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Purpose: County-level health departments (HD) have lead roles in COVID-19 response, with actions guided by 15 federal capabilities We sought to assess Illinois HD perception of preparation and ability to accomplish these, and how perceptions might vary by population size and rurality Methods: We attempted contact with all 96 certified HD (4/6-24/2020) and administered a survey asking 4 identical questions regarding each capability: 1) adequacy of past preparation (pastPreparation);2) current ability to accomplish (currAbility);3) sufficiency of current resources (Resources);and 4) sufficiency of local cooperation and support (Cooperation;all Likert 1-5) Rurality was assessed with Rural-Urban Continuum Codes (levels 1-9;stratified as metropolitan [1-3] and nonmetropolitan [4-9]);and population data obtained from the Census Bureau T-test, chi-square and regression analyses were conducted with SAS Results: We received 34 surveys representing 40/100 counties with HDs (11 metropolitan and 23 nonmetropolitan) Respondents/non-respondents did not differ by population or rurality Ave-currAbility (currAbility averaged across all 15 capabilities) was positively associated with population (+3 57E-6;p=0 027), and nonmetropolitan counties reported significantly lower ave-Cooperation (3 37 [95% CI 3 16-3 57] vs 3 93 [3 47-4 38]) For individual capabilities, nonmetropolitan HDs reported significantly lesser Resources for capabilities #2-Community Recovery, #3-Emergency Operation Coordination, and #5-Fatality Management;and lesser Cooperation for #2, #8 and #9-Medical Countermeasure and Material Dispensing, #10-Medical Surge, and #15-Volunteer Management Conclusions: Though metropolitan areas experience greater numbers of COVID-19 cases, many rural communities experience comparably high infection rates Though statewide HD response is critical, many nonmetropolitan HDs report comparatively lesser ability to meet this challenge

OBJECTIVES: Obesogenic environments (OE) to induce the fat accumulation are also risk factors of related chronic diseases. In previous studies, the metabolic rate for energy consumption such as BMR and RMR were significantly correlated with obesity prevalence. Our purpose was to find the scientific evidences of the connection among energy consumption (BMR or RMR), genomics related to BMR or RMR, and obesity prevalence in children and adults. METHODS: Using Affymetrix® Genome-Wide Human SNP 6.0 Array, genes related with BMR/RMR and BMI were screened in children (n = 512) from the cohort study, and adult subjects (N = 77) from the cross-sectional study. The correlation or association between SNPs of those genes and obesity prevalence or OE was estimated by Cochran-Armitage trend test and logistic regression. The data for family history, anthropometrics, blood biochemistry, dietary intakes and other OE were used in this study. RESULTS: Hsp40/C6 gene was highly associated with both of obesity (BMI) and (BMR/RMR) in children. However, Hsp40/C6 gene was not significantly confirmed in adults, and all significant genes derived from adults were not screened in children. We found that several OE and genes affect obesity prevalence in children or adults are different. We focused on the mutant types of five SNPs in Hsp40/C6 gene derived from children were associated with obesity by reducing energy consumption. CONCLUSIONS: We discovered the different genes related to energy consumption in obese children and adults. The mutation of Hsp40/C6 may change energy consumption and eventually induce obesity. These findings will contribute to find the mechanism of Hsp40/C6 gene on the fat accumulation in future. FUNDING SOURCES: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (2019R1A2C1008434).

In 2011, the Korean government designated hospitals with certain structural characteristics as specialty hospitals. This study compared the inpatient charges and length of stay of patients with joint diseases treated at these specialty hospitals with those of patients treated at small general hospitals. In addition, the study investigated whether the designation of certain hospitals as specialty hospitals had an effect on inpatient charges and length of stay. Multi-level models were used to perform regression analyses on inpatient claims data (N=268,809) for 2010-2012 because of the hierarchical structure of the data. The inpatient charge at specialty hospitals was 19% greater than that at small general hospitals, but the length of stay was 21% shorter. After adjusting for patient and hospital level confounders, specialty hospitals had a higher inpatient charge (34.6%) and a reduced length of stay (31.7%). However, the effect of specialty hospital designation on inpatient charge (2.7% higher) and length of stay (2.3% longer) was relatively smaller. Among the patient characteristics, female gender, age, and severity of illness were positively associated with inpatient charge and length of stay. In terms of location, hospitals in metropolitan area had higher inpatient charges (5.5%), but much shorter length of stay (-14%). Several structural factors, such as occupancy rate, bed size, number of outpatients and nurses were positively associated with both inpatient charges and length of stay. However, number of specialists was positively associated with inpatient charges, but negatively associated with length of stay. In sum, this study found that specialty hospitals treating joint diseases tend to incur higher charges but produce shorter length of stay, compared to their counterparts. Specialty hospitals' overcharging behaviors, although shorter length of stay, suggest that policy makers could introduce bundled payments for the joint procedures. To promote a successful specialty hospital system, a broader discussion and investigation that includes quality measures as well as real cost of care should be initiated.