Heatwaves increase road traffic injury morbidity risk and burden in China and its provinces.

Previous studies have demonstrated health impacts of climate change, but evidence on heatwaves' associations with road traffic injury (RTI) is limited. In this study, individual information of RTI cases in May-September during 2006-2021 in China were obtained from the National Injury Surveillance System. Daily maximum temperatures (TMmax) during 2006-2021 were collected from the ERA-5 reanalysis, and the projected daily TMmax during 2020-2099 were obtained from the latest Coupled Model Intercomparison Project Phase 6 Shared Socioeconomic Pathways scenarios (SSPs). We used a time-stratified case-crossover analysis to investigate the association between short-term exposure (lag01 days) to heatwaves (exceeding the 92.5th percentile of daily TMmax for ≥ three consecutive days) and RTI, and to project heatwave-related RTI until 2099 across China. Finally, a total of 1 031 082 RTI cases were included in the analyses. Compared with non-heatwaves, the risks of RTI increased by 3.61 % during heatwaves. Greater associations were found in people aged 15-64 years, in people with transportation occupation, for non-motor traffic vehicle injuries, for severe RTI cases, and in Western China particularly in Qinghai province. We projected substantial increases in attributable fraction (AF) of heatwave-related RTI in the future, particularly in Western and Southwest China. The national average increase in AF (per decade) during 2020s-2090s was 0.036 % for SSP1-2.6 scenario, and 0.267 % for SSP5-8.5 scenario. This study provided evidence on the associations of heatwaves with RTI, and the heatwave-related RTI will substantially increase in the future.

Joint effects of heat-humidity compound events on drowning mortality in Southern China.

BACKGROUND: Several previous studies have examined the association of ambient temperature with drowning. However, no study has investigated the effects of heat-humidity compound events on drowning mortality. METHODS: The drowning mortality data and meteorological data during the five hottest months (May to September) were collected from 46 cities in Southern China (2013-2018 in Guangdong, Hunan and Zhejiang provinces). Distributed lag non-linear model was first conducted to examine the association between heat-humidity compound events and drowning mortality at city level. Then, meta-analysis was employed to pool the city-specific exposure-response associations. Finally, we analysed the additive interaction of heat and humidity on drowning mortality. RESULTS: Compared with wet-non-hot days, dry-hot days had greater effects (excess rate (ER)=32.34%, 95% CI: 24.64 to 40.50) on drowning mortality than wet-hot days (ER=14.38%, 95%CI: 6.80 to 22.50). During dry-hot days, males (ER=42.40%, 95% CI: 31.92 to 53.72), adolescents aged 0-14 years (ER=45.00%, 95% CI: 21.98 to 72.35) and urban city (ER=36.91%, 95% CI: 23.87 to 51.32) showed higher drowning mortality risk than their counterparts. For wet-hot days, males, adolescents and urban city had higher ERs than their counterparts. Attributable fraction (AF) of drowning attributed to dry-hot days was 23.83% (95% CI: 21.67 to 26.99) which was significantly higher than that for wet-hot days (11.32%, 95% CI: 9.64 to 13.48%). We also observed that high temperature and low humidity had an additive interaction on drowning mortality. CONCLUSION: We found that dry-hot days had greater drowning mortality risk and burden than wet-hot days, and high temperature and low humidity might have synergy on drowning mortality.

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis.

Increasing evidence suggests that body composition is associated with the development of acute pancreatitis (AP). This study aimed to investigate the applicability of body composition in predicting AP severity. Data of 213 patients with AP from Affiliated Hospital of Putian University (AHOPTU) were included in this study, whilst data of 173 patients with AP from Fujian Medical University Union Hospital (FMUUH) were used for external validation. Patients were classified into the non-severe and severe groups according to AP severity. After seven days of treatment, in patients from AHOPTU, the difference in skeletal muscle index before and after treatment (ΔSMI) was significantly higher (P = 0.002), while the skeletal muscle radiodensity before treatment (PreSMR) was significantly lower (P = 0.042) in the non-severe group than in the severe group. The multivariate logistic regression model also revealed that the ΔSMI and PreSMR were independent risk factors for AP severity. The optimal cut-off values of ΔSMI and PreSMR were 1.0 and 43.7, respectively. The following metabolic score (SMS) was established to predict AP severity: 0: ΔSMI < 1.0 and PreSMR < 43.7; 1: ΔSMI ≥ 1.0 and PreSMR < 43.7 or ΔSMI < 1.0 and PreSMR ≥ 43.7; 3: ΔSMI ≥ 1.0 and PreSMR ≥ 43.7. In patients from AHOPTU and FMUUH, the areas under the curves (AUC) for this model were 0.764 and 0.741, respectively. ΔSMI and PreSMR can accurately predict AP severity. It is recommended to routinely evaluate the statuses of patients with AP using the predictive model presented in this study for individualized treatment.

