Limited implementation of California’s Healthy Default Beverage law for children’s meals sold online

Objective: To reduce children’s sugar-sweetened beverage intake, California’s Healthy-By-Default Beverage law (SB1192) mandates only unflavored dairy/non-dairy milk or water be the default drinks with restaurant children’s meals. The objective of this study is to examine consistency with this law for meals sold through online platforms from restaurants in low-income California neighborhoods. Design: This observational, cross-sectional study examines beverage availability, upcharges (additional cost), and presentation of beverage options consistent with SB1192 (using 4 increasingly restrictive criteria) within a random sample of quick service restaurants (QSRs) in SNAP-Ed eligible census tracts selling children’s meals online from November 2020-April 2021. Setting: Low-income California neighborhoods (n=226 census tracts). Participants: QSRs that sold children’s meals online via a restaurant-specific platform, DoorDash, GrubHub, and/or UberEats (n=631 observations from 254 QSRs). Results: Seventy percent of observations offered water; 63% offered unflavored milk. Among all beverages, water was most likely to have an upcharge; among observations offering water (n=445), 41% had an upcharge (average $0.51). Among observations offering unflavored milk (n=396), 11% had an upcharge (average $0.38). No observations upcharged for soda (regular or diet). Implementation consistency with SB1192 ranged from 40.5% (using the least restrictive criteria) to 5.6% (most restrictive) of observations. Conclusions: Based on observations from restaurant websites and three of the most popular online ordering platforms, most California QSRs located in low-income neighborhoods are not offering children’s meal beverages consistent with the state’s Healthy-By-Default Beverage law. As the popularity of online ordering increases, further work to ensure restaurants are offering healthy default beverages with children’s meals sold online is necessary.

Examining Social Vulnerability and the Association With COVID-19 Incidence in Harris County, Texas

Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.

Índices de vulnerabilidade social e programático para idosos que vivem no domicilio

Objectives: Build Social and Programmatic Vulnerability indices for older people living at home and verify the association of vulnerability components (individual, social and programmatic). Methods: It is a population based study, household and transversal survey type, conducted with 701 community older adults. Descriptive and bivariate exploratory spatial analysis was conducted (p≤ 0.05) as well as analysis of Main Components. Results: By means of the indices, it was observed that peripheral census tracts presented very high social vulnerability levels and that the main variables representative of the programmatic component – access to dentist via SUS, medications, and search of the same care location. It was verified that only 3.9% of the older adults did not present some level of vulnerability (individual, social and programmatic). Conclusion: Older adults are exposed to multiple vulnerability conditions, and Social and Programmatic Vulnerability indices are important tools for managers’ decision making. Objetivos: Construir los índices de vulnerabilidad social y vulnerabilidad de programas para personas mayores que viven en el hogar y verificar la asociación entre los componentes de vulnerabilidad (individual, social y programática). Métodos: Este es un estudio basado en la población del tipo de encuesta de hogares y transversal realizada con 701 miembros de la comunidad de ancianos. Se realizaron análisis exploratorios, descriptivos y bivariados (p≤ 0.05) y componentes principales. Resultados: A través de los índices, se descubrió que los sectores censales periféricos mostraban niveles muy altos de vulnerabilidad social y que las principales variables representativas del componente programático eran: acceso al dentista por SUS, medicamentos y demanda desde el mismo lugar de servicio. Se encontró que solo el 3.9% de los ancianos no tenían ninguna condición de vulnerabilidad (individual, social y programática). Conclusión: Los ancianos están expuestos a múltiples condiciones de vulnerabilidad; Los índices de vulnerabilidad social y programática son herramientas importantes para la toma de decisiones por parte de los gerentes. Objetivos: Construir os Índices de Vulnerabilidade Social e de Vulnerabilidade Programática para idosos que vivem no domicílio e verificar a associação entre os componentes da vulnerabilidade (individual, social e programática). Métodos: Trata-se de um estudo de base populacional do tipo inquérito domiciliar e transversal conduzido com 701 idosos comunitários. Realizou-se análise espacial exploratório, descritiva e bivariada (p≤ 0,05) e de Componentes Principais. Resultados: Através dos índices, verificou-se que os setores censitários periféricos apresentaram níveis de vulnerabilidade social muito elevados e que as principais variáveis representativas do componente programático foram - acesso ao dentista pelo SUS, à medicamentos e procura do mesmo local de atendimento. Constatou-se que apenas 3,9% dos idosos não apresentavam nenhuma condição de vulnerabilidade (individual, social e programático). Conclusão: Os idosos estão expostos as múltiplas condições de vulnerabilidade; sendo os Índices de Vulnerabilidade Social e Programática importantes ferramentas de tomada de decisão pelos gestores.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., a Neighborhood Disadvantage Index, a Murder Rate Index, and a Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel and clinic staff who do not have prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Imposing Contiguity Constraints in Political Districting Models

