A geographic analysis of population density thresholds in the influenza pandemic of 1918–19
Identifieur interne : 000640 ( Pmc/Checkpoint ); précédent : 000639; suivant : 000641A geographic analysis of population density thresholds in the influenza pandemic of 1918–19
Auteurs : Siddharth Chandra [États-Unis] ; Eva Kassens-Noor [États-Unis] ; Goran Kuljanin [États-Unis] ; Joshua Vertalka [États-Unis]Source :
- International Journal of Health Geographics [ 1476-072X ] ; 2013.
Abstract
Geographic 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.
Using 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.
The 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%).
This 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.
The online version of this article (doi:10.1186/1476-072X-12-9) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/1476-072X-12-9
PubMed: 23425498
PubMed Central: 3641965
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uniqKey="Kassens Noor E" first="Eva" last="Kassens-Noor">Eva Kassens-Noor</name><affiliation wicri:level="2"><nlm:aff id="Aff2">Urban and Transport Planning in the School of Planning,, Design, and Construction and Global Urban Studies Program,, 552 W Circle Drive, Room 201E,, 48824, East Lansing,, MI, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Urban and Transport Planning in the School of Planning,, Design, and Construction and Global Urban Studies Program,, 552 W Circle Drive, Room 201E,, 48824, East Lansing,, MI</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author><author><name sortKey="Kuljanin, Goran" sort="Kuljanin, Goran" uniqKey="Kuljanin G" first="Goran" last="Kuljanin">Goran Kuljanin</name><affiliation wicri:level="2"><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.17088.36</institution-id><institution-id institution-id-type="ISNI">0000 0001 2150 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last="Chandra">Siddharth Chandra</name><affiliation wicri:level="2"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.17088.36</institution-id><institution-id institution-id-type="ISNI">0000000121501785</institution-id><institution>Asian Studies Center,,</institution><institution>Michigan State University,,</institution></institution-wrap>427 N Shaw Lane, Room 301,, 48824, East Lansing,, MI, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>427 N Shaw Lane, Room 301,, 48824, East Lansing,, MI</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author><author><name sortKey="Kassens Noor, Eva" sort="Kassens Noor, Eva" uniqKey="Kassens Noor E" first="Eva" last="Kassens-Noor">Eva Kassens-Noor</name><affiliation wicri:level="2"><nlm:aff id="Aff2">Urban and Transport Planning in the School of Planning,, Design, and Construction and Global Urban Studies Program,, 552 W Circle Drive, Room 201E,, 48824, East Lansing,, MI, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Urban and Transport Planning in the School of Planning,, Design, and Construction and Global Urban Studies Program,, 552 W Circle Drive, Room 201E,, 48824, East Lansing,, MI</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author><author><name sortKey="Kuljanin, Goran" sort="Kuljanin, Goran" uniqKey="Kuljanin G" first="Goran" last="Kuljanin">Goran Kuljanin</name><affiliation wicri:level="2"><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.17088.36</institution-id><institution-id institution-id-type="ISNI">0000 0001 2150 1785</institution-id><institution>Department of Psychology,,</institution></institution-wrap>Psychology Building 316 Physics Room 262,, 48824, East Lansing,, MI, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Psychology Building 316 Physics Room 262,, 48824, East Lansing,, MI</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author><author><name sortKey="Vertalka, Joshua" sort="Vertalka, Joshua" uniqKey="Vertalka J" first="Joshua" last="Vertalka">Joshua Vertalka</name><affiliation wicri:level="2"><nlm:aff id="Aff4">Department of Geography,, 673 Auditorium Road, Room 116,, 48824, East Lansing,, MI, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Geography,, 673 Auditorium Road, Room 116,, 48824, East Lansing,, MI</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author></analytic><series><title level="j">International Journal of Health Geographics</title><idno type="eISSN">1476-072X</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Geographic 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.</p></sec><sec><title>Methods</title><p>Using 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.</p></sec><sec><title>Results</title><p>The 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%).