Geospatial characteristics of measles transmission in China during 2005-2014
Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination p...
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description | Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously. |
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Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005474</identifier><identifier>PMID: 28376097</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Children ; Children & youth ; China - epidemiology ; Cluster Analysis ; Demographics ; Disease control ; Disease Outbreaks ; Disease prevention ; Disease transmission ; Economic development ; Environmental health ; Epidemics ; Eradication ; Geospatial data ; Health sciences ; Humans ; Immunization ; Incidence ; Infectious diseases ; Measles ; Measles - epidemiology ; Measles - transmission ; Medicine and Health Sciences ; Migrant workers ; Migration ; Minority & ethnic groups ; Outbreaks ; People and Places ; Population ; Population Surveillance ; Prevalence studies (Epidemiology) ; Public health ; Regional development ; Sampling ; Social Class ; Social Sciences ; Statistics ; Vaccination ; Vaccines ; Workers</subject><ispartof>PLoS computational biology, 2017-04, Vol.13 (4), p.e1005474-e1005474</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Yang W, Wen L, Li S-L, Chen K, Zhang W-Y, Shaman J (2017) Geospatial characteristics of measles transmission in China during 2005?2014. PLoS Comput Biol 13(4): e1005474. https://doi.org/10.1371/journal.pcbi.1005474</rights><rights>2017 Yang et al 2017 Yang et al</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Yang W, Wen L, Li S-L, Chen K, Zhang W-Y, Shaman J (2017) Geospatial characteristics of measles transmission in China during 2005?2014. PLoS Comput Biol 13(4): e1005474. https://doi.org/10.1371/journal.pcbi.1005474</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c661t-e122528d71305330a357015c96935b2c47c0a13aab93b06387efe79a765b9b683</citedby><cites>FETCH-LOGICAL-c661t-e122528d71305330a357015c96935b2c47c0a13aab93b06387efe79a765b9b683</cites><orcidid>0000-0002-7216-7809 ; 0000-0002-7555-9728</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395235/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395235/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28376097$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ferrari, Matthew (Matt)</contributor><creatorcontrib>Yang, Wan</creatorcontrib><creatorcontrib>Wen, Liang</creatorcontrib><creatorcontrib>Li, Shen-Long</creatorcontrib><creatorcontrib>Chen, Kai</creatorcontrib><creatorcontrib>Zhang, Wen-Yi</creatorcontrib><creatorcontrib>Shaman, Jeffrey</creatorcontrib><title>Geospatial characteristics of measles transmission in China during 2005-2014</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.</description><subject>Biology and Life Sciences</subject><subject>Children</subject><subject>Children & youth</subject><subject>China - epidemiology</subject><subject>Cluster Analysis</subject><subject>Demographics</subject><subject>Disease control</subject><subject>Disease Outbreaks</subject><subject>Disease prevention</subject><subject>Disease transmission</subject><subject>Economic development</subject><subject>Environmental health</subject><subject>Epidemics</subject><subject>Eradication</subject><subject>Geospatial data</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Immunization</subject><subject>Incidence</subject><subject>Infectious diseases</subject><subject>Measles</subject><subject>Measles - epidemiology</subject><subject>Measles - transmission</subject><subject>Medicine and Health Sciences</subject><subject>Migrant workers</subject><subject>Migration</subject><subject>Minority & ethnic groups</subject><subject>Outbreaks</subject><subject>People and Places</subject><subject>Population</subject><subject>Population Surveillance</subject><subject>Prevalence studies (Epidemiology)</subject><subject>Public health</subject><subject>Regional development</subject><subject>Sampling</subject><subject>Social Class</subject><subject>Social