A three-year multifaceted intervention to prevent obesity in children of Mexican-heritage.

BACKGROUND: Obesity and overweight have increased dramatically in the United States over the last decades. The complexity of interrelated causal factors that result in obesity needs to be addressed within the cultural dynamic of sub-populations. In this study, we sought to estimate the effects of a multifaceted, community-based intervention on body mass index (BMI) among Mexican-heritage children. METHODS: Niños Sanos, Familia Sana (Healthy Children, Healthy Family) was a quasi-experimental intervention study designed to reduce the rate of BMI growth among Mexican-heritage children in California's Central Valley. Two rural communities were matched based on demographic and environmental characteristics and were assigned as the intervention or comparison community. The three-year intervention included parent workshops on nutrition and physical activity; school-based nutrition lessons and enhanced physical education program for children; and a monthly voucher for fruits and vegetables. Eligible children were between 3 and 8 years old at baseline. Intent-to-treat analyses were estimated using linear mixed-effect models with random intercepts. We ran a series of models for each gender where predictors were fixed except interactions between age groups and obesity status at baseline with intervention to determine the magnitude of impact on BMI. RESULTS: At baseline, mean (SD) BMI z-score (zBMI) was 0.97 (0.98) in the intervention group (n = 387) and 0.98 (1.02) in the comparison group (n = 313) (NS). The intervention was significantly associated with log-transformed BMI (ß = 0.04 (0.02), P = 0.03) and zBMI (ß = 0.25 (0.12), P = 0.04) among boys and log-transformed BMI among obese girls (ß = - 0.04 (0.02), P = 0.04). The intervention was significantly and inversely associated with BMI in obese boys and girls across all age groups and normal weight boys in the oldest group (over 6 years) relative to their counterparts in the comparison community. CONCLUSIONS: A community-based, multifaceted intervention was effective at slowing the rate of BMI growth among Mexican-heritage children. Our findings suggest that practitioners should consider strategies that address gender disparities and work with a variety of stakeholders to target childhood obesity. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01900613 . Registered 16th July 2013.

A Web-Based Application to Improve Data Collection in an Interventional Study Targeting Childhood Obesity: Pre-Post Analysis.

BACKGROUND: Although participatory action research (PAR) studies have proliferated in recent years, the development of technological resources to manage these types of projects has not kept pace. Few studies show how Web-based applications can be used to efficiently manage the data collection process. OBJECTIVE: This study described the development, use, and impact of a Web-based application to facilitate data management in Niños Sanos, Familia Sana (Healthy Children, Healthy Family), an interventional multifaceted PAR field study. METHODS: We described the transformation of the data management process and evaluated the impact of the application in terms of time efficiency of data collection and engagement of community-based data collectors. We defined time efficiency as the total number of days it took to collect 3 main surveys, per year of data collection. The engagement of data collectors was assessed based on qualitative reports. RESULTS: The amount of time it took to perform a round of data collection was reduced after implementation of the field team application (between 382 and 383 days and 198 and 233 days). Secondary data were also collected in a tighter time frame around collection of the primary outcome, and communication among data collectors, the field staff, and the research team was streamlined. In focus groups, community-based data collectors reported feeling more empowered and engaged in the data collection process after implementation of the application. CONCLUSIONS: A Web-based management application was successful in improving data collection time efficiency and engagement among data collectors.

Purchases Made with a Fruit and Vegetable Voucher in a Rural Mexican-Heritage Community.

Recent recommendations for US food assistance programs are intended to ensure foods provided through these programs help households consume a varied, healthful diet. From a policy viewpoint, it is important to examine the impact of economic incentives to purchase healthy foods across subpopulations, particularly low-income Latinos, who comprise 40% of the WIC program nationwide. Our aim was to determine how rural, Mexican-heritage households (N = 227) residing in California's Central Valley distributed fruit and vegetable (F/V) voucher spending among F/V subgroups and specific items over a 1-year period. Households contained at least one child who was between 3 and 8 years old at baseline and had a parent of Mexican-heritage. F/V voucher purchase data were collected via grocery store scanners. Expenditure and frequency shares of subgroups and individual items were analyzed to determine purchasing habits. Fruits were the most commonly purchased subgroup, representing 55% of spending and 45% of frequency. Households allocated low percentages of their voucher to dark green and red/orange vegetables-7 and 9% respectively. Approximately 20% of purchases were good potassium sources and 30% of purchases were good fiber sources. Many of the most frequently purchased items were of cultural significance (tomatillo, chayote, chili/jalapeño pepper, and Mexican squash). This study suggests that economic incentives can contribute important nutrients to participants' diets and targeted vouchers provided by food assistance programs should continue to include culturally important foods and be aware of the cultural values of their participants.

Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020.

OBJECTIVE: To rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques. SETTING: One hundred and eighty countries' patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020. DESIGN: Using the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries' final scores for each measure. PRIMARY OUTCOME: The primary outcomes are the COVID-19 cases and fatality grades in 2020. RESULTS: The United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries' preparedness. CONCLUSION: COVID-19 fatality can be a good proxy for countries' resources and system's resilience in managing the pandemic. These findings suggest that countries' economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.