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

Effects of weather and air pollution on outpatient visits for insect-and-mite-caused dermatitis: an empirical and predictive analysis

BACKGROUND

Dermatitis caused by insects and mites, diagnosed as papular urticaria or scabies, is a common skin disease. However, there is still a lack of studies about the effects of weather and air pollution on outpatient visits for this disease. This study aims to explore the impacts of meteorological and environmental factors on daily visits of dermatitis outpatients.

METHODS

Analyses are conducted on a total of 43,101 outpatient visiting records during the years 2015-2020 from the largest dermatology specialist hospital in Guangzhou, China. Hierarchical cluster models based on Pearson correlation between risk factors are utilized to select regression variables. Linear regression models are fitted to identify the statistically significant associations between the risk factors and daily visits, taking into account the short-term effects of temperatures. Permutation importance is adopted to evaluate the predictive ability of these factors.

RESULTS

Short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. In terms of total outpatients, the one-day lagged temperature not only has a significant impact on daily visits, but also has the highest median value of permutation importance. This conclusion is robust across most subgroups except for subgroups of summer and scabies, wherein the three-day lagged temperature has a negative effect. By contrast, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. Moreover, weekdays, holidays and trends have significant impacts on daily visits, but with weak predictive abilities.

CONCLUSIONS

Our study suggests that short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. Nevertheless, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

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 PM_2.5, 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 PM_2.5 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 PM_2.5 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.

Concentration prediction and spatial origin analysis of criteria air pollutants in Shanghai

Severe air pollution events still occur frequently in Shanghai. In order to predict when Shanghai air quality satisfies the National Ambient Air Quality Standards of China (NAAQSC) and identify potential source areas of criteria air pollutants for the regional joint prevention and control of air pollution, concentration data of PM_2.5, PM₁₀, SO₂, NO₂ and O₃ were collected in 2014-2022 at fourteen monitoring sites across Shanghai and surrounding areas. A first - order rate equation with harmonic regression analysis was employed for time series analysis and concentration prediction. Decreasing concentrations were observed widely over all sites except O₃ and NO₂. It is very likely that the secondary NAAQSC standards for PM_x, and SO₂ would be met by 2025 and O₃ and NO₂ would likely become the critical pollutants that determine air quality level after 2025. Regional transport was predominant for PM_x and SO₂ pollution. A 3D - CWT multisite joint location method was developed to identify their potential source areas at different spatial resolutions. Weighting function correction was assigned via information entropy of endpoint numbers in each cell. A probabilistic parameter WI_PSA was proposed to quantify and normalize the probability that grid cells are source areas in order to achieve fourteen - site joint location, and it was comparable and compatible at different spatial resolutions. Potential source areas of PM_2.5 and PM₁₀ were similar, including Henan, Shandong, Hebei and Anhui, while origin domains of SO₂ mainly covered Henan and Hebei. In all seasons, air pollution that was transported to Shanghai (i.e., PM_x and SO₂) originated mainly from the North China Plain; the contribution of marine sources was neglectable.

Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Air pollution seriously threats to human health. Understanding the health effects of air pollution is of great importance for developing countermeasures. However, little is known about the real-time impacts of air pollution on the human heath in a comprehensive way in developing nations, like China. To fill this research gap, the Chinese urbanites' health were sensed from more than 210.82 million Weibo (Chinese Twitter) data in 2017. The association between air pollution and the health sensing were quantified through generalized additive models, based on which the sensitivities and adaptions to air pollution in 70 China's cities were assessed. The results documented that the Weibo data can well sense urbanites' health in real time. With the different geographical characteristics and socio-economic conditions, the Chinese residents have adaption to air pollution, indicated by the spatial heterogeneity of the sensitivities to air pollution. Cities with good air quality in South China and East China were more sensitive to air pollution, while cities with worse air quality in Northwest China and North China were less sensitive. This research provides a new perspective and methodologies for health sensing and the health effect of air pollution.

Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records

BACKGROUND

In the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change.

METHODS

We examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I.

RESULTS

We observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 ˚C) to the mean (16.2 ˚C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: -0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications.

DISCUSSION

Increasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

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 >95^th 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.

Examining the Relationships Between Air Pollutants and the Incidence of Acute Aortic Dissection with Electronic Medical Data in a Moderately Polluted Area of Northwest China

This paper explored whether air pollutants influenced acute aortic dissection (AAD) incidence in a moderately polluted area. A total of 494 AAD patients’ data from 2013 to 2016 were analyzed. The results showed that AAD had the strongest associations with PM10, SO2, NO2, CO, and O3 on the day before an AAD incident (lag1) and with PM2.5 two days before an incident (lag2) in single-pollutant model. In the three-pollutant model, PM10 was associated with the highest risk of adverse effects (RR = 1.37, 95% CI: 1.22, 1.53), whereas PM2.5 was associated with the lowest risk (RR = .83, 95% CI: .79, .88). Both PM2.5 and PM10 were affected by season, and SO2 was significantly different between heating and non-heating seasons as well. This study revealed significant associations between short-term PM2.5, PM10, and SO2 exposure and daily AAD incidence, showing that PM10 and SO2 were strong predictors of AAD incidence in a moderately polluted area.