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BackgroundThe internet has caused the explosive growth of medical information and has greatly improved the availability of medical knowledge. This makes the internet one of the main ways for residents to obtain medical information and knowledge before seeking medical treatment. However, little has been researched on how the internet affects medical decisions. ObjectiveThe purpose of this study was to explore the associations between internet behaviors and medical decisions among Chinese adults aged 18 or over, including whether to go to the hospital and which level of medical institution to choose. MethodsWith the adult residents (≥18 years old) in 12 regions including urban and rural areas taken as the research objects, the differences in medical choices of adults with various characteristics were analyzed, and generalized linear mixed models were adopted to analyze the longitudinal data of the China Health Nutrition Survey from 2006 to 2015. ResultsAdult groups with different ages, genders, education levels, regions, places of residence, severities of illness and injury, years of suffering from hypertension, and history of chronic diseases showed diverse medical decisions, and the differences were statistically significant (P

Big data and artificial intelligence technologies have played a positive role in the prevention and control of COVID-19 outbreaks. However, its application and future trends has not been comprehensively discussed. Starting from the problems faced by the prevention and control of COVID-19, this study provided an overview of the common big data and artificial intelligence technologies and their practical application cases in the prevention and control of COVID-19 based on the introduction of the advantages of big data and artificial intelligence technologies, then discussed the application of big data and artificial intelligence technologies focusing on three elements of infectious source, route of transmission and susceptible population from the three stages that before, during, and after the COVID-19 outbreak based on the Haddon model perspective. The results of the study are important for clarifying the positive role of big data and artificial intelligence technologies in each stage of COVID-19 epidemic as well as their directions of development and application, further improving the efficiency and quality of the prevention and control of COVID-19, and effectively responding to new infectious diseases in the future.

Particulate matter (PM10) changes have been confirmed as one of the contributory factors affecting human health, the association between PM10 pollution and the hospitalization of chronic obstructive pulmonary disease (COPD) with comorbidity diseases was rarely reported. The same inpatient more than twice times admissions with COPD illness from January 1, 2016 to December 31, 2021 were identified from hospitals in the 17 cities of Henan, Central China. City-specific associations were firstly estimated using the case time series (CTS) model and then combined to obtain the regional average association. The multivariate meta-analytic model produces pooled estimates of the set of coefficients representing the PM10-COPD hospitalizations association across the 17 cities. Cause-specific hospitalization analyses were performed by COPD patients with different comorbidity combinations. A total of 34,348 elderly (age ≥ 65) subjects were analyzed and with a total of 35,122.35 person-years. These coefficients can be used to compute the linear exposure–response curve expressed as relative risk (RR) in per 10 µg/m3 increase in PM10 at lag03, which was 1.0091 (95% CI 1.0070–1.0112) for COPD with comorbidity, 1.0089 (95% CI 1.0067–1.0110) for COPD with circulatory system diseases, 1.0079 (95% CI 1.0052–1.0105) for COPD with respiratory system diseases, 1.0076 (95% CI 1.0032–1.0121) for COPD with endocrine system diseases, and 1.0087 (95% CI 1.0013–1.0162) for COPD with genitourinary system diseases, respectively. Some heterogeneity was found across cities, with estimates ranging from 1.0227 in the Puyang and Jiaozuo to 1.0053 in Henan Provance, China. The effect of higher PM10, on average, was higher in studies for northern cities, with a steeper raise in risk: per 10 µg/m3 increase in PM10, the RR from 1.0062 (95% CI 1.0030–1.0093) for the 10th percentile of latitude to 1.0124 (95% CI 1.0089–1.0160) for the 90th percentile. Our findings indicated that PM10 exposure may increase the risk of hospitalizations for COPD with comorbidity. Moreover, there might be a higher morbidity risk associated with PM10 in northern latitudes, indicating that stricter air quality standards could potentially reduce PM10-related morbidity among individuals with COPD. These findings have implications for the implementation of effective clean air interventions aligned with national climate policies. [ABSTRACT FROM AUTHOR]

Given the increasing environmental pollution around the world, air pollutants such as particulate matters (PM) have been becoming typical carcinogenic factors to cause lung cancer. The PM with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) plays an important role in lung cancer mortality. However, effects of PM2.5 on the mortality of lung cancer patients have not been investigated well. The purpose of this study is to assess the associations between PM2.5 exposure and lung cancer-related mortality, by regions and races. This systematic review was developed and conducted according to PRISMA protocol. A comprehensive literature search for peer-reviewed studies published up to October 2021 was conducted using the electronic databases PubMed, EMBASE, and Cochrane Library. The identified records were carefully evaluated following the PICOS criteria. Relative risks (RRs) and 95% confidence interval (CI) of lung cancer mortality associated with PM2.5 were extracted and meta-analyzed. The Chi-squared test, Begg's test, and Eggerr analysis were performed to assess the heterogeneity and publication bias. Sensitivity analysis and subgroup analysis were also conducted to make sure the authenticity and stability of the present study. In total, 16 studies met the inclusion criteria. For the overall effects of PM2.5 on lung cancer-related mortality, there was heterogeneity among the included studies (I2 = 62.30%, P < 0.01); thus, the random effects model was chosen to conduct related analysis. The pooled adjusted RR of lung cancer mortality associated with PM2.5 was 1.12 (95% CI: 1.09–1.15) for per 10 µg/m3 increase in PM2.5 concentrations. For different continents, the RRs of subgroup analysis in North-America, Asia, and Europe were 1.16 (1.09–1.22), 1.08 (CI: 1.07–1.09), and 1.14 (CI: 1.04–1.23), respectively. Subgroup analysis by race showed that for Mongoloid RR = 1.08 (1.07–1.09), and for Caucasoid RR = 1.13 (CI: 1.11–1.15). With per 10 µg/m3 increase in PM2.5, the mortality rate of Caucasoid lung cancer patients increased by 1.13 times, greater than that of the Mongoloid. Increase in PM2.5 concentrations is associated positively with the mortality of lung cancer patients. For different regions and races, the adverse effects of PM2.5 are discrepant. Our Findings can provide necessary evidence to protect public health further from air pollution. [ABSTRACT FROM AUTHOR]