Background: The global epidemic of novel coronavirus pneumonia (COVID-19) has resulted in substantial healthcare resource consumption. Since patients’ hospital length of stay (LoS) is at stake in the process, an investigation of COVID-19 patients’ LoS and its risk factors becomes urgent for a better understanding of regional capabilities to cope with COVID-19 outbreaks.Methods: First, we obtained retrospective data of confirmed COVID-19 patients in Sichuan province via National Notifiable Diseases Reporting System (NNDRS) and field surveys, including their demographic, epidemiological, clinical characteristics and LoS. Then we estimated the relationship between LoS and the possibly determinant factors, including demographic characteristics of confirmed patients, individual treatment behavior, local medical resources and hospital grade. The Kaplan-Meier method and the Cox Proportional Hazards Model were applied for single factor and multi-factor survival analysis.Results: From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment, including 271 (50%) patients aged ≥ 45, 285 (53%) males, and 450 patients (84%) with mild symptoms. The median LoS was 19 (interquartile range (IQR): 14–23, range: 3–41) days. Univariate analysis showed that age and clinical grade were strongly related to LoS (P<0.01). Adjusted multivariate analysis showed that the longer LoS was associated with those aged ≥ 45 (Hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.60–0.91), admission to provincial hospital (HR: 0.73, 95% CI: 0.54–0.99), and severe illness (HR: 0.66, 95% CI: 0.48–0.90). By contrast, the shorter LoS was linked with residential areas with more than 5.5 healthcare workers per 1,000 population (HR: 1.32, 95% CI: 1.05–1.65). Neither gender factor nor time interval from illness onset to diagnosis showed significant impact on LoS.Conclusions: Understanding COVID-19 patients’ hospital LoS and its risk factors is critical for governments’ efficient allocation of resources in respective regions. In areas with older and more vulnerable population and in want of primary medical resources, early reserving and strengthening of the construction of multi-level medical institutions are strongly suggested to cope with COVID-19 outbreaks.

Obesity is a multifactorial abnormality which has an underlying genetic control but requires environmental influences to trigger. Numerous epidemiological studies have examined the roles of physical inactivity and dietary factors in obesity development. Interactions between obesity-related genes and these lifestyles have also been confirmed. However, less attention has been paid to these complex relationship between cigarette smoking, alcohol drinking and obesity. The purpose of this study was to assess whether cigarette smoking and alcohol drinking were associated with body mass index (BMI), and whether these lifestyle factors modified the genetic variance of BMI. Subjects were twins recruited through the Chinese National Twin Registry, aged 18 to 79 years, and the sample comprised 6121 complete male twin pairs. Information on height, weight, cigarette smoking and alcohol drinking status were assessed with self-report questionnaires. The associations of cigarette smoking and alcohol drinking with BMI were evaluated by linear regression models. Further, structure equation models were conducted to estimate whether cigarette smoking and alcohol drinking status modified the degree of genetic variance of BMI. After adjustment for a variety of socio-demographic and lifestyle factors, former smokers had higher BMI (β = 0.475; 95 % CI, 0.196 to 0.754) whereas moderate to heavy smokers had lower BMI (β = −0.115; 95 % CI, −0.223 to −0.007) when compared with nonsmokers. BMI decreased with increased cigarette pack-years (β = −0.008; 95 % CI, −0.013 to −0.003). These effects still existed substantially in within-MZ twin pair analyses. By contrast, current alcohol drinking had no significant influence on BMI when additionally controlled for shared factors in within-pair analyses. Genetic modification by alcohol drinking was statistically significant for BMI (β = −0.137; 95 % CI, −0.215 to −0.058), with the intake of alcohol decreasing the additive genetic component of BMI. Cigarette smoking was negatively associated with BMI independent of genetic influences. The influence of genes on BMI was moderated by alcohol drinking, such that for individuals who were regular drinkers, genetic factors became less influential. Our findings highlight gene-alcohol interaction in finding candidate genes of BMI and elucidating the etiological factors of obesity.

