Heatwave and upper urinary tract stones morbidity: effect modification by heatwave definitions, disease subtypes, and vulnerable populations.

As heatwave occurs with increased frequency and intensity, the disease burden for urolithiasis, a heat-specific disease, will increase. However, heatwave effect on urolithiasis subtypes morbidity and optimal heatwave definition for urolithiasis remain unclear. Distributed lagged linear models were used to assess the associations between 32 defined heatwave and upper urinary tract stones morbidity. Relative risk (RR) and attributable fraction (AF) of upper urinary tract stone morbidity associated with heatwave of different intensities (low, middle, and high) were pooled by meta-analysis. Optimal heatwave definition was selected based on the combined score of AF, RR, and quasi-Akaike Information Criterion (QAIC) value. Stratified analyses were conducted to investigate the modification effects of gender, age, and disease subtypes. Association between heatwave and upper urinary tract stones morbidity was mainly for ureteral calculus, and AF was highest for low-intensity heatwave. This study's optimal heatwave was defined as average temperature > 93rd percentile for ≥ 2 consecutive days, with AF of 7.40% (95% CI: 2.02%, 11.27%). Heatwave was associated with ureteral calculus morbidity in males and middle-aged adults. While heatwave effect was statistically insignificant in females and other age groups. Managers should develop appropriate definitions to address heatwave based on regional characteristics and focus on heatwave effects on urolithiasis.

Effects of intra- and inter-day temperature change on acute upper respiratory infections among college students, assessments of three temperature change indicators.

Background: Acute upper respiratory infection (AURI) is a significant disease affecting all age groups worldwide. The differences in the impacts of different temperature change indicators, such as diurnal temperature range (DTR), temperature variation (TV), and temperature change between neighboring days (TCN), on AURI morbidity, are not clear. Methods: We collected data on 87,186 AURI patients during 2014-2019 in Zhengzhou. Distributed lag non-linear model was adopted to examine the effects of different temperature change indicators on AURI. We calculated and compared the attributable fractions (AF) of AURI morbidity caused by various indicators. We used stratified analysis to investigate the modification effects of season and gender. Results: With the increase in DTR and TV, the risk of AURI tended to increase; the corresponding AF values (95% eCI) higher than the references (5% position of the DTR or TV distribution) were 24.26% (15.46%, 32.05%), 23.10% (15.59%, 29.20%), and 19.24% (13.90%, 24.63%) for DTR, TV0 - 1, and TV0 - 7, respectively. The harmful effects of TCN on AURI mainly occurred when the temperature dropped (TCN < 0), and the AF value of TCN below the reference (0°C) was 3.42% (1.60%, 5.14%). The harm of DTR and TV were statistically significant in spring, autumn and winter, but not in summer, while the harm of TCN mainly occurred in winter. Three indicators have statistically significant effects on both males and females. Conclusions: High DTR and TV may induce AURI morbidity, while the harm of TCN occurs when the temperature drops. The impacts of DTR and TV on AURI are higher than that of TCN, and the impact of few-day TV is higher than that of multi-day TV. The adverse effects of DTR and TV are significant except in summer, while the hazards of TCN mainly occur in winter.

Modification effects of immigration status and comorbidities on associations of heat and heatwave with stroke morbidity.

