Objective: To investigate the association between ambient air pollution and stroke morbidity in different subgroups and seasons. Methods: We performed a time-series analysis based on generalised linear models to study the short-term exposure–response relationships between air pollution and stroke hospitalisations, and conducted subgroup analyses to identify possible sensitive populations. Results: For every 10 µg/m ³ increase in the concentration of air pollutants, across lag 0–3 days, the relative risk of stroke hospitalisation was 1.029 (95% CI 1.013 to 1.045) for PM _2.5 , 1.054 (95% CI 1.031 to 1.077) for NO ₂ and 1.012 (95% CI 1.002 to 1.022) for O ₃ . Subgroup analyses showed that statistically significant associations were found in both men and women, middle-aged and older populations, and both cerebral infarction and intracerebral haemorrhage. The seasonal analyses showed that statistically significant associations were found only in the winter. Conclusions: Our study indicates that short-term exposure to PM _2.5 , NO ₂ and O ₃ may induce stroke morbidity, and the government should take actions to mitigate air pollution and protect sensitive populations.

Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.

Objective Diurnal temperature range (DTR) is an important meteorological indicator of global climate change; high values of DTR may induce stroke morbidity, while the related high-risk periods and sensitive populations are not clear. This study aims to evaluate the effects of DTR on first-ever strokes in different seasons and in relation to sensitive populations. Methods We collected data on 142 569 first-ever strokes during 2005–2016 in Shenzhen. We fitted a time-series Poisson model in our study, estimating the associations between DTR and first-ever strokes, with a distributed lag non-linear model. Then, we calculated strokes attributable to high DTR in different genders, age groups, education levels and stroke subtypes. Results High DTR had a significant association with first-ever strokes, and the risk of stroke increased with the rise of DTR in the summer and winter. In total, 3.65% (95% empirical CI (eCI) 1.81% to 5.53%) of first-ever strokes were attributable to high DTR (5.5°C and higher) in the summer, while 2.42% (95% eCI 0.05% to 4.42%) were attributable to high DTR (8°C and higher) in the winter. In the summer, attributable fraction (AF) was significant in both genders, middle-aged and old patients, patients with different levels of education, as well as patients with cerebral infarction (CBI); in the winter, AF was significant in middle-aged patients, patients with primary and lower education level, as well as patients with CBI. Conclusions High DTR may trigger first-ever strokes in the summer and winter, and CBI is more sensitive than intracerebral haemorrhage to DTR. Most people are sensitive to high DTR in the summer, while middle-aged and low-education populations are sensitive in the winter. It is recommended that the DTR values be reported and emphasised in weather forecast services, together with the forecasts of heat and cold.

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011–2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)₅₂ model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

Background: Arrhythmia is a common cardiovascular event that is associated with increased cardiovascular health risks. Previous studies that have explored the association between air pollution and arrhythmia have obtained inconsistent results, and the association between the two in China is unclear. Methods: We collected daily data on air pollutants and meteorological factors from 1st January 2014 to 31st December 2016, along with daily outpatient visits for arrhythmia in Hangzhou, China. We used a quasi-Poisson regression along with a distributed lag nonlinear model to study the association between air pollution and arrhythmia morbidity. Results: The results of the single-pollutant model showed that each increase of 10 μg/m³ of Fine particulate matter (PM_2.5), Coarse particulate matter (PM₁₀), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), and Ozone (O₃) resulted in increases of 0.6% (− 0.9, 2.2%), 0.7% (− 0.4, 1.7%), 11.9% (4.5, 19.9%), 6.7% (3.6, 9.9%), and − 0.9% (− 2.9, 1.2%), respectively, in outpatient visits for arrhythmia; each increase of 1 mg/m³ increase of carbon monoxide (CO) resulted in increase of 11.3% (− 5.9, 31.6%) in arrhythmia. The short-term effects of air pollution on arrhythmia lasted 3 days, and the most harmful effects were observed on the same day that the pollution occurred. Results of the subgroup analyses showed that SO₂ and NO₂ affected both men and women, but differences between the sexes were not statistically significant. The effect of SO₂ on the middle-aged population was statistically significant. The effect of NO₂ was significant in both the young and middle-aged population, and no significant difference was found between them. Significant effects of air pollution on arrhythmia were only detected in the cold season. The results of the two-pollutants model and the single-pollutant model were similar. Conclusions: SO₂ and NO₂ may induce arrhythmia, and the harmful effects are primarily observed in the cold season. There is no evidence of PM_2.5, PM₁₀, CO and O₃ increasing arrhythmia risk. Special attention should be given to sensitive populations during the high-risk period.

Purposes: Observational studies indicate that birth weight and childhood obesity are associated with essential hypertension, but their causal effect on essential hypertension remains unclear. The aim of our study is to elucidate the causal relationship between birth weight, childhood obesity, and essential hypertension by Mendelian randomization (MR) with genetic variants as instrumental variables (IVs). Methods: We identified IVs based on single nucleotide polymorphisms (SNPs) associated with birth weight (n = 160 295) and childhood obesity (n = 6889, 1509 cases and 5380 controls) from the meta-analysis of a genome-wide association study. Summary level data from the UK Biobank essential hypertension consortium (n = 463 010, 54 358 cases and 408 652 controls) was used to analyze the relationship between IVs and essential hypertension. Two MR analysis methods, two threshold values of selecting IVs, and leave-one-out analysis were used to ensure the robustness of the results. Results: Genetic predisposition to higher birth weight did not increase the risk of essential hypertension. In contrast, per one standard deviation increase in childhood body mass index was significantly associated with an increased risk of essential hypertension (odds ratio = 1.0075, 95% confidence interval: 1.0035–1.0116) when using seven SNPs that achieved genome-wide significance (P < 5 × 10⁻⁸). Sensitivity analysis and MR-Egger regression indicated that the results were robust and not influenced by pleiotropy. Conclusions: No evidence of an association between birth weight and essential hypertension was found. Childhood obesity, however, showed a causal relationship with the risk of essential hypertension, which was helpful to understand the mechanisms of essential hypertension and develop strategies for its prevention.