Background: The prevalence of obesity and cardiometabolic diseases continues to rise globally and obesity is a significant risk factor for cardiometabolic diseases. However, to our knowledge, evidence of the relative roles of genes and the environment underlying obesity and cardiometabolic disease traits and the correlations between them are still lacking, as is how they change with age. Method: Data were obtained from the Chinese National Twin Registry (CNTR). A total of 1421 twin pairs were included. Univariate structural equation models (SEMs) were performed to evaluate the heritability of BMI and cardiometabolic traits, which included blood hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Bivariate SEMs were used to assess the genetic/environmental correlations between them. The study population was divided into three groups for analysis: ≤50, 51–60, and >60 years old to assess the changes in heritability and genetic/environmental correlations with ageing. Results: Univariate SEMs showed a high heritability of BMI (72%) and cardiometabolic traits, which ranged from 30% (HbA1c) to 69% (HDL-C). With age increasing, the heritability of all phenotypes has different degrees of declining trends. Among these, BMI, SBP, and DBP presented significant monotonous declining trends. The bivariate SEMs indicated that BMI correlated with all cardiometabolic traits. The genetic correlations were estimated to range from 0.14 (BMI and LDL-C) to 0.39 (BMI and DBP), while the environmental correlations ranged from 0.13 (BMI and TC/LDL-C) to 0.31 (BMI and TG). The genetic contributions underlying the correlations between BMI and SBP and DBP, TC, TG, and HDL-C showed a progressive decrease as age groups increased. In contrast, environmental correlations displayed a significant increasing trend for HbA1c, SBP, and DBP. Conclusions: The findings suggest that genetic and environmental factors have essential effects on BMI and all cardiometabolic traits. However, as age groups increased, genetic influences presented varying degrees of decrement for BMI and most cardiometabolic traits, suggesting the increasing importance of environments. Genetic factors played a consistently larger role than environmental factors in the phenotypic correlations between BMI and cardiometabolic traits. Nevertheless, the relative magnitudes of genetic and environmental factors may change over time.

Objective: Allocation of medical resource is essential to a strong public health system in response to COVID-19. Analysis of confirmed COVID-19 patients’ hospital length of stay in Sichuan can be informative to decision-making in other regions of the world. Design: A retrospective cross-sectional study. Data and Method: Data from confirmed COVID-19 cases in Sichuan Province were obtained from the National Notifiable Diseases Reporting System (NNDRS) and field survey. We collected information on demographic, epidemiological, clinical characteristics, and the length of hospital stay for confirmed patients. We conducted an exploratory analysis using adjusted multivariate cox-proportional models. Participants: A total of 538 confirmed patients of COVID-19 infection in Sichuan Province from January to March 2020. Outcome measure: The length of hospital stay after admissions for confirmed patients. Results: From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment. Among these, 271 (50%) were 45 years of age or above, 285 (53%) were male, 450 (84%) were considered as having mild symptoms. The median hospital length of stay was 19 days (interquartile range (IQR): 14-23, Range: 3-41). Adjusted multivariate analysis showed that longer hospital length of stay was associated with factors aged 45 and over (HR: 0.74, 95% CI: 0.60-0.91), those admitted to provincial hospital (HR: 0.73, 95% CI: 0.54-0.99), and those with serious illness (HR: 0.66, 95% CI: 0.48-0.90); living in areas with more than 5.5 healthcare workers per 1000 population (HR: 1.32, 95% CI: 1.05-1.65) was associated with shorter hospital length of stay. There was no gender difference. Conclusions: Preparation control measures of COVID-19 should involve the allocation of sufficient medical resources, especially in areas with older vulnerable populations and in areas that lack basic medical resources. Strengths and limitations of this study: ▸ Patients at least 45 years, those with serious illness, those living in areas with fewer healthcare workers per 1,000 people, and those admitted to higher levels of hospitalization had longer lengths of hospitalization, while gender, time interval from onset to visit the hospital had no effect on the length of the hospital stay. ▸ Preparation of timely evidence-based prevention and control measures for COVID-19 involve allocation of sufficient medical resources, especially in areas with older vulnerable populations and in areas that lack basic medical resources. ▸ Based on findings, it is of great significance to strengthen the construction of multi-level medical institutions in response to public health emergencies and occupation of medical resources. ▸ The characteristics of inpatients can be further subdivided to obtain more detailed inpatient characteristics.

