Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking

Hinojosa, Alberto M. Ortega; Davies, Molly; Jarjour, Sarah; Burnett, Richard T.; Mann, Jason R.; Hughes, Edward G.; Balmes, John R.; Turner, Michelle C.; Jerrett, Michael

doi:10.1016/j.envres.2014.07.029

Cited by 28 publications

(17 citation statements)

References 25 publications

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“…These two algorithms will select different sets of variables, thereby reducing the likelihood of important variables being omitted. Boruta and LASSO have been used for variable selection for various types of data, such as survey, medical and genomic data [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ].…”

Section: Methodsmentioning

confidence: 99%

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Atuegwu

Oncken

Laubenbacher

et al. 2020

IJERPH

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E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18–34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

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Section: Methodsmentioning

confidence: 99%

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Atuegwu

Oncken

Laubenbacher

et al. 2020

IJERPH

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“…We obtained estimates by ZIP code of population, median income, percent of the population over 65, percent of the population living in owner-occupied housing, and percent of the population with less than a high school diploma from the 2000 US Census. We used smoking prevalence estimates derived from Behavioral Risk Factor Surveillance System (BRFSS) data by ZIP code for the 2006-2010 time period based on the 2000 census ZIP codes (Ortega Hinojosa et al, 2014). For the ZIP codes (N ¼ 66, 8.5%) in our analysis that were created after 2000, we used county-level estimates.…”

Section: Covariate Datamentioning

confidence: 99%

Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach

Reid

Jerrett

Tager

et al. 2016

Environmental Research

Self Cite

152

170

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We investigated health effects associated with fine particulate matter during a long-lived, large wildfire complex in northern California in the summer of 2008. We estimated exposure to PM2.5 for each day using an exposure prediction model created through data-adaptive machine learning methods from a large set of spatiotemporal data sets. We then used Poisson generalized estimating equations to calculate the effect of exposure to 24-hour average PM2.5 on cardiovascular and respiratory hospitalizations and ED visits. We further assessed effect modification by sex, age, and area-level socioeconomic status (SES). We observed a linear increase in risk for asthma hospitalizations (RR=1.07, 95% CI=(1.05, 1.10) per 5µg/m(3) increase) and asthma ED visits (RR=1.06, 95% CI=(1.05, 1.07) per 5µg/m(3) increase) with increasing PM2.5 during the wildfires. ED visits for chronic obstructive pulmonary disease (COPD) were associated with PM2.5 during the fires (RR=1.02 (95% CI=(1.01, 1.04) per 5µg/m(3) increase) and this effect was significantly different from that found before the fires but not after. We did not find consistent effects of wildfire smoke on other health outcomes. The effect of PM2.5 during the wildfire period was more pronounced in women compared to men and in adults, ages 20-64, compared to children and adults 65 or older. We also found some effect modification by area-level median income for respiratory ED visits during the wildfires, with the highest effects observed in the ZIP codes with the lowest median income. Using a novel spatiotemporal exposure model, we found some evidence of differential susceptibility to exposure to wildfire smoke.

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“…Besides the obesogen effect of tobacco smoking [23–25], an increasing number of associative studies have suggested that inhaled environmental pollutants, combined with unhealthy diet and lifestyle, are associated with a propensity to obesity, metabolic syndrome, and insulin resistance, and are able to contribute to chronic non transmissible diseases [26], including cardiovascular disease and type 2 diabetes mellitus, all conditions that are characterized by systemic inflammation, in both adults [27–29] and children [23]. In particular, the association between obesity and PM2.5 has been extensively evaluated by a meta-analysis including three large prospective cohort studies and 14 panel studies with short-term follow-up [30].…”

Section: Inhaled Pollution and Obesitymentioning

confidence: 99%

Low serum vitamin D-status, air pollution and obesity: A dangerous liaison

Barrea¹,

Savastano²,

Somma

et al. 2016

Rev Endocr Metab Disord

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The aim of this review is to provide a general overview of the possible associations among the vitamin D status, air pollution and obesity. Sunlight exposure accounts in humans for more than 90 % of the production of vitamin D. Among emerging factors influencing sunlight-induced synthesis of vitamin D, prospective and observational studies proved that air pollution constitutes an independent risk factor in the pathogenesis of vitamin D hypovitaminosis. In addition, environmental pollutants can affect risk of obesity when inhaled, in combination with unhealthy diet and lifestyle. In turn, obesity is closely associated with a low vitamin D status and many possible mechanisms have been proposed to explain this association. The associations of air pollution with low vitamin D status on the hand and with obesity on the other hand, could provide a rationale for considering obesity as a further link between air pollution and low vitamin D status. In this respect, a vicious cycle could operate among low vitamin D status, air pollution, and obesity, with additive detrimental effects on cardio-metabolic risk in obese individuals. Besides vitamin D supplementation, nutrient combination, used to maximize the protective effects against air pollution, might also contribute to improve the vitamin D status by attenuating the “obesogen” effects of air pollution.

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Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking

Cited by 28 publications

References 25 publications

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach

Low serum vitamin D-status, air pollution and obesity: A dangerous liaison

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