The correlation between food insecurity and infant mortality in North Carolina.

OBJECTIVE: Food insecurity (FI) affects approximately 11·1 % of US households and is related to worsened infant outcomes. Evidence in lower income countries links FI and infant mortality rates (IMR), but there are limited data in the USA. This study examines the relationship between FI and IMR in North Carolina (NC). DESIGN: NC county-level health data were used from the 2019 Robert Woods Johnson Foundation County Health Rankings. The dependent variable was county-level IMR. Eighteen county-level independent variables were selected and a multivariable linear regression was performed. The independent variable, FI, was based on the United States Department of Agriculture's Food Security Supplement to the Current Population Survey. SETTING: NC counties. PARTICIPANTS: Residents of NC, county-level data. RESULTS: The mean NC county-level IMR was 7·9 per 1000 live births compared with 5·8 nationally. The average percentage of county population reporting FI was 15·4 % in the state v. 11·8 % nationally. Three variables statistically significantly predicted county IMR: percent of county population reporting FI; county population and percent population with diabetes (P values, respectively, < 0·04; < 0·05; < 0·03). These variables explained 42·4 % of the variance of county-level IMR. With the largest standardised coefficient (0·247), FI was the strongest predictor of IMR. CONCLUSIONS: FI, low birth weight and diabetes are positively correlated with infant mortality. While correlation is not causation, addressing FI as part of multifaceted social determinants of health might improve county-level IMR in NC.

Decreases in smoking and vaping during COVID-19 stay-at-home orders among a cohort of young adults in the United States.

In Spring 2020, most US states and territories implemented stay-at-home orders to slow transmission of the novel coronavirus SARS-CoV2, the cause of COVID-19. Little is known about the impact of stay-at-home orders on tobacco and nicotine use including among young adults. The current study examined participants (N = 1727) completing three recent survey waves from a longitudinal cohort of young adults recruited in 2010 from North Carolina and Virginia, USA: Wave 13 (Spring 2019), Wave 14 (Fall 2019), and Wave 15 (Spring 2020) to assess changes in cigarette and e-cigarette use. We conducted logistic regression analyses to compare the odds that participants reported smoking or vaping in Wave 14 relative to Wave 13 to establish if there was a trend of use pre-pandemic. Then, we conducted logistic regression analyses to compare the odds that participants reported smoking or vaping in Wave 15 relative to Wave 14 to determine the impact of COVID-19 stay-at-home orders. When comparing the odds of reporting tobacco use at Wave 14 to Wave 13, no differences emerged (p > 0.05). However, when comparing tobacco use at Wave 15 to Wave 14, participants had 40% lower odds of reporting past 30-day cigarette use (p = 0.02) and 50% lower odds of reporting past 30-day e-cigarette use (p < 0.01). The current study provides initial evidence that young adults may have reduced their tobacco and nicotine use during the stay-at-home orders. However, more work is needed to determine the long-term impact of the COVID-19 pandemic on tobacco use and cessation in this population.