</p></sec><sec><title>Conclusions</title><p>This 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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/1476-072X-12-9) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Hobday, Rap" uniqKey="Hobday R">RAP Hobday</name></author><author><name sortKey="Cason, Jwp" uniqKey="Cason J">JWP Cason</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lezzoni, L" uniqKey="Lezzoni L">L Lezzoni</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Mamelund, S E" uniqKey="Mamelund S">S-E 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Int J Health Geogr</journal-id><journal-id journal-id-type="iso-abbrev">Int J Health Geogr</journal-id><journal-title-group><journal-title>International Journal of Health Geographics</journal-title></journal-title-group><issn pub-type="epub">1476-072X</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23425498</article-id><article-id pub-id-type="pmc">3641965</article-id><article-id pub-id-type="publisher-id">536</article-id><article-id pub-id-type="doi">10.1186/1476-072X-12-9</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>A geographic analysis of population density thresholds in the influenza pandemic of 1918–19</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Chandra</surname><given-names>Siddharth</given-names></name><address><email>chandr45@msu.edu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Kassens-Noor</surname><given-names>Eva</given-names></name><address><email>ekn@msu.edu</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Kuljanin</surname><given-names>Goran</given-names></name><address><email>gkuljanin@gmail.com</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Vertalka</surname><given-names>Joshua</given-names></name><address><email>vertalk2@msu.edu</email></address><xref ref-type="aff" rid="Aff4">4</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.17088.36</institution-id><institution-id institution-id-type="ISNI">0000000121501785</institution-id><institution>Asian Studies Center,,</institution><institution>Michigan State University,,</institution></institution-wrap>427 N Shaw Lane, Room 301,, 48824, East Lansing,, MI, USA</aff><aff id="Aff2"><label>2</label>Urban and Transport Planning in the School of Planning,, Design, and Construction and Global Urban Studies Program,, 552 W Circle Drive, Room 201E,, 48824, East Lansing,, MI, USA</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="GRID">grid.17088.36</institution-id><institution-id institution-id-type="ISNI">0000 0001 2150 1785</institution-id><institution>Department of Psychology,,</institution></institution-wrap>Psychology Building 316 Physics Room 262,, 48824, East Lansing,, MI, USA</aff><aff id="Aff4"><label>4</label>Department of Geography,, 673 Auditorium Road, Room 116,, 48824, East Lansing,, MI, USA</aff></contrib-group><pub-date pub-type="epub"><day>20</day><month>2</month><year>2013</year></pub-date><pub-date pub-type="pmc-release"><day>20</day><month>2</month><year>2013</year></pub-date><pub-date pub-type="collection"><year>2013</year></pub-date><volume>12</volume><elocation-id>9</elocation-id><history><date date-type="received"><day>15</day><month>10</month><year>2012</year></date><date date-type="accepted"><day>13</day><month>1</month><year>2013</year></date></history><permissions><copyright-statement>© Chandra et al.; licensee BioMed Central Ltd. 2013</copyright-statement><license license-type="OpenAccess"><license-p>This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Geographic 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.</p></sec><sec><title>Methods</title><p>Using 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.</p></sec><sec><title>Results</title><p>The 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%).</p></sec><sec><title>Conclusions</title><p>This 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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/1476-072X-12-9) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Influenza</kwd><kwd>Population loss</kwd><kwd>Population density</kwd><kwd>Thresholds</kwd><kwd>Spatial distribution</kwd><kwd>Population growth</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2013</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Michigan</li></region></list><tree><country name="États-Unis"><region name="Michigan"><name sortKey="Chandra, Siddharth" sort="Chandra, Siddharth" uniqKey="Chandra S" first="Siddharth" last="Chandra">Siddharth Chandra</name></region><name sortKey="Kassens Noor, Eva" sort="Kassens Noor, Eva" uniqKey="Kassens Noor E" first="Eva" last="Kassens-Noor">Eva Kassens-Noor</name><name sortKey="Kuljanin, Goran" sort="Kuljanin, Goran" uniqKey="Kuljanin G" first="Goran" last="Kuljanin">Goran Kuljanin</name><name sortKey="Vertalka, Joshua" sort="Vertalka, Joshua" uniqKey="Vertalka J" first="Joshua" last="Vertalka">Joshua Vertalka</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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