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      Age and Racial/Ethnic Disparities in Prepregnancy Smoking Among Women Who Delivered Live Births

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          Abstract

          Introduction

          Prenatal smoking prevalence remains high in the United States. To reduce prenatal smoking prevalence, efforts should focus on delivering evidence-based cessation interventions to women who are most likely to smoke before pregnancy. Our objective was to identify groups with the highest prepregnancy smoking prevalence by age within 6 racial/ethnic groups.

          Methods

          We analyzed data from 186,064 women with a recent live birth from 32 states and New York City from the 2004-2008 Pregnancy Risk Assessment Monitoring System (PRAMS), a population-based survey of postpartum women. We calculated self-reported smoking prevalence during the 3 months before pregnancy for 6 maternal racial/ethnic groups by maternal age (18-24 y or ≥25 y). For each racial/ethnic group, we modeled the probability of smoking by age, adjusting for education, Medicaid enrollment, parity, pregnancy intention, state of residence, and year of birth.

          Results

          Younger women had higher prepregnancy smoking prevalence (33.2%) than older women (17.6%), overall and in all racial/ethnic groups. Smoking prevalences were higher among younger non-Hispanic whites (46.4%), younger Alaska Natives (55.6%), and younger American Indians (46.9%). After adjusting for confounders, younger non-Hispanic whites, Hispanics, Alaska Natives, and Asian/Pacific Islanders were 1.12 to 1.50 times as likely to smoke as their older counterparts.

          Conclusion

          Age-appropriate and culturally specific tobacco control interventions should be integrated into reproductive health settings to reach younger non-Hispanic white, Alaska Native, and American Indian women before they become pregnant.

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          Most cited references 21

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          The epidemiology of smoking during pregnancy: smoking prevalence, maternal characteristics, and pregnancy outcomes.

          The prevalence of smoking during pregnancy varies markedly across countries. In many industrialized countries, prevalence rates appear to have peaked and begun to decline, whereas in other countries smoking is becoming increasingly common among young women. Randomized controlled trials have shown that smoking interventions during pregnancy have had limited success. Smoking during pregnancy is in many countries recognized as the most important preventable risk factor for an unsuccessful pregnancy outcome. Smoking is causally associated with fetal growth restriction, and increasing evidence also suggests that smoking may cause stillbirth, preterm birth, placental abruption, and possibly also sudden infant death syndrome. Smoking during pregnancy also is generally associated with increased risks of spontaneous abortions, ectopic pregnancies, and placenta previa and may increase risks of behavioral disorders in childhood. Smoking during pregnancy will continue to be an important risk factor for maternal and fetal outcomes during pregnancy.
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            Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

            There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.
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              Healthy People 2010: Understanding andImproving Health

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                Author and article information

                Contributors
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention
                ,
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Journal
                Prev Chronic Dis
                Preventing Chronic Disease
                Centers for Disease Control and Prevention
                1545-1151
                November 2011
                15 October 2011
                : 8
                : 6
                Affiliations
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
                PCDv86_11_0018
                3221563
                22005614
                Categories
                Original Research
                Peer Reviewed

                Health & Social care

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