key: cord-0030049-bc3bs4go
authors: Puka, Klajdi; Buckley, Charlotte; Mulia, Nina; Lasserre, Aurélie M.; Rehm, Jürgen; Probst, Charlotte
title: Educational Attainment and Lifestyle Risk Factors Associated With All-Cause Mortality in the US
date: 2022-04-08
journal: JAMA Health Forum
DOI: 10.1001/jamahealthforum.2022.0401
sha: f90a38218b8ed93ed4f4a67025cb99e4ac59c25b
doc_id: 30049
cord_uid: bc3bs4go

IMPORTANCE: The US has experienced increasing socioeconomic inequalities and stagnating life expectancy. Past studies have not disentangled 2 mechanisms thought to underlie socioeconomic inequalities in health, differential exposure and differential vulnerability, that have different policy implications. OBJECTIVE: To evaluate the extent to which the association between socioeconomic status (SES) and all-cause mortality can be decomposed into a direct effect of SES, indirect effects through lifestyle factors (differential exposure), and joint effects of SES with lifestyle factors (differential vulnerability). DESIGN, SETTING, AND PARTICIPANTS: This nationwide, population-based cohort study used the cross-sectional US National Health Interview Survey linked to the National Death Index. Civilian, noninstitutionalized US adults aged 25 to 84 years were included from the 1997 to 2014 National Health Interview Survey and were followed up until December 31, 2015. Data were analyzed from May 1 to October 31, 2021. A causal mediation model using an additive hazard and marginal structural approach was used. EXPOSURES: Both SES (operationalized as educational attainment) and lifestyle risk factors (smoking, alcohol use, obesity, and physical inactivity) were assessed using self-reported questionnaires. MAIN OUTCOMES AND MEASURES: Time to all-cause mortality. RESULTS: Participants included 415 764 adults (mean [SD] age, 49.4 [15.8] years; 55% women; 64% non-Hispanic White), of whom 45% had low educational attainment and 27% had high educational attainment. Participants were followed up for a mean (SD) of 8.8 (5.2) years during which 49 096 deaths (12%) were observed. Low educational attainment (compared with high) was associated with 83.6 (men; 95% CI, 81.8-85.5) and 54.8 (women; 95% CI, 53.4-56.2) additional deaths per 10 000 person-years, of which 66% (men) and 80% (women) were explained by lifestyle factors. Inequalities in mortality were primarily a result of greater exposure and clustering of unhealthy lifestyle factors among low SES groups; with some exceptions among women, little evidence of differential vulnerability was identified. CONCLUSIONS AND RELEVANCE: In this cohort study, differential exposure to lifestyle risk factors was an important mediator of socioeconomic inequalities in mortality. Public health interventions are needed, particularly among low SES groups, to address smoking, physical inactivity, alcohol use, and the socioenvironmental contexts within which these risk factors develop.

I (men: up to 20 (women) or 40 (men) grams per day; women: up to 20 grams per day), 4) category II (men:21-40 (women) or 41-60 (men) grams per day; women: 21-40 grams per day), 5) category III (men: ≥41 (women) or ≥61 (men) grams per day; women: ≥41 grams per day).

Assuming 14 grams of pure alcohol per standard drink, these categories of alcohol use are equivalent to: category I (up to 10 (women) or 20 (men) drinks per week), category II (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (women) or 20-30 (men) drinks per week), and category III (>20 (women) or >30 (men) drinks per week). Category I drinking behavior was used as the reference category, given that this group comprised of the majority of the population and because never drinkers may have poorer health outcomes. 1 Lastly, as a sensitivity analysis, alcohol use was indexed using heavy episodic drinking (HED) based on the number of heavy drinking days (≥5 drinks a day) in the past 12 months. This indicator is not a measure of "binge drinking," generally defined as 5+ drinks (sometimes 4+ for women) on a single drinking occasion. HED was categorized into four categories based on the number of heavy drinking days in the past 12 months: no HED (0 heavy drinking days), HED less than once a month (1-11 heavy drinking days), HED at least once a month but less than once a week (12-51 heavy drinking days), and HED once a week or more (52-365 heavy drinking days).

With respect to smoking, participants were asked to report whether they 1) have smoked at least 100 cigarettes over their entire life, and 2) whether they currently smoke.

Smoking was categorized as never smokers (reference category), former smokers, current some day smokers, and current everyday smokers.

With respect to physical activity, participants were asked to report 1) how often they performed a) vigorous or b) light-moderate leisure-time physical activities of at least 10 minutes that caused a) heavy sweating or large increases in breathing or heart rate or b) only light sweating or a slight to moderate increase in breathing or heart rate, and 2) how long they do those a) vigorous or b) light or moderate leisure-time physical activities each time. No timeframe (e.g., over the past year, or past month) was specified for either question. The length of physical activity per week was calculated and combined, assuming that 1 minute of vigorous physical activity is equivalent to 2 minutes of moderate physical activity 2 . Physical activity was categorized using WHO recommendations of 150-300 minutes of moderate-intensity physical activity per week 3 .

