Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread

Background During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks. Methods In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations. Results Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers $$R_e$$ R e . Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London. Conclusions Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30–79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20–1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19–3.19) and 28.19 (95% CI = 14.85–53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22–15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions

Fatigue Properties and Damage Mechanism of a Cr-Mn Austenite Steel

The fatigue properties and the damage mechanism of a Cr-Mn austenite steel were investigated using four-point bend fatigue testing. The stress-number of cycles to failure (S-N) curve of the Cr-Mn austenite steel was measured at room temperature, at the frequency of f=20 Hz and the stress ratio of R=0.1. The fatigue strength of this Cr-Mn austenite steel was measured to be 503 MPa in the maximum stress. Multiple cracks are initiated on the sample surface after fatigue failure tests, and usually only one or two of them can lead to the final failure of the samples. Most of the cracks are initiated at the {111 }primary slip bands, especially within coarse grains. When a fatigue crack meets a new grain, it adapts to slip bands in this grain and hardly extends along the foregoing route in the previous grain. A crack is deflected at a grain boundary by crack plane twisting and tiling on the grain boundary plane, causing fracture steps on the fracture surface

Maintaining healthy sleep patterns and frailty transitions: a prospective Chinese study

Background: Little is known about the effects of maintaining healthy sleep patterns on frailty transitions. Methods: Based on 23,847 Chinese adults aged 30–79 in a prospective cohort study, we examined the associations between sleep patterns and frailty transitions. Healthy sleep patterns included sleep duration at 7 or 8 h/d, without insomnia disorder, and no snoring. Participants who persisted with a healthy sleep pattern in both surveys were defined as maintaining a healthy sleep pattern and scored one point. We used 27 phenotypes to construct a frailty index and defined three statuses: robust, prefrail, and frail. Frailty transitions were defined as the change of frailty status between the 2 surveys: improved, worsened, and remained. Log-binomial regression was used to calculate the prevalence ratio (PR) to assess the effect of sleep patterns on frailty transitions. Results: During a median follow-up of 8.0 years among 23,847 adults, 45.5% of robust participants, and 10.8% of prefrail participants worsened their frailty status, while 18.6% of prefrail participants improved. Among robust participants at baseline, individuals who maintained sleep duration of 7 or 8 h/ds, without insomnia disorder, and no-snoring were less likely to worsen their frailty status; the corresponding PRs (95% CIs) were 0.92 (0.89–0.96), 0.76 (0.74–0.77), and 0.85 (0.82–0.88), respectively. Similar results were observed among prefrail participants maintaining healthy sleep patterns. Maintaining healthy sleep duration and without snoring, also raised the probability of improving the frailty status; the corresponding PRs were 1.09 (1.00–1.18) and 1.42 (1.31–1.54), respectively. Besides, a dose-response relationship was observed between constantly healthy sleep scores and the risk of frailty transitions (P for trend Conclusions: Maintaining a comprehensive healthy sleep pattern was positively associated with a lower risk of worsening frailty status and a higher probability of improving frailty status among Chinese adults

Impacts of solid fuel use versus smoking on life expectancy at age 30 years in the rural and urban Chinese population: a prospective cohort study

Background: The impact of solid fuel use on life expectancy (LE) in less-developed countries remains unclear. We aimed to evaluate the potential impact of household solid fuel use on LE in the rural and urban Chinese population, with the effect of smoking as a reference. Methods: We used data from China Kadoorie Biobank (CKB) of 484,915 participants aged 30–79 free of coronary heart disease, stroke, or cancer at baseline. Analyses were performed separately for solid fuel use for cooking, solid fuel use for heating, and smoking, with participants exposed to the other two sources excluded. Solid fuels refer to coal and wood, and clean fuels refer to electricity, gas, and central heating. We used a flexible parametric Royston-Parmar model to estimate hazard ratios of all-cause mortality and predict LE at age 30. Findings: Totally, 185,077, 95,228, and 230,995 participants were included in cooking-, heating-, and smoking-related analyses, respectively. During a median follow-up of approximately 12.1 years, 12,725, 7,531, and 18,878 deaths were recorded in the respective analysis. Compared with clean fuel users who reported cooking with ventilation, participants who used solid fuels with ventilation and without ventilation had a difference in LE (95% confidence interval [CI]) at age 30 of −1.72 (−2.88, −0.57) and −2.62 (−4.16, −1.05) years for men and −1.33 (−1.85, −0.81) and −1.35 (−2.02, −0.67) years for women, respectively. The difference in LE (95% CI) for heating was −2.23 (−3.51, −0.95) years for men and −1.28 (−2.08, −0.48) years for women. In rural men, the LE reduction (95% CI) related to solid fuel use for cooking (−2.55; −4.51, −0.58) or heating (−3.26; −6.09, 0.44) was more than that related to smoking (−1.71; −2.54, −0.89). Conversely, in urban men, the LE reduction (95% CI) related to smoking (−3.06; −3.56, −2.56) was more than that related to solid fuel use for cooking (−1.28; −2.61, 0.05) and heating (−1.90; −3.16, −0.65). Similar results were observed in women but with a smaller magnitude. Interpretation: In this Chinese population, the harm to LE from household use of solid fuels was greater than that from smoking in rural residents. Conversely, the negative impact of smoking was greater than solid fuel use in urban residents. Our findings highlight the complexity and diversity of the factors affecting LE in less-developed populations

