ArticleNature communications2019
Introducing risk inequality metrics in tuberculosis policy development.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Community-Wide Active Case Finding for Tuberculosis: Time to Use the Evidence We Have.Tropical medicine and infectious disease · 2024Review
- Detecting and quantifying heterogeneity in susceptibility using contact tracing data.PLoS computational biology · 2024Article
- Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.Scientific reports · 2024Article
- Article
- Considering equity in priority setting using transmission models: Recommendations and data needs.Epidemics · 2022Review
- The impact of active case finding on transmission dynamics of tuberculosis: A modelling study.PloS one · 2021Article
- Modelling the epidemiology of residual Plasmodium vivax malaria in a heterogeneous host population: A case study in the Amazon Basin.PLoS computational biology · 2020Article
- The effects of individual nonheritable variation on fitness estimation and coexistence.Ecology and evolution · 2019Article
- Article
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Authors and funding
10 authors.
Funding
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.
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Registered trials
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