Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil
Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerab...
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| creator | Raymundo, Carlos Eduardo Oliveira, Marcella Cini Eleuterio, Tatiana de Araujo André, Suzana Rosa da Silva, Marcele Gonçalves Queiroz, Eny Regina da Silva Medronho, Roberto de Andrade |
| description | Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country.
This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).
The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.
Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness. |
| doi_str_mv | 10.1371/journal.pone.0247794 |
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This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).
The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.
Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0247794</identifier><identifier>PMID: 33647044</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Antiviral agents ; Bayesian analysis ; Brazil ; Brazil - epidemiology ; Cities - epidemiology ; Coronaviruses ; COVID-19 ; COVID-19 - diagnosis ; COVID-19 - epidemiology ; COVID-19 - mortality ; Data analysis ; Decision analysis ; Decision making ; Demography ; Disease control ; Disease transmission ; Distribution ; Empirical analysis ; Epidemics ; Fatalities ; Female ; Humans ; Incidence ; Income inequality ; Male ; Medicine and Health Sciences ; Mortality ; Municipalities ; Nurses - statistics & numerical data ; Pandemics ; People and places ; Research and Analysis Methods ; Respiratory diseases ; Risk Factors ; Severe acute respiratory syndrome coronavirus 2 ; Social aspects ; Social distancing ; Sociodemographics ; Socioeconomic Factors ; Spatial Analysis ; Spatial Regression ; Statistical analysis ; Statistical methods ; Vaccines ; Viral diseases</subject><ispartof>PloS one, 2021-03, Vol.16 (3), p.e0247794-e0247794</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Raymundo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Raymundo et al 2021 Raymundo et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-cf52b009cd10c21fcb8a2f1766e0a8250da1aadf757e25f4b155e3113765e6403</citedby><cites>FETCH-LOGICAL-c692t-cf52b009cd10c21fcb8a2f1766e0a8250da1aadf757e25f4b155e3113765e6403</cites><orcidid>0000-0002-4150-4403</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/PMC7920392/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920392/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33647044$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Raymundo, Carlos Eduardo</creatorcontrib><creatorcontrib>Oliveira, Marcella Cini</creatorcontrib><creatorcontrib>Eleuterio, Tatiana de Araujo</creatorcontrib><creatorcontrib>André, Suzana Rosa</creatorcontrib><creatorcontrib>da Silva, Marcele Gonçalves</creatorcontrib><creatorcontrib>Queiroz, Eny Regina da Silva</creatorcontrib><creatorcontrib>Medronho, Roberto de Andrade</creatorcontrib><title>Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country.
This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).
The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.
Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.</description><subject>Antiviral agents</subject><subject>Bayesian analysis</subject><subject>Brazil</subject><subject>Brazil - epidemiology</subject><subject>Cities - epidemiology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - diagnosis</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - mortality</subject><subject>Data analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Demography</subject><subject>Disease control</subject><subject>Disease transmission</subject><subject>Distribution</subject><subject>Empirical analysis</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Female</subject><subject>Humans</subject><subject>Incidence</subject><subject>Income inequality</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Mortality</subject><subject>Municipalities</subject><subject>Nurses - statistics & numerical data</subject><subject>Pandemics</subject><subject>People and places</subject><subject>Research and Analysis Methods</subject><subject>Respiratory diseases</subject><subject>Risk Factors</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Social aspects</subject><subject>Social