A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar

BMC Public Health

Research Article

Gouri Sadananda¹  Andry Andriamiadanarivo²  Ideal Ambinintsoa²  Kimmerling Razafindrina²  Roger Mario Rabetombosoa³  Ashley Hazel⁴  Niaina Rakotosamimanana⁵  Simon Grandjean Lapierre⁶  Astrid M. Knoblauch⁷  Lai Yu Tsang⁸  Peter M. Small⁸  Christine Pando⁸ 

[1] Case Western Reserve University, 10900 Euclid Ave, 44106, Cleveland, OH, USA;Centre ValBio Research Station, BP 33 Ranomafana, Ifanadiana, Madagascar;Centre ValBio Research Station, BP 33 Ranomafana, Ifanadiana, Madagascar;Institut Pasteur de Madagascar, 101, Ambohitrakely, Antananarivo, Madagascar;Francis I. Proctor Foundation, University of California, San Francisco, 490 Illinois Street, 2nd Floor, 94110, San Francisco, CA, USA;Institut Pasteur de Madagascar, 101, Ambohitrakely, Antananarivo, Madagascar;Institut Pasteur de Madagascar, 101, Ambohitrakely, Antananarivo, Madagascar;Centre de Recherche du Centre Hospitalier de L, Université de Montréal, 900 Saint-Denis, H2X 3H8, Montréal, Canada;Université de Montréal, 2900 Edouard Montpetit, H3T 1J4, Montreal, Canada;Institut Pasteur de Madagascar, 101, Ambohitrakely, Antananarivo, Madagascar;Swiss Tropical and Public Health Institute, Allschwil, Switzerland;University of Basel, Basel, Switzerland;Stony Brook University, 101 Nicolls Road, 11794-8343, Stony Brook, NY, USA;

关键词: Tuberculosis;    Public Health;    Modeling;    Social network analysis;   

DOI  :  10.1186/s12889-023-16425-w

 received in 2023-02-06, accepted in 2023-07-31,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundQuality 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.MethodsWe 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.ResultsInter- 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.ConclusionIn 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 3

Fig. 6

Fig. 2

Fig. 2

13690_2023_1151_Article_IEq17.gif

13690_2023_1151_Article_IEq14.gif

Fig. 4