Associations of COVID-19 lockdown with birth weight in China.

Background: During the special period of the global spread of COVID-19, pregnant women are sensitive groups to the impacts of COVID-19 epidemic. However, the effects of lockdown measures implemented in response to the COVID-19 on fetal birthweight remain unclear. Objectives: This study investigated the associations of COVID-19 lockdown with birth weight in Chinese population. Methods: We collected 730,153 data of participants from hospitals of five cities in the south of China, we defined the time period of level I response (1/23-2/24/2020) as level I lockdown, and women who were pregnant during level I lockdown as the exposure group. Women who were pregnant during the same calendar month from 2015 to 2019 were defined as the unexposed group. We quantitatively estimate the individual cumulative exposure dose by giving different weights to days with different emergency response levels. Generalized linear regression models were used to estimate the association between COVID-19 lockdown exposure with birth weight and risk of low birth weight (<2,500 g) and macrosomia (>4,000 g). Results: The birth weight of the exposed group is heavier than the unexposed group (3,238.52 vs. 3,224.11 g: adjusted ß = 24.39 g [95% CI: 21.88, 26.91 g]). The exposed group had a higher risk of macrosomia (2.8% vs. 2.6%; adjusted OR = 1.17 [95% CI: 1.12, 1.22]). More obvious associations were found between COVID-19 lockdown and macrosomia in women who experienced the lockdown in their early pregnancy. Women who experienced the lockdown at their 4-7 weeks of pregnancy showed statistically significant heavier birth weight than unexposed group (after adjustment): ß = 1.28 (95% CI: 1.11, 1.46) g. We also observed a positive association between cumulative exposure dose of COVID-19 lockdown in all pregnant women and birth weight, after divided into four groups, Q1: ß = 32.95 (95% CI: 28.16, 37.75) g; Q2: ß = 18.88 (95% CI: 14.12, 23.64) g; Q3: ß = 19.50 (95% CI: 14.73, 24.28) g; Q4: ß = 21.82 (95% CI: 17.08, 26.56) g. However, there was no statistically significant difference in the risk of low birth weight between exposed and unexposed groups. Conclusions: The COVID-19 lockdown measures were associated with a heavier birth weight and a higher risk of macrosomia. Early pregnancy periods may be a more susceptible exposure window for a heavier birth weight and a higher risk of macrosomia. We also observed a positive association between cumulative exposure dose of COVID-19 lockdown and birth weight. The government and health institutions should pay attention to the long-term health of the infants born during the COVID-19 lockdown period, and follow up these mothers and infants is necessary.

Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence.

BACKGROUND: Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) and its specific components with hypertension and blood pressure is limited. METHODS: We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007-2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. RESULTS: Each interquartile range (IQR = 16.80 µg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR µg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42-, NO3-, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. CONCLUSIONS: These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China.

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

Storage and dynamics of soil organic carbon in allochthonous-dominated and nitrogen-limited natural and planted mangrove forests in southern Thailand.

Mangrove forests can help to mitigate climate change by storing a significant amount of carbon (C) in soils. Planted mangrove forests have been established to combat anthropogenic threats posed by climate change. However, the efficiency of planted forests in terms of soil organic carbon (SOC) storage and dynamics relative to that of natural forests is unclear. We assessed SOC and nutrient storage, SOC sources and drivers in a natural and a planted forest in southern Thailand. Although the planted forest stored more C and nutrients than the natural forest, the early-stage planted forest was not a strong sink relative to mudflat. Both forests were predominated by allochthonous organic C and nitrogen limited, with total nitrogen being a major driver of SOC in both cases. SOC showed a significant decline along land-to-sea and depth gradients as a result of soil texture, nutrient availability, and pH in the natural forest.

Ambient PM2.5 and cardiopulmonary mortality in the oldest-old people in China: A national time-stratified case-crossover study.