distancing</subject><subject>Sociodemographics</subject><subject>Socioeconomic Factors</subject><subject>Spatial Analysis</subject><subject>Spatial Regression</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Vaccines</subject><subject>Viral diseases</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2LEzEUhgdR3A_9B6IDguhFa74zuRHW-lVYKLq6tyGTSdqUdNJNZhbXX2-6nV06sheSi4TkOe_Jec8pihcQTCHm8P069LFVfroNrZkCRDgX5FFxDAVGE4YAfnxwPipOUloDQHHF2NPiCGNGOCDkuPh-sVWdU75UWesmuVQGW84Wl_NPEyhK12rXmFab_NyU3cqUKWgXGrMJy6i2K6dLHdrO_O4yWn6M6o_zz4onVvlkng_7afHry-efs2-T88XX-ezsfKKZQN1EW4pqAIRuINAIWl1XClnIGTNAVYiCRkGlGsspN4haUkNKDYa5dEYNIwCfFq_2ulsfkhzcSBIRQSqWDaKZmO-JJqi13Ea3UfFGBuXk7UWIS6li57Q3skJCcMgs1ZQTAG2la4gIqRshBFAaZq0PQ7a-3phGm7aLyo9Exy-tW8lluJZcZP8FygJvB4EYrnqTOrlxSRvvVWtCf_tvSgDniGf09T_ow9UN1FLlAlxrQ86rd6LyjFFMKM4VZWr6AJVX7qHLvTPW5ftRwLtRwNDfpepTkvOLH__PLi7H7JsDdmWU71Yp-L5zoU1jkOxBHUNK0dh7kyGQu8m_c0PuJl8Ok5_DXh426D7obtTxX5GC-8c</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Raymundo, Carlos Eduardo</creator><creator>Oliveira, Marcella Cini</creator><creator>Eleuterio, Tatiana de Araujo</creator><creator>André, Suzana Rosa</creator><creator>da Silva, Marcele Gonçalves</creator><creator>Queiroz, Eny Regina da Silva</creator><creator>Medronho, Roberto de Andrade</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4150-4403</orcidid></search><sort><creationdate>20210301</creationdate><title>Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil</title><author>Raymundo, Carlos Eduardo ; Oliveira, Marcella Cini ; Eleuterio, Tatiana de Araujo ; André, Suzana Rosa ; da Silva, Marcele Gonçalves ; Queiroz, Eny Regina da Silva ; Medronho, Roberto de Andrade</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-cf52b009cd10c21fcb8a2f1766e0a8250da1aadf757e25f4b155e3113765e6403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Antiviral agents</topic><topic>Bayesian analysis</topic><topic>Brazil</topic><topic>Brazil - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Raymundo, Carlos Eduardo</au><au>Oliveira, Marcella Cini</au><au>Eleuterio, Tatiana de Araujo</au><au>André, Suzana Rosa</au><au>da Silva, Marcele Gonçalves</au><au>Queiroz, Eny Regina da Silva</au><au>Medronho, Roberto de Andrade</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-03-01</date><risdate>2021</risdate><volume>16</volume><issue>3</issue><spage>e0247794</spage><epage>e0247794</epage><pages>e0247794-e0247794</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country.
This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).
The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.
Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33647044</pmid><doi>10.1371/journal.pone.0247794</doi><tpages>e0247794</tpages><orcidid>https://orcid.org/0000-0002-4150-4403</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Antiviral agents Bayesian analysis Brazil Brazil - epidemiology Cities - epidemiology Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - epidemiology COVID-19 - mortality Data analysis Decision analysis Decision making Demography Disease control Disease transmission Distribution Empirical analysis Epidemics Fatalities Female Humans Incidence Income inequality Male Medicine and Health Sciences Mortality Municipalities Nurses - statistics & numerical data Pandemics People and places Research and Analysis Methods Respiratory diseases Risk Factors Severe acute respiratory syndrome coronavirus 2 Social aspects Social distancing Sociodemographics Socioeconomic Factors Spatial Analysis Spatial Regression Statistical analysis Statistical methods Vaccines Viral diseases |
| title | Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T08%3A45%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spatial%20analysis%20of%20COVID-19%20incidence%20and%20the%20sociodemographic%20context%20in%20Brazil&rft.jtitle=PloS%20one&rft.au=Raymundo,%20Carlos%20Eduardo&rft.date=2021-03-01&rft.volume=16&rft.issue=3&rft.spage=e0247794&rft.epage=e0247794&rft.pages=e0247794-e0247794&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0247794&rft_dat=%3Cgale_plos_%3EA653453971%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2494863715&rft_id=info:pmid/33647044&rft_galeid=A653453971&rft_doaj_id=oai_doaj_org_article_8299716f5c57401f8cb1244bd9990ac1&rfr_iscdi=true |