Association of dietary patterns, circulating lipid profile, and risk of obesity

Objective The aim of this study was to simultaneously explore the associations of major dietary patterns (DP) with lipid profiles and the associations of these profiles with general and central obesity risks and to evaluate the extent to which the metabolites mediate such associations. Methods Habitual food consumption of 4778 participants with an average age of 47.0 from the China Kadoorie Biobank was collected using a 12-item food frequency questionnaire. Plasma samples were analyzed via targeted nuclear magnetic resonance (NMR) spectroscopy to quantify 129 lipid-related metabolites. Anthropometric information was measured by trained staff. Results Two DPs were derived by factor analysis. The newly affluent southern pattern was characterized by high intakes of rice, meat, poultry, and fish, whereas the balanced pattern was characterized by consuming meat, poultry, fish, fresh fruit, fresh vegetables, dairy, eggs, and soybean. The newly affluent southern pattern was positively associated with 45 metabolites, which were positively associated with risks of obesity at the same time. The global lipid profile potentially explained 30.9%, 34.7%, and 53.1% of the effects of this DP on general obesity, waist circumference-defined central obesity, and waist-hip ratio-defined central obesity, respectively. Conclusions The newly affluent southern pattern points to an altered lipid profile, which showed higher general and central obesity risks. These findings partly suggest the biological mechanism for the obesogenic effects of this DP

Healthy lifestyle and life expectancy free of major chronic diseases at age 40 in Chinese population: a prospective cohort study

Background: A healthy lifestyle has been associated with a longer life expectancy (LE). However, whether it also helps achieve gains in LE free of major non-communicable diseases (NCDs) and its share of total LE in Chinese adults remains unknown. Methods: We used data from China Kadoorie Biobank (CKB) of 451,233 adults aged 30-79 free of heart disease, stroke, cancer, chronic obstructive pulmonary disease (COPD), and asthma at baseline. Low-risk lifestyle factors included never smoking or quitting for reasons other than illness, no excessive alcohol use, being physically active, healthy eating habits, and healthy body shape. We built multistate life tables for individuals with different risk levels of lifestyle factors to calculate LE with and without diseases (cardiovascular diseases [CVDs], cancer, chronic respiratory diseases [CRDs, including COPD and asthma]) at age 40. For life table calculation, we used prevalence of lifestyle factors, transition rates, and hazard ratios (HRs) for three transitions (disease-free to disease onset, disease-free to death, and presence of disease to all-cause mortality). Findings: During a median follow-up of 11.1 years, we documented 111,002 new CVD cases, 24,635 cancer cases, 12,506 CRD cases, and 34,740 deaths. The adjusted HRs (95% confidence intervals [CIs]) of men adopting all five versus 0-1 low-risk factors was 0.56 (0.50, 0.63), 0.40 (0.20, 0.80), and 0.64 (0.50, 0.83) for baseline to disease, baseline to death, and disease to death, respectively; the corresponding values for women were 0.69 (0.64, 0.75), 0.57 (0.34, 0.94), and 0.57 (0.47, 0.69). The LE free of the three NCDs (95%CI) at age 40 for individuals with 0-1 low-risk factor was on average 23.9 (23.2, 24.6) years for men and 24.2 (23.5, 24.9) years for women. For individuals adopting all five low-risk factors, it was 30.2 (28.8, 31.6) years for men and 28.4 (27.2, 29.6) years for women, with an increase of 6.3 (5.1, 7.5) years (men) and 4.2 (3.6, 5.4) years (women). Correspondingly, the proportion of LE free of the three NCDs to total LE increased from 73.1% to 76.3% for men and from 67.6% to 68.4% for women. Interpretation: Our findings suggest that promoting healthy lifestyles through public health interventions could be associated with increased LE free of major NCDs and 'relative compression of morbidity' in the Chinese population

Association between physical activity and cancer risk among Chinese adults: a 10-year prospective study

Background: In China, the quantity of physical activity differs from that in Western countries. Substantial uncertainty remains about the relevance of physical activity for cancer subtypes among Chinese adults. Objective: This study aimed to investigate the association between total daily physical activity and the incidence of common types of cancer. Methods: A total of 53,269 participants aged 30–79 years were derived from the Wuzhong subcohort of the China Kadoorie Biobank study during 2004–2008. We included 52,938 cancer-free participants in the final analysis. Incident cancers were identified through linkage with the health insurance system and death registries. Cox proportional hazard models were introduced to assess the associations of total daily physical activity with the incidence of 6 common types of cancer. Results: During a follow-up of 10.1 years, 3,674 cases of cancer were identified, including 794 (21.6%) from stomach cancer, 722 (19.7%) from lung cancer, 458 (12.5%) from colorectal cancer, 338 (9.2%) from liver cancer, 250 (6.8%) from breast cancer, and 231 (6.3%) from oesophageal cancer. Compared to the participants in the lowest quartile of physical activity levels, those in the highest quartile had an 11% lower risk for total cancer incidence (hazard ratio [HR]: 0.89, 95% confidence interval [CI]: 0.81–0.99), 25% lower risk for lung cancer incidence (HR: 0.75, 95% CI: 0.60–0.94), and 26% lower risk for colorectal cancer incidence (HR: 0.74, 95% CI: 0.55–1.00). There were significant interactions of physical activity with sex and smoking on total cancer (both P for interaction  Conclusions: Higher physical activity levels are associated with a reduced risk of total, lung, and colorectal cancer