Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities.

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-µg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Estimates of the global burden of cancer-related deaths attributable to residential exposure to petrochemical industrial complexes from 2020 to 2040.

The petrochemical industry is a major industrial emitter of greenhouse gas (CO2) and environmental pollution, posing health risks to nearby communities. Although previous studies have indicated that residents living near petrochemical industrial complexes are at a higher risk of cancer, they have focused on local or regional burdens. This study aimed to estimate the global cancer burden attributable to residential exposure to petrochemical industrial complexes. The geographical coordinates of petrochemical plants and oil refineries were retrieved and verified from published sources. The ArcGIS software and global population data were used to estimate the number of people living within specific distances (exposed population). The exposure time window was framed as ranging from 1992 to 2035, extending to the latest period of the exposure time window for all cancer types to estimate the attributable deaths between 2020 and 2040. The relative risk of cancer was estimated from 15 published studies. Population attributable fraction (PAF) method was used to estimate the risk of cancer attributable to residential exposure and calculate the number of cancer-related deaths. Our findings indicate that >300 million people worldwide will be estimated to live near petrochemical industrial complexes by 2040. The overall global burden of cancer-related deaths was 19,083 in 2020, and it is estimated to increase to 27,366 deaths by 2040. The region with the highest attributable cancer deaths due to exposure is the high-income region, which had 10,584 deaths in 2020 and is expected to reach 13,414 deaths by 2040. Residential exposure to petrochemical industrial complexes could contribute to global cancer deaths, even if the proportion is relatively small, and proactive measures are required to mitigate the cancer burdens among these residents. Enforcing emissions regulations, improving monitoring, educating communities, and fostering collaboration are vital to protecting residents' health.

Dose-response relationship between lung function and chest imaging response to silica exposures in artificial stone manufacturing workers.

BACKGROUND: Occupational exposure to artificial stone, a popular material used for countertops, can cause accelerated silicosis, but the precise relationship between silica dose and disease development is unclear. OBJECTIVES: This study evaluated the impact of silica exposure on lung function and chest imaging in artificial stone manufacturing workers. METHODS: Questionnaire and spirometry assessments were administered to workers in two plants. A high-exposure subset underwent further evaluation, including chest CT and DLco. Weighting factors, assigned as proxies for silica exposure, were based on work tasks. Individual cumulative exposures were estimated using area concentration measurements and time spent in specific areas. Exposure-response associations were analyzed using linear and logistic regression models. RESULTS: Among 65 participants, the mean cumulative silica exposure was 3.61 mg/m3-year (range 0.0001 to 44.4). Each 1 mg/m3-year increase was associated with a 0.46% reduction in FVC, a 0.45% reduction in FEV1, and increased lung function abnormality risk (aOR = 1.27, 95% CI = 1.03-1.56). Weighting factors correlated with cumulative exposures (Spearman correlation = 0.59, p < 0.0001), and weighted tenure was associated with lung function abnormalities (aOR = 1.04, 95% CI = 1.01-1.09). Of 37 high-exposure workers, 19 underwent chest CT, with 12 (63%) showing abnormal opacities. Combining respiratory symptoms, lung function, and chest X-ray achieved 91.7% sensitivity and 75% specificity for predicting chest CT abnormalities. CONCLUSION: Lung function and chest CT abnormalities occur commonly in artificial stone workers. For high-exposure individuals, abnormalities on health screening could prompt further chest CT examination to facilitate early silicosis detection.

Ozone-related acute excess mortality projected to increase in the absence of climate and air quality controls consistent with the Paris Agreement.

Short-term exposure to ground-level ozone in cities is associated with increased mortality and is expected to worsen with climate and emission changes. However, no study has yet comprehensively assessed future ozone-related acute mortality across diverse geographic areas, various climate scenarios, and using CMIP6 multi-model ensembles, limiting our knowledge on future changes in global ozone-related acute mortality and our ability to design targeted health policies. Here, we combine CMIP6 simulations and epidemiological data from 406 cities in 20 countries or regions. We find that ozone-related deaths in 406 cities will increase by 45 to 6,200 deaths/year between 2010 and 2014 and between 2050 and 2054, with attributable fractions increasing in all climate scenarios (from 0.17% to 0.22% total deaths), except the single scenario consistent with the Paris Climate Agreement (declines from 0.17% to 0.15% total deaths). These findings stress the need for more stringent air quality regulations, as current standards in many countries are inadequate.

