Spatial analysis of COVID-19 clusters and contextual factors in New York City
•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn. Identifying areas with low access to testing and high case...
Gespeichert in:
| Veröffentlicht in: | Spatial and spatio-temporal epidemiology 2020-08, Vol.34, p.100355-100355, Article 100355 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 100355 |
|---|---|
| container_issue | |
| container_start_page | 100355 |
| container_title | Spatial and spatio-temporal epidemiology |
| container_volume | 34 |
| creator | Cordes, Jack Castro, Marcia C. |
| description | •Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn.
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives. |
| doi_str_mv | 10.1016/j.sste.2020.100355 |
| format | Article |
| fullrecord | <record><control><sourceid>elsevier_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7306208</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1877584520300332</els_id><sourcerecordid>S1877584520300332</sourcerecordid><originalsourceid>FETCH-LOGICAL-c570t-c4809ea6169bd815ef8e5b057f8d985ca1be72f78003f59a7734a7824dfcfbc93</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EoqXwAyyQfyDFTuLYkRASCq9KhS54SKwsx7HBJU0q2y3073EUqGDDakYz997RHACOMRpjhLPT-dg5r8YxirsBSgjZAUPMKI0II8nutk_JABw4N0coYwkj-2CQxAzRFKEhuHtYCm9EDUUj6o0zDrYaFrPnyWWEcyjrVbhgXdhWULaNV59-FcRaSN-GsWngvfqAL619h4Xxm0Owp0Xt1NF3HYGn66vH4jaazm4mxcU0koQiH8mUoVyJDGd5WTFMlGaKlIhQzaqcESlwqWisKQtPaZILSpNUUBanlZa6lHkyAud97nJVLlQlVeOtqPnSmoWwG94Kw_9uGvPGX9s1pwnKYsRCQNwHSNs6Z5XeejHiHVw-5x1c3sHlPdxgOvl9dWv5oRkEZ71Ahd_XRlnupFGNVJWxSnpetea__C_yiYwW</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Spatial analysis of COVID-19 clusters and contextual factors in New York City</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Cordes, Jack ; Castro, Marcia C.</creator><creatorcontrib>Cordes, Jack ; Castro, Marcia C.</creatorcontrib><description>•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn.
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.</description><identifier>ISSN: 1877-5845</identifier><identifier>EISSN: 1877-5853</identifier><identifier>DOI: 10.1016/j.sste.2020.100355</identifier><identifier>PMID: 32807400</identifier><language>eng</language><publisher>Netherlands: Elsevier Ltd</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Clinical Laboratory Techniques - statistics & numerical data ; Cluster Analysis ; Communicable Diseases, Emerging - epidemiology ; Coronavirus Infections - diagnosis ; Coronavirus Infections - epidemiology ; COVID-19 ; COVID-19 Testing ; Disease Outbreaks - statistics & numerical data ; Female ; Health inequalities ; Health Status Disparities ; Healthcare Disparities - economics ; Healthcare Disparities - ethnology ; Humans ; Infectious disease ; Male ; Middle Aged ; New York City - epidemiology ; Pandemics - statistics & numerical data ; Pneumonia, Viral - diagnosis ; Pneumonia, Viral - epidemiology ; Risk Assessment ; Spatial Analysis ; Urban health ; Urban Health - economics ; Urban Health - ethnology ; Urban Population</subject><ispartof>Spatial and spatio-temporal epidemiology, 2020-08, Vol.34, p.100355-100355, Article 100355</ispartof><rights>2020</rights><rights>Copyright © 2020. Published by Elsevier Ltd.</rights><rights>2020 Elsevier Ltd. All rights reserved. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c570t-c4809ea6169bd815ef8e5b057f8d985ca1be72f78003f59a7734a7824dfcfbc93</citedby><cites>FETCH-LOGICAL-c570t-c4809ea6169bd815ef8e5b057f8d985ca1be72f78003f59a7734a7824dfcfbc93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1877584520300332$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32807400$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cordes, Jack</creatorcontrib><creatorcontrib>Castro, Marcia C.</creatorcontrib><title>Spatial analysis of COVID-19 clusters and contextual factors in New York City</title><title>Spatial and spatio-temporal epidemiology</title><addtitle>Spat Spatiotemporal Epidemiol</addtitle><description>•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn.