BACKGROUND: Evidence on the associations of fine particulate matter (PM2.5) with cardiopulmonary mortality in the oldest-old (aged 80+ years) people remains limited. METHODS: We conducted a time-stratified case-crossover study of 1,475,459 deaths from cardiopulmonary diseases in China to estimate the associations between short-term exposure to ambient PM2.5 and cardiopulmonary mortality among the oldest-old people. FINDINGS: Each 10 µg/m3 increase in PM2.5 concentration (6-day moving average [lag05]) was associated with higher mortality from cardiopulmonary diseases (excess risks [ERs] = 1.69%, 95% confidence interval [CI]: 1.54%, 1.84%), cardiovascular diseases (ER = 1.72%, 95% CI: 1.54%, 1.90%), and respiratory diseases (ER = 1.62%, 95% CI: 1.33%, 1.91%). Compared to the other groups, females (ER = 1.94%, 95% CI: 1.73%, 2.15%) (p for difference test = 0.043) and those aged 95-99 years (ER = 2.31%, 95% CI: 1.61%, 3.02%) (aged 80-85 years old was the reference, p for difference test = 0.770) presented greater mortality risks. We found 14 specific cardiopulmonary causes associated with PM2.5, out of which emphysema (ER = 3.20%, 95% CI: 1.57%, 4.86%) had the largest association. Out of the total deaths, 6.27% (attributable fraction [AF], 95% CI: 5.72%, 6.82%) were ascribed to short-term PM2.5 exposure. CONCLUSIONS: This study provides evidence of PM2.5-induced cardiopulmonary mortality and calls for targeted prevention actions for the oldest-old people. FUNDING: This work was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Foreign Expert Program of the Ministry of Science and Technology, the Natural Science Foundation of Guangdong, China, and the Science and Technology Program of Guangzhou.

Muscle Strength Decline Caused by Eperisone Hydrochloride: A Case Report.

A 65-year-old female with thoracic spinal stenosis and incomplete paraplegia underwent T11-T12 posterior thoracic interbody fusion. During postoperative rehabilitation, she experienced thigh pain, involuntary lower limb convulsions, and muscle fatigue. Despite being prescribed eperisone hydrochloride for relief, her muscle strength decreased after 14 doses. This adverse effect, not listed in the latest Chinese medication instructions, subsided 4 days after discontinuation. This case suggests eperisone hydrochloride potentially caused reversible muscle strength decline, highlighting its potential unsuitability for incomplete paraplegia patients due to possible further muscle strength reduction. We propose updating the medication instructions to alert clinicians to this risk.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence.

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Prognostic value of platelet combined with serum procalcitonin in patients with sepsis.

Sepsis, a common and life-threatening condition in critically ill patients, is a leading cause of death in intensive care units. Over the past few decades, there has been significant improvement in the understanding and management of sepsis. However, the mortality rate remains unacceptably high, posing a prominent challenge in modern medicine and a significant global disease burden. A total of 295 patients with sepsis admitted to the hospital from January 2021 to December 2022 were collected and divided into survival group and death group according to their 28-day survival status. The differences in general clinical data and laboratory indicators between the 2 groups were compared. Receiver operating characteristic curve analysis was used to evaluate the predictive value of platelet (PLT) and procalcitonin (PCT) for the prognosis of sepsis patients within 28 days. A total of 295 patients were diagnosed with sepsis, and 79 died, with a mortality rate of 26.78%. The PLT level in the death group was lower than that in the survival group; the PCT level in the death group was higher than that in the survival group. The receiver operating characteristic curve showed that the area under the curve of PCT and PLT for evaluating the prognosis of sepsis patients were 0.808 and 0.804, respectively. Kaplan-Meier survival analysis showed that the 28-day survival rate of the low PLT level group was 19.0% and that of the high PLT level group was 93.1% at the node of 214.97 × 109/L, and the difference between the 2 groups was statistically significant (χ2 = 216.538, P < .001). The 28-day survival rate of the low PCT level group was 93.4% and that of the high PCT level group was 51.7% at the node of 2.85 ng/mL, and the difference between the 2 groups was statistically significant (χ2 = 63.437, P < .001). There was a negative correlation between PCT level and PLT level (r = -0.412, P < .001). Platelet combined with serum procalcitonin detection has high predictive value for judging the 28-day prognosis of sepsis, and it can be used as an index for evaluating the patient's condition and prognosis, and is worthy of clinical promotion and application.

The comparison of mortality burden between exposure to dry-cold events and wet-cold events: A nationwide study in China.

