Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries.

COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: -0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries.

Importance: The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space. Objective: To estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach. Design, Setting, and Participants: This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023. Exposure: Day-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations. Main Outcomes and Measures: Day-to-day changes in county- or municipality-level all-cause mortality rates. Results: Among the 8 963 352 deaths in the 4 study regions, a 10-µg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-µg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2). Conclusions and Relevance: The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

A unified framework for assessing interaction effects among environmental exposures in epidemiologic studies: A case study on temperature, air pollution, and kidney-related conditions in New York state.

BACKGROUND: There are various methods to assess interaction effects. However, current methods have limitations, and quantification of interaction effects is rarely performed. This study aimed to develop a unified quantitative framework for assessing interaction effects. METHODS: We proposed a novel framework using log-linear models with a product term(s) across the exposures that generates parametric bi-variate association and interaction effect surfaces and allows flexible functional forms for exposures in the interaction term(s). In a case study, we assessed the interaction effects between temperature and air pollution (i.e., PM2.5, NO2, and O3) on risk for kidney-related conditions in New York State (2007-2016) using a case-crossover design with conditional logistic models. Our measures of exposure were the moving averages at lag 0-5 days for air pollution (linear) and daytime mean outdoor wet-bulb globe temperature (WBGT; using a natural cubic spline). RESULTS: We derived closed-form expressions for the magnitude of multiplicative interaction effects (the joint relative risk divided by the product of the two conditional relative risks) and their uncertainties. In the case study, we found a Bonferroni-corrected significant multiplicative interaction effect (IE) between outdoor WBGT at the 99th percentile (median as the reference) and (1) PM2.5 (per 5 µg/m3 increase, IE = 1.052; 95 % confidence interval [CI]: 1.019, 1.087) for acute kidney failure and (2) O3 (per 5 ppb increase; IE = 1.022; 95 % CI: 1.008, 1.036) for urolithiasis (the latter being inconclusive based on the sensitivity analysis). CONCLUSIONS: Our framework allows different functional forms of exposure variables in the interaction term, quantifies the magnitudes of entire-exposure-range (in addition to discrete exposure level) multiplicative interaction effects and their uncertainties in a categorical or continuous (linear or non-linear) manner, and harmonizes the two-way evaluation of effect modification. The case study underscores co-consideration of heat and air pollution when estimating health burden and designing heat/pollution alert systems.

Beauty Inside Out: Examining Beauty Product Use Among Diverse Women and Femme-Identifying Individuals in Northern Manhattan and South Bronx Through an Environmental Justice Framework.

The disproportionate use of chemical straighteners and skin lighteners by women of color is a growing public health concern given the link between product use and adverse health effects. Prior studies examined product use as an individual choice but neglected social-structural factors, which influence beauty perceptions and personal decisions around product use. We used a community-based participatory research approach to characterize product use by demographics and investigated how racialized beauty norms impact use among 297 women and femme-identifying individuals in Northern Manhattan and the South Bronx. Product use varied by race/ethnicity, nativity, and messaging from family and peers. Black respondents were more likely to ever use chemical straighteners than non-Black respondents (OR = 2.0; 95% CI = 1.2-3.2), as were respondents who heard that family members express a preference for straight hair compared with respondents whose family members expressed mixed preferences about hairstyles (OR = 2.0; 95% CI = 1.1-3.7). Compared with non-Asian respondents and respondents born in the United States, Asian respondents and respondents born in other countries, respectively, had threefold higher odds of ever using skin lighteners (Asian: OR = 3.2; 95% CI = 1.4-7.0; born in other countries: OR = 3.4; 95% CI = 1.9-6.1). Respondents' perceptions that others believe straight hair or lighter skin confer benefits such as beauty, professionalism, or youth were associated with greater use of chemical straighteners and skin lighteners. These findings highlight the pervasiveness of racialized beauty norms and point to the need to reduce the demand for and sale of these products through community education, market-based strategies, and public policy.

Floods and Diarrhea Risk in Young Children in Low- and Middle-Income Countries.