Sciences</subject><subject>Statistics</subject><subject>Vaccination</subject><subject>Vaccines</subject><subject>Workers</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk1vEzEQhlcIREvhHyBYiQscEmyPPy9IVQQlUgQSH2fL6_UmjjZ2sHcR_fd1yLZqUC_IB4_Gz7zjeTVV9RKjOQaB32_jmILp53vb-DlGiFFBH1XnmDGYCWDy8b34rHqW8xahEir-tDojEgRHSpxXqysX894M3vS13Zhk7OCSz4O3uY5dvXMm9y7XQzIh73zOPobah3qx8cHU7Zh8WNekNJ8RhOnz6kln-uxeTPdF9fPTxx-Lz7PV16vl4nI1s5zjYeYwIYzIVmBADAAZYAJhZhVXwBpiqbDIYDCmUdAgDlK4zgllBGeNariEi-r1UXffx6wnI7LGUikAoAQKsTwSbTRbvU9-Z9K1jsbrv4mY1tqkMmTvtDTCMUwdl1bSTnQKpO1oy9pGOipAFa0PU7ex2bnWulDc6E9ET1-C3-h1_K0ZKEaAFYG3k0CKv0aXB12ctK7vTXBxPPxbUsoJVbygb_5BH55uotamDOBDF0tfexDVl1SBACAUFWr-AFVO63bexuA6X_InBe9OCgozuD_D2ow56-X3b__Bfjll6ZG1KeacXHfnHUb6sMu3Q-rDLutpl0vZq_u-3xXdLi_cACTa7Do</recordid><startdate>20170401</startdate><enddate>20170401</enddate><creator>Yang, Wan</creator><creator>Wen, Liang</creator><creator>Li, Shen-Long</creator><creator>Chen, Kai</creator><creator>Zhang, Wen-Yi</creator><creator>Shaman, Jeffrey</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7216-7809</orcidid><orcidid>https://orcid.org/0000-0002-7555-9728</orcidid></search><sort><creationdate>20170401</creationdate><title>Geospatial characteristics of measles transmission in China during 2005-2014</title><author>Yang, Wan ; Wen, Liang ; Li, Shen-Long ; Chen, Kai ; Zhang, Wen-Yi ; Shaman, Jeffrey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c661t-e122528d71305330a357015c96935b2c47c0a13aab93b06387efe79a765b9b683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Biology and Life Sciences</topic><topic>Children</topic><topic>Children & youth</topic><topic>China - epidemiology</topic><topic>Cluster Analysis</topic><topic>Demographics</topic><topic>Disease control</topic><topic>Disease Outbreaks</topic><topic>Disease prevention</topic><topic>Disease transmission</topic><topic>Economic development</topic><topic>Environmental health</topic><topic>Epidemics</topic><topic>Eradication</topic><topic>Geospatial data</topic><topic>Health sciences</topic><topic>Humans</topic><topic>Immunization</topic><topic>Incidence</topic><topic>Infectious diseases</topic><topic>Measles</topic><topic>Measles - epidemiology</topic><topic>Measles - transmission</topic><topic>Medicine and Health Sciences</topic><topic>Migrant workers</topic><topic>Migration</topic><topic>Minority & ethnic groups</topic><topic>Outbreaks</topic><topic>People and Places</topic><topic>Population</topic><topic>Population Surveillance</topic><topic>Prevalence studies (Epidemiology)</topic><topic>Public health</topic><topic>Regional development</topic><topic>Sampling</topic><topic>Social Class</topic><topic>Social Sciences</topic><topic>Statistics</topic><topic>Vaccination</topic><topic>Vaccines</topic><topic>Workers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Wan</creatorcontrib><creatorcontrib>Wen, Liang</creatorcontrib><creatorcontrib>Li, Shen-Long</creatorcontrib><creatorcontrib>Chen, Kai</creatorcontrib><creatorcontrib>Zhang, Wen-Yi</creatorcontrib><creatorcontrib>Shaman, Jeffrey</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Wan</au><au>Wen, Liang</au><au>Li, Shen-Long</au><au>Chen, Kai</au><au>Zhang, Wen-Yi</au><au>Shaman, Jeffrey</au><au>Ferrari, Matthew (Matt)</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Geospatial characteristics of measles transmission in China during 2005-2014</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2017-04-01</date><risdate>2017</risdate><volume>13</volume><issue>4</issue><spage>e1005474</spage><epage>e1005474</epage><pages>e1005474-e1005474</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28376097</pmid><doi>10.1371/journal.pcbi.1005474</doi><orcidid>https://orcid.org/0000-0002-7216-7809</orcidid><orcidid>https://orcid.org/0000-0002-7555-9728</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biology and Life Sciences Children Children & youth China - epidemiology Cluster Analysis Demographics Disease control Disease Outbreaks Disease prevention Disease transmission Economic development Environmental health Epidemics Eradication Geospatial data Health sciences Humans Immunization Incidence Infectious diseases Measles Measles - epidemiology Measles - transmission Medicine and Health Sciences Migrant workers Migration Minority & ethnic groups Outbreaks People and Places Population Population Surveillance Prevalence studies (Epidemiology) Public health Regional development Sampling Social Class Social Sciences Statistics Vaccination Vaccines Workers |
title | Geospatial characteristics of measles transmission in China during 2005-2014 |
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