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Tuberculosis and Social Networks:A Narrative Review on How Social Network Data and Metrics Help Explain Tuberculosis Transmission


Affiliations
1 Department of Health Economics, National Institute for Research in Tuberculosis, No. 1, Mayor Sathyamoorthy Road Chetpet, Chennai 600 031, India
2 Department of Statistics, School of Public Health, SRM University Campus, Kattankulathur, Kancheepuram 603 203, India
 

Social network data of tuberculosis (TB) patients could explain the source and pattern of disease spread. A review of the published literature highlights that social network data could identify hidden social or epidemiological links among TB patients and improved TB case finding. Index and betweenness position of patients explained TB transmission. Social networks of TB cases were centred in the hotspots of alcohol, substance uses, sexual activities and hospitals. Multiple sources of TB infection in the community were identified. The findings highlight the potential of social network methods for understanding TB spread in high-burden countries like India.

Keywords

Epidemiological Links, Hotspots, Social Network Matrics, Transmission, Tuberculosis.
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  • Christakis, N. and Fowler, J. H., The collective dynamics of smoking in a large social network. N. Engl. J. Med., 2008, 358(21), 2249–2258.

  • Kim, J. W. et al., Social network analysis reveals the negative effects of attention – deficit/hyperactivity disorder (ADHD) symptoms on friend-based student networks. PLOS ONE (internet). 2015, 10(11), e0142782; http://dx.plos.org/10.1371/journal.pone.0142782.

  • Schneider, J. et al., Social network and risk-taking behavior most associated with rapid HIV testing, circumcision and preexposure prophylaxis acceptability among high-risk. Indian Men. AIDS Patient Care STDs, 2012, 26(10), 631–640; http://online.liebertpub.com/doi/abs/10.1089/apc.2012.0188

  • Hawe, P., A glossary of terms for navigating the field of social network analysis. J. Epidemiol. Commun. Heal, 2004; 58(12), 971–975; http://jech.bmj.com/cgi/doi/10.1136/jech.2003.014530

  • Nelson, L. E. et al., Predictors of condom use among peer social networks of men who have sex with men in Ghana, West Africa. PLoS ONE, 2015, 10(1), e0115504; http://dx.plos.org/10.1371/journal.pone.0115504.

  • Hurt, C. B. et al., Investigating a sexual network of black men who have sex with men: implications for transmission and prevention of HIV infection in the United States. J. Acquir. Immune Defic. Syndr., 2012, 61(4), 515–521; http://www.scopus.com/inward/record.url?eid=2-s2.0-84870295414&partnerID=tZOtx3y1%5Cnhttp://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00126334-201212010-00016

  • Schneider, J. A. et al., Network mixing and network influences most linked to HIV infection and risk behavior in the HIV epidemic among black men who have sex with men. Am. J. Public Health, 2013, 103(1), e28–e36.

  • Latkin, C., Yang, C., Tobin, K., Penniman, T., Patterson, J. and Spikes, P., Differences in the social networks of African American men who have sex with men only and those who have sex with men and women. Am. J. Public Health, 2011, 101(10), e18– e23.

  • Peterson, J. L., Rothenberg, R., Kraft, J. M., Beeker, C. and Trotter, R., Perceived condom norms and HIV risks among social and sexual networks of young African American men who have sex with men. Health Educ. Res., 2009, 24(1), 119–127.

  • Cook, V. J. et al., Transmission network analysis in tuberculosis contact investigations. J. Infect. Dis., 2007, 196(10), 1517–1527; https://academic.oup.com/jid/article-lookup/doi/10.1086/523109

  • McElroy, P. D. et al., A network-informed approach to investigating a tuberculosis outbreak: implications for enhancing contact investigations. Int. J. Tuber. Lung Dis., 2003, 7(12 suppl. 3), S486–S493.

  • Cook, V. J., Shah, L., Gardy, J. and Bourgeois, A.-C., Recommendations on modern contact investigation methods for enhancing tuberculosis control. Int. J. Tuber. Lung Dis., 2011, 16(3), 297–305.

  • Fonseca, B. P. F., Da Silva, M. V. P., De Araújo, K. M., Sampaio, R. B. and Moraes, M. O., Network analysis for science and technology management: evidence from tuberculosis research in Fiocruz, Brazil. PLoS ONE, 2017, 12(8).

  • Al-Azem, A., Social network analysis in tuberculosis control among the aboriginal population of Manitoba. Diss. Abstr Int., 2006; http://www.csa.com/partners/viewrecord.php?requester=gs&collection=ENV&recid=8949307.

  • Cozatt, D. M., Social network analysis (SNA) of clark county, Nevada tuberculosis (TB) case and contact investigation data to determine pediatric risk factors for disease transmission. UNLV thesis, Disst., Professional papers, Capstones, 2014, no. 2253; https://digitalscholarship.univ.edu/thesesdissertations/2253

  • Jackson, A. D. et al., Characterising transmission of a tuberculosis genotype in Scotland: a qualitative approach to social network enquiry. Int. J. Tuber. Lung Dis., 2009, 13(4), 486–493.

  • Munang, M. L. et al., Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom. Int. J. Tuber. Lung Dis., 2016, 20(10), 1300–1305; http://openurl.ingenta.com/content/xref?genre=article& issn=1027-3719&volume=20&issue=10&spage=1300

  • Fitzpatrick, L. K. et al., A preventable outbreak of tuberculosis investigated through an intricate social network. J. Clin. Infect. Dis., 2001, 33(11), 1801–1806.

