Sociodemographic Variation in Children's Health Behaviors During the COVID-19 Pandemic.

Background: Societal changes during the COVID-19 pandemic may affect children's health behaviors and exacerbate disparities. This study aimed to describe children's health behaviors during the COVID-19 pandemic, how they vary by sociodemographic characteristics, and the extent to which parent coping strategies mitigate the impact of pandemic-related financial strain on these behaviors. Methods: This study used pooled data from 50 cohorts in the Environmental influences on Child Health Outcomes Program. Children or parent proxies reported sociodemographic characteristics, health behaviors, and parent coping strategies. Results: Of 3315 children aged 3-17 years, 49% were female and 57% were non-Hispanic white. Children of parents who reported food access as a source of stress were 35% less likely to engage in a higher level of physical activity. Children of parents who changed their work schedule to care for their children had 82 fewer min/day of screen time and 13 more min/day of sleep compared with children of parents who maintained their schedule. Parents changing their work schedule were also associated with a 31% lower odds of the child consuming sugar-sweetened beverages. Conclusions: Parents experiencing pandemic-related financial strain may need additional support to promote healthy behaviors. Understanding how changes in parent work schedules support shorter screen time and longer sleep duration can inform future interventions.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups.

BACKGROUND: In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups. METHODS: The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement. RESULTS: In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03). CONCLUSIONS: The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

Impacts of the New York State COVID-19 Disaster Emergency Orders on prescription dispensing for opioids and medication for opioid use disorder.

AIMS: To examine whether the March 2020 New York State (NYS) SARS-CoV-2 Emergency Orders were associated with an initial surge in opioid dispensing and a longer-term reduction in access to medications for opioid use disorder (MOUD). DESIGN: Time-series analyses of the dispensing of non-MOUD opioid and MOUD prescriptions using IQVIA's Longitudinal Prescription Claims Database (n=16,087,429) in NYS by week, from 1/1/2018, to 7/31/2020. IQVIA is a multinational company that provides biopharmaceutical development and commercial outsourcing services. SETTING AND PARTICIPANTS: NYS Zone Improvement Plan (ZIP) Codes (n=1,218) in which prescriptions were dispensed. MEASUREMENT: For each ZIP Code, for each week, the following dispensing measures were calculated: total weekly morphine milligram equivalents/day (MME/day); total weekly MME/day dispensed via prescriptions for ≤7 days; and the count of MOUD prescriptions dispensed. Differences in dispensing metrics, comparing each week in 2020 with corresponding weeks in 2019, were calculated for each ZIP Code. RESULTS: During the study period, weekly MME/day per ZIP Code of dispensed non-MOUD opioids steadily declined. Compared with the difference in dispensing between 2019 and 2020 during the first week in 2020, there was a significantly larger drop in dispensed weekly total MME/day beginning March 21 2020 and lasting until the week of April 17 (p<0.05 for each week). Mean weekly total MME/day dispensed from March 21 to April 17, 2020 was 17.07% lower (95% confidence interval [CI] 13.97%, 20.17%) than in the 4 weeks before March 21, almost entirely due to a drop in MME/day dispensed for prescriptions of ≤7 days. There was not a discernable drop in MOUD dispensing associated with the period of the Emergency Orders. CONCLUSIONS AND RELEVANCE: New York State Emergency Orders in March 2020 to reduce SARS-CoV-2 transmission and preserve hospital capacity appeared to be associated with a decline in dispensing of opioids not used as MOUD. Access to MOUD appeared to be unaffected by the Orders, likely because of policy initiatives by the Substance Abuse and Mental Health Services Administration.

Changes in BMI During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Experts hypothesized increased weight gain in children associated with the coronavirus disease 2019 (COVID-19) pandemic. Our objective was to evaluate whether the rate of change of child body mass index (BMI) increased during the COVID-19 pandemic compared with prepandemic years. METHODS: The study population of 1996 children ages 2 to 19 years with at least 1 BMI measure before and during the COVID-19 pandemic was drawn from 38 pediatric cohorts across the United States participating in the Environmental Influences on Child Health Outcomes-wide cohort study. We modeled change in BMI using linear mixed models, adjusting for age, sex, race, ethnicity, maternal education, income, baseline BMI category, and type of BMI measure. Data collection and analysis were approved by the local institutional review board of each institution or by the central Environmental Influences on Child Health Outcomes institutional review board. RESULTS: BMI increased during the COVID-19 pandemic compared with previous years (0.24 higher annual gain in BMI during the pandemic compared with previous years, 95% confidence interval 0.02 to 0.45). Children with BMI in the obese range compared with the healthy weight range were at higher risk for excess BMI gain during the pandemic, whereas children in higher-income households were at decreased risk of BMI gain. CONCLUSIONS: One effect of the COVID-19 pandemic is an increase in annual BMI gain during the COVID-19 pandemic compared with the 3 previous years among children in our national cohort. This increased risk among US children may worsen a critical threat to public health and health equity.

The COVID-19 Pandemic as a Threat Multiplier for Childhood Health Disparities: Evidence from St. Louis, MO.

The COVID-19 pandemic has highlighted socioeconomic and racial health disparities in the USA. In this study, we examined the COVID-19 pandemic as a threat multiplier for childhood health disparities by evaluating health behavior changes among urban St. Louis, MO, children (ages 6-14) during the COVID-19 pandemic. From 27 October to 10 December 2020, 122 parents/guardians reported on their children's health behaviors (Eating, Sleeping, Physical activity, Time outside, Time with friends in-person, Time with friends remotely, Time using media for educational proposes, Time using media for non-educational proposes, and Social connectedness) prior to and during the COVID-19 pandemic. We ran K-means cluster analyses to identify distinct health behavior cluster profiles. Relative risks were determined to evaluate behavioral differences between the two clusters. Two distinct cluster profiles were identified: a High Impact profile (n = 49) and a Moderate Impact profile (n = 73). Children in the High Impact cluster had a greater risk of being diagnosed with COVID-19, developed worsened eating habits (RR = 2.10; 95% CI = 1.50-2.93), spent less time sleeping, and spent less time outdoors (RR = 1.55; 95% CI = 1.03-2.43) than the Moderate Impact cluster. The High Impact cluster was more likely to include Black children and children from single-adult households than the Moderate Impact cluster (both p < 0.05). Our findings suggest that the COVID-19 pandemic may be a threat multiplier for childhood health disparities. Further research is needed to better understand the long-term effects of the COVID-19 pandemic on children's health.

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing, case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of neighborhood characteristics were independently associated with COVID-19 testing, case, and death rates. For example, the proportions of Blacks and Hispanics in a ZCTA were positively associated with COVID-19 case rate. Contrary to the conventional hypothesis, neighborhoods with low-density housing experienced higher COVID-19 case rates. In addition, demographic changes (e.g. out-migration) during the pandemic may bias the estimates of COVID-19 rates. Future research should further investigate these neighborhood-level factors and their interactions over time to better understand the mechanisms by which they affect COVID-19.