BACKGROUND
COVID-19 is a significant threat to people's mental health and social well-being. The research examined the effects of social determinants of health on COVID-19 related stress, family's stress and discord, and personal diagnosis of COVID-19.
METHODS
In November 2020, the data collection was conducted from 97 counties in North Carolina (
N
= 1500). Adult residents in North Carolina completed an online COVID-19 impact survey conducted using quota-based sampling on race, income, and county to provide a rapid quasi-representative assessment of COVID impact. The study investigated the variables in a structural model through structural equation modeling. For data analysis, IBM SPSS and AMOS were deployed.
RESULTS
Social determinants of health had direct effects on COVID-19 related stress (
β
= .66,
p
< .001,
r
2 = .43), family's stress and discord (
β
= .73,
p
< .001,
r
2 = .53), and personal diagnosis of COVID-19 (
β
= .52,
p
< .001,
r
2 = .27). These findings indicate that underserved populations experienced higher stress and discord at both individual and family levels and more severe COVID-19 symptoms. Moreover, black participants, whose family income and food access declined significantly more, had worse stress, discord, and COVID-19 symptoms than white participants.
CONCLUSIONS
The study suggests that the government and health professionals enhance mental health and family support service accessibility for underprivileged populations through telehealth and community health programs to prevent associated social and health issues such as suicide, violence, and cancer.
The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.