Adverse Childhood Experiences and Diabetes Risk in Mississippi Adults.

Despite Mississippi's high diabetes prevalence and the growing literature finding significant associations between adverse childhood experiences (ACEs) and diabetes, no research has examined the relationship between ACEs and diabetes risk in Mississippi adults. This study utilized data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) to determine if such a relationship existed. Data for Mississippi respondents were weighted to account for nonresponse bias and non-coverage errors. Each respondent's total ACE exposure score was calculated based on the number of ACE categories experienced. Multivariate logistic regression was utilized to model the relationship between diabetes and ACE categories and diabetes and total ACE exposure scores. Variables that were significant at p<0.05 were retained in the final (best-fitting) models. All models were adjusted for sex, age, race, level of education, income, and body mass index (BMI). After adjusting for covariates, those experiencing physical abuse (adjusted odds ratio (AOR) 1.72, 95% CI 1.69; 1.75) or sexual abuse (AOR 1.56, 95% CI 1.53; 1.58) had the highest odds of ever being diagnosed with diabetes. Experiencing one ACE (AOR 1.02, 95% CI 1.01; 1.03) was associated with slightly higher odds of having diabetes, while experiencing seven ACE categories (AOR 2.20, 95% CI 2.10; 2.31) had the highest odds. Overall, this study shows a strong association between ACEs and a diagnosis of diabetes in the state of Mississippi. This relationship represents an important focus area for prevention efforts in legislation, public health campaigns, and universal screening procedures in primary care that may decrease the prevalence and burden of diabetes in Mississippi.

Prediction of tropospheric ozone concentrations by using the design system approach.

Data on the concentrations of non-methane hydrocarbons (NMHC), nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), and meteorological parameters (air temperature and solar radiation) were used to predict the concentration of tropospheric ozone using the Design-Ease software. These data were collected on hourly basis over a 12-month period. Sampling of the data was conducted automatically. The effect of the NMHC, NO, NO2,CO, temperature and solar radiation variables in predicting ozone concentrations was examined under two scenarios: (i) when NO is included with the absence of NO2; and (ii) when NO2 is addressed with the absence of NO. The results of these two scenarios were validated against ozone actual data. The predicted concentration of ozone in the second scenario (i.e., when NO2 is addressed) was in better agreement with the real observations. In addition, the paper indicated that statistical models of hourly surface ozone concentrations require interactions and non-linear relationships between predictor variables in order to accurately capture the ozone behavior.