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7 results for “premature mortality”

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zenodo36/100

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States

<p>This repo includes supporting material for the publication:</p> <p>Qiu, M., Zigler, C. M., &amp; Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States.&nbsp;<em>Science Advances</em>,&nbsp;<em>8</em>(48), eabn8762.</p> <p>Please download and unzip the file &quot;<a href="https://zenodo.org/api/files/bef77248-ca65-4ae6-9a92-ad00fc068665/mhqiu/wind_pollution_EJ-v1.0.zip?versionId=a9b5b8e9-273c-4431-a101-4f9a4c331675">mhqiu/wind_pollution_EJ-v1.0.zip</a>&quot;. Please see README for a full description of the sample data included in this repo.&nbsp;</p> <p><strong>README</strong></p> <p><strong>1. Regression results:&nbsp;</strong><br> <em>EGU_regression_scenario_results.xlsx&nbsp;</em><br> It includes regression results for each EGU in our sample (1264 EGUs in total).&nbsp;</p> <p><strong>2. Air quality simulation results:</strong><br> <strong>2.1 GEOS-Chem simulation</strong><br> <em>GC_daily_pm25_o3_scenarios.nc&nbsp;</em><br> It contains surface level annual mean PM2.5 and MDA8 O3 concentration under different emission scenarios. We include four scenarios in total:<br> - baseline scenario: air quality **without** the amount of wind power associated with 2014 RPS targets&nbsp;<br> - expost scenario: &nbsp;air quality with the wind power associated with 2014 RPS targets under the current dispatch decisions<br> - health damage minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the health damage<br> - CO2 minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the CO2 emissions</p> <p>Therefore, to calculate the air quality impacts of wind power under different dispatch decisions: &nbsp;<br> current (ex post) = ex post - baseline. &nbsp;<br> health damage minimizing = &nbsp;health damage minimizing - baseline. &nbsp;<br> CO2 minimizing scenario = CO2 minimizing - baseline. &nbsp;</p> <p><strong>2.2 InMAP simulations</strong><br> <em>InMAP/xx.shp&nbsp;&nbsp;</em><br> The shapefiles contain annual mean PM2.5 concentration simulated with InMAP under different emission scenarios. &nbsp;<br> baseline.shp: baseline scenario &nbsp;<br> ex post.shp: ex post scenario &nbsp;<br> health_damage_minimizing.shp: health damage minimizing scenario &nbsp;<br> co2_minimizing.shp: CO2 minimizing scenario</p> <p>Descriptions of the four scenarios are the same as above for GEOS-Chem.</p> <p><strong>3. County-level air quality change for different demographic groups (GEOS-Chem)</strong><br> <em>county_pm_o3_changes_by_groups_GEOS_CHEM.xlsx&nbsp;&nbsp;</em><br> This file contains changes in county-level simulated PM2.5 and O3 concentrations due to wind power under different scenarios. It also includes the total population at the county level and the population for each subgroup. This data can be used to calculate the distributional effects of air quality benefits across different population groups.</p> <p>&nbsp;</p> <p>&nbsp;</p>

openother-openNov 2022View details →
dryad32/100

Data from: Population characteristics, mechanisms of primary care and premature mortality in England: a cross-sectional study

Objectives. Health systems with strong primary care tend to have better population outcomes, but in many countries demand for care is growing. We sought to identify mechanisms of primary care that influence premature mortality. Design. We developed a conceptual model of the mechanisms by which primary care influences premature mortality, and undertook a cross-sectional study in which population and primary care variables reflecting the model were used to explain variations in mortality under aged 75 years. The premature standardised mortality ratios (SMRs) for each practice, available from the Department of Health, had been calculated from numbers of deaths in the five years 2006-10. A regression model was undertaken with explanatory variables for the year 2009/10, and repeated to check stability using data for 2008/09 and 2010/11. Setting: All general practices in England were eligible for inclusion, and of the total of 8290, complete data were available for 7858. Results. Population variables, particularly deprivation, were the most powerful predictors of premature mortality, but the mechanisms of primary care depicted in our model also affected mortality. The number of GPs/1000 population and detection of hypertension were negatively associated with mortality. In less deprived practices, continuity of care was also negatively associated with mortality. Conclusions. Greater supply of primary care is associated with lower premature mortality even in a health system that has strong primary care (England). Health systems need to sustain the capacity of primary care to deliver effective care, and should assist primary care providers in identifying and meeting the needs of socio-economically deprived groups.

opencc-zeroDec 2015View details →
dryad32/100

Data from: Population characteristics, mechanisms of primary care and premature mortality in England: a cross-sectional study

Open the record for dataset details and reuse information.

publicJan 2016View details →
dryad28/100

Data from: Ablation of insulin-producing cells prevents obesity but not premature mortality caused by a high-sugar diet in Drosophila

Open the record for dataset details and reuse information.

publicNov 2014View details →
geo24/100

Consequences of high temperatures and premature mortality on the transcriptome and blood physiology of wild adult sockeye salmon (Oncorhynchus nerka)

GEO Series GSE33586. Oncorhynchus mykiss; Oncorhynchus nerka; Salmo salar; Oncorhynchus tshawytscha; Osmerus mordax; Coregonus clupeaformis. 40 samples. Type: Expression profiling by array.

openGEO-OpenMay 2012View details →
geo24/100

Loss of the long non-coding RNA, Malat1, accelerates premature aging and mortality

GEO Series GSE130392. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJul 2022View details →
ClinicalTrials.gov24/100

Predictive Score for Neonatal Mortality for Women With Premature Rupture of Membranes Between 22 and 28 Weeks of Gestation

ClinicalTrials.gov study NCT03108404. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

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International Brain Laboratory public data

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OpenNeuro

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