Importance: Climate change is associated with more frequent and intense floods. Current research on the association between flood exposure and diarrhea risk is limited mainly to short-term and event-specific analyses. Moreover, how prior drought or water, sanitation, and hygiene (WaSH) practices influence this association remains largely unknown. Objective: To examine the association between flood exposure and diarrhea risk among children younger than 5 years and to evaluate the compounding influence of prior drought and effect modification by WaSH. Design, Setting, and Participants: This cross-sectional study included multicluster surveys conducted by the Demographic and Health Surveys Program in 43 low- and middle-income countries during 2009 through 2019. This study included children younger than 5 years in all households from each survey cluster. Collected data were analyzed between September 1 and December 31, 2022. Exposures: Historical flood events during 2009 through 2019 were obtained from the Dartmouth Flood Observatory. Main Outcome and Measures: The main outcome was diarrhea prevalence among children younger than 5 years in the 2 weeks before the survey was conducted. Results were analyzed by binomial generalized linear mixed-effects logistic regression models with nested random intercepts for country and survey cluster. Results: Among 639 250 children making up the complete data series (excluding 274 847 children with missing values for diarrhea or baseline characteristics), 6365 (mean [SD] age, 28.9 [17.2] months; 3214 boys [50.5%]; 3151 girls [49.5%]) were exposed to floods during the 8 weeks after a flood started. The prevalence of diarrhea was 13.2% (n = 839) among exposed children and 12.7% (n = 80 337) among unexposed children. Exposure to floods was associated with increased diarrhea risk, with the highest odds ratio (OR) observed during the second to fourth weeks after floods started (OR, 1.35; 95% CI, 1.05-1.73). When floods were stratified by severity and duration, significant associations were observed only for extreme floods (OR during the third to fifth weeks, 2.07; 95% CI, 1.37-3.11) or floods lasting more than 2 weeks (OR during the second to fourth weeks, 1.47; 95% CI, 1.13-1.92), with significantly stronger associations than for less extreme floods or shorter-duration floods, respectively. The OR during the first 4 weeks after the start of floods was significantly higher for floods preceded by a 6-month or longer drought (12-month drought OR, 1.96; 95% CI, 1.53-2.52) than for floods not preceded by a 6-month or longer drought (12-month drought OR, 1.00; 95% CI, 0.79-1.27). Conclusions: These findings suggest that floods, especially severe floods, long-duration floods, and floods preceded by drought, are associated with an increased risk of diarrhea among children younger than 5 years living in low- and middle-income countries. With the projected increasing frequency and intensity of floods and drought under climate change, greater collective efforts are needed to protect children's health from these compounding events.

Associations between short-term exposure to PM2.5, NO2 and O3 pollution and kidney-related conditions and the role of temperature-adjustment specification: A case-crossover study in New York state.

Epidemiologic evidence on the relationship between air pollution and kidney disease remains inconclusive. We evaluated associations between short-term exposure to PM2.5, NO2 and O3 and unplanned hospital visits for seven kidney-related conditions (acute kidney failure [AKF], urolithiasis, glomerular diseases [GD], renal tubulo-interstitial diseases, chronic kidney disease, dysnatremia, and volume depletion; n = 1,209,934) in New York State (2007-2016). We applied a case-crossover design with conditional logistic regression, controlling for temperature, dew point temperature, wind speed, and solar radiation. We used a three-pollutant model at lag 0-5 days of exposure as our main model. We also assessed the influence of model adjustment using different specifications of temperature by comparing seven temperature metrics (e.g., dry-bulb temperature, heat index) and five intraday temperature measures (e.g., daily mean, daily minimum, nighttime mean), according to model performance and association magnitudes between air pollutants and kidney-related conditions. In our main models, we adjusted for daytime mean outdoor wet-bulb globe temperature, which showed good model performance across all kidney-related conditions. We observed the odds ratios (ORs) for 5 µg/m3 increase in daily mean PM2.5 to be 1.013 (95% confidence interval [CI]: 1.001, 1.025) for AKF, 1.107 (95% CI: 1.018, 1.203) for GD, and 1.027 (95% CI: 1.015, 1.038) for volume depletion; and the OR for 5 ppb increase in daily 1-hour maximum NO2 to be 1.014 (95% CI; 1.008, 1.021) for AKF. We observed no associations with daily 8-hour maximum O3 exposure. Association estimates varied by adjustment for different intraday temperature measures: estimates adjusted for measures with poorer model performance resulted in the greatest deviation from estimates adjusted for daytime mean, especially for AKF and volume depletion. Our findings indicate that short-term exposure to PM2.5 and NO2 is a risk factor for specific kidney-related conditions and underscore the need for careful adjustment of temperature in air pollution epidemiologic studies.

