Secondhand Smoke Exposure at Home and/or in a Vehicle: Differences Between Urban and Non-Urban Adolescents in the United States, From 2015 to 2018

Home Smoke-Free Policies as Children Age: Urban, Rural, and Suburban Differences

INTRODUCTION

Parents/guardians can effectively reduce tobacco smoking and secondhand smoke exposure among youth by adopting and enforcing rules against indoor tobacco smoking (ie, home smoke-free policies). We investigate home smoke-free policies from childhood to adolescence in the United States and across rural, suburban, and urban households.

AIMS AND METHODS

We analyzed 2019-2020 National Survey of Children's Health data from n = 5,955 parents of youth aged 0-17, living at home with a tobacco smoker in the United States (U.S). Geographical categories were: rural, suburban, and urban. Home smoke-free policy reflected prohibiting tobacco smoking inside the home. Weighted logistic regressions examined the (1) association between youth age and home smoke-free policies, (2) interaction between geographic category and youth age, and (3) differing associations between youth age and home smoke-free policies by geography. Models controlled for youth race, ethnicity, sex, parental education, household annual income, and home structure.

RESULTS

Approximately 13.2% of U.S. households with a smoker did not have a home smoke-free policy. Stratified analyses found one-year increase in youth age was associated with lower odds of having a home smoke-free policy in rural (aOR:0.91; 95%CI: 0.87-0.95) and urban (aOR: 0.96; 95%CI: 0.92-1.00; p = .039), but not suburban (aOR:1.00; 95%CI: 0.95-1.05) households, controlling for covariates.

CONCLUSION

Odds of having a smoke-free home in the U.S. declined significantly in rural (9% per year) and urban (4%) but not suburban (0%) households. We quantify declines in home smoke-free policies as children age and identify geographic disparities for this environmental determinant of health.

IMPLICATIONS

Health promotion efforts targeting secondhand smoke prevention is needed, particularly for parents of older youth. Furthermore, there is a clear geographic bias in secondhand smoke exposure among all youth particularly older youth. Tailored interventions are needed to address geographic disparities in secondhand smoke exposure among rural and urban youth.

Rural and urban differences in perceptions, behaviors, and health care disruptions during the COVID-19 pandemic

Purpose

The objective of this study was to assess perceptions, health behaviors, and disruptions related to the COVID‐19 pandemic in a largely rural, Midwestern state, and to examine differences between rural and urban respondents.

Methods

A questionnaire was mailed August 2020 to a sample of 10,009 registered voters in Iowa ages 18‐100 years, with oversampling from 6 select rural counties. Previously validated and tested items assessed COVID‐19 precautions, health care disruptions, emotional reactions, health behavior changes, telehealth and experiences with the internet, and demographic characteristics.

Findings

There were 4,048 respondents (40% response rate); 65% were rural and 35% were urban residents. The average age of respondents was 58.3 years and 45% of respondents identified as female. Rural respondents reported less concern about COVID‐19 in their community (29% vs 40%, P<.001) and lower perceived importance of social distancing (51% vs 64%, P<.001). Urban respondents more often reported experiencing disruption to daily living, stronger negative emotional reactions, and displayed more pronounced behavior change compared to their rural counterparts. For example, urban respondents reported more pandemic‐related job losses (6% vs 4%, P = .05), disruptions to daily activities (48% vs 35%, P<.001), and use of telehealth services during the pandemic (24% vs 16%, P<.001).

Conclusions

The majority of respondents reported disruptions to normal activities, medical appointment cancellations, and emotional distress during the first 6 months of the pandemic. The impact of the pandemic on urban residents appeared to be greater than for rural respondents. Timing of pandemic spread and varying beliefs are potential explanations.