When everyone's doing it: The relative effects of geographical context and social determinants of health on teen birth rates.

Geographic disparities in teen birth rates in the U.S. persist, despite overall reductions over the last two decades. Research suggests these disparities might be driven by spatial variations in social determinants of health (SDOH). An alternative view is that "place" or "geographical context" affects teen birth rates so that they would remain uneven across the U.S. even if all SDOH were constant. We use multiscale geographically weighted regression (MGWR) to quantify the relative effects of geographical context, independent of SDOH, on county-level teen birth rates across the U.S. Findings indicate that even if all counties had identical compositions with respect to SDOH, strong geographic disparities in teen birth rates would still persist. Additionally, local parameter estimates show the relationships between several components of SDOH and teen birth rates vary over space in both direction and magnitude, confirming that global regression techniques commonly employed to examine these relationships likely obscure meaningful contextual differences in these relationships. Findings from this analysis suggest that reducing geographic disparities in teen birth rates will require not only ameliorating differences in SDOH across counties but also combating community norms that contribute to high rates of teen birth, particularly in the southern U.S. Further, the results suggest that if geographical context is not incorporated into models of SDOH, the effects of such determinants may be interpreted incorrectly.

GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation.

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform those in another, etc.? Answers to these questions require explanations of the model structure, the choice of parameters, and contextualization of the findings with respect to their geographic context. This is particularly true for local forms of regression models which are focused on the role of locational context in determining human behavior. In this paper, we present GeoExplainer, a visual analytics framework designed to support analysts in creating explanative documentation that summarizes and contextualizes their spatial analyses. As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results. As analysts explore the model results, all visualizations and annotations can be captured in an interactive report generation widget. We demonstrate our framework using a case study modeling the determinants of voting in the 2016 US Presidential Election.

Spatial distributions in disaster risk vulnerability for people with disabilities in the U.S.

Background: The spread of Coronavirus Disease 2019 (COVID-19) in the United States has centered the role of natural hazards such as pandemics into the public health sphere. The impacts of these hazards disproportionately affect people with disabilities, who are frequently in situations of social, political, or economic disadvantage. Because of these disadvantages, people with disabilities may have less access to necessary resources and services, putting them at risk due to unmet health needs. These disparities in access also highlight important regional, state, and county-level differences with regards to vulnerability and preparedness for natural hazards. Objective: The objective of this paper is to examine the relationship between disability and disaster risk in the United States. We examine the geographic variation in the relationship between risk from natural disasters and the percentage of people with disabilities living in a community. Because emergency management functions in the U.S. are directed and enacted at the county level, we also explore how these relationships change across U.S. counties. In addition to the overall prevalence of people with disabilities, we disaggregate the population of people with disabilities by gender, race, ethnicity, age, and disability impairment type. Methods: To measure risk of natural hazards, we use Expected Annual Loss index, a component of the 2020 National Risk Index, developed by Federal Emergency Management Agency, which identifies communities most at risk to18 natural hazards. We measure the percent of people with disabilities per county using the American Community Survey. We estimate the nationwide relationship between the proportion of people with disabilities and risk of natural hazards using ordinary least squares regression. To explore geographic differences in these relationships across the United States, we use a geographically weighted regression model to estimate local relationships for each county in the contiguous United States. We use mapping techniques to display regional differences across different disability demographic groups. Results: Counties with higher percentages of people with disabilities have a lower risk of natural disasters. Across the United States, a one percent increase in prevalence of people with disabilities in a county is associated with two percent decrease in the natural hazard risk score. Small but statistically significant regional differences exist as well. County-specific estimates range from a five percent decrease to a one percent increase. Stronger associations between risk and the prevalence of people with disabilities are observed in the Midwest and parts of the Southwest and West, whereas the relationship across racial groups is more scattered across the United States. Conclusion: In this study, nationwide results suggest that people with disabilities are more likely to live in communities with lower risk of natural hazards, but this relationship differs across U.S. counties and by demographic subgroups. These findings represent a contribution in further understanding the health and well-being of people with disabilities in the United States and the geographic variation therein.

A Replication of DiMaggio et al. (2020) in Phoenix, AZ.

This research replicates in Phoenix, Arizona a study originally conducted by DiMaggio et al. (2020) that investigated the associations between positive COVID-19 tests and demographic, socioeconomic, and racial characteristics in New York City at the ZIP Code Tabulation Area level. We extend that work through a conceptual replication that introduces covariates appropriate to Phoenix, AZ. Our direct replication, which focuses on that city's first wave of COVID-19 (May 31, 2020 to August 1, 2020), demonstrates that the framework used by DiMaggio et al. can be transferred across cities, but also identifies specification decisions that need careful consideration. Our conceptual replication identifies the proportion of Hispanic residents, rather than that of Black/African American residents, to be a key predictor of positive COVID-19 testing. This finding sheds light on the dynamics of race during the pandemic.

Reaching a Vulnerable Population: Why Adolescents Participate in the National School Lunch Program.