Associations between short-term temperature exposure and kidney-related conditions in New York State: The influence of temperature metrics across four dimensions.

BACKGROUND: Evidence describing the relationship between short-term temperature exposure and kidney-related conditions is insufficient. It remains unclear how temperature specification affects estimation of these associations. This study aimed to assess associations between short-term temperature exposure and seven kidney-related conditions and to evaluate the influence of temperature specification. METHODS: We obtained data on hospital encounters in New York State (2007-2016). We assessed associations with a case-crossover design using conditional logistic regression with distributed lag non-linear models. We compared model performance (i.e., AIC) and association curves using 1) five temperature spatial resolutions; 2) temperature on an absolute versus relative scale; 3) seven temperature metrics incorporating humidity, wind speed, and/or solar radiation; and 4) five intraday temperature measures (e.g., daily minimum and daytime mean). RESULTS: We included 1,209,934 unplanned adult encounters. Temperature metric and intraday measure had considerably greater influence than spatial resolution and temperature scale. For outcomes not associated with temperature exposure, almost all metrics or intraday measures showed good model performance; for outcomes associated with temperature, there were meaningful differences in performance across metrics or intraday measures. For parsimony, we modelled daytime mean outdoor wet-bulb globe temperature, which showed good performance for all outcomes. At lag 0-6 days, we observed increased risk at the 95th percentile of temperature versus the minimum morbidity temperature for acute kidney failure (odds ratio [OR] = 1.36, 95% confidence interval [CI]: 1.09, 1.69), urolithiasis (OR = 1.41, 95% CI: 1.16, 1.70), dysnatremia (OR = 1.26, 95% CI: 1.01, 1.59), and volume depletion (OR = 1.88, 95% CI: 1.41, 2.51), but not for glomerular diseases, renal tubulo-interstitial diseases, and chronic kidney disease. CONCLUSIONS: High-temperature exposure over one week is a risk factor for acute kidney failure, urolithiasis, dysnatremia, and volume depletion. The differential model performance across temperature metrics and intraday measures indicates the importance of careful selection of exposure metrics when estimating temperature-related health burden.

Temperature variability and birthweight: Epidemiological evidence from Africa.

BACKGROUND: Mounting evidence supports an association between nonoptimal ambient temperatures (i.e., heat or cold) and risk of low birthweight (LBW) (<2500 g), while the effect of temperature variability (TV) is largely unknown. We aimed to quantify the association between TV and risk of LBW in Africa. METHODS: Data on birthweight in 37 countries during 1990-2020 were collected from the Demographic and Health Surveys program. We calculated overall, intraday, and interday TV during the entire pregnancy and each trimester using hourly temperatures at âˆ¼ 9 km resolution from ERA5-Land. We employed generalized linear mixed logistic regression, with random effects for country and survey cluster, to quantify the association between LBW and three separate TV metrics. RESULTS: In total there were 33,863 (10.2%) LBW births out of 333,618 records. We found a J-shaped association between TV and LBW. Compared to the reference TV where the lowest risk was observed, extremely high (97.5th percentile) overall, intraday, and interday TV during the entire pregnancy increased the odds of LBW birth by 37.3% (26.7-48.8%), 24.1% (16.4-32.3%), and 15.1% (6.9-24.0%), respectively. In total, 7.3% of all LBW births in Africa were attributable to elevated overall TV. These associations were observed in dry climate zones, but not in tropical or temperate zones. CONCLUSIONS: Our study suggests an adverse impact of TV on the risk of LBW in Africa, according to three different TV definitions, underlining the significance of climate-health risk assessment in those most vulnerable to climate change.

Residential and Race/Ethnicity Disparities in Heat Vulnerability in the United States.

Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) "redlining" grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non-disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades "A"-"D," respectively. CEJST-defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non-disadvantaged tracts (16.68). The non-Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place- and race/ethnicity-based disparities in heat vulnerability.

Improving Water Quality in the Short Beach Neighborhood of Branford, Connecticut, 2019-A Citizen Science Project.

