District-specific Effects Research Articles

Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020

BackgroundScientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. MethodsA two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM2.5 and PM10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. ResultsA total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM2.5 corresponded to 1.91% (95% CI: 1.33–2.49%), 2.16% (95% CI: 1.08–3.25%), and 1.92% (95% CI: 1.46–2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0–8 days, lag 8 days, and lag 8 days. The effect size of PM2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM10 were 3.86% (95% CI: 2.48–5.27%), 3.73% (95% CI: 1.72–5.79%), and 3.92% (95% CI: 2.65–5.21%), respectively, at lag 0–13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. ConclusionsShort-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM2.5 was higher in patients with T2D than those without T2D.

Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories

Airborne laser scanning (ALS) data provide a detailed representation of forest canopy structure and are highly suitable for forest inventory applications. Providing three-dimensional data at a lower cost, digital aerial photogrammetry (DAP) has emerged as an alternative to ALS. Previous studies have compared the utility of ALS and DAP data for predicting forest attributes, however none of those studies used data acquired as part of large-area operational inventories. We used ALS, DAP and field data obtained from 836 plots and five operational inventories conducted in southeastern Norway. We compared local and regional modelling approaches for predicting basal area, number of stems, volume, Lorey's mean height, and dominant height. First, we developed district-, forest type-, and data source-specific nonlinear prediction models for all forest attributes (local models). Second, we fitted forest type- and data-source specific nonlinear models with pooled data from all districts (regional models), in which we included dummy variables to account for district-specific effects. We compared the accuracies of ALS- and DAP-based predictions made with local and regional models. Finally, we assessed how a reduced number of calibration plots affected the accuracies of ALS- and DAP- based predictions made with regional models. In general, ALS-based predictions made with local models were most accurate. District-specific effects needed to be accounted for in the regional models. ALS models required substantially fewer calibration plots than DAP models. The accuracies obtained with regional ALS models fitted with 50% of the available data were better than the accuracies obtained with local DAP models fitted with all the available data. Thus, by fitting regional ALS models with data from multiple inventories, field efforts can be reduced substantially while still obtaining better prediction accuracies than by fitting local DAP models.

Spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China.

Due to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10μm in aerodynamic diameter (PM10) and nitrogen dioxide (NO2) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM10 and NO2, and meteorological factors from January 1, 2009, to December 31, 2010, in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM10 and NO2 on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM and the strongest effects were identified for 2-day moving average lag 0-1. For every 10μg/m3 increases in concentrations of PM10 and NO2, the corresponding daily cardiovascular mortality increases in 0.31% (95%CI 0.15%, 0.46%) and 1.63% (95%CI 1.11%, 2.13%), respectively. The death risk associated with air pollutants varied across different geographic districts in Beijing. We found spatially varied adverse effects of air pollution on cardiovascular deaths in Beijing. But there was insufficient evidence to show the significant spatial heterogeneity in mortality effects of PM10 and NO2 in this study.

The spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China

Owing to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10 μm in aerodynamic diameter (PM10), nitrogen dioxide (NO2) and carbon monoxide (CO) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM10, NO2 and CO, and meteorological factors from 1 January 2009 to 31 December 2010 in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM10, NO2 and CO on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM, and the strongest effects were identified for 2-day moving average lag 0–1. The interquartile increases in concentrations of PM10, NO2 and CO were associated with 2.46 (95% confidence interval (CI), 1.22–3.72), 4.11 (95%CI, 2.82–5.42) and 2.23 (95%CI, 1.14–3.33) percentage increases in daily cardiovascular mortality by GAMM, respectively. The relative risk of each district compared with reference district was generally statistically significant. The death risk associated with air pollutants varies across different geographic districts in Beijing. The data indicate that the risk is high in suburban areas and rural counties. We found significant and spatially varied adverse effects of air pollution on cardiovascular deaths across the rural and urban areas in Beijing.

Acute Effects of Nitrogen Dioxide on Cardiovascular Mortality in Beijing: An Exploration of Spatial Heterogeneity and the District-specific Predictors

The exploration of spatial variation and predictors of the effects of nitrogen dioxide (NO2) on fatal health outcomes is still sparse. In a multilevel case-crossover study in Beijing, China, we used mixed Cox proportional hazard model to examine the citywide effects and conditional logistic regression to evaluate the district-specific effects of NO2 on cardiovascular mortality. District-specific predictors that could be related to the spatial pattern of NO2 effects were examined by robust regression models. We found that a 10 μg/m3 increase in daily mean NO2 concentration was associated with a 1.89% [95% confidence interval (CI): 1.33–2.45%], 2.07% (95% CI: 1.23–2.91%) and 1.95% (95% CI: 1.16–2.72%) increase in daily total cardiovascular (lag03), cerebrovascular (lag03) and ischemic heart disease (lag02) mortality, respectively. For spatial variation of NO2 effects across 16 districts, significant effects were only observed in 5, 4 and 2 districts for the above three outcomes, respectively. Generally, NO2 was likely having greater adverse effects on districts with larger population, higher consumption of coal and more civilian vehicles. Our results suggested independent and spatially varied effects of NO2 on total and subcategory cardiovascular mortalities. The identification of districts with higher risk can provide important insights for reducing NO2 related health hazards.

