Disparities in cross-city pandemic severity during the 1918 Influenza Pandemic remain poorly understood. This paper uses newly assembled historical data on annual mortality across 438 U.S. cities to explore the determinants of pandemic mortality. We assess the role of three broad factors: i) pre-pandemic population health and poverty, ii) air pollution, and iii) the timing of onset and proximity to military bases. Using regression analysis, we find that cities in the top tercile of the distribution of pre-pandemic infant mortality had 21 excess deaths per 10,000 residents in 1918 relative to cities in the bottom tercile. Similarly, cities in the top tercile of the distribution of proportion of illiterate residents had 21.3 excess deaths per 10,000 residents during the pandemic relative to cities in the bottom tercile. Cities in the top tercile of the distribution of coal-fired electricity generating capacity, an important source of urban air pollution, had 9.1 excess deaths per 10,000 residents in 1918 relative to cities in the bottom tercile. There was no statistically significant relationship between excess mortality and city proximity to World War I bases or the timing of onset. In a counterfactual analysis, the three statistically significant factors accounted for 50 percent of cross-city variation in excess mortality in 1918.
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http://dx.doi.org/10.1016/j.ehb.2019.03.010 | DOI Listing |
PLoS One
January 2025
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany.
Industrialization contributes to economic growth; however, its negative impacts cannot be overlooked. The emission of toxic pollutants into the atmosphere by industries poses a serious threat to both environmental and human health. We conducted a field study in the top three most polluted cities of Pakistan to quantify the impacts of industrial air pollution on the perceived health effects of households.
View Article and Find Full Text PDFSci Total Environ
December 2024
Discipline of Civil, Structural and Environmental Engineering, School of Engineering & Architecture, University College Cork, Ireland; Environmental Research Institute, University College Cork, Lee Rd, Sunday's Well, Cork T23 XE10, Ireland. Electronic address:
There is an urgent need to rapidly reduce greenhouse gas (GHG) emissions and, although human activity is a primary driver of emissions, a knowledge gap remains in terms of the key individual and collective drivers of emissions, and on how to harmonise citizen-led climate action with top-down emissions mitigation policy. In response to this, an urban decarbonisation framework which was informed by systems thinking was developed to support multi-level climate action and decision making. Another aim was to demonstrate the integration of a data-driven and activity-based GHG emissions model for individuals into the framework to enable decarbonisation.
View Article and Find Full Text PDFRisk Manag Healthc Policy
December 2024
Nursing College, Taibah University, Madinah, Saudi Arabia.
Purpose: This study investigated the daily practices of community nurses working in Primary Health Care Centers (PHCCs) and their learning needs.
Participants And Methods: This descriptive cross-sectional correlational study was guided by the eight sections of the Canadian Community Health Nursing Standards of Practice 2019 expressing daily clinical activities and learning needs based on a five-point Likert scale. Participants were recruited from three Saudi Arabian cities.
PLoS One
December 2024
College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City.
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