The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

Background With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available. Objective The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph. Methods We first collected images of the interiors of tobacco retailers in West Virginia and the District of Columbia during 2016 and 2018. The clearest photographs were selected and used to create a training and test data set. We then used a pretrained image classification network model, Inception V3, to discover the presence of tobacco logos and a unified object detection system, You Only Look Once V3, to identify logo locations. Results Our model was successful in identifying the presence of advertising within images, with a classification accuracy of over 75% for 8 of the 42 brands. Discovering the location of logos within a given photograph was more challenging because of the relatively small training data set, resulting in a mean average precision score of 0.72 and an intersection over union score of 0.62. Conclusions Our research provides preliminary evidence for a novel methodological approach that tobacco researchers and other public health practitioners can apply in the collection and processing of data for tobacco or other POS surveillance efforts. The resulting surveillance information can inform policy adoption, implementation, and enforcement. Limitations notwithstanding, our analysis shows the promise of using machine learning as part of a suite of tools to understand the tobacco retail environment, make policy recommendations, and design public health interventions at the municipal or other jurisdictional scale.

Objective:Studies assessing sociodemographic disparities in the tobacco retail environment have relied heavily on non-spatial analytical techniques, resulting in potentially misleading conclusions. We utilized a spatial analytical framework to evaluate neighborhood sociodemographic disparities in the tobacco retail environment in Washington, DC (DC) and the DC metropolitan statistical area (DC MSA).Methods:Retail tobacco availability for DC (n=177) and DC MSA (n=1,428) census tract was assessed using adaptive-bandwidth kernel density estimation. Density surfaces were constructed from DC (n=743) and DC MSA (n=4,539) geocoded tobacco retailers. Sociodemographics were obtained from the 2011-2015 American Community Survey. Spearman's correlations between sociodemographics and retail density were computed to account for spatial autocorrelation. Bivariate and multivariate spatial lag models were fit to predict retail density.Results:DC and DC MSA neighborhoods with a higher percentage of Hispanics were positively correlated with retail density (rho = .3392, P = .0001 and rho = .1191, P = .0000, respectively). DC neighborhoods with a higher percentage of African Americans were negatively correlated with retail density (rho = -.3774, P = .0000). This pattern was not significant in DC MSA neighborhoods. Bivariate and multivariate spatial lag models found a significant inverse relationship between the percentage of African Americans and retail density (Beta = -.0133, P = .0181 and Beta = -.0165, P = .0307, respectively).Conclusions:Associations between neighborhood sociodemographics and retail density were significant, although findings regarding African Americans are inconsistent with previous findings. Future studies should analyze other geographic areas, and account for spatial autocorrelation within their analytic framework.

Despite the declines in the prevalence of tobacco use over the past decades, tobacco use remains the leading preventable cause of morbidity and mortality in the United States.1 Unfortunately, the burden of tobacco-related disease and death is not shared equally across all populations.2 Low-income populations have greater rates of tobacco use, higher prevalence of tobacco-related diseases, and lower cessation rates compared with the general population.2 Researchers have hypothesized that disparities exist, in part, due to differing levels of exposure to tobacco retail outlets. Several studies have examined the density of tobacco outlets within defined geographic areas (eg, census tracts), finding density to be greater in areas with higher proportions of racial/ethnic minority and low-income residents. In their assessment of tobacco outlet density across the United States, Rodriguez et al found that greater tobacco outlet density, measured as the number of tobacco retailers per 1,000 people, was associated with greater proportions of African American, Hispanic, and low-income residents.3 Others have found similar results when examining tobacco outlet density within specific US cities,4-6 counties,7-9 and states.10-12 Higher tobacco outlet density has been found to be associated with greater intentions to smoke among youth13; increased tobacco use among youth13-16; initiation of cigarette use among young adults17; heavier smoking patterns among adults18; and reduced quit attempts.19 Several mechaSociodemographic diSparitieS in the tobacco retail environment in WaShington, dc: a Spatial perSpective

Objectives More than 250 US localities restrict sales of flavoured tobacco products (FTPs), but comprehensiveness varies, and many include retailer-based exemptions. The purpose of this study is to examine resulting changes in the US retail environment for FTPs if there was a hypothetical national tobacco control policy that would prohibit FTP sales in all retailers except (1) tobacco specialty stores or (2) tobacco specialty stores and alcohol outlets. Design and setting A cross-sectional analysis of the FTP retail environment in every US Census tract (n=74 133). FTP retailers (n=3 10 090) were enumerated using nine unique codes from a national business directory (n=296 716) and a national vape shop directory (n=13 374). Outcome measures We assessed FTP availability using static-bandwidth and adaptive-bandwidth kernel density estimation. We then calculated the proportion of FTP stores remaining and the mean density of FTP retailers under each policy scenario for the overall population, as well as across populations vulnerable to FTP use. Results Exempting tobacco specialty stores alone would leave 25 276 (8.2%) FTP retailers nationwide, while exempting both tobacco specialty stores and alcohol outlets would leave 54 091 (17.4%) retailers. On average, the per cent remaining FTP availability per 100 000 total population was 7.1% for a tobacco specialty store exemption and 18.1% for a tobacco specialty store and alcohol outlet exemption. Overall, density estimate trends for remaining FTP availability among racial/ethnic populations averaged across Census tracts mirrored total population density. However, estimates varied when stratified by metropolitan status. Compared with the national average, FTP availability would remain 47%–49% higher for all racial/ethnic groups in large metropolitan areas. Conclusions Retailer-based exemptions allow greater FTP availability compared with comprehensive policies which would reduce FTP availability to zero. Strong public policies have the greatest potential impact on reducing FTP availability, particularly among urban, and racial/ethnic minority populations.

Two years ago, the EU Commission initiated the development of a European Marine Observation and Data Network (EMODnet), which is one of the steps in Europe’s integrated maritime policy action plan. The biology part is currently being worked out by a consortium of European government agencies and research institutions and one US partner, and is spearheaded by the Flanders Marine Institute, who is also host of the European Ocean Biogeographic Information System (EurOBIS) and the World Register of Marine Species (WoRMS).