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Whether egg consumption plays a beneficial/detrimental role in affecting human health and longevity has been debated for decades. Large-scale cohort evidence from low- and middle-income populations are scarce. In this study, we aimed to assess the association of egg consumption with mortality in Chinese adults. A nationwide cohort of 30,835 participants ages 16–110 years were enrolled from 25 provincial regions in China’s mainland. Dietary intake (e.g., egg, meat, vegetable) was assessed by a food-frequency questionnaire. Cox proportional hazards models were used to examine associations between egg consumption and mortality, adjusting for demographic characteristics, dietary factors and health status. Dose–response relationships were investigated using the smoothing function of restricted cubic splines. Several subgroup analyses were performed. A total of 1651 all-cause deaths occurred during a median follow-up of 8.1 years. Egg consumption was associated with lower risks of mortality, with the lowest risk occurring in the group of moderate egg intake (3–6 times/week). Compared with non-consumers, the hazard ratios (95% confidence intervals) for mortality were 0.84 (0.72–0.97) for 3–6 times/week and 0.82 (0.69–0.98) for ≥7 times/week, whereas no significant associations were observed among the lower egg intake group (1–2/week). An approximately inverted J-shaped association was observed in three models, while restricting our analysis in the multivariable model (model 3) did not identify a significant violation for the linear relationship (p for nonlinear = 0.122). There were no statistically significant effect modifications in the subgroup analyses. Egg consumption may be associated with lower risks of mortality in Chinese adults. Our findings found moderate-to-high egg consumption might be beneficial for improving long-term health and longevity.

Existing PM2.5-morbidity studies using daily mean concentration as exposure metric may fail to capture intra-day variations of PM2.5 concentrations, resulting in underestimated health impacts to some extent. This study introduced a novel indicator, daily excessive concentration hours (DECH), defined as sums of per-hourly excessive concentrations of PM2.5 against a specific threshold within a day. PM2.5 DECHs were separately calculated as daily concentration-hours >8, 10, 15, 20, and 25 μg/m3 (abbreviations: DECH-8, DECH-10, DECH-15, DECH-20, and DECH-25). We adopted a time-stratified case-crossover design with conditional logistic regression models to compare risks of hospitalizations for chronic obstructive pulmonary disease (COPD) associated with PM2.5 mean and DECHs in Shenzhen, China. We observed highly comparable PM2.5-COPD associations using exposure metrics of daily mean and DECHs with above-defined thresholds. For instance, PM2.5 mean and DECHs showed similar increases in risks of COPD hospitalization for an interquartile range rise in exposure, with odds ratio estimates of 1.26 (95% confidence interval: 1.06–1.50) for PM2.5 mean, 1.24 (1.05–1.46) for DECH-10 and 1.21 (1.06–1.39) for DECH-25, respectively. Findings remained robust after further adjusting for gaseous pollutants (e.g., SO2, NO2, CO, and O3) and meteorologic factors (e.g., wind speed and air pressure). Our study strengthened the evidence that DECHs could come be as a novel exposure metric in health risk assessments associated with short-term exposure to ambient PM2.5.

Background Ambient particulate matter (PM) has been linked to adverse health outcomes, but evidence is still relatively limited for emergency department visits (EDVs). Most prior studies used daily mean PM as exposure assessment regardless of within-day variations, which might underestimate the potential acute health effects. In this study, we evaluated the hourly associations between PM and EDVs, so as to further understand the very short-term impacts of PM on EDVs. Methods Hourly data for all-cause EDVs, air pollutants and meteorological factors were collected from Shenzhen, China, between March 1, 2015 and February 28, 2018. A time-stratified case-crossover design with conditional logistic regression analysis was performed to assess the hourly associations between PM and EDVs, adjusting for hourly mean temperature and relative humidity. Subgroup analyses stratified by gender, age and clinic department were conducted to identify vulnerable populations. We further classified the whole year into warm (April to September) and cold (October to March of the next year) months to evaluate seasonal effect modification. Results Hourly mean all-cause EDVs peaked at 9 a.m. and 7 p.m., while PM2.5 and PM10 peaked at 8 a.m. and 8 p.m. The hourly associations between PM and EDVs appeared immediately and persisted for about 10 hours. All-cause EDVs increased 0.72% (95% confidence interval (CI): 0.33, 1.12) and 0.72% (95% CI: 0.45, 0.99) associated with per 10 μg/m3 rise in PM2.5 and PM10 at lag 0–10 h, respectively. Stronger effects of PM2.5 and PM10 on EDVs were observed among younger groups (age Conclusions Our study provided hourly perspective for the short-term associations between PM exposure and increased EDVs. The adverse impacts of PM on EDVs were only identified in cold season, and young people suffered more from both PM2.5 and PM10 exposures. These findings may have valuable implications for public policy promotion on air cleaning, as well as hospital management in optimizing allocation of resources on PM-sensitive conditions.

