evaluated health-related behaviors included smoking, physical activity, and fruit and vegetable consumption. Age-standardized percentages were reported using complex survey design parameters to enhance generalizability. Results The Mid-South population had increased rates of chronic disease and worse health-related behaviors than the rest of the U.S. population. Mid-South blacks had the highest percentages of obesity, diabetes, high blood pressure, and stroke of all subgroups, along with lower physical activity and fruit and vegetable consumption. In both races and regions, individuals with lower income and education had higher rates of chronic disease and unhealthy behaviors than those with higher income and education. However, black men in both regions had higher obesity and cancer rates in the higher education category. In general, education-level disparities were more pronounced in health-related behaviors, whereas income-level disparities were more pronounced in chronic health conditions. Conclusions Future studies should test tailored interventions to address the specific needs of population subgroups in order to improve their health.

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“…It is also central to answering the common question of how much of sociodemographic differences in mortality are “explained by” differences in smoking, obesity, and other behavioral factors. The answer to this question is normally a function of differences in the distribution of behavioral factors across socioeconomic groups (e.g., Mehta et al, 2015; Nandi et al, 2014). However, differences in the responsiveness of various groups to behavioral factors should also be assessed in such an inquiry.…”

Section: Discussionmentioning

confidence: 99%

Are major behavioral and sociodemographic risk factors for mortality additive or multiplicative in their effects?

Mehta

Preston

2016

Social Science & Medicine

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All individuals are subject to multiple risk factors for mortality. In this paper, we consider the nature of interactions between certain major sociodemographic and behavioral risk factors associated with all-cause mortality in the United States. We develop the formal logic pertaining to two forms of interaction between risk factors, additive and multiplicative relations. We then consider the general circumstances in which additive or multiplicative relations might be expected. We argue that expectations about interactions among socio-demographic variables, and their relation to behavioral variables, have been stated in terms of additivity. However, the statistical models typically used to estimate the relation between risk factors and mortality assume that risk factors act multiplicatively. We examine empirically the nature of interactions among five major risk factors associated with all-cause mortality: smoking, obesity, race, sex, and educational attainment. Data were drawn from the cross-sectional NHANES III (1988–1994) and NHANES 1999–2010 surveys, linked to death records through December 31, 2011. Our analytic sample comprised 35,604 respondents and 5,369 deaths. We find that obesity is additive with each of the remaining four variables. We speculate that its additivity is a reflection of the fact that obese status is generally achieved later in life. For all pairings of socio-demographic variables, risks are multiplicative. For survival chances, it is much more dangerous to be poorly educated if you are black or if you are male. And it is much riskier to be a male if you are black. These traits, established at birth or during childhood, literally result in deadly combinations. We conclude that the identification of interactions among risk factors can cast valuable light on the nature of the process being studied. It also has public health implications by identifying especially vulnerable groups and by properly identifying the proportion of deaths attributable to a risk factor.

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Dynamics of health behaviours and socioeconomic differences in mortality in the USA

Cited by 25 publications

References 40 publications

Contribution of time-varying measures of health behaviours to socioeconomic inequalities in mortality: how to understand the underlying mechanisms?

Contribution of time-varying measures of health behaviours to socioeconomic inequalities in mortality: how to understand the underlying mechanisms?

Sociodemographic Patterns of Chronic Disease: How the Mid-South Region Compares to the Rest of the Country

Are major behavioral and sociodemographic risk factors for mortality additive or multiplicative in their effects?

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