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Clinical Laboratory Techniques - statistics & numerical data</subject><subject>Cluster Analysis</subject><subject>Communicable Diseases, Emerging - epidemiology</subject><subject>Coronavirus Infections - diagnosis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>COVID-19</subject><subject>COVID-19 Testing</subject><subject>Disease Outbreaks - statistics & numerical data</subject><subject>Female</subject><subject>Health inequalities</subject><subject>Health Status Disparities</subject><subject>Healthcare Disparities - economics</subject><subject>Healthcare Disparities - ethnology</subject><subject>Humans</subject><subject>Infectious disease</subject><subject>Male</subject><subject>Middle Aged</subject><subject>New York City - epidemiology</subject><subject>Pandemics - statistics & numerical data</subject><subject>Pneumonia, Viral - diagnosis</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Risk Assessment</subject><subject>Spatial Analysis</subject><subject>Urban health</subject><subject>Urban Health - economics</subject><subject>Urban Health - ethnology</subject><subject>Urban Population</subject><issn>1877-5845</issn><issn>1877-5853</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtOwzAQRS0EoqXwAyyQfyDFTuLYkRASCq9KhS54SKwsx7HBJU0q2y3073EUqGDDakYz997RHACOMRpjhLPT-dg5r8YxirsBSgjZAUPMKI0II8nutk_JABw4N0coYwkj-2CQxAzRFKEhuHtYCm9EDUUj6o0zDrYaFrPnyWWEcyjrVbhgXdhWULaNV59-FcRaSN-GsWngvfqAL619h4Xxm0Owp0Xt1NF3HYGn66vH4jaazm4mxcU0koQiH8mUoVyJDGd5WTFMlGaKlIhQzaqcESlwqWisKQtPaZILSpNUUBanlZa6lHkyAud97nJVLlQlVeOtqPnSmoWwG94Kw_9uGvPGX9s1pwnKYsRCQNwHSNs6Z5XeejHiHVw-5x1c3sHlPdxgOvl9dWv5oRkEZ71Ahd_XRlnupFGNVJWxSnpetea__C_yiYwW</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Cordes, Jack</creator><creator>Castro, Marcia C.</creator><general>Elsevier Ltd</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>5PM</scope></search><sort><creationdate>20200801</creationdate><title>Spatial analysis of COVID-19 clusters and contextual factors in New York City</title><author>Cordes, Jack ; Castro, Marcia C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c570t-c4809ea6169bd815ef8e5b057f8d985ca1be72f78003f59a7734a7824dfcfbc93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Clinical Laboratory Techniques - statistics & numerical data</topic><topic>Cluster Analysis</topic><topic>Communicable Diseases, Emerging - epidemiology</topic><topic>Coronavirus Infections - diagnosis</topic><topic>Coronavirus Infections - epidemiology</topic><topic>COVID-19</topic><topic>COVID-19 Testing</topic><topic>Disease Outbreaks - statistics & numerical data</topic><topic>Female</topic><topic>Health inequalities</topic><topic>Health Status Disparities</topic><topic>Healthcare Disparities - economics</topic><topic>Healthcare Disparities - ethnology</topic><topic>Humans</topic><topic>Infectious disease</topic><topic>Male</topic><topic>Middle Aged</topic><topic>New York City - epidemiology</topic><topic>Pandemics - statistics & numerical data</topic><topic>Pneumonia, Viral - diagnosis</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Risk Assessment</topic><topic>Spatial Analysis</topic><topic>Urban health</topic><topic>Urban Health - economics</topic><topic>Urban Health - ethnology</topic><topic>Urban Population</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cordes, Jack</creatorcontrib><creatorcontrib>Castro, Marcia C.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Spatial and spatio-temporal epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cordes, Jack</au><au>Castro, Marcia C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial analysis of COVID-19 clusters and contextual factors in New York City</atitle><jtitle>Spatial and spatio-temporal epidemiology</jtitle><addtitle>Spat Spatiotemporal Epidemiol</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>34</volume><spage>100355</spage><epage>100355</epage><pages>100355-100355</pages><artnum>100355</artnum><issn>1877-5845</issn><eissn>1877-5853</eissn><abstract>•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn.
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.</abstract><cop>Netherlands</cop><pub>Elsevier Ltd</pub><pmid>32807400</pmid><doi>10.1016/j.sste.2020.100355</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1877-5845 |
| ispartof | Spatial and spatio-temporal epidemiology, 2020-08, Vol.34, p.100355-100355, Article 100355 |
| issn | 1877-5845 1877-5853 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7306208 |
| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Adult Aged Aged, 80 and over Clinical Laboratory Techniques - statistics & numerical data Cluster Analysis Communicable Diseases, Emerging - epidemiology Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology COVID-19 COVID-19 Testing Disease Outbreaks - statistics & numerical data Female Health inequalities Health Status Disparities Healthcare Disparities - economics Healthcare Disparities - ethnology Humans Infectious disease Male Middle Aged New York City - epidemiology Pandemics - statistics & numerical data Pneumonia, Viral - diagnosis Pneumonia, Viral - epidemiology Risk Assessment Spatial Analysis Urban health Urban Health - economics Urban Health - ethnology Urban Population |
| title | Spatial analysis of COVID-19 clusters and contextual factors in New York City |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T22%3A35%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spatial%20analysis%20of%20COVID-19%20clusters%20and%20contextual%20factors%20in%20New%20York%20City&rft.jtitle=Spatial%20and%20spatio-temporal%20epidemiology&rft.au=Cordes,%20Jack&rft.date=2020-08-01&rft.volume=34&rft.spage=100355&rft.epage=100355&rft.pages=100355-100355&rft.artnum=100355&rft.issn=1877-5845&rft.eissn=1877-5853&rft_id=info:doi/10.1016/j.sste.2020.100355&rft_dat=%3Celsevier_pubme%3ES1877584520300332%3C/elsevier_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/32807400&rft_els_id=S1877584520300332&rfr_iscdi=true |