BACKGROUND: Most previous studies have focused on the health effect of temperature or humidity, and few studies have explored the combined health effects of exposure to temperature and humidity. This study aims to estimate the relationship between humidity-cold events and mortality, and then to compare the mortality burden between exposure to dry-cold events and wet-cold events, and finally to explore whether there was an additive interaction of temperature and humidity on mortality. METHODS: In the study, Daily mortality data during 2006-2017 were collected from Centers for Disease Control and Prevention in China, and daily mean temperature and daily mean relative humidity data from 698 weather stations in China were obtained from the China Meteorological Data Sharing Service system. We first employed time-series design with a distributed lag nonlinear model and a multivariate meta-analysis model to examine the association between humidity-cold events with mortality. RESULTS: We found that humidity-cold events significantly increased mortality risk, and the effect of wet-cold events (RR:1.24, 95%CI:1.20,1.29) was higher than that of dry-cold events (RR:1.14, 95%CI:1.10,1.18). Dry-cold events and wet-cold events accounted for 2.41 % and 2.99 % excess deaths, respectively with higher burden for the elderly ≥85 years old, Central China and CVD. In addition, there is a synergistic additive interaction between low temperature and high humidity in winter. CONCLUSION: This study showed that humidity-cold events significantly increased mortality risk, and the effect of wet-cold events was higher than that of dry-cold events.

The association of heatwave with drowning mortality in five provinces of China.

Drowning is a serious public health problem in the world. Several studies have found that ambient temperature is associated with drowning, but few have investigated the effect of heatwave on drowning. This study aimed to explore the associations between heatwave and drowning mortality, and further estimate the mortality burden of drowning attributed to heatwave in China. Drowning mortality data were collected in 71 prefectures in China during 2013-2018 from provincial vital register system. Meteorological data at the same period were collected from European Centre for Medium-Range Weather Forecasts (ECMWF). A distributed lag non-linear model (DLNM) was first to explore the association between heatwave and drowning mortality in each prefecture. Secondly, the prefecture-specific associations were pooled using meta-analysis. Finally, attributable fractions (AFs) of drowning deaths caused by heatwave were estimated. Compared to normal day, the mortality risk of drowning significantly increased during heatwave (RR = 1.20, 95%CI: 1.18-1.23). Higher risks were observed in males (RR = 1.23, 95%CI: 1.20-1.27) than females (RR = 1.18, 95%CI: 1.13-1.23), in children aged 5-14 years old (RR = 1.24, 95%CI: 1.15-1.33) than other age groups, in urban city (RR = 1.32, 95%CI: 1.28-1.36) than rural area (RR = 1.09, 95%CI: 1.07-1.12) and in Jilin province (RR = 2.85, 95%CI: 1.61-5.06) than other provinces. The AF of drowning deaths due to heatwave was 11.4 % (95%CI: 10.0 %-12.9 %) during heatwave and 1.0 % (95%CI: 0.9 %-1.1 %) during study period, respectively. Moreover, the AFs during study period were higher for male (1.2 %, 95%CI: 1.0 %-1.3 %), children 5-14 years (1.1 %, 95%CI: 0.7 %-1.6 %), urban city (1.6 %, 95%CI: 1.4 %-1.8 %) than their correspondents. These differences were also observed in AFs during heatwave. We found that heatwave may significantly increase the mortality risk of drowning mortality, and its mortality burden attributable to heatwave was noteworthy. Targeted intervention should be carried out to decrease drowning mortality during heatwave.

The Combined Effects of Short-Term Exposure to Multiple Meteorological Factors on Unintentional Drowning Mortality: Large Case-Crossover Study.

BACKGROUND: Drowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. OBJECTIVE: We aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. METHODS: Unintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. RESULTS: A total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and -9.5% to the overall effects, respectively. CONCLUSIONS: This study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Effectiveness of COVID-19 Vaccination Against SARS-CoV-2 Omicron Variant Infection and Symptoms - China, December 2022-February 2023.

What is already known about this topic?: A considerable percentage of the population has received both primary and booster vaccinations, which could potentially provide protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infections and related symptoms. What is added by this report?: The self-reported infection rate, as determined from an online survey, reached its peak (15.5%) between December 19 and 21, 2022, with an estimated 82.4% of individuals in China being infected as of February 7, 2023. During the epidemic, the effectiveness of booster vaccinations against SARS-CoV-2 Omicron infection was found to be 49.0% within three months of vaccination and 37.9% between 3 and 6 months following vaccination. Furthermore, the vaccine effectiveness of the booster vaccination in relation to symptom prevention varied from 48.7% to 83.2% within three months and from 25.9% to 69.0% between 3 and 6 months post-booster vaccination. What are the implications for public health practice?: The development and production of efficacious vaccines, together with prompt vaccinations or emergency vaccinations, have the potential to mitigate the epidemic's impact and safeguard public health.