Fungal Spore Richness and Abundance of Allergenic Taxa: Comparing a Portable Impactor and Passive Trap Indoors and Outdoors in an Urban Setting.

Fungal spores are common airborne allergens, and fungal richness has been implicated in allergic disease. Amplicon sequencing of environmental DNA from air samples is a promising method to estimate fungal spore richness with semi-quantification of hundreds of taxa and can be combined with quantitative PCR to derive abundance estimates. However, it remains unclear how the choice of air sampling method influences these estimates. This study compared active sampling with a portable impactor and passive sampling with a passive trap over different durations to estimate fungal spore richness and the abundance of allergenic taxa. Air sampling was conducted indoors and outdoors at 12 residences, including repeated measurements with a portable impactor and passive traps with 1-day and 7-day durations. ITS2 amplicon sequence data were transformed to spore equivalents estimated by quantitative PCR, repeated active samples were combined, and abundance-based rarefaction was performed to standardize sample coverage for estimation of genus-level richness and spore abundance. Rarefied fungal richness was similar between methods indoors but higher for passive traps with a 7-day duration outdoors. Rarefied abundance of allergenic genera was similar between methods but some genera had lower abundance for passive traps with a 1-day duration, which differed indoors and outdoors indicating stochasticity in the collection of spores on collocated samplers. This study found that similar estimates of fungal spore richness and abundance of allergenic taxa can be obtained using a portable impactor or a passive trap within one day and that increased passive sample duration provides limited additional information.

Tropical cyclone-specific mortality risks and the periods of concern: A multicountry time-series study.

BACKGROUND: More intense tropical cyclones (TCs) are expected in the future under a warming climate scenario, but little is known about their mortality effect pattern across countries and over decades. We aim to evaluate the TC-specific mortality risks, periods of concern (POC) and characterize the spatiotemporal pattern and exposure-response (ER) relationships on a multicountry scale. METHODS AND FINDINGS: Daily all-cause, cardiovascular, and respiratory mortality among the general population were collected from 494 locations in 18 countries or territories during 1980 to 2019. Daily TC exposures were defined when the maximum sustained windspeed associated with a TC was ≥34 knots using a parametric wind field model at a 0.5° × 0.5° resolution. We first estimated the TC-specific mortality risks and POC using an advanced flexible statistical framework of mixed Poisson model, accounting for the population changes, natural variation, seasonal and day of the week effects. Then, a mixed meta-regression model was used to pool the TC-specific mortality risks to estimate the overall and country-specific ER relationships of TC characteristics (windspeed, rainfall, and year) with mortality. Overall, 47.7 million all-cause, 15.5 million cardiovascular, and 4.9 million respiratory deaths and 382 TCs were included in our analyses. An overall average POC of around 20 days was observed for TC-related all-cause and cardiopulmonary mortality, with relatively longer POC for the United States of America, Brazil, and Taiwan (>30 days). The TC-specific relative risks (RR) varied substantially, ranging from 1.04 to 1.42, 1.07 to 1.77, and 1.12 to 1.92 among the top 100 TCs with highest RRs for all-cause, cardiovascular, and respiratory mortality, respectively. At country level, relatively higher TC-related mortality risks were observed in Guatemala, Brazil, and New Zealand for all-cause, cardiovascular, and respiratory mortality, respectively. We found an overall monotonically increasing and approximately linear ER curve of TC-related maximum sustained windspeed and cumulative rainfall with mortality, with heterogeneous patterns across countries and regions. The TC-related mortality risks were generally decreasing from 1980 to 2019, especially for the Philippines, Taiwan, and the USA, whereas potentially increasing trends in TC-related all-cause and cardiovascular mortality risks were observed for Japan. CONCLUSIONS: The TC mortality risks and POC varied greatly across TC events, locations, and countries. To minimize the TC-related health burdens, targeted strategies are particularly needed for different countries and regions, integrating epidemiological evidence on region-specific POC and ER curves that consider across-TC variability.

