Associations of socioeconomic status with infectious diseases mediated by lifestyle, environmental pollution and chronic comorbidities: a comprehensive evaluation based on UK Biobank.

BACKGROUND: Socioeconomic status (SES) inequity was recognized as a driver of some certain infectious diseases. However, few studies evaluated the association between SES and the burden of overall infections, and even fewer identified preventable mediators. This study aimed to assess the association between SES and overall infectious diseases burden, and the potential roles of factors including lifestyle, environmental pollution, chronic disease history. METHODS: We included 401,009 participants from the UK Biobank (UKB) and defined the infection status for each participant according to their diagnosis records. Latent class analysis (LCA) was used to define SES for each participant. We further defined healthy lifestyle score, environment pollution score (EPS) and four types of chronic comorbidities. We used multivariate logistic regression to test the associations between the four above covariates and infectious diseases. Then, we performed the mediation and interaction analysis to explain the relationships between SES and other variables on infectious diseases. Finally, we employed seven types of sensitivity analyses, including considering the Townsend deprivation index as an area level SES variable, repeating our main analysis for some individual or composite factors and in some subgroups, as well as in an external data from the US National Health and Nutrition Examination Survey, to verify the main results. RESULTS: In UKB, 60,771 (15.2%) participants were diagnosed with infectious diseases during follow-up. Lower SES [odds ratio (OR) = 1.5570] were associated with higher risk of overall infections. Lifestyle score mediated 2.9% of effects from SES, which ranged from 2.9 to 4.0% in different infection subtypes, while cardiovascular disease (CVD) mediated a proportion of 6.2% with a range from 2.1 to 6.8%. In addition, SES showed significant negative interaction with lifestyle score (OR = 0.8650) and a history of cancer (OR = 0.9096), while a significant synergy interaction was observed between SES and EPS (OR = 1.0024). In subgroup analysis, we found that males and African (AFR) with lower SES showed much higher infection risk. Results from sensitivity and validation analyses showed relative consistent with the main analysis. CONCLUSIONS: Low SES is shown to be an important risk factor for infectious disease, part of which may be mediated by poor lifestyle and chronic comorbidities. Efforts to enhance health education and improve the quality of living environment may help reduce burden of infectious disease, especially for people with low SES.

Decision-Making Behaviour Evolution Among Pork Supply and Demand Subjects Under Normalisation of COVID-19 Prevention and Control: A Case Study in China.

Affected by the coronavirus disease 2019 (COVID-19), there were short-term uncertainties in China's live pig industry supply chain. Due to the insufficient supply of pork, the price of pork rose from 33.21 yuan/kg at the end of 2019 to 37.46 yuan/kg in mid-February and fell to 26.41 yuan/kg in mid-May. To restore pig supply and stabilise prices, China issued relevant policies. Given the current effective control of COVID-19 in China, this paper constructed an evolutionary game model of China's pork supply and demand stakeholders under normalisation of COVID-19 prevention and control, analysed the behavioural strategies of consumers, government, and pig farmers, used MATLAB software for data simulation, and expounded on the evolution path and the characteristic rule of tripartite decision-making behaviours. The results showed that government supervision costs, evaluation of government by consumers and pig farmers, government subsidies to pig farmers and consumers, and the proportion of stakeholder behaviours affected the formation of tripartite relationships. The results provide a useful reference for the government to formulate effective policies, increase pig supply, and stabilise pork prices.

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019.

On 31 December 2019, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei province, China, and caused the outbreak of the Coronavirus Disease 2019 (COVID-19). To date, computed tomography (CT) findings have been recommended as major evidence for the clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the imaging characteristics and changes throughout the disease course in patients with COVID-19 in order to provide some help for clinicians. Typical CT findings included bilateral ground-glass opacity, pulmonary consolidation, and prominent distribution in the posterior and peripheral parts of the lungs. This review also provides a comparison between COVID-19 and other diseases that have similar CT findings. Since most patients with COVID-19 infection share typical imaging features, radiological examinations have an irreplaceable role in screening, diagnosis and monitoring treatment effects in clinical practice.