Mortality burden based on the associations of ambient PM2.5 with cause-specific mortality in China: Evidence from a death-spectrum wide association study (DWAS).

Although studies have estimated the associations of PM2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM2.5. Thus, this study investigated the association of short-term exposure to PM2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013-2018 were collected from six provinces in China. Daily PM2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM2.5. Each 10 µg/m3 increase in PM2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM2.5 concentrations < 50 µg/m3. Stratified analyses showed greater ERs in females (1.01 %, 95 %CI: 0.91 %, 1.11 %), children ≤ 5 years (2.17 %, 95 %CI: 0.85 %, 3.51 %), and old people ≥ 70 years. We identified 33 specific causes (level 2) of death which had significant associations with PM2.5, including 16 circulatory diseases, 9 respiratory diseases, and 8 other causes. The PAF estimated based on the overall association between PM2.5 and total mortality was 3.16 % (95 %CI: 2.89 %, 3.40 %). However, the PAF was reduced to 2.88 % (95 %CI: 1.88 %, 3.81 %) using the associations of PM2.5 with 33 level 2 causes of death, based on which 250.15 (95 %CI: 163.29, 330.93) thousand deaths were attributable to short-term PM2.5 exposure across China in 2019. Overall, this study provided a comprehensive picture on the death-spectrum wide association between PM2.5 and morality in China. We observed robust positive cause-specific associations of PM2.5 with mortality risk, which may provide more precise basis in assessing the mortality burden of air pollution.

The impact of mortality underreporting on the association of ambient temperature and PM10 with mortality risk in time series study.

Properly analyzing and reporting data remains a challenging task in epidemiologic research, as underreporting of data is often overlooked. The evaluation on the effect of underreporting remains understudied. In this study, we examined the effect of different scenarios of mortality underreporting on the relationship between PM10, temperature, and mortality. Mortality data, PM10, and temperature data in seven cities were obtained from Provincial Center for Disease Control and Prevention (CDC), China Meteorological Data Sharing Service System, and China National Environmental Monitoring Center, respectively. A time-series design with a distributed lag nonlinear model (DLNM) was used to examine the effects of five mortality underreporting scenarios: 1) Random underreporting of mortality; 2) Underreporting is monotonically increasing (MI) or monotonically decreasing (MD); 3) Underreporting due to holiday and weekends; 4) Underreporting occurs before the 20th day of each month, and these underreporting will be added after the 20th day of the month; and 5) Underreporting due to holiday, weekends, MI, and MD. We observed that underreporting at random (UAR) scenario had little effect on the association between PM10, temperature, and daily mortality. However, other four underreporting not at random (UNAR) scenarios mentioned above had varying degrees of influence on the association between PM10, temperature, and daily mortality. Additionally, in addition to imputation under UAR, the variation of minimum mortality temperature (MMT) and attributable fraction (AF) of mortality attributed to temperature in the same imputation scenarios is inconsistent in different cities. Finally, we observed that the pooled excess risk (ER) below MMT was negatively associated with mortality and the pooled ER above MMT was positively associated with mortality. This study showed that UNAR impacted the association between PM10, temperature, and mortality, and potential underreporting should be dealt with before analyzing data to avoid drawing invalid conclusions.

Effects of temperature, relative humidity, and illumination on the entomological parameters of Aedes albopictus: an experimental study.