Urinary heavy metals and attention-deficit/hyperactivity symptoms of preschool children: a mixed-exposure analysis.

The neurotoxic effects of certain heavy metals are well established, but only a few studies have investigated the joint effect of concurrent exposure to multiple ones. The study aims to evaluate the association between mixed exposure to neurotoxic metals and the psychosocial behavior of preschool children. Using a stratified sampling strategy, we recruited participants from 105 kindergartens in 41 townships of Taiwan and excluded those with blood lead levels ≥ 3.5 µg/L. The first-morning void urines were collected and analyzed for cadmium, manganese, arsenic, chromium, lead, and nickel concentrations using inductively coupled plasma mass spectrometry. We applied the parentally reported Strengths and Difficulties Questionnaire (SDQ) and Swanson, Nolan, and Pelham IV (SNAP-IV) scales to evaluate the psychosocial behaviors. Multiple linear regressions were utilized to evaluate the associations between each heavy metal and the outcomes, while the mixed effect of concurrent exposure was estimated by using a Quantile g-computation approach. A total of 977 preschool children were included in the study, and the mean (SD) age was 5.7 (0.7) years old. In single pollutant models, we observed adverse effects of urinary manganese, nickel, arsenic, and lead on the specific subsets of SDQ. Furthermore, the combined effect of six heavy metals significantly affected the hyperactivity/inattention symptoms (beta = 0.46, 95% CI: 0.13-0.78, with all metals increased by one quartile), and chromium and lead were the two major contributors. Similar detrimental effects of urinary cadmium and lead were also observed in the SNAP-IV subsets, although the joint effect analysis was not significant. The study provided evidence that concurrent exposure to multiple heavy metals may exert increased risks of hyperactivity/inattention in children compared to single pollutant exposure. Further studies are needed to verify our findings regarding mixed exposure to multiple neurotoxic metals.

Attention-deficit hyperactivity disorder in children is related to maternal screen time during early childhood in Taiwan: a national prospective cohort study.

BACKGROUND: It is unclear to familial screen time in early childhood is associated with the subsequent diagnosis of attention-deficit and hyperactivity disorder (ADHD). Our study is to evaluate the association between screen time during early childhood in families and the incidence of ADHD. METHODS: We conducted a population-based birth cohort study by using the Taiwan Birth Cohort Study, which recruited 24 200 mother-child pairs when children were 6 months old. Screen time exposure for children and parents were collected at the age of 18 and 36 months. Whether the child has ever been diagnosed with ADHD was determined at a follow-up interview at age 8. Factors including socioeconomic factors and screen time were analyzed using logistic regression to determine their association with the rate of ADHD. RESULTS: A total of 16 651 term singletons were included in the final analysis. Of them, 382 (2.3%) were diagnosed as having ADHD before the age of 8 years. No significant relationship between children's or fathers' screen time and ADHD was noted. When compared to children whose mothers spent less time on screens, those whose mothers spent more than 3 h a day on screens when the child was 3 years old exhibited a higher incidence of ADHD (adjusted OR [aOR]: 1.31, 95% CI: 1.03-1.66). CONCLUSION: Higher maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in this population-based study. However, children's screen time did not find related to ADHD. We found that it was the mother's screen time, who typically serves as the primary caregiver in our study participants, not the child's, that mattered. In addition to superficial screen use time, future research is needed to replicate the findings and clarify mechanisms underlying this association.

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis.

OBJECTIVE: To investigate potential interactive effects of fine particulate matter (PM2.5) and ozone (O3) on daily mortality at global level. DESIGN: Two stage time series analysis. SETTING: 372 cities across 19 countries and regions. POPULATION: Daily counts of deaths from all causes, cardiovascular disease, and respiratory disease. MAIN OUTCOME MEASURE: Daily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM2.5 and O3 in association with mortality. RESULTS: During the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 µg/m3 increment in PM2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O3 concentration; and for a 10 µg/m3 increase in O3 ranged from 0.04% (-0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM2.5 and O3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM2.5 and O3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons. CONCLUSION: The findings of this study suggest a synergistic effect of PM2.5 and O3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants.