BACKGROUND: Heat and heatwave have been associated with stroke morbidity, but it is still unclear whether immigrants from different geographic regions and patients with comorbidity are more vulnerable to heat and heatwave. METHODS: Time-stratified case-crossover design combined with generalized additive quasi-Poisson models were used to quantify the relative risks (RRs) of heat and heatwave on first-ever stroke morbidity during 0-7 lag days. Attributable fractions (AFs) were estimated to assess the first-ever stroke morbidity burden due to heat and heatwave. Stratified analyses for sex, age, disease subtypes, resident characteristics, and comorbidity type were performed to identify potential modification effects. RESULTS: Heat and heatwave were associated with first-ever stroke morbidity, with the AF of 2.535% (95% empirical confidence interval (eCI) = 0.748, 4.205) and 2.409% (95% confidence interval (CI) = 1.228, 3.400), respectively. Among northern and southern immigrants, the AF for heat was 2.806% (0.031, 5.069) and 2.798% (0.757, 4.428), respectively, and the AF for heatwave was 2.918% (0.561, 4.618) and 2.387% (1.174, 3.398), respectively, but the effects of both on natives were statistically insignificant. Among patients with hypertension, dyslipidemia, or diabetes, the AF for heat was 3.318% (1.225, 5.007), 4.237% (1.037, 6.770), and 4.860% (1.171, 7.827), respectively, and the AF for heatwave was 2.960% (1.701, 3.993), 2.771% (0.704, 4.308), and 2.652% (0.653, 4.185), respectively. However, the effects of both on patients without comorbidity were statistically insignificant. CONCLUSION: Heat and heatwave are associated with an increased risk of first-ever stroke morbidity among immigrants and those with comorbid hypertension, dyslipidemia, or diabetes, with the effects primarily due to non-native individuals. DATA ACCESS STATEMENT: The author(s) are not authorized to share the data.

A dynamic nomogram to predict invasive fungal super-infection during healthcare-associated bacterial infection in intensive care unit patients: an ambispective cohort study in China.

Objectives: Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare-associated bacterial infection (HABI). Methods: An ambispective cohort study was conducted in ICU patients with HABI from a tertiary hospital of China. Predictors of IFSI were selected by both the least absolute shrinkage and selection operator (LASSO) method and the two-way stepwise method. The predictive performance of two models built by logistic regression was internal-validated and compared. Then external validity was assessed and a web-based nomogram was deployed. Results: Between Jan 1, 2019 and June 30, 2023, 12,305 patients with HABI were screened in 14 ICUs, of whom 372 (3.0%) developed IFSI. Among the fungal strains causing IFSI, the most common was C.albicans (34.7%) with a decreasing proportion, followed by C.tropicalis (30.9%), A.fumigatus (13.9%) and C.glabrata (10.1%) with increasing proportions year by year. Compared with LASSO-model that included five predictors (combination of priority antimicrobials, immunosuppressant, MDRO, aCCI and S.aureus), the discriminability of stepwise-model was improved by 6.8% after adding two more predictors of COVID-19 and microbiological test before antibiotics use (P<0.01).And the stepwise-model showed similar discriminability in the derivation (the area under curve, AUC=0.87) and external validation cohorts (AUC=0.84, P=0.46). No significant gaps existed between the proportion of actual diagnosed IFSI and the frequency of IFSI predicted by both two models in derivation cohort and by stepwise-model in external validation cohort (P=0.16, 0.30 and 0.35, respectively). Conclusion: The incidence of IFSI in ICU patients with HABI appeared to be a temporal rising, and our externally validated nomogram will facilitate the development of targeted and timely prevention and control measures based on specific risks of IFSI.

Bidirectional modification effects on nonlinear associations of summer temperature and air pollution with first-ever stroke morbidity.

High temperature and air pollution may induce stroke morbidity. However, whether associations between high temperature and air pollution with stroke morbidity are modified by each other is still unclear. Data on 23,578 first-ever stroke patients in Shenzhen, China, during the summers of 2014-2018 were collected. Distributed lag nonlinear models were used to assess the modifying effects of air pollution stratified by the median for the associations between summer temperature and stroke morbidity at 0-3 lag days; modifying effects of temperature stratified by the minimum morbidity temperature on the associations between air pollution and stroke morbidity at the same lags were also estimated. The attributable risks of high temperature and high pollution on stroke morbidity were quantified. Stratified analyses of gender, age, migration type, and complication type were conducted to assess vulnerable population characteristics. Summer high temperature may induce stroke morbidity at high-level PM2.5, PM10, O3, SO2, and NO2 conditions, with attributable fraction (AF) of 2.982% (95% empirical confidence interval [eCI]: 0.943, 4.929), 3.113% (0.948, 5.200), 2.841% (0.943, 4.620), 3.617% (1.539, 5.470), and 2.048% (0.279, 3.637), respectively. High-temperature effects were statistically insignificant at corresponding low-level air pollution conditions. High-level PM2.5, PM10, and O3 may induce stroke morbidity at high-temperature conditions, with AF of 3.664% (0.036, 7.196), 4.129% (0.076, 7.963), and 4.574% (1.009, 7.762), respectively. High-level PM2.5, PM10, and O3 were not associated with stroke morbidity at low-temperature conditions. The effects of high temperature and high pollution on stroke morbidity were statistically significant among immigrants and patients with hypertension, dyslipidemia, or diabetes but insignificant among natives and patients without complications. The associations of summer temperature and air pollution with first-ever stroke morbidity may be enhanced bidirectionally. Publicity on the health risks of combined high temperature and high pollution events should be strengthened to raise protection awareness of relevant vulnerable populations.