Aedes albopictus (Diptera: Culicidae) is a mosquito from Asia that can transmit a variety of diseases. This paper aimed to explore the effects of temperature, relative humidity, and illumination on the entomological parameters related to the population growth of Aedes albopictus, and provide specific parameters for developing dynamic models of mosquito-borne infectious disease. We used artificial simulation lab experiments, and set 27 different meteorological conditions to observe and record mosquito's hatching time, emergence time, longevity of adult females, and oviposition amount. We then applied generalized additive model (GAM) and polynomial regression to formulate the effects of temperature, relative humidity, and illumination on the biological characteristics of Aedes albopictus. Our results showed that hatchability closely related to temperature and illumination. The immature stage and the survival time of adult female mosquitoes were associated with temperature and relative humidity. The oviposition rate related to temperature, relative humidity, and illumination. Under the control of relative humidity and illumination, ecological characteristics of mosquitoes such as hatching rate, transition rate, longevity, and oviposition rate had an inverted J shape with temperature, and the thresholds were 31.2 °C, 32.1 °C, 17.7 °C, and 25.7 °C, respectively. The parameter expressions of Aedes albopictus using meteorological factors as predictors under different stages were established. Meteorological factors especially temperature significantly influence the development of Aedes albopictus under different physiological stages. The established formulas of ecological parameters can provide important information for modeling mosquito-borne infectious diseases.

Improved MR temperature imaging at 0.5 T using view-sharing accelerated multiecho thermometry for MR-guided laser interstitial thermal therapy.

The aim of the current study was to improve temperature-monitoring precision using multiecho proton resonance frequency shift-based thermometry with view-sharing acceleration for MR-guided laser interstitial thermal therapy (MRgLITT) on a 0.5-T low-field MR system. Both precision and speed of the temperature measurement for clinical MRgLITT treatments suffer at low field, due to reduced image signal-to-noise ratio (SNR), decreased temperature-induced phase changes, and limited RF receiver channels. In this work, a bipolar multiecho gradient-recalled echo sequence with a temperature-to-noise ratio optimal weighted echo combination is applied to improve the temperature precision. A view-sharing-based approach is utilized to accelerate signal acquisitions while preserving image SNRs. The method was evaluated using ex vivo (pork and pig brain) LITT heating experiments and in vivo (human brain) nonheating experiments on a high-performance 0.5-T scanner. In terms of results, (1) after echo combination, multiecho thermometry (i.e., ~7.5-40.5 ms, 7 TEs) provides ~1.5-1.9 times higher temperature precision than the no echo combination case (i.e., TE7 = 40.5 ms) within the same readout bandwidth. Additionally, echo registration is necessary for the bipolar multiecho sequence; (2) for a threefold acceleration, the view-sharing approach with variable-density subsampling shows around 1.8 times lower temperature errors than the GRAPPA method. Particularly for view-sharing, variable-density subsampling performs better than Interleave subsampling; and (3) ex vivo heating and in vivo nonheating experiments demonstrated that the temperature accuracy was less than 0.5 ° C and that the temperature precision was less than 0.6 ° C using the proposed 0.5-T thermometry. It was concluded that view-sharing accelerated multiecho thermometry is a practical temperature measurement approach for MRgLITT at 0.5 T.

Joint Associations of Short-Term Exposure to Ambient Air Pollutants with Hospital Admission of Ischemic Stroke.

BACKGROUND: Studies have estimated the associations of short-term exposure to ambient air pollution with ischemic stroke. However, the joint associations of ischemic stroke with air pollution as a mixture remain unknown. METHODS: We employed a time-stratified case-crossover study to investigate 824,808 ischemic stroke patients across China. We calculated daily mean concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5), maximum 8-h average for O3 (MDA8 O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) across all monitoring stations in the city where the IS patients resided. We conducted conditional logistic regression models to estimate the exposure-response associations. RESULTS: Results from single-pollutant models showed positive associations of hospital admission for ischemic stroke with PM2.5 (excess risk [ER] = 0.38%, 95% confidence interval [CI]: 0.29% to 0.47%, for 10 µg/m3), MDA8 O3 (ER = 0.29%, 95% CI: 0.18% to 0.40%, for 10 µg/m3), NO2 (ER = 1.15%, 95% CI: 0.92% to 1.39%, for 10 µg/m3), SO2 (ER = 0.82%, 95% CI: 0.53% to 1.11%, for 10 µg/m3) and CO (ER = 3.47%, 95% CI: 2.70% to 4.26%, for 1 mg/m3). The joint associations (ER) with all air pollutants (for interquartile range width increases in each pollutant) estimated by the single-pollutant model was 8.73% and was 4.27% by the multipollutant model. The joint attributable fraction of ischemic stroke attributable to air pollutants based on the multipollutant model was 7%. CONCLUSIONS: Short-term exposures to PM2.5, MDA8 O3, NO2, SO2, and CO were positively associated with increased risks of hospital admission for ischemic stroke. The joint associations of air pollutants with ischemic stroke might be overestimated using single-pollutant models. See video abstract at, http://links.lww.com/EDE/C8.