Background

Diseases occur in populations whose individuals differ in essential characteristics, such as exposure to the causative agent, susceptibility given exposure, and infectiousness upon infection in the case of infectious diseases.



Discussion

Concepts developed in demography more than 30 years ago assert that variability between individuals affects substantially the estimation of overall population risk from disease incidence data. Methods that ignore individual heterogeneity tend to underestimate overall risk and lead to overoptimistic expectations for control. Concerned that this phenomenon is frequently overlooked in epidemiology, here we feature its significance for interpreting global data on human tuberculosis and predicting the impact of control measures.



Summary

We show that population-wide interventions have the greatest impact in populations where all individuals face an equal risk. Lowering variability in risk has great potential to increase the impact of interventions. Reducing inequality, therefore, empowers health interventions, which in turn improves health, further reducing inequality, in a virtuous circle

Barreto, Maurício L.

Glaziou, Philippe

Gomes, Gabriela

Medley, Graham F.

Rodrigues, Laura C.

Squire, Bertie

Wallinga, Jacco

LSTM Online Archive

DEBATE Open AccessEnd TB strategy: the need to reduce riskinequalitiesM. Gabriela M. Gomes1,2,3*, Maurício L. Barreto4,5, Philippe Glaziou6, Graham F. Medley7, Laura C. Rodrigues7,Jacco Wallinga8,9 and S. Bertel Squire1AbstractBackground: Diseases occur in populations whose individuals differ in essential characteristics, such as exposureto the causative agent, susceptibility given exposure, and infectiousness upon infection in the case of infectiousdiseases.Discussion: Concepts developed in demography more than 30 years ago assert that variability between individualsaffects substantially the estimation of overall population risk from disease incidence data. Methods that ignoreindividual heterogeneity tend to underestimate overall risk and lead to overoptimistic expectations for control.Concerned that this phenomenon is frequently overlooked in epidemiology, here we feature its significance forinterpreting global data on human tuberculosis and predicting the impact of control measures.Summary: We show that population-wide interventions have the greatest impact in populations where all individualsface an equal risk. Lowering variability in risk has great potential to increase the impact of interventions. Reducinginequality, therefore, empowers health interventions, which in turn improves health, further reducing inequality, in avirtuous circle.Keywords: Tuberculosis, Heterogeneity, Cohort selection, Social inequality, Intervention impactBackgroundThe World Health Organization (WHO) has defined anew strategy for tuberculosis (TB) prevention, care andcontrol – the End TB Strategy – and set, among itstargets, to reduce the incidence rate of the disease by90 % in 20 years (2015–2035) [1]. There is some urgencyfor TB control as drug resistance increases and popula-tions become more mobile – TB becomes more difficultto treat while effectively infecting the world’s poorestcommunities no matter where they live. Consequently,WHO estimates that the reduction in global incidencemust accelerate from the current 2 % per year [2] tomeet new targets, and advocates strong social protectionin addition to universal health care [3]. The markedheterogeneity in TB risk makes its dynamics particularlyresponsive to synergistic policies, such as targeted socialmeasures or targeted HIV treatment to reduce variation,combined with TB interventions targeting the whole popu-lation to reduce disease. Adding to previous studies, whichhave assessed targeting control at the poor [4] or imple-menting population-wide measures with fixed stratifica-tion of disease risk [5], here we examine how the impactof universal coverage interventions, such as a hypotheticalvaccine against TB, depends on the underlying risk distri-bution in a population. Universal coverage interventionshave greater impact on disease in populations where allindividuals face similar risks, indicating that reducinginequality empowers existing disease control tools.Misuse of population averagesIn tuberculosis, it is well known that some groups havemuch higher risk to acquire infection and develop dis-ease than the majority of the population: incarceratedprisoners (20 times higher in Brazil [6]), persons livingwith HIV (8 times higher in a South African community[7]), geographical hotspots within urban settings (3 timeshigher in people living in the poorest areas in Rio deJaneiro [8]), among others. Behind these differences aredifferences in basic parameters, which measure how* Correspondence: Gabriela.Gomes@lstmed.ac.uk1Liverpool School of Tropical Medicine, Liverpool, UK2CIBIO-InBIO, Centro de Investigação em Biodiversidade e RecursosGenéticos, Universidade do Porto, Vila do Conde, PortugalFull list of author information is available at the end of the article© 2016 Gomes et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Gomes et al. BMC Infectious Diseases  (2016) 16:132 DOI 10.1186/s12879-016-1464-8frequently each person is likely to meet an infectiouscase of TB and become infected, as determined by theway social mixing patterns combine with individual vul-nerabilities [4]. These effective contact rates are then av-eraged and scaled into the so-called basic reproductionnumber, R0, defined as the average number of secondarydisease cases caused by an average infectious person in atotally susceptible population. Similarly, the incidence ofTB in the population is usually presented as a singlenumber, which implicitly represents a weighted averageof the incidences in groups of different sizes and relativerisks. All this averaging would be fine if disease probabil-ities were linearly related with exposure intensity, butincidence describes a non-linear relationship with R0(Fig. 1). This relation has a concave form due to thestronger depletion of susceptible hosts at higher trans-mission intensities. As a result, averaging incidencesleads to underestimated R0 and overoptimistic