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Introducing Risk Inequality Metrics in Tuberculosis Policy Development M.Gabriela M.Gomes, Juliane F

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Introducing Risk Inequality Metrics in Tuberculosis Policy Development M.Gabriela M.Gomes, Juliane F Introducing risk inequality metrics in tuberculosis policy development M.Gabriela M.Gomes, Juliane F. Oliveira, Adelmo Bertolde, Diepreye Ayabina, Tuan Anh Nguyen, Ethel Leonor Noia Maciel, Raquel Duarte, Binh Hoa Nguyen, Priya B Shete, Christian Lienhardt To cite this version: M.Gabriela M.Gomes, Juliane F. Oliveira, Adelmo Bertolde, Diepreye Ayabina, Tuan Anh Nguyen, et al.. Introducing risk inequality metrics in tuberculosis policy development. Nature Communications, Nature Publishing Group, 2019, 10, pp.2480. 10.1038/s41467-019-10447-y. hal-02474655 HAL Id: hal-02474655 https://hal.archives-ouvertes.fr/hal-02474655 Submitted on 11 Feb 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ARTICLE https://doi.org/10.1038/s41467-019-10447-y OPEN Introducing risk inequality metrics in tuberculosis policy development M. Gabriela M. Gomes 1,2, Juliane F. Oliveira 2, Adelmo Bertolde3, Diepreye Ayabina1, Tuan Anh Nguyen4, Ethel L. Maciel5, Raquel Duarte6, Binh Hoa Nguyen4, Priya B. Shete7 & Christian Lienhardt8,9 Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure 1234567890():,; to account for variation in individual risk leads to substantial biases that impair data inter- pretation 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 fundamen- tally 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 cal- culated and reported by national tuberculosis programs for use in policy development and modeling. 1 Liverpool School of Tropical Medicine, Liverpool L3 5QA, United Kingdom. 2 CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão 4485-661, Portugal. 3 Departamento de Estatística, Universidade Federal do Espírito Santo, Vitória, Espírito Santo 29075-910, Brazil. 4 National Lung Hospital, Hanoi 10000, Vietnam. 5 Laboratório de Epidemiologia, Universidade Federal do Espírito Santo, Vitória, Espírito Santo 29047-105, Brazil. 6 Faculdade de Medicina, and EPIUnit, Instituto de Saúde Pública, Universidade do Porto, Porto 4050-091, Portugal. 7 Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, CA 94110, USA. 8 Global TB Programme, World Health Organization, 1211 Geneva 27, Geneva, Switzerland. 9 Unité Mixte Internationale TransVIHMI (UMI 233 IRD – U1175 INSERM – Université de Montpellier), Institut de Recherche pour le Développement (IRD), Montpellier 34394, France. Correspondence and requests for materials should be addressed to M.G.M. G. (email: [email protected]) NATURE COMMUNICATIONS | (2019) 10:2480 | https://doi.org/10.1038/s41467-019-10447-y | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-10447-y uberculosis (TB) is a leading cause of morbidity and dividing the population in sufficiently large groups and recording mortality worldwide, accounting for over 10 million new occurrences in each of them. Then incidence rates can be cal- T 1 cases annually . Although allusions are often made to the culated per group, and ranked. Supplementary Fig. 1 illustrates disproportionate effect of TB on the poorest and socially mar- the population of a hypothetical country comprising low and ginalized groups2,3, robust metrics to quantify risk inequality in high-risk individuals distributed geographically (but dividing by TB are lacking. Data reported by the World Health Organization age or income level, for example, applied singly or in combina- (WHO), which mathematical models often rely on for calibra- tion, could also serve our statistical purposes). Forasmuch as tions and projections, are typically in the form of country-level individuals are nonuniformly distributed, disease incidence will averages that do not describe heterogeneity within populations. In vary between groups and carry information about variation in keeping with the spirit of the Sustainable Development Goals individual risks. agenda4, we postulate that mathematical models that account for Here we adopt concepts and tools developed in economics to heterogeneity and inequality may best reflect the potential impact measure inequality in wealth, such as the Lorenz curve14 and the of TB prevention and care strategies in achieving disease elim- Gini coefficient15, and modify them into suitable indicators of ination. Further, we hypothesize that disease incidence patterns in disease risk inequality. We then calculate a risk inequality coef- a population reflect unobserved heterogeneity and may be used to ficient for three countries—Vietnam, Brazil, and Portugal, inform model development and implementation. representing high to low TB burdens—and derive country- Variation in individual characteristics has a generally recog- specific risk distributions to inform transmission models. The nized impact on the dynamics of populations, and pathogen resulting models are applied to investigate the conditions for transmission is no exception5. In infectious diseases, hetero- reducing TB incidence by 90% between 2015 and 2035, one of geneities in transmission have been shown to have specific effects the targets set by the WHO’s End TB Strategy16. The results differ fi on the basic reproduction number, R0, in ways which are unique signi cantly from those obtained by a homogeneous approx- to these systems6–10. In TB, as in other communicable diseases, imation of the same models. We find that by considering het- this approach motivated the proliferation of efforts to collect data erogeneity, control efforts result in a lower impact on disease on contact patterns and superspreading events, to unravel pro- burden, except in special circumstances which we highlight. More cesses that may affect transmission indices and models. The need generally, we elucidate how model predictability relies on certain to account for variation in disease risk, however, is not unfamiliar forms of heterogeneity but not others, and propose a practical in epidemiology at large, where so-called frailty terms are more scheme for summarizing inequality in disease risk to be used in generally included in models to improve the accuracy of data modeling and policy development for TB and other diseases. analysis11. The premise is that variation in the risk of acquiring a disease (whether infectious or not) goes beyond what is captured by measured factors (typically age, malnutrition, comorbidities, Results habits, social contacts, etc), and a distribution of unobserved Risk inequality coefficient (RIC). Figure 1 depicts Lorenz heterogeneity can be inferred from incidence trends in a holistic curves14 for TB occurrences in the populations of Vietnam, Brazil manner. Such distributions are needed for eliminating biases in and Portugal structured by municipalities (level 2 administrative interpretation and prediction12,13, and can be utilized in con- divisions), enabling the calculation of a Gini coefficient15 that we junction with more common reductionist approaches, which are refer to as the risk inequality coefficient (RIC) (Methods). To required when there is desire to target interventions at individuals inform mathematical models of TB transmission with two risk with specific characteristics. groups12,17, we discretize risk such that 4% of the population Individual risk of infection or disease relates to a probability of experiences higher risk than the remaining 96%. This cut-off is responding to a stimulus and, therefore, direct measurement consistent with previous studies17,18, although it could have been would require the recording of responses to many exposures to set arbitrarily as the procedure does not depend on how we obtain the frequency at which the outcome of interest occurs. In discretize what is conceivably a continuous risk distribution. The TB, this is unfeasible due to the relatively low frequency of disease Lorenz curves corresponding to the discretization, which are episodes and the extremely variable time period between expo- depicted by the dashed lines in Fig. 1a, are then used as an sure and disease development, but may be approximated by sub- approximation to the original solid curves with the same RIC. 100 a Vietnam (RIC = 0.30) b Vietnam c Brazil d Portugal Brazil (RIC = 0.46) 80 Portugal (RIC = 0.32) 60 40 TB: 60 TB: TB notifications (%) TB: 50 20 200 40 80 150 30 60 100 20 40 0 50 10 20 0 20406080100 0 0 0 Population (%) Fig. 1 Risk inequality coefficient. a Lorenz curves calculated from notification data stratified by level 2 administrative divisions (697 districts in Vietnam; 5127 municipalities in Brazil; 308 municipalities
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