An approach to the diagnosis of lumbar disc herniation using deep learning models.

Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc. Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist's labeling and annotation, and then the deep learning models were trained using the training/validation dataset. Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH. Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.

Does daily energy and macronutrient intake differ between work and non-workdays in shift workers? A mixed methods study.

Shift workers are at increased risk of obesity and metabolic diseases, but their eating patterns on work and non-workdays are understudied. We aimed to examine whether energy intake and macronutrient intake of day and night shift nurses were different during work and non-workdays. We used a mixed-methods approach to study food intake of shift working nurses from two hospitals during day and night shifts. Participants completed baseline questionnaires about eating behaviour, sleep, chronotype, mood and shift work disorder. Participants then completed a 4-d food diary which included a non-workday prior to the first shift, the first and last shift (either day or night) and the following non-workday. After completion of the food diaries, we used semi-structured interviews to explore the qualitative aspects of eating behaviours. Seventy-nine shift-working nurses participated in the study. Daily energy intake was not significantly different on work and non-workdays in day or night shift workers (p > 0.05). Whilst macronutrient consumption was also not different between day and night shift workers (p > 0.05), sugar intake was higher in day compared to night shift workers (p = 0.02) on the non-workday prior to the first workday. In qualitative interviews, participants reported their eating to be different on day and night shifts as well as work and non-workdays. Eating behaviour in day and night shift workers was highly influenced by food availability, convenience, peers, and family members. Nurses qualitatively report that night and day shifts result in them eating differently despite no statistically discernible difference in energy intake.

Rapid increase in the risk of heat-related mortality.

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives.

Global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019: a multi-country time-series study.

BACKGROUND: The global spatiotemporal pattern of mortality risk and burden attributable to tropical cyclones is unclear. We aimed to evaluate the global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019. METHODS: The wind speed associated with cyclones from 1980 to 2019 was estimated globally through a parametric wind field model at a grid resolution of 0·5°â€ˆ× 0·5°. A total of 341 locations with daily mortality and temperature data from 14 countries that experienced at least one tropical cyclone day (a day with maximum sustained wind speed associated with cyclones ≥17·5 m/s) during the study period were included. A conditional quasi-Poisson regression with distributed lag non-linear model was applied to assess the tropical cyclone-mortality association. A meta-regression model was fitted to evaluate potential contributing factors and estimate grid cell-specific tropical cyclone effects. FINDINGS: Tropical cyclone exposure was associated with an overall 6% (95% CI 4-8) increase in mortality in the first 2 weeks following exposure. Globally, an estimate of 97 430 excess deaths (95% empirical CI [eCI] 71 651-126 438) per decade were observed over the 2 weeks following exposure to tropical cyclones, accounting for 20·7 (95% eCI 15·2-26·9) excess deaths per 100 000 residents (excess death rate) and 3·3 (95% eCI 2·4-4·3) excess deaths per 1000 deaths (excess death ratio) over 1980-2019. The mortality burden exhibited substantial temporal and spatial variation. East Asia and south Asia had the highest number of excess deaths during 1980-2019: 28 744 (95% eCI 16 863-42 188) and 27 267 (21 157-34 058) excess deaths per decade, respectively. In contrast, the regions with the highest excess death ratios and rates were southeast Asia and Latin America and the Caribbean. From 1980-99 to 2000-19, marked increases in tropical cyclone-related excess death numbers were observed globally, especially for Latin America and the Caribbean and south Asia. Grid cell-level and country-level results revealed further heterogeneous spatiotemporal patterns such as the high and increasing tropical cyclone-related mortality burden in Caribbean countries or regions. INTERPRETATION: Globally, short-term exposure to tropical cyclones was associated with a significant mortality burden, with highly heterogeneous spatiotemporal patterns. In-depth exploration of tropical cyclone epidemiology for those countries and regions estimated to have the highest and increasing tropical cyclone-related mortality burdens is urgently needed to help inform the development of targeted actions against the increasing adverse health impacts of tropical cyclones under a changing climate. FUNDING: Australian Research Council and Australian National Health and Medical Research Council.