PURPOSE: Fourteen percent of households with children under 18 years were food insecure in 2018. However, participation in the National School Lunch Program (NSLP) is lower among adolescents compared to younger children. This analysis examined, in a national sample of middle and high school students, the reasons why adolescents participate in the NSLP. METHODS: This analysis used data from the School Nutrition and Meal Cost Study collected from adolescents (ages 10-19) attending middle and high schools with a known household food security status (n = 1,106). Adolescents were asked their number one reason for eating the school lunch. Results were compared by school level, income-eligibility for free or reduced price meals, and household food security status. A logistic regression examined the sociodemographic factors associated with adolescents' number one reason for eating the school lunch. RESULTS: The most frequently cited reason for participating in school lunch was hunger. Adolescents who were income-eligible for free or reduced price meals and those from food insecure households were significantly more likely to report hunger as their main reason for participating in the NSLP compared to those who were not income-eligible and those who were from food secure households, respectively. After controlling for characteristics of schools and school food authorities and student demographics, income eligibility was the only student characteristic that emerged as a significant predictor of reporting hunger as the main reason for participation. CONCLUSIONS: The results demonstrate that adolescents who regularly participate in the NSLP do so due to hunger, particularly if they are from low-income families.

Analyzing the Association between Student Weight Status and School Meal Participation: Evidence from the School Nutrition and Meal Cost Study.

Childhood obesity remains a pressing public health concern. Children consume a substantial amount of their caloric intake while in school, making the passage of the Healthy Hunger-Free Kids Act (HHFKA) in 2010 and the subsequent improvements to the school meal standards a key policy change. Using data from the School Nutrition and Meal Cost Study, this paper seeks to re-examine the association between students' (N = 1963) weight status and participation in the National School Lunch Program (NSLP) and School Breakfast Program (SBP) since the implementation of these policy changes to determine whether, and how, this relationship has changed. After controlling for a wide array of student characteristics and school-level fixed effects, findings from the multivariate regression analyses indicate that usual participation in the school meal programs has no clear association with students' weight status, which contradicts findings from earlier studies conducted prior to the passage of the HHFKA. These findings are discussed in relation to changes in the demographic composition of usual NSLP participants over time.

Estimating the incidence of unintended births and pregnancies at the sub-state level to inform program design.

OBJECTIVES: Unintended (mistimed or unwanted) pregnancies occur frequently in the United States and have negative effects. When designing prevention programs and intervention strategies for the provision of comprehensive birth control methods, it is necessary to identify (1) populations at high risk of unintended pregnancy, and (2) geographic areas with a concentration of need. METHODS: To estimate the proportion and incidence of unintended births and pregnancies for regions in Missouri, two machine-learning prediction models were developed using data from the National Survey of Family Growth and the Missouri Pregnancy Risk Assessment Monitoring System. Each model was applied to Missouri birth certificate data from 2014 to 2016 to estimate the number of unintended births and pregnancies across regions in Missouri. Population sizes from the American Community Survey were incorporated to estimate the incidence of unintended births and pregnancies. RESULTS: About 24,500 (34.0%) of the live births in Missouri each year were estimated to have resulted from unintended pregnancies: about 25 per 1,000 women (ages 15 to 45) annually. Further, 40,000 pregnancies (39.7%) were unintended each year: about 41 per 1,000 women annually. Unintended pregnancy was concentrated in Missouri's largest urban areas, and annual incidence varied substantially across regions. CONCLUSIONS: Our proposed methodology was feasible to implement. Random forest modeling identified factors in the data that best predicted unintended birth and pregnancy and outperformed other approaches. Maternal age, marital status, health insurance status, parity, and month that prenatal care began predict unintended pregnancy among women with a recent live birth. Using this approach to estimate the rates of unintended births and pregnancies across regions within Missouri revealed substantial within-state variation in the proportion and incidence of unintended pregnancy. States and other agencies could use this study's results or methods to better target interventions to reduce unintended pregnancy or address other public health needs.

Disparities in the Healthfulness of School Food Environments and the Nutritional Quality of School Lunches.

The Healthy, Hunger-Free Kids Act (HHFKA), a public law in the United States passed in 2010, sought to improve the healthfulness of the school food environment by requiring updated nutrition standards for school meals and competitive foods. Studies conducted since the passage of the HHFKA indicate improvements in the food environment overall, but few studies have examined whether these improvements varied by the socioeconomic and racial/ethnic composition of students in schools. To better understand the extent of disparities in the school food environment after HHFKA, this paper examined differences in the healthfulness of school food environments and the nutritional quality of school lunches by the school poverty level and racial/ethnic composition of students using data from the School Nutrition and Meal Cost Study. Results from chi-square analyses showed lower proportions of high poverty, majority black, and majority Hispanic schools had access to competitive foods, while higher proportions of these schools had a school wellness policy in addition to a district wellness policy. The overall nutritional quality of school lunches, as measured by total Healthy Eating Index (HEI)-2010 scores, did not vary significantly across school types, although some HEI component scores did. From these findings, we concluded that there were disparities in the school food environment based on the socioeconomic and racial/ethnic composition of students in schools, but no significant disparities in the overall nutritional quality of school lunches were found.