Asset Creation through National Rural Employment Guarantee Scheme (NREGS) and its Impact on West Bengal Agriculture: A District Level Analysis

An important objective of National Rural Employment Guarantee Scheme presently known as Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is to raise agricultural productivity through creating different productive assets related to agriculture. The present study investigates whether different kinds of asset creation activities related to agriculture under NREGS have any impact on the cropping intensity of West Bengal over the years. We have taken 18 districts in West Bengal and the time period is considered up to 2013-14. We primarily carried out Random Effect estimation without considering the district specific effects. The result shows that work like ‘micro irrigation’ and ‘rural connectivity’ through NREGS has created a positive but small impact on cropping intensity. It is further observed that average rain fall and average harvest price of principal crops in the previous period play a significant role to enhance the cropping intensity of West Bengal. Considering few bottlenecks of random effect estimation, we as supplementary carried out the Fixed effect estimation to judge both cross district disparity in cropping intensity through district specific effects and variation over the time periods via examining time specific effects.Â

Effect of Climate on Productivity of Pigeonpea and Cotton in Andhra Pradesh – A Panel Data Regression

This paper was attempted to examine the effect of temperature and rainfall on the productivity of two important crops-pigeonpea and cotton in Andhra Pradesh following panel data regression approach. Using the district level time series data, the yield of each of these two crops was regressed on maximum temperature during kharif, rainfall quantity and number of rainy days for the period 1990–2002, in a one-way and two-way fixed effect models of panel regression using Least Squares Dummy Variable Method. Comparison of district specific effects revealed statistically significant differences between districts in case of pigeonpea. The response coefficients for rainfall and number of rainy days were found significant at 5 per cent level. A positive impact on yield of pigeonpea at the rate of 1.9 kg/ha was observed for a 10 mm rise in rainfall. The number of rainy days was found to have a significant negative relationship with yield. In case of cotton, half of the major cotton growing districts differed significantly in mean yield levels. A significant reduction in yield at a rate of 13 kg/ha for every 1°C rise in the maximum temperature was observed. The yield of cotton was found to increase by 0.7 kg/ha for every 10 mm increase in the rainfall.

The spatial characteristics of ambient particulate matter and daily mortality in the urban area of Beijing, China

Few epidemiological studies have reported the spatial characteristics of the association between particulate matter <10μm in aerodynamic diameter (PM10) and mortality in China. This study explored the spatial characteristics of the association between ambient PM10 and mortality in the urban area of Beijing, China. We collected daily data on air pollution, weather conditions and mortality in the eight urban districts of Beijing from Jan. 1st 2008 to Dec. 31st 2009. A Poisson Generalized Additive Model (GAM) was used to examine the district-specific effects of PM10 on cause-specific mortality. A Poisson Generalized Additive Mixed Model (GAMM) was used to examine the urban-wide association between PM10 and cause-specific mortality while controlling for the random effects of districts, compared with GAM which did not control for the random effects of districts. The inter-quartile ranges (IQRs) of annual PM10 ranged from 83.5μg/m3 (Chaoyang district) to 96.0μg/m3 (Shijingshan district). A 96.0μg/m3 increase of PM10 was associated with a 7.52% (95%CI: 1.78%–13.56%) increase of cardiovascular mortality in Shijingshan district while an 87.0μg/m3 increase of PM10 was associated with a 7.68% (95%CI: 0.08%–15.86%) increase of respiratory deaths in Dongcheng district. The urban-wide effects derived from GAMM showed that an 88.0μg/m3 increase of PM10 was associated with an increase of 1.30% (95%CI: 0.45%–2.16%), 2.60% (95%CI: 0.14%–5.11%) in non-accidental and respiratory mortality, illustrating, higher results than those from the GAM. In conclusion, there is spatial variation in ambient PM10 concentration as well as in the effects of PM10 on cause-specific mortality in the urban area of Beijing. Additionally, GAMM model may be more effective in estimating the spatial association between urban-wide PM10 and cause-specific mortality.