Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China.

BACKGROUND: Positive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error. METHODS: This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season. RESULTS: A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 µg/m3 increase in PM2.5 (lag0-2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01-1.45%) and 1.95% (95% CI 1.63-2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season. CONCLUSIONS: This study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.

Impact of summer heat on mortality and years of life lost: Application of a novel indicator of daily excess hourly heat.

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.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Subject clustering by IF-PCA and several recent methods.

Subject clustering (i.e., the use of measured features to cluster subjects, such as patients or cells, into multiple groups) is a problem of significant interest. In recent years, many approaches have been proposed, among which unsupervised deep learning (UDL) has received much attention. Two interesting questions are 1) how to combine the strengths of UDL and other approaches and 2) how these approaches compare to each other. We combine the variational auto-encoder (VAE), a popular UDL approach, with the recent idea of influential feature-principal component analysis (IF-PCA) and propose IF-VAE as a new method for subject clustering. We study IF-VAE and compare it with several other methods (including IF-PCA, VAE, Seurat, and SC3) on 10 gene microarray data sets and eight single-cell RNA-seq data sets. We find that IF-VAE shows significant improvement over VAE, but still underperforms compared to IF-PCA. We also find that IF-PCA is quite competitive, slightly outperforming Seurat and SC3 over the eight single-cell data sets. IF-PCA is conceptually simple and permits delicate analysis. We demonstrate that IF-PCA is capable of achieving phase transition in a rare/weak model. Comparatively, Seurat and SC3 are more complex and theoretically difficult to analyze (for these reasons, their optimality remains unclear).

Assessing PM2.5-associated risk of hospitalization for COPD: an application of daily excessive concentration hours.

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.

Fine particulate matter and cause-specific mortality in the Hong Kong elder patients with chronic kidney disease.

Emerging epidemiologic studies suggested that particulate matter (PM) was a risk factor for the incidence of chronic kidney disease (CKD). However, few studies were conducted to examine whether PM was associated with cause-specific deaths in the CKD progression. This study aimed to estimate the association between fine particulate matter (PM2.5) and a spectrum of deaths among CKD patients. We took leverage of the Elderly Health Service cohort (n = 66,820), a large Hong Kong elderly cohort followed up till 2010. A total of 902 CKD incident patients in the cohort were identified during the follow-up period. We estimated yearly PM2.5 at the residential address for each CKD patient based on a satellite-based spatiotemporal model. We used Cox proportional hazards models with attained age as the underlying timescale to assess the association between long-term exposure to PM2.5 and cause-specific mortality among CKD patients. A total of 496 patients died during the follow-up, where 147 died from cardiovascular disease, 61 from respiratory disease and 154 from renal failure. The mortality hazard ratio (HR) per interquartile-range increase in PM2.5 (4.0 µg/m3) was 1.97 (95% confidence interval (CI): 1.34 to 2.91) for ischemic heart disease (IHD) among CKD patients, and was 1.42 (95%CI: 1.05 to 1.93) for CKD among those patients concomitantly with hypertension. Associations were not of statistical significance between PM2.5 and mortality hazard ratios of all-cause, stroke, and pneumonia among CKD patients. Our findings suggest that long-term exposure to PM2.5 may contribute to the CKD progression into ischemic heart diseases.

Utilizing daily excessive concentration hours to estimate cardiovascular mortality and years of life lost attributable to fine particulate matter in Tehran, Iran.

BACKGROUND: Evidence for associations between fine particulate matter (PM2.5) and cardiovascular diseases (CVDs) in Iran is scarce. Given large within-day variations of PM2.5 concentration, using the daily mean of PM2.5 (PM2.5mean) as exposure metric might bias the health-related assessment. This study applied a novel indicator, daily excessive concentration hours (DECH), to evaluate the effect of ambient PM2.5 on CVD mortality and years of life lost (YLL) in Tehran, the capital city of Iran. METHODS: Hourly concentration data for PM2.5, daily information for meteorology and records of registered cardiovascular deaths from 2012 to 2016 were obtained from Tehran, Iran. Daily excessive concentration hours of PM2.5 (PM2.5DECH) was defined as daily total concentration-hours exceeding 35 µg/m3. Using a time-series design, we applied generalized linear models to assess the attributable effects of PM2.5DECH and PM2.5mean on CVD mortality and YLL. RESULTS: For an interquartile range (IQR) rise in PM2.5DECH, total CVD mortality at lag 0-10 days and YLL at lag 0-8 days increased 2.26% (95% confidence interval (CI): 0.85-3.69%) and 23.24 (6.07-40.42) person years, respectively. Corresponding increases were 3.45% (1.44-5.49%) and 35.21 (10.85-59.58) person years for an IQR rise in PM2.5mean. Significant associations between PM2.5 pollution (i.e., PM2.5mean and PM2.5DECH) and cause-specific cardiovascular health (i.e., mortality and YLL) were only identified in stroke. Subgroup analyses showed that male and people aged 0-64 years suffered more from PM2.5 pollution. Furthermore, we attributed a greater CVD burden to PM2.5DECH (1.67% for mortality and 2.67% for YLL) than PM2.5mean (0.63% for mortality and 0.70% for YLL) during the study period. CONCLUSIONS: This study strengthened the evidence for the aggravated CVD mortality burden associated with short-term exposure to PM2.5. Our findings also suggested that PM2.5DECH might be a potential alternative indicator of exposure assessment in PM2.5-related health investigations.

Short-term impacts of ambient fine particulate matter on emergency department visits: Comparative analysis of three exposure metrics.

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.

Association between ambient air pollution exposure and infants small for gestational age in Huangshi, China: a cross-sectional study.

Small for gestational age (SGA) is defined as intrauterine growth retardation or small sample, referring to the 10th percentile of birth weight lower or two standard deviations less than the average weight at the same gestational age. SGA infants bring great economic and psychological burdens to families and society. The association between exposure to air pollution and SGA in underdeveloped cities with poor air quality remains unclear. Thus, this study is conducted to estimate the effects of maternal exposure to air pollutants on SGA numbers. Birth information was collected from the Huangshi Maternity and Children's Health Hospital from January 1st to December 31st in 2017. Data of pregnancy exposure were accessed using stationary monitors. These data included particulate matter less than or equal to 10 µm in aerodynamic diameter (PM10), particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Multivariate logistic regression models were performed to estimate the association between ambient air pollution and the risk of SGA during different exposure windows. It was found that a 1 µg/m3 increase in air pollution concentrations during the entire pregnancy was associated with a higher risk of SGA, with an adjusted odds ratio (OR) and 95% confidence interval (CI) of 1.055 (1.035-1.076), 1.084 (1.053-1.116), 1.000 (0.953-1.049), and 1.051 (0.968-1.141) for PM10, PM2.5, NO2, and SO2, respectively. Thus, it is suggested that exposure to air pollution is associated with an increased risk of SGA. The effects of PM10 and PM2.5 were more stable than NO2 and SO2.

Long-Term Exposure to Fine Particulate Matter and Academic Performance Among Children in North Carolina.

This cross-sectional study analyzes the association of exposure to fine particulate matter and academic performance among school age children in North Carolina.