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To move behavior-change efforts upstream, this study examines how behavior clustering operates within a social context among public housing residents. It uses an ego-centric, participant-reported network design to model homophily patterns in sugary beverage and food consumption, identifying individual and relational predictors.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-social-network-homophily-on-common-chronic-disease-risk-behaviors-among-public-housing-residents-research-open-access/457667/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-social-network-homophily-on-common-chronic-disease-risk-behaviors-among-public-housing-residents-research-open-access/457667.png","ImageObject",300,407,{"name":92,"@type":93},"SANS","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-03","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address about chronic disease risk behaviors?","Question",{"text":112,"@type":113},"Behavioral risk factors tend to cluster among connected individuals through homophily, making it difficult to design interventions based only on individual-level factors.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was social network information collected in this study?",{"text":117,"@type":113},"Participants (egos) reported their social contacts (alters) with whom they discuss important matters, share meals, and interact within public housing developments, and reported both their own and alters’ sugar-sweetened beverage and food consumption.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors best predicted homophily for sugar-sweetened beverages (SSB) and sugar-sweetened foods (SSF)?",{"text":121,"@type":113},"For SSB, the best-fitting model included daily contact, education homophily, and SSF homophily. For SSF, the best-fitting model included daily contact, education homophily, individual SSF consumption level, and SSB homophily.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},457667,1791053346,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090760505,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Casey et al. BMC Public Health (2026) 26:88 BMC Public Health  \n[https://doi.org/10.1186/s12889-025-25731-4](https://doi.org/10.1186/s12889-025-25731-4)  \nRESEARCH Open Access  \nPredicting social network homophilyon common chronic disease risk behaviors among public housing residents  \nSharon M. Casey 1,2, Mabel ine Velez 1,2, Robert McDonough 1, Raul I. Garcia 1, Neha Gondal3,4 and Brenda Heaton 1,2,5*  \nAbstract  \nBackground Attributes and behaviors tend to cluster (homophily) among connected individuals (social networks) . Accordingly, the design of effective interventions to address chronic disease risk behaviors at the individual level has proven challenging. To effectively move behavior-change interventions upstream, beyond the individual, an understanding of behavior clustering within a social context is required.  \nMethods This ego-centric/participant reported social network study aimed to identify individual-level (gender, behavior) and relational (closeness) factors that predict homophily on the consumption frequency of both sugarsweetened beverages (SSB) and sugar-sweetened foods (SSF) among residents of public housing developments in Boston, MA. Egos/participants (n = 272) named alters/social contacts (n = 889) with whom they discuss important matters, share meals, and interact within their housing development. Egos reported sociodemographics, relationship attributes and health behaviors, including SSB and SSF consumption for themselves and alters. Data were collected between March 2019–2020.  \nResults Multilevel regression models evaluated homophily on SSB and SSF. The best fitting model predicting homophily on SSB included daily contact (OR 1. 99, 95% CI:1 . 33, 2 . 98), education homophily (OR 1. 68, 95% CI:1 . 15, 2.46), and SSF homophily (OR 1 . 79, 95% CI:1 . 21, 2 . 66) . The best fitting model predicting homophily on SSF included daily contact (OR 1. 72, 95% CI:1 . 11, 2 . 68), education homophily (OR 1. 75, 95% CI:1 . 15, 2 . 67), individual SSF consumption level (OR 0 . 55, 95% CI:0 . 29, 1. 07), and SSB homophily (OR 1. 89, 95% CI:1 . 23, 2 . 89) .  \nConclusions Shared common chronic disease risk behaviors within social networks can be predicted by network attributes, holding promise for multilevel approaches to behavior change.  \nKeywords Health risk behaviors, Social network analysis, Public housing, Chronic disease, Social networks  \n*Correspondence:  \nBrenda Heaton  \n[brenda.heaton@hsc.utah.edu](brenda.heaton@hsc.utah.edu)  \n1Department of Health Policy and Health Services Research, Boston University Henry M. Goldman School of Dental Medicine, Boston, MA, USA  \n2Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA  \n3Department of Sociology, Boston University, Boston, MA, USA 4Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA  \n5University of Utah School of Dentistry, 530 Wakara Way, Salt Lake,  \nUT 84108, USA  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommon","cbCaipxTILyclqt7","https://ap.wps.com/l/cbCaipxTILyclqt7","pdf",1254102,11,"English","# Abstract\n# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What problem does the study address about chronic disease risk behaviors?\",\"answer\":\"Behavioral risk factors tend to cluster among connected individuals through homophily, making it difficult to design interventions based only on individual-level factors.\"},{\"question\":\"How was social network information collected in this study?\",\"answer\":\"Participants (egos) reported their social contacts (alters) with whom they discuss important matters, share meals, and interact within public housing developments, and reported both their own and alters’ sugar-sweetened beverage and food consumption.\"},{\"question\":\"Which factors best predicted homophily for sugar-sweetened beverages (SSB) and sugar-sweetened foods (SSF)?\",\"answer\":\"For SSB, the best-fitting model included daily contact, education homophily, and SSF homophily. For SSF, the best-fitting model included daily contact, education homophily, individual SSF consumption level, and SSB homophily.\"}]","Predicting social network homophily on common chronic disease risk behaviors among public housing residents - Research Open Access | PDF",1790750101,28]