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      Persistent disparities in smoking among rural Appalachians: evidence from the Mountain Air Project

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          Abstract

          Background

          Adult smoking prevalence in Central Appalachia is the highest in the United States, yet few epidemiologic studies describe the smoking behaviors of this population. Using a community-based approach, the Mountain Air Project (MAP) recruited the largest adult cohort from Central Appalachia, allowing us to examine prevalence and patterns of smoking behavior.

          Methods

          A cross-sectional epidemiologic study of 972 participants aged 21 years and older was undertaken 2015–2017, with a response rate of 82%. Prevalence ratios and 95% confidence intervals for current smoking (compared to nonsmokers) were computed for the entire cohort then stratified by multiple characteristics, including respiratory health. Adjusted prevalence ratios for current smoking versus not smoking were also computed.

          Results

          MAP participants reported current smoking prevalence (33%) more than double the national adult smoking prevalence. Current smoking among participants with a reported diagnosis of chronic obstructive pulmonary disease and emphysema was 51.5 and 53.3%, respectively. Compared to participants age 65 years and older, those age 45 years or younger reported double the prevalence of smoking (PR: 2.04, 95% CI: 1.51–2.74). Adjusted analyses identified younger age, lower education, unmet financial need, and depression to be significantly associated with current smoking.

          Conclusions

          Despite declining rates of smoking across the United States, smoking remains a persistent challenge in Central Appalachia, which continues to face marked disparities in education funding and tobacco control policies that have benefitted much of the rest of the nation. Compared with national data, our cohort demonstrated higher rates of smoking among younger populations and reported a greater intensity of cigarette use.

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          Most cited references54

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

            Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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              Estimating the relative risk in cohort studies and clinical trials of common outcomes.

              Logistic regression yields an adjusted odds ratio that approximates the adjusted relative risk when disease incidence is rare (<10%), while adjusting for potential confounders. For more common outcomes, the odds ratio always overstates the relative risk, sometimes dramatically. The purpose of this paper is to discuss the incorrect application of a proposed method to estimate an adjusted relative risk from an adjusted odds ratio, which has quickly gained popularity in medical and public health research, and to describe alternative statistical methods for estimating an adjusted relative risk when the outcome is common. Hypothetical data are used to illustrate statistical methods with readily accessible computer software.
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                Author and article information

                Contributors
                kathryn.cardarelli@uky.edu
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                2 February 2021
                2 February 2021
                2021
                : 21
                : 270
                Affiliations
                [1 ]GRID grid.266539.d, ISNI 0000 0004 1936 8438, College of Public Health, , University of Kentucky, ; Lexington, KY USA
                [2 ]GRID grid.266539.d, ISNI 0000 0004 1936 8438, Center for Health Equity Transformation, , University of Kentucky, ; Lexington, KY USA
                [3 ]GRID grid.266539.d, ISNI 0000 0004 1936 8438, College of Medicine, , University of Kentucky, ; Lexington, KY USA
                Author information
                http://orcid.org/0000-0001-6695-7671
                Article
                10334
                10.1186/s12889-021-10334-6
                7856720
                33530976
                4b298eff-1a5f-4978-8170-602f9729a61e
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 September 2020
                : 25 January 2021
                Funding
                Funded by: National Institutes of Health (US)
                Award ID: R01ES024771
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                smoking,tobacco,appalachia,health inequities,disparities,rural health,respiratory health
                Public health
                smoking, tobacco, appalachia, health inequities, disparities, rural health, respiratory health

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