  • Klovdahl, A. S. et al., Networks and tuberculosis: an undetected community outbreak involving public places. Soc. Sci. Med., 2001, 52(5), 681–694.

  • Andre, M. et al., Transmission network analysis to complement routine tuberculosis contact investigations. Am. J. Public Health, 2007, 97(3), 470–477.

  • Chamie, G. et al., Identifying locations of recent TB transmission in rural Uganda: a multidisciplinary approach. Trop. Med. Int. Heal., 2015, 20(4), 537–545.

  • MMWR, HIV-related tuberculosis in a transgender network at Baltimore, Maryland, and New York city area. CDC, 2000, vol.49, p. 317–320; https://www.cdc.gov/mmwr/preview/mmwrhtml/mm4915a1.htm

  • Gandhi, N. R. et al., Nosocomial transmission of extensively drugresistant tuberculosis in a rural hospital in South Africa. J. Infect.Dis., 2013, 207(1), 9–17; http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3523793%7B&%7Dtool=pmcentrez%7B&%7Drendertype=abstract%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3523793&tool=pmcentrez&rendertype= abstract

  • Sekandi, J. N. et al., Four degrees of separation: social contacts and health providers influence the steps to final diagnosis of active tuberculosis patients in Urban Uganda. BMC Infect. Dis., 2015, 15(1), 361; http://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-015-1084-8

  • Kawatsu, L., Izumi, K., Uchimura, K., Urakawa, M., Ohkado, A. and Takahashi, I., Can social network analysis assist in the prioritisation of contacts in a tuberculosis contact investigation? Int. J. Tuber. Lung Dis., 2015, 19, 1293–1299.

  • Gardy, J. L. et al., Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N. Engl. J. Med., 2011, 364(8), 730–739; http://www.nejm.org/doi/abs/10.1056/NEJMoa1003176.

  • Devlin, S. and Passmore, E., Ongoing transmission of tuberculosis in Aboriginal communities in NSW. N S W Public Health Bull., 2013, 24(1), 38–42; http://www.ncbi.nlm.nih.gov/pubmed/23849029

  • Wood, R. et al., Indoor social networks in a south African township: potential contribution of location to tuberculosis transmission. PLoS ONE, 2012, 7(6), e39246.

  • Shah, N. S. et al., Transmission of extensively drug-resistant tuberculosis in South Africa. N. Engl. J. Med., 2017, 376(3), 243– 253; http://dx.doi.org/10.1056/NEJMoa1604544.

  • Rothenberg, R. B., McElroy, P. D., Wilce, M. A. and Muth, S. Q., Contact tracing: comparing the approaches for sexually transmitted diseases and tuberculosis. Int. J. Tuber. Lung Dis., 2003; 7(12: suppl. 3), S342–S348; http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed9&NEWS=N&AN=38004707

  • Cook, V. J. and Elwood, R. K., The burden of tuberculosis in Aboriginal communities of BC. Br. Columbia Med. J., 2008, 50, 219.

  • Revised National Tuberculosis Control Program, National strategic plan for tuberculosis elimination 2017–2025, 2017; http://tbcindia.gov.in/WriteReadData/NSP Draft 20.02.2017 1.pdf

  • Fujimoto, K., Williams, M. L. and Ross, M. W., a network analysis of relationship dynamics in sexual dyads as correlates of HIV risk misperceptions among high-risk MSM. Sex Transm. Infect., 2015, 91(2), 130–134; http://sti.bmj.com/cgi/doi/10.1136/sextrans-2014-051742

  • Tucker, J. D. et al., Female sex worker social networks and STI/HIV prevention in South China. PLoS ONE, 2011, 6(9), e24816.

  • Dowdy, D. W., Golub, J. E., Chaisson, R. E. and Saraceni, V., Heterogeneity in tuberculosis transmission and the role of geographic hotspots in propagating epidemics. Proc. Natl. Acad. Sci., 2012, 109(24), 9557–9562; http://www.pnas.org/cgi/doi/10.1073/pnas.1203517109


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  • Tuberculosis and Social Networks:A Narrative Review on How Social Network Data and Metrics Help Explain Tuberculosis Transmission

Abstract Views: 220  |  PDF Views: 67

Authors

Karikalan Nagarajan
Department of Health Economics, National Institute for Research in Tuberculosis, No. 1, Mayor Sathyamoorthy Road Chetpet, Chennai 600 031, India
Bagavan Das
Department of Statistics, School of Public Health, SRM University Campus, Kattankulathur, Kancheepuram 603 203, India

Abstract


Social network data of tuberculosis (TB) patients could explain the source and pattern of disease spread. A review of the published literature highlights that social network data could identify hidden social or epidemiological links among TB patients and improved TB case finding. Index and betweenness position of patients explained TB transmission. Social networks of TB cases were centred in the hotspots of alcohol, substance uses, sexual activities and hospitals. Multiple sources of TB infection in the community were identified. The findings highlight the potential of social network methods for understanding TB spread in high-burden countries like India.

Keywords


Epidemiological Links, Hotspots, Social Network Matrics, Transmission, Tuberculosis.

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DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1068-1080