Background Previous studies have widely assessed heat-mortality relationships across global regions, while the epidemiological evidence regarding the heat effect on years of life lost (YLL) is relatively sparse. Current investigations using daily mean data cannot take hourly temperature variation into consideration and may underestimate heat effects. We developed a novel indicator, daily excess hourly heat (DEHH), to precisely evaluate the potential heat effects on mortality and YLL. Methods Hourly data on temperature and daily information, including concentrations of air pollutants, relative humidity, and records of all registered deaths were obtained in Wuhan, China during the warm seasons (May–September) of 2009–2012. DEHH, developed in this study, is defined as daily total hourly temperatures that exceed a specific heat threshold. By performing time series regression analyses, we assessed the changes in daily mortality and YLL per interquartile range (IQR) increase in DEHH across different lag days. Results The heat threshold evaluated by the Akaike Information Criterion for DEHH calculation is 30 °C (92th percentile of whole-year mean temperature distribution). Daily average DEHH was 13.9 °C, with an IQR of 19.9 °C. Linear exposure-response curves were found between DEHH and two health outcomes. Generally, heat effects lasted for 2–3 days and DEHH at lag 0–1 was most strongly associated with increased mortality and YLL. The effects were especially remarkable for stroke and ischemic heart disease mortality. Most intense effect on YLL was found in non-accidental deaths (20.11, 95% confidence interval: 8.90–31.33) at lag 0–1. More DEHH-related mortality and YLL from cardiovascular deaths were observed among males. People aged 0–74 years and males suffered more from YLL burden due to high temperatures. Conclusions Our study demonstrated that DEHH may be an alternative indicator to precisely measure heat effects on daily mortality and YLL. Further DEHH-based evidence from large scale investigations is needed so as to better understand heat-associated health burden and improve public response to extremely high temperatures.

Existing PM

Background Ambient PM1 (particulate matter with aerodynamic diameter ≤1 μm) is an important contribution of PM2.5 mass. However, little is known worldwide regarding the PM1-associated health effects due to a wide lack of ground-based PM1 measurements from air monitoring stations. Methods We collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015–2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PM1 and PM2.5. Results PM1 and PM2.5 showed significant adverse effects on respiratory disease hospitalizations, while no evident associations with PM1–2.5 were identified. Admission risks for total respiratory diseases were 1.09 (95% confidence interval: 1.04 to 1.14) and 1.06 (1.02 to 1.10), corresponding to per 10 μg/m3 rise in exposure to PM1 and PM2.5 at lag 0–2 days, respectively. Both PM1 and PM2.5 were strongly associated with increased admission for pneumonia and chronic obstructive pulmonary diseases, but exhibited no effects on asthma and upper respiratory tract infection. Largely comparable risk estimates were observed between male and female patients. Groups aged 0–14 years and 45–74 years were significantly affected by PM1- and PM2.5-associated risks. PM-hospitalization associations exhibited a clear seasonal pattern, with significantly larger risks in cold season than those in warm season among some subgroups. Conclusions Our study suggested that PM1 rather than PM1–2.5 contributed to PM2.5-induced risks of hospitalization for respiratory diseases and effects of PM1 and PM2.5 mainly occurred in cold season.

Ambient PMWe collected daily records of all-cause emergency department visits (EDVs) and ground measurements of ambient air pollutants and meteorological factors in Guangzhou and Shenzhen, China, 2015-2016. Case-crossover design and conditional logistic regression models were used to comparatively assess the short-term effects of ambient PMPMOur study added comparative evidence for increased EDVs risks associated with short-term exposures to ambient PM

Ambient PMWe collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015-2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PMPMOur study suggested that PM