Incidence, temporal trends and risk factors of puerperal infection in Mainland China: a meta-analysis of epidemiological studies from recent decade (2010-2020).

BACKGROUND: Puerperal infection (PI) is a severe threat to maternal health. The incidence and risk of PI should be accurately quantified and conveyed for prior decision-making. This study aims to assess the quality of the published literature on the epidemiology of PI, and synthesize them to identify the temporal trends and risk factors of PI occurring in Mainland China. METHODS: This review was registered in PROSPERO (CRD42021267399). Putting a time frame on 2010 to March 2022, we searched Cochrane library, Embase, Google Scholar, MEDLINE, Web of Science, China biology medicine, China national knowledge infrastructure and Chinese medical current contents, and performed a meta-analysis and meta-regression to pool the incidence of PI and the effects of risk factors on PI. RESULTS: A total of 49 eligible studies with 133,938 participants from 17 provinces were included. The pooled incidence of PI was 4.95% (95%CIs, 4.46-5.43), and there was a statistical association between the incidence of PI following caesarean section and the median year of data collection. Gestational hypertension (OR = 2.14), Gestational diabetes mellitus (OR = 1.82), primipara (OR = 0.81), genital tract inflammation (OR = 2.51), anemia during pregnancy (OR = 2.28), caesarean section (OR = 2.03), episiotomy (OR = 2.64), premature rupture of membrane (OR = 2.54), prolonged labor (OR = 1.32), placenta remnant (OR = 2.59) and postpartum hemorrhage (OR = 2.43) have significant association with PI. CONCLUSIONS: Maternal infection remains a crucial complication during puerperium in Mainland China, which showed a nationwide temporal rising following caesarean section in the past decade. The opportunity to prevent unnecessary PI exists in several simple but necessary measures and it's urgent for clinicians and policymakers to focus joint efforts on promoting the bundle of evidence-based practices.

Short-term effects of fine particulate matter constituents on myocardial infarction death.

Existing evidence suggested that short-term exposure to fine particulate matter (PM2.5) may increase the risk of death from myocardial infarction (MI), while PM2.5 constituents responsible for this association has not been determined. We collected 12,927 MI deaths from 32 counties in southern China during 2011-2013. County-level exposures of ambient PM2.5 and its 5 constituents (i.e., elemental carbon (EC), organic carbon (OC), sulfate (SO42-), ammonium (NH4+), and nitrate (NO3-)) were aggregated from gridded datasets predicted by Community Multiscale Air Quality Modeling System. We employed a space-time-stratified case-crossover design and conditional logistic regression models to quantify the association of MI mortality with short-term exposure to PM2.5 and its constituents across various lag days. Over the study period, the daily mean PM2.5 mass concentration was 77.8 (standard deviation (SD) = 72.7) µg/m3. We estimated an odds ratio of 1.038 (95% confidence interval (CI): 1.003-1.074), 1.038 (1.013-1.063) and 1.057 (1.023-1.097) for MI mortality associated with per interquartile range (IQR) increase in the 3-day moving-average exposure to PM2.5 (IQR = 76.3 µg/m3), EC (4.1 µg/m3) and OC (9.1 µg/m3), respectively. We did not identify significant association between MI death and exposure to water-soluble ions (SO42-, NH4+ and NO3-). Likelihood ratio tests supported no evident violations of linear assumptions for constituents-MI associations. Subgroup analyses showed stronger associations between MI death and EC/OC exposure in the elderly, males and cold months. Short-term exposure to PM2.5 constituents, particularly those carbonaceous aerosols, was associated with increased risks of MI mortality.

Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study.

BACKGROUND: Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States. METHODS: ILI, meteorological factors, and PM2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors. RESULTS: A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: ß = -6.110, P = 0.021; SLM: ß = -5.496, P = 0.022; SEM: ß = -6.150, P = 0.022). CONCLUSION: The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

Trends of Incidence, Mortality, and Risk Factors for Lower Respiratory Infections among Children under 5 Years in China from 2000 to 2019.

BACKGROUND: Understanding the temporal trends in the burden of lower respiratory tract infections (LRI) and their attributable risk factors in children under 5 years is important for effective prevention strategies. METHODS: We used incidence, mortality, and attributable risk factors of LRI among children under 5 years from the Global Burden of Diseases database to analyze health patterns in 33 provincial administrative units in China from 2000 to 2019. Trends were examined using the annual average percentage change (AAPC) by the joinpoint regression method. RESULTS: The rates of incidence and mortality for under-5 LRI in China were 18.1 and 4134.3 per 100,000 children in 2019, with an AAPC decrease of 4.1% and 11.0% from 2000, respectively. In recent years, the under-5 LRI incidence rate has decreased significantly in 11 provinces (Guangdong, Guangxi, Guizhou, Hainan, Heilongjiang, Jiangxi, Qinghai, Sichuan, Xinjiang, Xizang, and Zhejiang) and remained stable in the other 22 provinces. The case fatality ratio was associated with the Human Development Index and the Health Resource Density Index. The largest decline in risk factors of deaths was household air pollution from solid fuels. CONCLUSIONS: The burden of under-5 LRI in China and the provinces has declined significantly, with variation across provinces. Further efforts are needed to promote child health through the development of measures to control major risk factors.

Projections of heatwave-attributable mortality under climate change and future population scenarios in China.

Background: In China, most previous projections of heat-related mortality have been based on modeling studies using global climate models (GCMs), which can help to elucidate the risks of extreme heat events in a changing climate. However, spatiotemporal changes in the health effects of climate change considering specific regional characteristics remain poorly understood. We aimed to use credible climate and population projections to estimate future heatwave-attributable deaths under different emission scenarios and to explore the drivers underlying these patterns of changes. Methods: We derived climate data from a regional climate model driven by three CMIP5 GCM models and calculated future heatwaves in China under Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP8.5. The future gridded population data were based on Shared Socioeconomic Pathway 2 assumption with different fertility rates. By applying climate zone-specific exposure-response functions to mortality during heatwave events, we projected the scale of heatwave-attributable deaths under each RCP scenario. We further analyzed the factors driving changes in heatwave-related deaths and main sources of uncertainty using a decomposition method. We compared differences in death burden under the 1.5°C target, which is closely related to achieving carbon neutrality by mid-century. Findings: The number of heatwave-related deaths will increase continuously to the mid-century even under RCP2.6 and RCP4.5 scenarios, and will continue increasing throughout the century under RCP8.5. There will be 20,303 deaths caused by heatwaves in 2090 under RCP2.6, 35,025 under RCP4.5, and 72,260 under RCP8.5, with half of all heatwave-related deaths in any scenario concentrated in east and central China. Climate effects are the main driver for the increase in attributable deaths in the near future till 2060, explaining 78% of the total change. Subsequent population decline cannot offset the losses caused by higher incidence of heatwaves and an aging population under RCP8.5. Although health loss under the 1.5°C warming scenario is 1.6-fold higher than the baseline period 1986-2005, limiting the temperature rise to 1.5°C can reduce the annual mortality burden in China by 3,534 deaths in 2090 compared with RCP2.6 scenarios. Interpretation: With accelerating climate change and population aging, the effects of future heatwaves on human health in China are likely to increase continuously even under a low emission scenario. Significant health benefits are expected if the optimistic 1.5°C goal is achieved, suggesting that carbon neutrality by mid-century is a critical target for China's sustainable development. Policymakers need to tighten climate mitigation policies tailored to local conditions while enhancing climate resilience technically and infrastructurally, especially for vulnerable elderly people. Funding: National Key R&D Program of China (2018YFA0606200), Wellcome Trust (209734/Z/17/Z), Natural Science Foundation of China (41790471), and Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004).