To evaluate the interaction (joint effects) of education and lifestyle risk factors on mortality (Objective 1), Aalen's additive hazard models were used to directly estimate additive interaction 4,5 . Additive interaction was the focus given that it is of greater importance for public health 4 . An additional benefit is that the coefficients obtained from Aalen models are collapsible, unlike those obtained from Cox models; 6 that is, one can meaningfully compare parameter estimates from Aalen models with different covariates, but should not do so for Cox models since the addition of a covariate could shift the baseline hazard rather than simply altering the slops of the hazard function. The hazard of all-cause mortality for person i at age t was modeled as a linear function of the exposure (E; education), the lifestyle risk factor (M), their interaction (E x M), covariates (C), and an unspecified baseline hazard (λ 0 ):

With regard to interpretation, α 3 directly estimates the number of additional events per person year at risk due to their additive interaction. This semiparametric model is flexible and can incorporate time-varying covariate effects (i.e., β(t), where the effect of the covariate is not constant over time). Each lifestyle risk factor was evaluated one at a time, and models were adjusted for age (used as the time scale), and the following categorical variables: race/ethnicity, marital status, and survey year. Separate models were estimated for men and women given that sex has been suggested to be an effect modifier of socioeconomic inequalities on all-cause mortality 7 . The graphical techniques and tests described by Scheike and Martinussen 5 suggested that race/ethnicity, smoking, and physical activity should be modeled as age-varying effects; this is equivalent to a violating the proportional hazards assumption in Cox models.

Aalen models are flexible and the effects of race/ethnicity were included in the model as age-varying. Sensitivity analyses by age subgroups (where the age-invariant assumption was met)

were used to examine the impact of modeling smoking and physical activity as age-invariant.

The marginal structural approach described by Lange et al. [8] [9] [10] was used to evaluate the extent to which lifestyle risk factors mediated the relationship between education and mortality (Objective 2). Briefly, this flexible approach uses a counterfactual framework and allows for the direct parameterization of natural direct and indirect effects, multiple mediators, and exposure-mediator interactions. The total effect of education on mortality was decomposed into three components: the average pure direct effect, the average pure indirect effect through each mediator (indicating differential exposure), and the average effect of the mediated interaction between education and each mediator (indicating differential vulnerability). The proportion of the total effect mediated by each lifestyle risk factor was also calculated. We fit an additive hazard model including all lifestyle risk factors (alcohol use, smoking, BMI, physical activity) and covariates (age [used as the time scale], race/ethnicity, marital status, and survey year), and fit separate models for men and women. Robust standard errors were not used given the size of the sample and computational limitations, despite the fact that the analyses were conducted on a specialized computing cluster. 5 All analyses were completed in R 3.6.3, using the timereg package (version 1.9.8) 5 . The timereg package does not allow for complex sampling designs and survey weights were not utilized.

The statistical code for this manuscript is publicly available at https://github.com/kpuka/SIMAH_clean/tree/main/Puka_2022_SES_x_Lifestyles

As sensitivity analyses, the analyses described above were repeated with small modifications.

First, alcohol use was indexed using heavy episodic drinking (HED) based on the number of heavy drinking days (≥5 drinks a day) in the past 12 months (eTables 2 to 4). Second, analyses were stratified analyses by age group, to evaluate the impact of modeling smoking and physical eTable 1. Characteristics at Baseline among participants with complete and missing data. The model adjusted for age (as timescale), race/ethnicity, marital status, and survey year; for simplicity, only the effect of low education (relative to high education) is presented. CI: confidence interval. a Proportion mediated is the ratio between the effect and the total effect x 100 The model adjusted for age (as timescale), race/ethnicity, marital status, and survey year; for simplicity, only the effect of low education (relative to high education) is presented.CI: confidence interval. a Proportion mediated is the ratio between the effect and the total effect x 100 2 (0, 4) BMI: differential vulnerability -0.5 (-2.0, 0.9) -1 (-3, 1) Physical activity: differential exposure 14.7 (13.5, 15.9) 22 (20, 24) Physical activity: differential vulnerability 4.4 (2.9, 5.9) 7 (4, 9) The model adjusted for age (as timescale), race/ethnicity, marital status, and survey year; for simplicity, only the effect of low education (relative to high education) is presented. CI: confidence interval. a Proportion mediated is the ratio between the effect and the total effect x 100

Associations between self-reported illness and non-drinking in young adults

Department of Health Human Services

World Health Organization 2020 guidelines on physical activity and sedentary behaviour

Additive Interaction in Survival Analysis: Use of the Additive Hazards Model

Dynamic Regression models for survival data

On collapsibility and confounding bias in Cox and Aalen regression models

Reducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach

Assessing Natural Direct and Indirect Effects Through Multiple Pathways

A Simple Unified Approach for Estimating Natural Direct and Indirect Effects

Direct and indirect effects in a survival context

The model was adjusted for age (as timescale), sex, race/ethnicity, marital status, and survey year. Bolded text highlights significant interactions. CI: confidence interval.