Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child.

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.

Design and Implementation of the Surveys of Women: Protocol for an Address-Based Sampling Multimodal Study.

BACKGROUND: Studies conducted in the United States such as the National Survey of Family Growth (NSFG) and the Pregnancy Risk Assessment Monitoring System (PRAMS) collect data on pregnancy intentions to aid in improving health education, services, and programs. PRAMS collects data from specific sites, and NSFG is a national household-based survey. Like NSFG, the Surveys of Women was designed to survey participants residing in households using an address-based sample and a multimode data collection approach. The Surveys of Women collects data from eligible participants in 9 states within the United States on contraception use, reproductive health, and pregnancy intentions. In this paper, we focus on the baseline data collection protocol, including sample design, data collection procedures, and data processing. We also include a brief discussion on the follow-up and endline survey methodologies. Our goal is to inform other researchers on methods to consider when fielding a household-level reproductive health survey. OBJECTIVE: The Surveys of Women was developed to support state-specific research and evaluation projects, with an overall goal of understanding contraceptive health practices among women aged 18-44 years. The project collects data from respondents in 9 different states (Arizona, Alabama, Delaware, Iowa, Maryland, New Jersey, Ohio, South Carolina, and Wisconsin) over multiple rounds. METHODS: Households were selected at random using address-based sampling methods. This project includes a cross-sectional baseline survey, 2 or 3 follow-up surveys with an opt-in panel of respondents, and a cross-sectional endline survey. Each round of data collection uses a multimode design through the use of a programmed web survey and a formatted hard copy questionnaire. Participants from the randomly selected households access their personalized surveys through a web survey or mail in a hard copy questionnaire. To maximize responses, these surveys follow a rigorous schedule of various prompts bolstering the survey implementation design, and the participants received a modest monetary incentive. RESULTS: This is an ongoing project with results published separately by the evaluation teams involved with data analysis. CONCLUSIONS: The methods used in the first baseline survey informed modifications to the methods used in subsequent statewide surveys. Data collected from this project will provide insight into women's reproductive health, contraceptive use, and abortion attitudes in the 9 selected states. The long-term goal of the project is to use a data collection methodology that collects data from a representative sample of participants to assess changes in reproductive health behaviors over time. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40675.

Making Sense of Sensor Data: How Local Environmental Conditions Add Value to Social Science Research.

Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The U.S. federally funded Array of Things (AoT) project has deployed more than 140 such sensor nodes throughout the City of Chicago. This paper combines a year's worth of AoT sensor data with household data collected from 450 elderly Chicagoans in order to explore the feasibility of using previously unavailable data on local environmental conditions to improve traditional neighborhood research. Specifically, we pilot the use of AoT sensor data to overcome limitations in research linking air pollution to poor physical and mental health and find support for recent findings that exposure to pollutants contributes to both respiratory and dementia-related diseases. We expect that this support will become even stronger as sensing technologies continue to improve and more AoT nodes come online, enabling additional applications to social science research where environmental context matters.

Sample Design and Estimation in the National Social Life, Health, and Aging Project: Round 3 (2015-2016).

OBJECTIVES: This article, and corresponding articles for the earlier rounds of the National Social Life, Health, and Aging Project (NSHAP), provide the scientific underpinning for the statistical analysis of NSHAP data. The 2015-2016 round of data collection for NSHAP comprised the third wave of data collection for the original cohort born 1920-1947 (C1) and the first wave of data collection for a second cohort born 1948-1965 (C2). Here we describe (a) our protocol for reinterviewing C1; (b) our approach to the sample design for C2, including the frame construction, stratification, clustering, and within-household selection; and (c) the construction of cross-sectional weights for the entire 2015-2016 sample when analyzed at the individual level or when analyzed as a sample of cohabiting couples. We also provide guidance on computing design-based standard errors. METHODS: The sample for C2 was drawn independently of the C1 sample using the NORC U.S. National Sampling Frame. A probability sample of households containing at least one individual born 1948-1965 was drawn, and from these, each age-eligible individual was included together with their cohabiting spouse or partner (even if not age-eligible). This C2 sample was combined with the C1 sample to yield a sample representative of the U.S. population of adults born 1920-1965. RESULTS: Among C1, we conducted 2,409 interviews corresponding to a 91% conditional response rate (i.e., among previous respondents); the unconditional three-wave response rate for the original C1 sample was 71%. Among C2, we conducted 2,368 interviews corresponding to a response rate of 76%. DISCUSSION: Together C1 and C2 permit inference about the U.S. population of home-dwelling adults born from 1920 to 1965. In addition, three waves of data from C1 are now available, permitting longitudinal analyses of health outcomes and their determinants among older adults.

Image Processing for Public Health Surveillance of Tobacco Point-of-Sale Advertising: Machine Learning-Based Methodology.

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.

Sociodemographic Disparities in the Tobacco Retail Environment in Washington, DC: A Spatial Perspective.

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.

Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP.

OBJECTIVES: The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible. METHOD: There were 2 important innovations. First, the scope of the study was expanded by collecting data from coresident spouses or romantic partners. Second, to maximize the representativeness of the Wave 2 data, nonrespondents from Wave 1 were again approached for interview in the Wave 2 sample. RESULTS: The overall unconditional response rate for the Wave 2 panel was 74%; the conditional response rate of Wave 1 respondents was 89%; the conditional response rate of partners was 84%; and the conversion rate for Wave 1 nonrespondents was 26%. DISCUSSION: The inclusion of coresident partners enhanced the study by allowing the examination of how intimate, household relationships are related to health trajectories and by augmenting the size of the NSHAP sample size for this and future waves. The uncommon strategy of returning to Wave 1 nonrespondents reduced potential bias by ensuring that to the extent possible the whole of the original sample forms the basis for the field effort. NSHAP Wave 2 achieved its field objectives of consolidating the panel, recruiting their resident spouses or romantic partners, and converting a significant proportion of Wave 1 nonrespondents.

The onset of depression during the great recession: foreclosure and older adult mental health.

OBJECTIVES: We examined neighborhood-level foreclosure rates and their association with onset of depressive symptoms in older adults. METHODS: We linked data from the National Social Life, Health, and Aging Project (2005-2006 and 2010-2011 waves), a longitudinal, nationally representative survey, to data on zip code-level foreclosure rates, and predicted the onset of depressive symptoms using logit-linked regression. RESULTS: Multiple stages of the foreclosure process predicted the onset of depressive symptoms, with adjustment for demographic characteristics and changes in household assets, neighborhood poverty, and visible neighborhood disorder. A large increase in the number of notices of default (odds ratio [OR] = 1.75; 95% confidence interval [CI] = 1.14, 2.67) and properties returning to ownership by the bank (OR = 1.62; 95% CI = 1.06, 2.47) were associated with depressive symptoms. A large increase in properties going to auction was suggestive of such an association (OR = 1.45; 95% CI = 0.96, 2.19). Age, fewer years of education, and functional limitations also were predictive. CONCLUSIONS: Increases in neighborhood-level foreclosure represent an important risk factor for depression in older adults. These results accord with previous studies suggesting that the effects of economic crises are typically first experienced through deficits in emotional well-being.