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Record W1940768770 · doi:10.1017/s002205071800058x

Pollution, Infectious Disease, and Mortality: Evidence from the 1918 Spanish Influenza Pandemic

2015· article· en· W1940768770 on OpenAlexafffund
Karen Clay, Joshua Lewis, Edson Severnini

Bibliographic record

VenueThe Journal of Economic History · 2015
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversité de Montréal
FundersCarnegie Mellon UniversityUniversité de MontréalUniversity of Chicago
KeywordsPandemicInfluenza pandemicVirologyDiseaseInfectious disease (medical specialty)PollutionGeographyMedicineEnvironmental healthCoronavirus disease 2019 (COVID-19)BiologyInternal medicine

Abstract

fetched live from OpenAlex

The 1918 Influenza Pandemic killed millions worldwide and hundreds of thousands in the United States. This article studies the impact of air pollution on pandemic mortality. The analysis combines a panel dataset on infant and all-age mortality with a novel measure of air pollution based on the burning of coal in a large sample of U.S. cities. We estimate that air pollution contributed significantly to pandemic mortality. Cities that used more coal experienced tens of thousands of excess deaths in 1918 relative to cities that used less coal with similar pre-pandemic socioeconomic conditions and baseline health. Factors related to poverty, public health, and the timing of onset also affected pandemic mortality. The findings support recent medical evidence on the link between air pollution and influenza infection, and suggest that poor air quality was an important cause of mortality during the pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.384
GPT teacher head0.400
Teacher spread0.015 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2015
Admission routes2
Has abstractyes

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