Bonnie E. Shook-Sa

Comparison of Sampling Strategies for Tobacco Retailer Inspections to Maximize Coverage in Vulnerable Areas and Minimize Cost

Introduction In the United States, tens of thousands of inspections of tobacco retailers are conducted each year. Various sampling choices can reduce travel costs, emphasize enforcement in areas with greater noncompliance, and allow for comparability between states and over time. We sought to develop a model sampling strategy for state tobacco retailer inspections. Methods Using a 2014 list of 10,161 North Carolina tobacco retailers, we compared results from simple random sampling; stratified, clustered at the ZIP code sampling; and, stratified, clustered at the census tract sampling. We conducted a simulation of repeated sampling and compared approaches for their comparative level of precision, coverage, and retailer dispersion. Results While maintaining an adequate design effect and statistical precision appropriate for a public health enforcement program, both stratified, clustered ZIP- and tract-based approaches were feasible. Both ZIP and tract strategies yielded improvements over simple random sampling, with relative improvements, respectively, of average distance between retailers (reduced 5.0% and 1.9%), percent Black residents in sampled neighborhoods (increased 17.2% and 32.6%), percent Hispanic residents in sampled neighborhoods (reduced 2.2% and increased 18.3%), percentage of sampled retailers located near schools (increased 61.3% and 37.5%), and poverty rate in sampled neighborhoods (increased 14.0% and 38.2%). Conclusions States can make retailer inspections more efficient and targeted with stratified, clustered sampling. Use of statistically appropriate sampling strategies like these should be considered by states, researchers, and the Food and Drug Administration to improve program impact and allow for comparisons over time and across states. Implications The authors present a model tobacco retailer sampling strategy for promoting compliance and reducing costs that could be used by US states and the Food and Drug Administration (FDA). The design is feasible to implement in North Carolina. Use of the sampling design would help document the impact of FDAs compliance and enforcement program, save money, and emphasize inspections in areas where they are needed most. FDA should consider requiring probability-based sampling in their inspections contracts with states and private contractors.

Sensitivity of sexual victimization estimates to definitional and measurement decisions

Self-report surveys are subject to measurement error associated with variation in the methodology employed. The current analysis uses data from the Campus Climate Survey Validation Study (CCSVS) to examine the impact that measurement decisions have on estimates. The findings demonstrate that asking victims to provide detailed information in an effort to properly place incidents in time and classify incidents by type resulted in relatively minor decreases in estimate magnitude. Ultimately, asking respondents to provide or confirm additional incident-level information for proper classification resulted in more complete information with very little impact on estimates.

Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States

Asthma is an inflammatory airway disease that affects 22 million Americans in the United States. Research has found associations between impaired respiratory function, including asthma and increased symptoms among asthmatics, and common indoor air pollutants, including tobacco smoke exposure and volatile organic compounds (VOCs). However, findings linking VOC exposure and asthma are inconsistent and studies are of mixed quality due to design limitations, challenges measuring VOC exposure, small sample sizes, and suboptimal statistical methodologies. Because of the correlation between tobacco smoke exposure and VOCs, and associations between both tobacco smoke and VOCs with respiratory function, it is crucial that statistical methodology employed to assess links between respiratory function and individual air pollutants control for these complex relationships. This research uses Structural Equation Modeling (SEM) to assess the relationships between respiratory function, tobacco smoke exposure, and VOC exposure among a nationally-representative sample of adolescents. SEM allows for multiple outcome variables, the inclusion of both observed and latent variables, and controls the effects of confounding and correlated variables, which is critically important and is lacking in earlier studies when estimating the effects of correlated air pollutants on respiratory function. We find evidence of associations between respiratory function and some types of VOCs, even when controlling for the effects of tobacco smoke exposure and additional covariates. Furthermore, we find that poverty has an indirect effect on respiratory function through its relationships with tobacco smoke exposure and some types of VOCs. This analysis demonstrates how SEM is a robust analytic tool for assessing associations between respiratory function and multiple exposures to pollutants.