A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar
Abstract Background Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contri...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-023-16425-w |
| _version_ | 1797451292871753728 |
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| author | Christine Pando Ashley Hazel Lai Yu Tsang Kimmerling Razafindrina Andry Andriamiadanarivo Roger Mario Rabetombosoa Ideal Ambinintsoa Gouri Sadananda Peter M. Small Astrid M. Knoblauch Niaina Rakotosamimanana Simon Grandjean Lapierre |
| author_facet | Christine Pando Ashley Hazel Lai Yu Tsang Kimmerling Razafindrina Andry Andriamiadanarivo Roger Mario Rabetombosoa Ideal Ambinintsoa Gouri Sadananda Peter M. Small Astrid M. Knoblauch Niaina Rakotosamimanana Simon Grandjean Lapierre |
| author_sort | Christine Pando |
| collection | DOAJ |
| description | Abstract Background Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission. Methods We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction. Results Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%–79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination. Conclusion In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities’ resiliency to TB introduction decreases as their interconnectivity increases. “Top down” population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling. |
| first_indexed | 2024-03-09T14:51:35Z |
| format | Article |
| id | doaj.art-c0a02d4cc3b641a2b7a7bea460379382 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-09T14:51:35Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-c0a02d4cc3b641a2b7a7bea4603793822023-11-26T14:26:11ZengBMCBMC Public Health1471-24582023-08-0123111110.1186/s12889-023-16425-wA social network analysis model approach to understand tuberculosis transmission in remote rural MadagascarChristine Pando0Ashley Hazel1Lai Yu Tsang2Kimmerling Razafindrina3Andry Andriamiadanarivo4Roger Mario Rabetombosoa5Ideal Ambinintsoa6Gouri Sadananda7Peter M. Small8Astrid M. Knoblauch9Niaina Rakotosamimanana10Simon Grandjean Lapierre11Stony Brook UniversityFrancis I. Proctor Foundation, University of California, San FranciscoStony Brook UniversityCentre ValBio Research StationCentre ValBio Research StationCentre ValBio Research StationCentre ValBio Research StationCase Western Reserve UniversityStony Brook UniversityInstitut Pasteur de MadagascarInstitut Pasteur de MadagascarInstitut Pasteur de MadagascarAbstract Background Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission. Methods We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction. Results Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%–79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination. Conclusion In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities’ resiliency to TB introduction decreases as their interconnectivity increases. “Top down” population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.https://doi.org/10.1186/s12889-023-16425-wTuberculosisPublic HealthModelingSocial network analysis |
| spellingShingle | Christine Pando Ashley Hazel Lai Yu Tsang Kimmerling Razafindrina Andry Andriamiadanarivo Roger Mario Rabetombosoa Ideal Ambinintsoa Gouri Sadananda Peter M. Small Astrid M. Knoblauch Niaina Rakotosamimanana Simon Grandjean Lapierre A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar BMC Public Health Tuberculosis Public Health Modeling Social network analysis |
| title | A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar |
| title_full | A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar |
| title_fullStr | A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar |
| title_full_unstemmed | A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar |
| title_short | A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar |
| title_sort | social network analysis model approach to understand tuberculosis transmission in remote rural madagascar |
| topic | Tuberculosis Public Health Modeling Social network analysis |
| url | https://doi.org/10.1186/s12889-023-16425-w |
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