For nearly 60 years, operations research techniques have assisted in the creation of political districting plans, beginning with an integer programming model. This model, which seeks compactness as its objective, tends to generate districts that are contiguous, or nearly so, but provides no guarantee of contiguity. In the paper “Imposing contiguity constraints in political districting models” by Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd, the authors consider and analyze four different contiguity models (two old and two new). Their computer implementation can handle redistricting instances as large as Indiana (1,511 census tracts). Their fastest approach uses a branch-and-cut algorithm, where contiguity constraints are added in a callback. Critically, many variables can be fixed to zero a priori by Lagrangian arguments. All test instances and source code are publicly available.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Feline sporotrichosis

Over the past two decades, many Brazilian cities have been reporting an increasing incidence and spread of feline sporotrichosis. The disease is neglected, and little is known about the causal processes underlying its epidemic occurrence. This study characterized the spatiotemporal dynamics of feline sporotrichosis in Guarulhos. Moreover, we proposed and tested a causal explanation for its occurrence and zoonotic transmission, giving a key role to social vulnerability. A direct acyclic graph represented the causal explanation, while Bayesian spatial models supported its test as well as the attribution of a risk-based priority index to the census tracts of the city. Between 2011 and 2017, the disease grew exponentially and the spatial spread increased. The model findings showed a dose-response pattern between an index of social vulnerability and the incidence of feline sporotrichosis. This pattern was not strictly monotonic, so some census tracts received a higher priority index than others with higher vulnerability. According to our causal explanation, there will not be effective prevention of feline and zoonotic sporotrichosis as long as social inequities continue imposing precarious livelihoods.

Using Emerging hot Spot Analysis to Explore Spatiotemporal Patterns of Housing Vacancy in Ohio Metropolitan Statistical Areas

We highlight the use of a newer method—emerging hot spot analysis of space-time cubes from defined locations—for examining the spread of housing vacancy in large, Ohio MSAs. Using this method, we discovered that many Ohio MSAs concurrently experienced spread, contraction, and vacancy stabilization in census tracts located adjacent to, or within close proximity of, one another. These results indicate that vacancy proliferation is not solely a matter of geographic determinism, whereby high vacancy in one tract predicts high vacancy in neighboring tracts in future years. We also found that vacancy spread at the tract level is associated with population dynamics at the neighborhood, city, and MSA levels. Our findings suggest that vacancy reduction initiatives should account for population trends at various geographic scales, not just physical conditions within a particular neighborhood or tract.

Improving assessment of urban racial segregation by partitioning a region into racial enclaves

Frequently, a single-value metric is needed to rank urban regions with respect to the level of multiracial segregation or to compare a segregation level of a single urban region at two different times. Assessment of segregation depends not only on a metric used but also on a choice of region’s partitioning. The standard practice is to partition the region into single-scale subregions. In the United States, census tracts are the subregions of choice. Census aggregation units including tracts are delineated without direct regard to racial homogeneity and are in fact heterogeneous. Consequently, using tracts as subdivisions leads to the underestimation of the segregation level of the entire region. Here we propose to partition a region into racial enclaves - units having boundaries that align with transitions between different racial compositions. By reflecting true demographic structure, such units minimize their internal racial inhomogeneity resulting in improved assessment of segregation. Enclaves are defined as aggregates of adjacent census blocks (smallest and the most racially homogeneous census units) of similar composition. In a typical US urban region effective population size of enclaves is an order of magnitude larger than the size of a census tract and yet the segregation calculated based on enclaves is larger than segregation based on census tracts. The proposed methodology is described and applied to a set of 61 largest cities in the U.S. in their metropolitan statistical areas (MSAs) as well as their urban areas (UAs) boundaries using 1990 and 2010 block-level data. The method is compared to the standard methodology using correlations between cities’ segregation rankings.

Geographic distribution of availability of adult day services in Missouri

Adult day service (ADS) is an important component of long-term supportive services. Geographic availability of ADS is an essential factor for aging in place especially for people with assistance needs. This study aims to examine the geographic distribution of availability of ADS and its relationship with the disadvantaged characteristics of neighborhoods. Data from the Missouri Department of Health and Senior Services and the American Community Survey were utilized. Geographic availability of ADS was measured as capacity (number of clients served) of ADS centers per week divided by the number of people who were 65 or older and under poverty at the census tract level. To examine neighborhood disadvantaged characteristics, principal component analysis was applied to construct a socioeconomic deprivation index (SDI). Using geographic information systems, we mapped ADS centers, geographic availability of ADS, and SDI scores. Pearson correlation coefficient was calculated between geographic availability of ADS and SDI scores. In 92.3% of the census tracts in Missouri, ADS centers are not available. Further, ADS centers are less likely to locate in rural areas or census tracts with higher numbers of residents 65 or older and poor. Also, lower availability of ADS was associated with higher levels of neighborhood disadvantage at a marginal level (r = - 0.163). Our findings suggested that strategies should be identified to provide ADS in rural areas, especially in the areas with higher levels of neighborhood disadvantage. Further investigation on the geographic distribution of ADS accessibility and its association with neighborhood characteristics is warranted.