Background Ambient PM2.5 has been identified as the top leading cause of risk-attributable deaths worldwide, particularly in China. Evidence suggested that PM1 contributed the most majority of PM2.5 concentrations in Chinese cities. However, epidemiologic knowledge to date is of wide lack regarding PM1-associated health effects. Methods We collected daily records of all-cause emergency department visits (EDVs) and ground measurements of ambient air pollutants and meteorological factors in Guangzhou and Shenzhen, China, 2015–2016. Case-crossover design and conditional logistic regression models were used to comparatively assess the short-term effects of ambient PM1, PM2.5, and PM10 on EDVs. Stratified analyses by gender, age and season were performed to identify vulnerable groups and periods. Results PM1, PM2.5 and PM10 were all significantly associated with increased EDVs in both cities. Population risks for EDVs increased by 2.2% [95% confidence interval, 1.8 to 2.6] in Guangzhou and 1.7% [1.0 to 2.4] in Shenzhen, for a 10 μg/m3 rise in PM1 at lag 0–1 days and lag 0–4 days, respectively. Relatively lower risks were found to be associated with PM2.5 and PM10. PM-EDVs associations exhibited no gender differences, but varied across age groups. Compared with adults and the elderly, children under 14 years-of-age suffered higher PM-induced risks. Results from both cities suggested greatly significant effect modification by season, with consistently stronger PM-EDVs associations during cold months. Conclusions Our study added comparative evidence for increased EDVs risks associated with short-term exposures to ambient PM1, PM2.5 and PM10. Besides, PM-associated effects were significantly stronger among children and during cold months.

Background Research argued that daily excessive concentration hours (DECH) could be more informative through accounting for within-day variations, when assessing population-level exposure to ambient fine particle (PM2.5). However, few studies have comparatively investigated PM2.5-associated risks using DECH and two common metrics of daily mean and hourly peak concentration. Methods We collected daily records of all-cause emergency department visits (EDVs) and hourly data on air pollutants and meteorological factors from Shenzhen, China, 2015–2018. According to guidelines proposed by the World Health Organization, DECH was calculated by summing up daily concentrations exceeding 25 μg/m3. Based on time-stratified case-crossover design, we adopted conditional logistic regression models to assess short-term attributable risks of EDVs associated with PM2.5 using three exposure metrics. Results DECH and daily average of PM2.5 strongly elevated risks of EDVs, while less evident associations were observed using hourly peak metric. Estimated excess relative risks at lag 0 day were 0.56% (95% confidence interval [CI]: 0.21 to 0.91), 0.69% (95% CI: 0.25 to 1.13) and 0.37% (95% CI: 0.02 to 0.76), respectively, associated with an interquartile range increase in DECH (420.2 μg/m3), 24-h average (24.9 μg/m3) and hourly peak concentration (38 μg/m3). More emergency visits could be attributed to DECH than daily mean PM2.5, with attributable fractions of 2.02% (95% CI: 1.42 to 2.61) and 1.09% (95% CI: 0.69 to 1.49), respectively. Conclusions This study added evidence for increased risk of EDVs associated with exposure to ambient PM2.5. DECH was a potential alternative exposure metric for PM2.5 assessment, which may have implications for future revision of air quality standards.

Background Short-term exposure to ambient air pollution (AAP) has been widely linked to asthma morbidity such as outpatient and emergency visits and hospitalization. Up to date, however, the acute effects of AAP exposure on asthma mortality remained largely unknown possibly due to the rare occurrence of asthma deaths. Objectives This study aimed to investigate whether the death risks from asthma are triggered by short-term AAP exposures in a Chinese urban population who suffered poor air quality. Methods 1385 asthma death cases were identified from the total 61.3 thousand death records in two urban districts in Wuhan, central China, 2003–2013. We performed a time-stratified case-crossover design and conditional logistic regression models were applied to assess short-term associations of air pollutants (particulate matter with aerodynamic diameter ≤10 μm [PM10], sulfur dioxide [SO2], and nitrogen dioxide [NO2]) along different exposure days with asthma mortality. Results A total of 1385 case days and 4668 control days were investigated during the study period. Daily mean concentrations of PM10, SO2, and NO2 were 116.6 μg/m3, 53.4 μg/m3, and 60.7 μg/m3, respectively. A 10 μg/m3 rise in exposure to SO2 and NO2 at 2 days prior to death (lag 2 days), was associated with increased asthma death risks of 2.9% (odds ratio [OR] = 1.029, 95% confidence interval [CI]: 1.006, 1.053) and 4.3% (OR = 1.043, 95% CI: 1.012, 1.076), respectively. No evident PM10-asthma associations were identified in all subpopulations except for those aged 85 + years (OR = 1.022, 95% CI: 1.001 to 1.044). In our stratified analyses, significant effects of SO2 and NO2 were only observed in female, the older elderly, 7+ years-educated and unmarried persons, as well as those died outside the hospital and in cold season. We observed some evidence for effect modification by age, with p-values of 0.032 for PM10 and 0.051 for SO2, suggesting higher vulnerability to air pollution among the older asthma cases. Conclusions This study provided suggestive evidence for the short-term association between air pollution exposure and asthma death in highly polluted urban areas. Several days’ high-level exposures to air pollutants, particularly SO2 and NO2, may elevate risks of asthma death. Our findings highlighted the potential health benefits from intervention and preventive actions targeted to reducing AAP exposure among asthma patients.

