Real-Time Geospatial analysis Identifies Gaps in Covid-19 Vaccination in a Minority Population

COVID-19 vaccination is being rapidly rolled out in the US and many other countries, and it is crucial to provide fast and accurate assessment of vaccination coverage and vaccination gaps to make strategic adjustments promoting vaccine coverage. We reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population living in South Texas on the US-Mexico Border, to inform vaccination campaign strategies. We developed 4 rank-based approaches to evaluate the vaccination gap at the census tract level, which considered both population vulnerability and vaccination priority and eligibility. We identified areas with the highest vaccination gaps using different assessment approaches. Real-time geospatial analysis to identify vaccination gaps is critical to rapidly increase vaccination uptake, and to reach herd immunity in the vulnerable and the vaccine hesitant groups. Our results assisted the City of Brownsville Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and deploying services to areas identified as high vaccination gap. The analyses and responses can be adopted in other locations

How does community context influence coalitions in the formation stage? a multiple case study based on the Community Coalition Action Theory

<p>Abstract</p> <p>Background</p> <p>Community coalitions are rooted in complex and dynamic community systems. Despite recognition that environmental factors affect coalition behavior, few studies have examined how community context impacts coalition formation. Using the Community Coalition Action theory as an organizing framework, the current study employs multiple case study methodology to examine how five domains of community context affect coalitions in the formation stage of coalition development. Domains are history of collaboration, geography, community demographics and economic conditions, community politics and history, and community norms and values.</p> <p>Methods</p> <p>Data were from 8 sites that participated in an evaluation of a healthy cities and communities initiative in California. Twenty-three focus groups were conducted with coalition members, and 76 semi-structured interviews were conducted with local coordinators and coalition leaders. Cross-site analyses were conducted to identify the ways contextual domains influenced selection of the lead agency, coalition membership, staffing and leadership, and coalition processes and structures.</p> <p>Results</p> <p>History of collaboration influenced all four coalition factors examined, from lead agency selection to coalition structure. Geography influenced coalition formation largely through membership and staffing, whereas the demographic and economic makeup of the community had an impact on coalition membership, staffing, and infrastructure for coalition processes. The influence of community politics, history, norms and values was most noticeable on coalition membership.</p> <p>Conclusions</p> <p>Findings contribute to an ecologic and theory-based understanding of the range of ways community context influences coalitions in their formative stage.</p

Who Comes Back? A Longitudinal Analysis of the Reentry Behavior of Exiting Teachers

While a large literature examines the factors that lead teachers to leave teaching, few studies have examined what factors affect teachers’ decisions to reenter the profession. Drawing on research on the role of family characteristics in predicting teacher work behavior, we examine predictors of reentry. We employ survival analysis of time to reentry for exiting teachers using longitudinal data from the 1979 National Longitudinal Surveys of Youth. We find that younger, better paid, and more experienced teachers are more likely to reenter. We also find that women are more likely to return to teaching than men. Child rearing plays an important role in this difference. Women are less likely to reenter with young children at home. We conclude that reentrants may be an important source of teacher labor supply and that policies focused on the needs of teachers with young children may be effective ways for districts to attract returning teachers.</jats:p

Recruitment or Preparation? Investigating the Effects of Teacher Characteristics and Student Teaching

Some believe the solution to improving instructional quality in K-12 schools lies in identifying and recruiting certain kinds of individuals to the profession (e.g., academically talented, stronger commitment). Others believe that talented or committed individuals cannot become effective or enduring teachers without adequate preparation. Most prior literature examines either recruitment or preparation, rather than weighing evidence for both simultaneously. In addition, most prior research investigates the effects of either approach on only a single outcome, rather than considering multiple outcomes at once. Drawing on pre- and poststudent teaching surveys of more than 1,000 prospective teachers in a large, urban district, this study uses a unique strategy to disentangle the effects of one dimension of preparation (student teaching) from the effects of teacher characteristics on a number of measures for teachers’ self-perceived instructional quality and career plans. The findings indicate that career plans are more often related to teacher characteristics, whereas self-perceived instructional quality is more often related to features of clinical preparation. Implications for recruitment and preparation are discussed.</jats:p

Teachers’ Preferences to Teach Underserved Students

To increase the supply of teachers into underserved schools, teacher educators and policymakers commonly use two approaches: (a) recruit individuals who already report strong preferences to work in underserved schools or (b) design pre-service preparation to increase preferences. Using survey and administrative data on more than 1,000 teachers in a large, urban district, this study provides some of the first district-level evidence for both approaches. Individuals with stronger underserved preferences and teachers of color were more likely to enter underserved schools. Underserved preferences also increased across pre-service student teaching, although increases were mostly unrelated to working with underserved student populations.</jats:p

A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data

In mean-based approaches to dietary data analysis, it is possible for potentially important associations at the tails of the intake distribution, where inadequacy or excess is greatest, to be obscured due to unobserved heterogeneity. Participants in the upper or lower tails of dietary intake data will potentially have the greatest change in their behavior when presented with a health behavior intervention; thus, alternative statistical methods to modeling these relationships are needed to fully describe the impact of the intervention. Using data from Tu Salud ¡Si Cuenta! (Your Health Matters!) at Home Intervention, we aimed to compare traditional mean-based regression to quantile regression for describing the impact of a health behavior intervention on healthy and unhealthy eating indices. The mean-based regression model identified no differences in dietary intake between intervention and standard care groups. In contrast, the quantile regression indicated a nonconstant relationship between the unhealthy eating index and study groups at the upper tail of the unhealthy eating index distribution. The traditional mean-based linear regression was unable to fully describe the intervention effect on healthy and unhealthy eating, resulting in a limited understanding of the association

Weight loss and weight gain among participants in a community-based weight loss Challenge

Abstract Background To describe the characteristics of participants who registered for multiple annual offerings of a community-based weight loss program called The Challenge, and to determine participant characteristics associated with weight change over multiple offerings of The Challenge occurring during the years 2010–2016. Methods Multivariable linear mixed effects analyses were conducted to describe percent weight change within and between offerings of The Challenge by participant characteristics. Results There were 669 and 575 participants included in the within and between analyses, respectively, for offerings of The Challenge. Among the 434 participants who lost weight in their first attempt at The Challenge and completed the initial weigh-in for a subsequent offering of The Challenge, 22.4% maintained their weight loss or had greater weight loss by the next Challenge, 40.3% gained back some weight, and 37.3% gained back all or more of the weight they lost during their first Challenge. Men had a significantly greater percent weight loss compared to women in their first and second Challenge and men were more likely to gain weight between Challenges. Participants who returned to more Challenges had a greater accumulated percent weight loss compared to those who returned to fewer Challenges. Conclusions The current weight loss Challenge appears to contribute to helping a percentage of participants lose weight and maintain some or all of the weight loss