Pretherapy 18F-FDG PET/CT in Predicting Disease Relapse in Patients With Immunoglobulin G4-Related Disease: A Prospective Study.

OBJECTIVE: To investigate whether the levels of inflammation detected by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) can predict disease relapse in immunoglobulin G4-related disease (IgG4-RD) patients receiving standard induction steroid therapy. MATERIALS AND METHODS: This prospective study analyzed pretherapy FDG PET/CT images from 48 patients (mean age, 63 ± 12.9 years; 45 males and 3 females) diagnosed with IgG4-RD between September 2008 and February 2018, who subsequently received standard induction steroid therapy as the first-line treatment. Multivariable Cox proportional hazards models were used to identify the potential prognostic factors associated with relapse-free survival (RFS). RESULTS: The median follow-up time for the entire cohort was 1913 days (interquartile range [IQR], 803-2929 days). Relapse occurred in 81.3% (39/48) patients during the follow-up period. The median time to relapse was 210 days (IQR, 140-308 days) after completion of standardized induction steroid therapy. Among the 17 parameters analyzed, Cox proportional hazard analysis identified whole-body total lesion glycolysis (WTLG) > 600 on FDG-PET as an independent risk factor for disease relapse (median RFS, 175 vs. 308 days; adjusted hazard ratio, 2.196 [95% confidence interval: 1.080-4.374]; P = 0.030). CONCLUSION: WTLG on pretherapy FDG PET/CT was the only significant factor associated with RFS in IgG-RD patients receiving standard steroid induction therapy.

Burden of cardiovascular disease attributable to long-term exposure to ambient PM2.5 concentration and the cost-benefit analysis for the optimal control level.

Environmental exposure to fine particulate matter PM2.5 is known to be associated with many hazardous health effects, including cardiovascular diseases (CVDs). To reduce the related health burden, it is crucial that policy-makers throughout the world set regulation levels according to their own evidence-based study outcomes. However, there appears to be a lack of decision-making methods for the control level of PM2.5 based on the burden of disease. In this study, 117,882 CVD-free participants (≥30-years-old) of the MJ Health Database were followed-up (for a median of 9 years) between 2007 and 2017. Each participant's residential address was matched to the 3× 3 km grid PM2.5 concentration estimates with a 5-year average for long-term exposure. We used a time-dependent nonlinear weight-transformation Cox regression model for the concentration-response function (CRF) between exposure to PM2.5 and CVD incidence. Town/district-specific PM2.5-attributable years of life in disability (YLDs) in CVD incidence were calculated by using the relative risk (RR) of the PM2.5 concentration level relative to the reference level. A cost-benefit analysis was proposed by assessing the trade-off between the gain in avoidable YLDs (given a reference level at u and considering mitigation cost) versus the loss in unavoidable YLDs by not setting at the lowest observed health effect level u0. The CRF varied across different areas with dissimilar PM2.5 exposure ranges. Areas with low PM2.5 concentrations and population sizes provided crucial information for the CVD health effect at the lower end. Additionally, women and older participants were more susceptible. The avoided town/district-specific YLDs in CVD incidence due to lower RRs ranged from 0 to 3000 person-years comparing the PM2.5 concentration levels in 2019 with the levels in 2011. Based on the cost-benefit analysis, an annual PM2.5 concentration of 13 µg/m3 would be optimal, which provides a guideline for the updated regulation level (currently at 15 µg/m3). The proposed cost-benefit analysis method may be applied to other countries/regions for regulation levels that are most suitable for their air pollution status and population health.

Risk of type 2 diabetes after diagnosed gestational diabetes is enhanced by exposure to PM2.5.