Background Temperature variability (TV) is closely associated with climate change, but there is no unified TV definition worldwide. Two novel composite TV indexes were developed recently by calculating the standard deviations of several days’ daily maximum and minimum temperatures (TVdaily), or hourly mean temperatures (TVhourly). Objectives This study aimed to compare the mortality risks and burden associated with TVdaily and TVhourly using large time-series datasets collected from multiple locations in China, United Kingdom and United States. Methods We collected daily mortality and hourly temperature data through 1987 to 2012 from 63 locations in China (8 communities, 2006–2012), United Kingdom (10 regions, 1990–2012), and USA (45 cities, 1987–2000). TV-mortality associations were investigated using a three-stage analytic approach separately for China, UK, and USA. First, we applied a time-series regression for each location to derive location-specific TV-mortality curves. A second-stage meta-analysis was then performed to pool these estimated associations for each country. Finally, we calculated mortality fraction attributable to TV based on above-described location-specific and pooled estimates. Results Our dataset totally consisted of 23, 089, 328 all-cause death cases, including 93, 750 from China, 7,573,716 from UK and 15, 421, 862 from USA, respectively. In despite of a relatively wide uncertainty in China, approximately linear relationships were consistently identified for TVdaily and TVhourly. In the three countries, generally similar lag patterns of TV effects were consistently observed for TVdaily and TVhourly. A 1 °C rise in TVdaily and TVhourly at lag 0–7 days was associated with mortality increases of 0.93% (95% confidence interval [CI]: 0.12, 1.74) and 0.97% (0.18, 1.77) in China, 0.33% (0.15, 0.51) and 0.41% (0.21, 0.60) in UK, and 0.55% (0.41, 0.70) and 0.51% (0.35, 0.66) in USA, respectively. Larger attributable fractions were estimated using TVdaily than those using TVhourly, with estimates at 0–10 days of 3.69% (0.51, 6.75) vs. 2.59% (0.10, 5.01) in China, 1.14% (0.54, 1.74) vs. 0.98% (0.55, 1.42) in UK, and 2.57% (1.97, 3.16) vs. 1.67% (1.15, 2.18) in USA, respectively. Our meta-regression analyses indicated higher vulnerability to TV-induced mortality risks in warmer locations. Conclusions Our study added multi-country evidence for increased mortality risk associated with short-term exposure to large temperature variability. Daily and hourly TV exposure metrics produced generally comparable risk effects, but the attributable mortality burden tended to be higher using TVdaily instead of TVhourly.

Compared with relative risk, attributable fraction (AF) is more informative when assessing the mortality burden due to some environmental exposures (e.g., ambient temperature). Up to date, however, available AF-based evidence linking temperature with mortality has been very sparse regionally and nationally, even for the leading mortality types such as cardiorespiratory deaths. This study aimed to quantify national and regional burden of cardiorespiratory mortality (CRM) attributable to ambient temperature in the USA, and to explore potential socioeconomic and demographic sources of spatial heterogeneity between communities. Daily CRM and weather data during 1987-2000 for 106 urban communities across the mainland of USA were acquired from the publicly available National Morbidity, Mortality and Air Pollution Study (NMMAPS). We did the data analysis using a three-stage analytic approach. We first applied quasi-Poisson regression incorporated with distributed lag nonlinear model to estimate community-specific temperature-CRM associations, then pooled these associations at the regional and national level through a multivariate meta-analysis, and finally estimated the temperature-AF of CRM and performed subgroup analyses stratified by community-level characteristics. Both low and high temperatures increased short-term CRM risk, while temperature-CRM associations varied by regions. Nationally, the fraction of cardiorespiratory deaths caused by the total non-optimum, low, and high temperatures was 7.58% (95% empirical confidence interval, 6.68-8.31%), 7.15% (6.31-7.85%), and 0.43% (0.37-0.46%), respectively. Greater temperature-AF was identified in two northern regions (i.e., Industrial Midwest and North East) and communities with lower temperature and longitude, higher latitude, and moderate humidity. Additionally, higher vulnerability appeared in locations with higher urbanization level, more aging population, less White race, and lower socioeconomic status. Ambient temperature may be responsible for a large fraction of cardiorespiratory deaths. Also, temperature-AF of CRM varied considerably by geographical and climatological factors, as well as community-level disparity in socioeconomic status.