Homophily and social mixing in a small community: Implications for infectious disease transmission
Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a...
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description | Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n = 384 index cases + n = 2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized. |
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Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n = 384 index cases + n = 2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0303677</identifier><identifier>PMID: 38805519</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; African Americans ; Age ; Age groups ; Aged ; Analysis ; Biology and life sciences ; Communicable diseases ; Community ; Consent ; COVID-19 - epidemiology ; COVID-19 - transmission ; Disease control ; Disease transmission ; Enrollments ; Epidemics ; Epidemiology ; Ethnic factors ; Ethnicity ; Female ; Gender ; Health aspects ; Health risks ; Humans ; Infections ; Infectious diseases ; Male ; Medical research ; Medicine and Health Sciences ; Medicine, Experimental ; Middle Aged ; Networks ; Peers ; People and places ; Population ; Public health ; Research and Analysis Methods ; Risk analysis ; Sampling designs ; SARS-CoV-2 ; Seeds ; Severe acute respiratory syndrome coronavirus 2 ; Social networks ; Sociodemographics ; Vaccination ; Viral diseases ; Young Adult</subject><ispartof>PloS one, 2024-05, Vol.19 (5), p.e0303677</ispartof><rights>Copyright: © 2024 Pasquale et al. 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.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Pasquale 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>2024 Pasquale et al 2024 Pasquale et al</rights><rights>2024 Pasquale et al. 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Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.</description><subject>Adolescent</subject><subject>Adult</subject><subject>African Americans</subject><subject>Age</subject><subject>Age groups</subject><subject>Aged</subject><subject>Analysis</subject><subject>Biology and life sciences</subject><subject>Communicable diseases</subject><subject>Community</subject><subject>Consent</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - transmission</subject><subject>Disease control</subject><subject>Disease transmission</subject><subject>Enrollments</subject><subject>Epidemics</subject><subject>Epidemiology</subject><subject>Ethnic factors</subject><subject>Ethnicity</subject><subject>Female</subject><subject>Gender</subject><subject>Health aspects</subject><subject>Health 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Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n = 384 index cases + n = 2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38805519</pmid><doi>10.1371/journal.pone.0303677</doi><tpages>e0303677</tpages><orcidid>https://orcid.org/0000-0001-6686-7844</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult African Americans Age Age groups Aged Analysis Biology and life sciences Communicable diseases Community Consent COVID-19 - epidemiology COVID-19 - transmission Disease control Disease transmission Enrollments Epidemics Epidemiology Ethnic factors Ethnicity Female Gender Health aspects Health risks Humans Infections Infectious diseases Male Medical research Medicine and Health Sciences Medicine, Experimental Middle Aged Networks Peers People and places Population Public health Research and Analysis Methods Risk analysis Sampling designs SARS-CoV-2 Seeds Severe acute respiratory syndrome coronavirus 2 Social networks Sociodemographics Vaccination Viral diseases Young Adult |
title | Homophily and social mixing in a small community: Implications for infectious disease transmission |
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