Evidence map›Paper›PMID 23425498›Full record

ArticleInternational journal of health geographics2013

A geographic analysis of population density thresholds in the influenza pandemic of 1918-19.

Siddharth Chandra, Eva Kassens-Noor, Goran Kuljanin, Joshua Vertalka

Abstract readHistorical Article
In one paragraph

Article in International journal of health geographics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  15. Resilient urban form to pandemics: Lessons from COVID-19.Medical journal of the Islamic Republic of Iran · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Siddharth ChandraAsian Studies Center, Michigan State University, 427 N Shaw Lane, Room 301, East Lansing, MI, 48824, USA. chandr45@msu.edu
Eva Kassens-Noor
Goran Kuljanin
Joshua Vertalka

Funding

Drug Consumption and Disease Mortality: Population-level AnalysesR21DA025917 · NIDA · MICHIGAN STATE UNIVERSITY · PI CHANDRA, SIDDHARTH · 2010 to 2011
$392k
NIDA NIH HHS 1R21DA025917-01A1NIDA NIH HHS R21 DA025917
6 · The paper itself

Abstract

backgroundGeographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918-19 in India, where over 15 million people died in the short span of less than one year.

methodsUsing data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918-19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts.

resultsThe results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%).

conclusionsThis paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.

Indexed as

CensusesPopulation DensityHistory, 20th CenturyHumansIndiaInfluenza, HumanPandemics

Identifiers

PMID23425498
PMCPMC3641965

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.