A spatio-temporal hierarchical model to account for temporal misalignment in American Community Survey explanatory variables.

The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.

Impact of Criterion Versus Norm-Referenced Assessment on the Quality of Life in Korean Medical Students.

BACKGROUND: Medical students are known to be subjected to immense stress under competitive curricula and have a high risk of depression, burnout, anxiety and sleep disorders. There is a global trend of switching from norm-referenced assessment (NRA) to criterion-referenced assessment (CRA), and these changes may have influenced the quality of life (QOL), sleep phase, sleep quality, stress, burnout, and depression of the medical students. We hypothesized that there is a significant difference of QOL between CRA and NRA and that sleep, stress, burnout, and depression are the main contributors. METHODS: By administering an online survey regarding QOL and its contributors to Korean medical students, 365 responses from 10 medical schools were recorded. To clarify the complex relationship between the multiple factors in play, we applied nonlinear machine learning algorithms and utilized causal structure learning techniques on the survey data. RESULTS: Students with CRA had lower scores in stress (68.16 ± 11.29, 76.03 ± 12.38, P < 0.001), burnout (48.09 ± 11.23, 55.93 ± 13.07, P < 0.001), depression (12.77 ± 9.82, 16.44 ± 11.27, P = 0.003) and higher scores in QOL (95.79 ± 16.20, 89.65 ± 16.28, P < 0.001) compared with students with NRA. Multiple linear regression, permutation importance of the random forest model and the causal structure model showed that depression, stress and burnout are the most influential factors of QOL of medical students. CONCLUSION: Medical students from schools that use CRA showed higher QOL scores, as well as lower burnout, stress and depression when compared with students from schools that use NRA. These results may be used as a basis for granting justification for the transition to CRA.

Pandemic and hospital avoidance: Evidence from the 2015 Middle East respiratory syndrome outbreak in South Korea.

Existing literature shows that people exhibit disease avoidance behaviors in response to contagious disease outbreaks. We examine hospital avoidance behaviors during the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. The outbreak provides an excellent setting for the analysis because unlike the coronavirus disease-19 (COVID-19) situation, no mandatory lockdown was imposed during the outbreak, and the economic impact was also not large. Hence, reduced hospital visits are likely to reflect the public's intention to avoid hospitals to protect themselves from getting infected with MERS. Moreover, the outbreak did not spread to the entire country and vanished after a short period of time, allowing us to consider the affected regions as the treatment group and the other regions as the control group without much concern of confounding by other factors. The data come from a government agency, which assesses (national) health insurance claims made by hospitals, and hence cover all outpatient visits in the country. We find that people reduced outpatient visits by about 17% in response to the MERS outbreak, and the response was the most intense when new cases were reported most frequently.