Dataset Information

Introducing risk inequality metrics in tuberculosis policy development.

ABSTRACT: Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to selection imposed by dynamic forces of infection. We introduce concrete metrics of risk inequality, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient (RIC) which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.

SUBMITTER: Gomes MGM

PROVIDER: S-EPMC6554307 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Introducing risk inequality metrics in tuberculosis policy development.

Gomes M Gabriela M MGM Oliveira Juliane F JF Bertolde Adelmo A Ayabina Diepreye D Nguyen Tuan Anh TA Maciel Ethel L EL Duarte Raquel R Nguyen Binh Hoa BH Shete Priya B PB Lienhardt Christian C

Nature communications 20190606 1

Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as ...[more]

PMID: 31171791

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Project description:OBJECTIVE:To explore antecedents, components and influencing factors on active case-finding (ACF) policy development and implementation. DESIGN:Scoping review, searching MEDLINE, Web of Science, the Cochrane Database of Systematic Reviews and the World Health Organization (WHO) Library from January 1968 to January 2018. We excluded studies focusing on latent tuberculosis (TB) infection, passive case-finding, childhood TB and studies about effectiveness, yield, accuracy and impact without descriptions of how this evidence has/could influence ACF policy or implementation. We included any type of study written in English, and conducted frequency and thematic analyses. RESULTS:Seventy-three articles fulfilled our eligibility criteria. Most (67%) were published after 2010. The studies were conducted in all WHO regions, but primarily in Africa (22%), Europe (23%) and the Western-Pacific region (12%). Forty-one percent of the studies were classified as quantitative, followed by reviews (22%) and qualitative studies (12%). Most articles focused on ACF for tuberculosis contacts (25%) or migrants (32%). Fourteen percent of the articles described community-based screening of high-risk populations. Fifty-nine percent of studies reported influencing factors for ACF implementation; mostly linked to the health system (eg, resources) and the community/individual (eg, social determinants of health). Only two articles highlighted factors influencing ACF policy development (eg, politics). Six articles described WHO's ACF-related recommendations as important antecedent for ACF. Key components of successful ACF implementation include health system capacity, mechanisms for integration, education and collaboration for ACF. CONCLUSION:We identified some main themes regarding the antecedents, components and influencing factors for ACF policy development and implementation. While we know much about facilitators and barriers for ACF policy implementation, we know less about how to strengthen those facilitators and how to overcome those barriers. A major knowledge gap remains when it comes to understanding which contextual factors influence ACF policy development. Research is required to understand, inform and improve ACF policy development and implementation.

Dataset Information

Introducing risk inequality metrics in tuberculosis policy development.

Publications

Introducing risk inequality metrics in tuberculosis policy development.

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