BACKGROUND: Air pollution and gestational diabetes mellitus (GDM) are both associated with increased diabetes mellitus (DM) occurrence. However, whether air pollutants modify the effects of GDM on the occurrence of DM has been unknown. This study aims to determine whether the effect of GDM on DM development can be modified by exposure to ambient air pollutants. METHODS: Women with one singleton birth delivery during 2004-14 according to the Taiwan Birth Certificate Database (TBCD) were included as the study cohort. Those newly diagnosed as having DM 1 year or later after childbirth were identified as DM cases. Controls were selected among women without DM diagnosis during follow-up. Personal residence was geocoded and linked with interpolated concentrations of air pollutants into township levels. Conditional logistic regression was used to determine the odds ratio (OR) of pollutant exposure and GDM, adjusting for age, smoking and meteorological variables. RESULTS: There were 9846 women who were newly diagnosed as having DM over a mean follow-up period of 10.2 years. We involved them and the 10-fold matching controls involved in our final analysis. The OR (odds ratio) (95% confidence interval, 95% CI) of DM occurrence per interquartile range increased in particulate matter (PM) smaller than or equal to 2.5 µm (PM2.5) and ozone (O3) was 1.31 (1.22-1.41) and 1.20 (1.16-1.25), respectively. The effects of PM exposure on DM development were significantly higher in the GDM group (OR: 2.46, 95% CI: 1.84-3.30) than in the non-GDM group (OR: 1.30, 95% CI: 1.21-1.40). CONCLUSIONS: Exposure to high levels of PM2.5 and O3 elevates the risk of DM. GDM acted synergistically in DM development with exposure to PM2.5 but not with that to O3.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

Air pollution during the perinatal period and neurodevelopment in children: A national population study in Taiwan.

AIM: To evaluate the association between ambient particulate matter no larger than 2.5 µm in diameter (PM2.5 ) during the prenatal and postnatal periods and infant neurodevelopmental parameters. METHOD: We conducted a population-based birth cohort study using the Taiwan Birth Cohort Study. Participants were assessed for developmental conditions through home interviews at 6 months and 18 months of age. Exposure to PM2.5 of mothers and infants during perinatal periods was estimated using hybrid kriging/land-use regression. The exposure was linked to each participant by home address. Logistic regression was then conducted to determine the risk of neurodevelopmental delay in relation to PM2.5 . RESULTS: A total of 17 683 term singletons without congenital malformations were included in the final analysis. PM2.5 during the second trimester was associated with increased risks of delays in gross motor neurodevelopmental milestones (adjusted odds ratio [aOR] 1.09 per 10 µg/m3 increase in exposure to PM2.5 ). Delayed fine motor development was also found to be related to exposure to PM2.5 in the second and third trimesters (aOR 1.06), as was personal-social skill (aOR 1.11 for the second trimester and 1.06 for the third). These neurodevelopmental parameters were unrelated to postnatal PM2.5 exposure. INTERPRETATION: Exposure to ambient PM2.5 during pregnancy was significantly related to delay in gross motor, fine motor, and personal-social development in this population-based study. WHAT THIS PAPER ADDS: Prenatal exposure to higher PM2.5 was associated with increased risk of delayed early neurodevelopment. The critical period for delayed gross motor development was the second trimester. The critical period for fine motor and personal-social development was the second and third trimesters.

Identification of serum metabolic signatures of environmental-leveled phthalate in the Taiwanese child population using NMR-based metabolomics.

Phthalates have become important environmental pollutants due to their high exposure frequency in daily life; thus, phthalates are prevalent in humans. Although several epidemiologic surveys have linked phthalates with several adverse health effects in humans, the molecular events underlying phthalate exposure have not been fully elucidated. The purpose of this study was to reveal associations between phthalate exposure and the serum metabolome in Taiwanese children using a metabolomic approach. A total of 256 Taiwanese children (8-10 years old) from two cohorts were enrolled in this study. Twelve urinary phthalate metabolites were analyzed by high-performance liquid chromatography/tandem mass spectrometry, while a nuclear magnetic resonance-based metabolomic approach was used to record serum metabolic profiles. The associations between metabolic profiles and phthalate levels were assessed by partial least squares analysis coupled with multiple linear regression analysis. Our results revealed that unique phthalate exposures, such as mono-isobutyl phthalate, mono-n-butyl phthalate, and mono (2-ethyl-5-oxohexyl) phthalate, were associated with distinct serum metabolite profiles. These phthalate-mediated metabolite changes may be associated with perturbed energy mechanisms, increased oxidative stress, and lipid metabolism. In conclusion, this study suggests that metabolomics is a valid approach to examine the effects of environmental-level phthalate on the serum metabolome. This study also highlighted potentially important phthalates and their possible effects on children.