Modeling COVID-19 Mortality Across 44 Countries: Face Covering May Reduce Deaths.

INTRODUCTION: Despite ongoing efforts to vaccinate communities against COVID-19, the necessity of face mask use in controlling the pandemic remains subject to debate. Several studies have investigated face masks and COVID-19, covering smaller and less diverse populations than this study's sample. This study examines a hypothesized association of face-covering mandates with COVID-19 mortality decline across 44 countries in 2 continents. METHODS: In a retrospective cohort study, changes in COVID-19‒related daily mortality rate per million population from February 15 to May 31, 2020 were compared between 27 countries with and 17 countries without face mask mandates in nearly 1 billion (911,446,220 total) people. Longitudinal mixed effect modeling was applied and adjusted for over 10 relevant demographic, social, clinical, and time-dependent confounders. RESULTS: Average COVID-19 mortality per million was 288.54 in countries without face mask policies and 48.40 in countries with face mask policies. In no mask countries, adjusted average daily increase was 0.1553 - 0.0017 X (days since the first case) log deaths per million, compared with 0.0900 - 0.0009 X (days since the first case) log deaths per million in the countries with a mandate. A total of 60 days into the pandemic, countries without face mask mandates had an average daily increase of 0.0533 deaths per million, compared with the average daily increase of 0.0360 deaths per million for countries with face mask mandates. CONCLUSIONS: This study's significant results show that face mask mandates were associated with lower COVID-19 deaths rates than the rates in countries without mandates. These findings support the use of face masks to prevent excess COVID-19 deaths and should be advised during airborne disease epidemics.

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.

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.