Objective Hypertension is a major risk factor and cause of many non-communicable diseases in China. While there have been studies on various diet and lifestyle risk factors, we do not know whether sleep duration has an association to blood pressure in southwest China. This predictor is useful in low-resource rural settings. We examined the association between sleep duration and hypertension in southwest China. Design Population-based cross-sectional study. Setting This study was part of the baseline survey of a large ongoing prospective cohort study, the China Kadoorie Biobank. Participants were enrolled in 15 townships of Pengzhou city in Sichuan province during 2004–2008. Participants 55 687 participants aged 30–79 years were included. Sleep duration was assessed by a self-reported questionnaire. Main outcome measures Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg, or prior physician-diagnosed hypertension in hospitals at the township (community) level or above. Results The prevalence of hypertension was 25.17%. The percentages of subjects with sleep durations of <6, 6, 7, 8 and ≥9 hours were 17.20%, 16.14%, 20.04%, 31.95% and 14.67%, respectively. In multivariable-adjusted analyses, the increased ORs of having hypertension were across those who reported ≥9 hours of sleep (men: 1.16, 95% CI 1.04 to 1.30; women: 1.19, 95% CI 1.08 to 1.32; general population: 1.17, 95% CI 1.08 to 1.26). The odds of hypertension was relatively flat until around 6.81 hours of sleep duration and then started to increase rapidly afterwards in subjects and a J-shaped pattern was observed. There was a U-shaped relationship between sleep duration and hypertension in females. Conclusion Long sleep duration was significantly associated with hypertension and a J-shaped pattern was observed among rural adults in southwest China, independent of potential confounders. However, this association was not obvious between short sleep duration and hypertension.

Objectives This study explored the prevalence of and individual influencing factors for metabolic syndrome (MS) as well as associated socioeconomic factors and regional aggregation. Design Four cross-sectional surveys were analysed for trends in MS and associations with socioeconomic and individual factors through multilevel logistic regression analyses. The risk associated with nutrient intake was also assessed through a dietary survey in 2015. Setting From 2010 to 2018, 8–15 counties/districts of West China were included. Participants A total of 28 274 adults were included in the prevalence analysis. A total of 23 708 adults were used to analyse the related factors. Results The overall prevalence of MS ranged from 21.4% to 27.8% over the 8 years, remaining basically stable within the 95% CI. Our study found that the urbanisation rate and hospital beds per 1000 people were positively associated with MS, and the number of doctors in healthcare institutions per 1000 persons was negatively associated with MS. The ORs for females, people with college education and higher and unmarried or single people were 1.49, 0.67 and 0.51, respectively (p<0.05). The ORs of people who smoked at least 20 cigarettes/day, ate more than 100 g of red meat/day, consumed fruit or vegetable juice and drank carbonated soft drinks less than weekly were 1.10, 1.16, 1.19–1.27 and 0.81–0.84, respectively. The ORs rose with increasing sedentary time and decreased with higher physical activity. Conclusion The high burden of MS, unreasonable proportions of energy and micronutrient intake and low percentage of high levels of physical activity were the major challenges to public health in western China. Improving the human resources component of medical services, such as the number of doctors, increasing the availability of public sports facilities and E-health tools and improving individual dietary quality and education might help prevent MS.