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Background: Retrospective and descriptive molecular epidemiology studies have shown that Mycobacterium tuberculosis whole genome sequencing can identify outbreaks and disease transmission events with higher resolution than conventional epidemiological investigations. Those studies have strengthened our understanding of genomic polymorphisms correlating with person-to-person transmission and helped resolve putative transmission clusters. To date, systematic genomic surveillance programs implemented for M. tuberculosis were only implemented in low-incidence settings. The purpose of this study is to determine whether there is an impact of routine M. tuberculosis whole genome sequencing on tuberculosis case detection in a high-incidence setting., Methods: A cluster randomized controlled trial will be performed. Forty-eight rural village groups (or Fokontany) in the Vohibato district of Madagascar will be randomized to one of three interventions arms. Arm 1 (standard of care) involves healthcare facility-based passive case detection with smear microscopy testing. Arm 2 (best practice) consists of active case finding and Xpert MTB/RIF Ultra PCR testing followed by household contact investigations. Arm 3 (novel intervention) includes the same interventions as arm 2, with addition of sputum culture and M. tuberculosis whole genome sequencing for all newly diagnosed cases. In arm 3, molecular suggested putative outbreaks are investigated, and additional TB suspects are appropriately tested. The intervention observational period will be 2 years. The primary outcome will be the number of detected cases/100,000/year in each arm after 1 year of intervention., Discussion: This study is designed to determine whether there is an impact of prospective whole genome sequencing-based molecular typing on tuberculosis case detection in high-incidence settings. Investigating potential outbreaks and focusing active case finding in spatiotemporal settings where disease transmission is suggested by genomic typing is hypothesized to improve case detection in rural communities., Trial Registration: ClinicalTrials.gov NCT05406453 . Retrospectively registered on June 6, 2022., (© 2024. The Author(s).)

Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children's MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zimmer 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.)

Targeted Next Generation Sequencing (tNGS) and Whole Genome Sequencing (WGS) are increasingly used for genotypic drug susceptibility testing (gDST) of Mycobacterium tuberculosis. Thirty-two multi-drugs resistant and 40 drug susceptible isolates from Madagascar were tested with Deeplex® Myc-TB and WGS using the Mykrobe analysis pipeline. Sixty-four of 72 (89 %) yielded concordant categorical gDST results for drugs tested by both assays. Mykrobe didn't detect pncA K96T, pncA Q141P, pncA H51P, pncA H82R, rrs C517T and rpsL K43R mutations, which were identified as minority variants in corresponding isolates by tNGS. One discrepancy (rrs C517T) was associated with insufficient sequencing depth on WGS. Deeplex® Myc-TB didn't detect inhA G-154A which isn't covered by the assay's amplification targets. Despite those targets being included in the Deeplex® Myc-TB assay, a pncA T47A and a deletion in gid were not identified in one isolate respectively. The evaluated WGS and tNGS gDST assays show high but imperfect concordance., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Philip Supply reports a relationship with GenoScreen that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Sub-Saharan Africa is rich in natural resources but also faces widespread poverty. The United Nations' Sustainable Development Goals brought increased attention to resource extraction projects, emphasizing their development potential in extraction regions. While mining companies are required to conduct environmental impact assessments, their effect on the project-affected communities' health mostly lacks systematic management, and their consideration of community perspectives is insufficient. Between March and May 2019, qualitative research was conducted at three industrial gold mines in Burkina Faso. Thirty-six participants, including community leaders, healthcare providers, and mining officials, were interviewed through key informant interviews about their perceptions on the impacts of mining operations on health, health determinants, and health service delivery. Disparities in perceptions were a key focus of the analysis. Mining officials reported mainly positive effects, while healthcare providers and community leaders described enhancing and adverse health impacts without clear trends observed regarding the extent of the impacts on health determinants. The perception of predominantly positive health impacts by mining officials represents a potential risk for insufficient acknowledgement of stakeholders' concerns and mining-related effects on community health in affected populations. Overall, this study enhances comprehension of the complex interplay between mining operations and health, emphasizing the need for comprehensive assessments, stakeholder involvement, and sustainable practices to mitigate negative impacts and promote the well-being of mining communities.

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)