A Cluster-Randomized Controlled Trial to assess the Impact of a Nutrition intervention On Dietary Behaviors among Early Care and Education Providers: the Create Healthy Futures Study

Create Healthy Futures is a self-paced, web-based intervention on improving healthy eating behaviors among Early Care and Education (ECE) providers. We examined the impact of web-based Create Healthy Futures on diet quality measured by the Alternative Healthy Eating Index (AHEI) 2010, dietary behaviors, and related psychosocial and environmental factors among ECE providers. A cluster randomized controlled trial (CRCT) was implemented with baseline surveys administered from October 2019-January 2020, intervention implementation from April-May 2020, and post-intervention from May 2020-August 2020. Centered-based ECE programs under the Pennsylvania Head Start Association (n = 12) were recruited and randomized to intervention (n = 5) or comparison (n = 7) groups. A total of 186 ECE providers completed the post-intervention surveys (retention rate: 86.1%). At baseline, 31.5% of ECE providers were food insecure. Pre-to-post intervention demonstrated no significant within-or-between-group changes in the AHEI-2010 diet quality scores. ECE providers in the intervention group reported a significant decrease from baseline to post-intervention in the number of days eating out (aMD = -0.8, CI:-1.6, -0.1

Design For a Cluster Randomized Controlled Trial to Evaluate the Effects of the Catch Healthy Smiles School-Based oral Health Promotion intervention among Elementary School Children

BACKGROUND: The top two oral diseases (tooth decay and gum disease) are preventable, yet dental caries is the most common childhood disease with 68% of children entering kindergarten having tooth decay. CATCH Healthy Smiles is a coordinated school health program to prevent cavities for students in kindergarten, 1st, and 2nd grade, and is based on the framework of Coordinated Approach to Child Health (CATCH), an evidence-based coordinated school health program. CATCH has undergone several cluster-randomized controlled trials (CRCT) demonstrating sustainable long-term effectiveness in incorporating the factors surrounding children, in improving eating and physical activity behaviors, and reductions in obesity prevalence among low-income, ethnically diverse children. The aim of this paper is to describe the design of the CATCH Healthy Smiles CRCT to determine the effectiveness of an oral health school-based behavioral intervention in reducing incidence of dental caries among children. METHODS: In this CRCT, 30 schools serving low-income, ethnically-diverse children in greater Houston area are recruited and randomized into intervention and comparison groups. From which, 1020 kindergarten children (n = 510 children from 15 schools for each group) will be recruited and followed through 2nd grade. The intervention consists of four components (classroom curriculum, toothbrushing routine, family outreach, and schoolwide coordinated activities) will be implemented for three years in the intervention schools, whereas the control schools will be offered free trainings and materials to implement a sun safety curriculum in the meantime. Outcome evaluation will be conducted at four time points throughout the study period, each consists of three components: dental assessment, child anthropometric measures, and parent survey. The dental assessment will use International Caries Detection and Assessment System (ICDAS) to measures the primary outcome of this study: incidence of dental caries in primary teeth as measured at the tooth surface level (dfs). The parent self-report survey measures secondary outcomes of this study, such as oral health related behavioral and psychosocial factors. A modified crude caries increment (mCCI) will be used to calculate the primary outcome of the CATCH Healthy Smiles CRCT, and a two-tailed test of the null hypothesis will be conducted to evaluate the intervention effect, while considering between- and within-cluster variances through computing the weighted-average of the mCCI ratios by cluster. CONCLUSION: If found to be effective, a platform for scalability, sustainability and dissemination of CATCH already exists, and opens a new line of research in school oral health. CLINICAL TRIALS IDENTIFIER: At ClinicalTrials.gov - NCT04632667

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\u27s 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\u27 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

Spatial Patterns of COVID-19 Vaccination Coverage by Social Vulnerability Index and Designated COVID-19 Vaccine Sites in Texas

Equitable access to the COVID-19 vaccine remains a public health priority. This study explores the association between ZIP Code–Tabulation Area level Social Vulnerability Indices (SVI) and COVID-19 vaccine coverage in Texas. A mixed-effects, multivariable, random-intercept negative binomial model was used to explore the association between ZIP Code–Tabulation Area level SVI and COVID-19 vaccination coverage stratified by the availability of a designated vaccine access site. Lower COVID-19 vaccine coverage was observed in ZIP codes with the highest overall SVIs (adjusted mean difference (aMD) = −13, 95% CI, −23.8 to −2.1, p p = 0.01) and housing and transportation theme (aMD = −18.3, 95% CI, −29.6 to −7.1, p p = 0.04) and Blacks (aMD = −3.7, 95% CI, −6.4 to −1, p = 0.01). SVI negatively impacted COVID-19 vaccine coverage in Texas. Access to vaccine sites did not address disparities related to vaccine coverage among minority populations. These findings are relevant to guide the distribution of COVID-19 vaccines in regions with similar demographic and geospatial characteristics

Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality.

Understanding sociodemographic, behavioral, clinical, and laboratory risk factors in patients diagnosed with COVID-19 is critically important, and requires building large and diverse COVID-19 cohorts with both retrospective information and prospective follow-up. A large Health Information Exchange (HIE) in Southeast Texas, which assembles and shares electronic health information among providers to facilitate patient care, was leveraged to identify COVID-19 patients, create a cohort, and identify risk factors for both favorable and unfavorable outcomes. The initial sample consists of 8,874 COVID-19 patients ascertained from the pandemic's onset to June 12th, 2020 and was created for the analyses shown here. We gathered demographic, lifestyle, laboratory, and clinical data from patient's encounters across the healthcare system. Tobacco use history was examined as a potential risk factor for COVID-19 fatality along with age, gender, race/ethnicity, body mass index (BMI), and number of comorbidities. Of the 8,874 patients included in the cohort, 475 died from COVID-19. Of the 5,356 patients who had information on history of tobacco use, over 26% were current or former tobacco users. Multivariable logistic regression showed that the odds of COVID-19 fatality increased among those who were older (odds ratio = 1.07, 95% CI 1.06, 1.08), male (1.91, 95% CI 1.58, 2.31), and had a history of tobacco use (2.45, 95% CI 1.93, 3.11). History of tobacco use remained significantly associated (1.65, 95% CI 1.27, 2.13) with COVID-19 fatality after adjusting for age, gender, and race/ethnicity. This effort demonstrates the impact of having an HIE to rapidly identify a cohort, aggregate sociodemographic, behavioral, clinical and laboratory data across disparate healthcare providers electronic health record (HER) systems, and follow the cohort over time. These HIE capabilities enable clinical specialists and epidemiologists to conduct outcomes analyses during the current COVID-19 pandemic and beyond. Tobacco use appears to be an important risk factor for COVID-19 related death