We initiated a collaboration between local government, academia, and citizen scientists to investigate high frequencies of elevated Escherichia coli bacteria levels in the coastal Short Beach neighborhood of Branford, Connecticut. Citizen scientist involvement enabled collection of short-duration postprecipitation outfall flow water samples (mean E. coli level = 4930 most probable number per 100 mL) and yielded insights into scientific collaboration with local residents. A records review and sanitary questionnaire identified aging properties with septic systems (3.3%) and holding tanks (0.6%) as potential sources of the E. coli contamination. (Am J Public Health. 2022;112(9):1261-1264. https://doi.org/10.2105/AJPH.2022.306943).

Associations between long-term drought and diarrhea among children under five in low- and middle-income countries.

Climate change is projected to intensify drought conditions, which may increase the risk of diarrheal diseases in children. We constructed log-binomial generalized linear mixed models to examine the association between diarrhea risk, ascertained from global-scale nationally representative Demographic and Health Surveys, and drought, represented by the standardized precipitation evapotranspiration index, among children under five in 51 low- and middle-income countries (LMICs). Exposure to 6-month mild or severe drought was associated with an increased diarrhea risk of 5% (95% confidence interval 3-7%) or 8% (5-11%), respectively. The association was stronger among children living in a household that needed longer time to collect water or had no access to water or soap/detergent for handwashing. The association for 24-month drought was strong in dry zones but weak or null in tropical or temperate zones, whereas that for 6-month drought was only observed in tropical or temperate zones. In this work we quantify the associations between exposure to long-term drought and elevated diarrhea risk among children under five in LMICs and suggest that the risk could be reduced through improved water, sanitation, and hygiene practices, made more urgent by the likely increase in drought due to climate change.

Long-term ozone exposure and cognitive impairment among Chinese older adults: A cohort study.

Ambient particulate matter pollution has been linked to impaired cognitive performance, but the effect of ambient ozone exposure on cognitive function remains largely unknown. We examined the association of long-term ozone exposure with the risk of cognitive impairment among a national representative cohort of 9,544 Chinese older adults (aged 65 years and over) with baseline normal cognition from the Chinese Longitudinal Healthy Longevity Survey (2005-2018). The ozone exposure of each participant was measured by annual mean ozone concentrations for the county of residence. Cognitive function was assessed by the Chinese version of the Mini-Mental State Examination (MMSE). We defined cognitive impairment as an MMSE score below 18 points accompanied by an MMSE score that declined ≥ 4 points from baseline. Cox proportional hazards models were applied to explore the association of ozone exposure with cognitive impairment. During the mean follow-up time of 6.5 years, 2,601 older adults developed cognitive impairment. Each 10-µg/m3 increase in annual mean ozone exposure was associated with a 10.4% increased risk of cognitive impairment. The exposure-response relationship between ozone exposure and risk of cognitive impairment showed a linear trend. Sensitivity analyses revealed the association to be robust. We found that older adults from Eastern, Central, and Southern China were particularly susceptible. Our results show that ozone is a risk factor for late-life cognitive decline. Reducing ambient ozone pollution may help delay the onset of cognitive impairment among older adults.

Relationships between short-term ambient temperature exposure and kidney disease hospitalizations in the warm season in Vietnam: A case-crossover study.

BACKGROUND: Under a warming climate, adverse health effects of heat are an increasing concern. We evaluated associations between short-term ambient temperature exposure and hospital admission for kidney disease in Vietnam. METHODS: We linked province-level meteorologic data with admission data from 14 province-level hospitals (2003-2015). We used a case-crossover design to evaluate associations between daily ambient temperature metrics (mean, maximum, and minimum temperature and mean heat index) and risk of hospitalization for four kidney disease subtypes: glomerular diseases, renal tubulo-interstitial diseases, chronic kidney disease, and urolithiasis, including lagged (≤lag 14 days) and cumulative (≤lag 0-6 days) associations, during the warm season. We also evaluated independent associations with extreme heat days (defined as days with daily maximum temperature >95th percentile of the provincial daily maximum temperature distribution). Akaike's information criterion and patterns of risk estimates across cumulative exposure time windows and single-day lags informed our selection of final models. RESULTS: We included 58,330 hospital admissions during the warm season. Daily mean temperature averaged over the same day and the previous six days (lag 0-6 days) was associated with risk of hospitalization for each kidney disease outcome with odds ratios (per 1 °C increase in daily mean temperature) of 1.07 (95% confidence interval [CI]: 0.99, 1.16) for glomerular diseases, 1.06 (95% CI: 0.96, 1.17) for renal tubulo-interstitial diseases, 1.12 (95% CI: 1.00, 1.24) for chronic kidney disease, and 1.09 (95% CI: 1.02, 1.16) for urolithiasis. We found no additional independent associations with extreme heat. Results for the four temperature metrics were similar. CONCLUSIONS: High ambient temperature was associated with increased risk of hospitalization for each kidney disease subtype, with the most convincing associations for chronic kidney disease and urolithiasis. Further laboratory and epidemiologic research is needed to confirm the findings and disentangle the underlying mechanisms.

Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model.

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.

Short-term associations between particulate matter air pollution and hospital admissions through the emergency room for urinary system disease in Beijing, China: A time-series study.

Evidence on the relationship between particulate matter air pollution and urinary system disease (UD) is scarce. This study aims to evaluate the associations between short-term exposures to PM2.5 and PM10 and risk of daily UD inpatient hospital admissions through the emergency room (ER-admissions) in Beijing. We obtained 41,203 weekday UD ER-admissions for secondary and tertiary hospitals in all 16 districts in Beijing during 2013-2018 from the Beijing Municipal Health Commission Information Center and obtained district-level air pollution concentrations based on 35 fixed monitoring stations in Beijing. We conducted a two-stage time-series analysis, with district-specific generalized linear models for each of Beijing's 16 districts, followed by random effects meta-analysis to obtain pooled risk estimates. We evaluated lagged and cumulative associations up to 30 days. In single-pollutant models, for both PM2.5 and PM10, cumulative exposure averaged over the day of admission and the previous 10 days (lag 0-10 days) showed the strongest association, with per interquartile range increases of PM2.5 or PM10 concentrations associated with a 7.5 % (95 % confidence interval [CI]: 3.0 %-12.2 %) or 6.0 % (95 % CI: 1.1 %-11.2 %) increased risk of daily UD hospital admissions, respectively. The risk estimates were robust to adjustment for co-pollutants and to a variety of sensitivity analyses. However, due to the strong correlation between PM2.5 and PM10 concentrations, we were unable to disentangle the respective relationships between these two exposures and UD risk. In this study, we found that short-term exposures to PM2.5 and PM10 are risk factors for UD morbidity and that cumulative exposure to PM pollution over a period of one to two weeks (i.e., 11 days) could be more important for UD risk than transient exposure during each of the respective single days.

Relationship between flood severity and risk of hospitalisation in the Mekong River Delta of Vietnam.

OBJECTIVE: To examine the relationship between flood severity and risk of hospitalisation in the Vietnam Mekong River Delta (MRD). METHODS: We obtained data on hospitalisations and hydro-meteorological factors during 2011-2014 for seven MRD provinces. We classified each day into a flood-season exposure period: the 2011 extreme annual flood (EAF); 2012-2014 routine annual floods (RAF); dry season and non-flood wet season (reference period). We used province-specific Poisson regression models to calculate hospitalisation incidence rate ratios (IRRs). We pooled IRRs across provinces using random-effects meta-analysis. RESULTS: During the EAF, non-external cause hospitalisations increased 7.2% (95% CI 3.2% to 11.4%); infectious disease hospitalisations increased 16.4% (4.3% to 29.8%) and respiratory disease hospitalisations increased 25.5% (15.5% to 36.4%). During the RAF, respiratory disease hospitalisations increased 8.2% (3.2% to 13.5%). During the dry season, hospitalisations decreased for non-external causes and for each specific cause except injuries. CONCLUSIONS: We observed a gradient of decreasing risk of hospitalisation from EAF to RAF/non-flood wet season to dry season. Adaptation measures should be strengthened to prepare for the increased probability of more frequent extreme floods in the future, driven by climate change.

Role of meteorological factors in the transmission of SARS-CoV-2 in the United States.

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculate the fraction of Rt attributable to these meteorological conditions. Lower air temperature (within the 20-40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased Rt. The fraction of Rt attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66-3.76%), 9.35% (95% eCI: 9.27-9.39%), and 4.44% (95% eCI: 4.38-4.47%), respectively. In total, 17.5% of Rt was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role.