Economic burden attributable to healthcare-associated infections in tertiary public hospitals of Central China: a multi-centre case-control study.

Healthcare-associated infection (HAI) is a major cause of morbidity, mortality and cost, which vary widely by region and hospital. In this case-control study, we calculated losses attributable to HAI in central China. A total of 2976 patients in 10 hospitals were enrolled, and the incidence rate of HAI (range, 0.88-4.15%) was significantly, but negatively associated with the cost per 1000 beds of its prevention (range, $24 929.76-$53 146.41; r = -0.76). The per capita economic loss attributable to HAIs was $2047.07 (interquartile range, $327.63-$6429.17), mainly from the pharmaceutical cost (median, $1044.39). The HAIs, which occurred in patients with commercial medical insurance, affected the haematologic system and caused by Acinetobacter baumannii, contributed most to the losses (median, $3881.55, $4734.20 and $9882.75, respectively). Furthermore, the economic losses attributable to device-associated infections and hospital-acquired multi-drug resistant bacteria were two to four times those of the controls. The burden attributable to HAI is heavy, and opportunities for easing this burden exist in several areas, including that strengthening antibiotic stewardship and practicing effective bundle of HAI prevention for patients carrying high-risk factors, for example, elders or those with catheterisations in healthcare institutions, and accelerating the medical insurance payment system reform based on diagnosis-related groups by policy-making departments.

Short-term exposure to fine particulate matter constituents and mortality: case-crossover evidence from 32 counties in China.

A growing number of studies associated increased mortality with exposures to specific fine particulate (PM2.5) constituents, while great heterogeneity exists between locations. In China, evidence linking PM2.5 constituents and mortality was extensively sparse. This study primarily aimed to quantify short-term associations between PM2.5 constituents and non-accidental mortality among the Chinese population. We collected daily mortality records from 32 counties in China between January 1, 2011, and December 31, 2013. Daily concentrations of main PM2.5 constituents (organic carbon (OC), elemental carbon (EC), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+)) were estimated using the modified Community Multiscale Air Quality model. Time-stratified case-crossover design with conditional logistic regression models was adopted to estimate mortality risks associated with short-term exposures to PM2.5 mass and its constituents. Stratification analyses were done by sex, age, and season. A total of 116,959 non-accidental deaths were investigated. PM2.5 concentrations on the day of death were averaged at 75.7 µg m-3 (control day: 75.6 µg m-3), with an interquartile range (IQR) of 65.2 µg m-3. Per IQR rise in PM2.5, EC, OC, NO3-, SO42-, and NH4+ at lag-04 day was associated with an increase in non-accidental mortality of 2.4% (95% confidence interval, (1.0-3.7), 1.7% (0.8-2.7), 2.9% (1.6-4.3), 2.1% (0.4-3.9), 1.0% (0.2-1.9), and 1.6% (0.3-2.9), respectively. Both PM2.5 mass and its constituents were strongly associated with elevated cardiovascular mortality risks, but only PM2.5, EC, and OC were positively associated with respiratory mortality at lag-3 day. PM2.5 mass and its constituents associated effects on mortality varied among sex- and age-specific subpopulations. Differences in the seasonal pattern of associations exist among PM2.5 constituents, with stronger effects related to EC and NO3- in warm months but SO42- and NH4+ in cold months. Short-term exposures to PM2.5 compositions were positively associated with increased risks of mortality, particularly those constituents from combustion-related sources.

A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.

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.

Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020.

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.

Birth weight, childhood obesity and risk of hypertension: a Mendelian randomization study.

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.

Associations between air pollution and outpatient visits for arrhythmia in Hangzhou, China.

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 (PM2.5), 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 PM2.5, PM10, CO and O3 increasing arrhythmia risk. Special attention should be given to sensitive populations during the high-risk period.