controlexpectations at the population level as recently demon-strated by a theoretical study applicable to infectiousdiseases more generally [9].Why there is a tendency to underestimate control effortsTo illustrate the idea, we use a transmission model forTB, developed previously [10] and replicated in theappendix. In Fig. 1a, b incidence data for different coun-tries are shown in a colour scheme defined by WHOregion [2], superimposed on how rates of TB incidencerelate to R0 according to two realisations of the model: ahomogenous assumption, where all individuals experi-ence the same rates of infection, disease progression andtransmission (Fig. 1a); and a heterogeneous implementa-tion where 4 % of individuals have a higher risk to con-tract the infection and infect others [8, 10, 11] (Fig. 1b).All parameters satisfy the same mean values across thetwo model realisations, allowing comparison and dem-onstrating how heterogeneity in risk has a striking abilityto modify the correspondence between incidence andR0. More specifically, to a given incidence, the heteroge-neous model associates a reproduction number that ishigher than that given by the corresponding homogeneouscounterpart, resulting in greater resilience to universalcoverage control efforts.For intuition, consider a population with some distri-bution of TB risk. The first people to be infected arethose with higher risk, leaving behind a susceptibilitypool with an average risk that decreases over time, by aprocess of cohort selection [12, 13], until the systemreaches equilibrium. Thus cohort selection acts effect-ively to suppress disease incidence, the more so thehigher the transmission intensity. Now suppose that TBtransmission is formulated into a model that collapsesthe risk distribution into its mean value. Cohort selec-tion no longer occurs because all individuals in thismodel have the same risk, and therefore the systemsustains a higher disease incidence by the time itreaches equilibrium. It follows that when two popula-tions present the same overall disease incidence, weshould expect a higher average risk where inequality ishigher, but this is not revealed in overall disease inci-dence. Interestingly, any universal coverage interven-tion that reduces (directly) transmission potential alsoreduces (indirectly) the suppression imposed by cohortselection, and this effect is greater where inequality ishigher. Therefore, intervention impact is effectivelylower under risk inequality and this is aggravated as theintervention progresses.Fig 1 Global tuberculosis incidence per 100,000 person-years. a, b,Endemic equilibrium states for model in appendix (black curves: a,homogeneous; b, heterogeneous). Colored lines represent the TBincidences reported by WHO in all countries, and associated R0.Countries are color-coded by WHO region: African (red); South-East Asia(yellow); Eastern Mediterranean (cyan); Western Pacific (green); Europe(blue); The Americas (magenta). c, Simulation of a vaccine that halvessusceptibility to infection and reduces reactivation rate by 90 %.Dashed and dash-dotted curves correspond to populations withvariance-to-mean ratios of 0 (homogeneous) and 20 (heterogeneous),respectively, in two epidemiological settings: baseline incidence of 1000per 100,000 person-years (red); and 50 per 100,000 person-years (grey)Gomes et al. BMC Infectious Diseases  (2016) 16:132 Page 2 of 4Universal coverage intervention impactIn Fig. 1c, we introduce a universal coverage interven-tion – for example a vaccine against TB. We considersettings that, in 2015, present mean incidences of 1000per 100,000 person-years (red), as in the highest burdenAfrican countries, and a more common scenario of 50per 100,000 person-years (grey). For each setting, wecontrast the time course of incidence rates under a vac-cine that effectively halves everybody’s susceptibility toinfection and reduces reactivation rate by 90 %, assum-ing that the risk distribution is homogeneous (dashed)or heterogeneous (dash-dotted). The impact of the inter-vention is consistently higher under homogeneous risks,although the magnitude of the difference can vary be-tween settings. In this case, the higher incidence settingshows greater difference in predicted impact: over20 years, the vaccine reduces incidence by 91 % or justby 10 %, depending on whether the variance-to-meanratio in risk is 0 (homogeneous) or 20 (heterogeneous)(Fig. 1c, red). The same simulation for a population pre-senting a lower baseline incidence of 50 per 100,000person-years leads to more consistent reductions by,respectively, 94 % or 92 % after 20 years although thereduction is slower when heterogeneity is accounted for(Fig. 1c, grey).Figure 2 summarises how the 20-year impact of thesimulated vaccination programme decays with risk het-erogeneity, considering a range of variance-to-meanratios between 0 and 20. An extended impact analysisover baseline incidences is provided in the appendix.Additional file 1: Figure S1 shows expected incidences20 years since the intervention has started, as well as aprojection of the same measures when new equilibriaare eventually established. Time to approach equilib-rium varies considerably across settings, appearing par-ticularly high in those settings which are closer toelimination, thus indicating the importance of accuratetime descriptions as elimination is approached.ConclusionsThe analysis provided here has profound implicationsfor the assessment of progression towards control tar-gets, such as those recently set by the WHO [1]. Wehave used a hypothetical vaccine to quantify how hetero-geneity in individual disease risk can compromise theperformance of control programmes. In this exercise,the recommended target of 90 % reduction in incidenceby 2035 can be comfortably met in settings where theincidence is 50 per 100,000. In contrast, when incidenceis much higher, e.g. 1000 per 100,000, then considerablymore effort is required. Averaging model parametersover groups of very different risk tends to unrealisticallyassign low values to transmission indices, such as R0,leading to overoptimistic control expectations especiallyin high incidence regions.In highlighting the sensitivity of programme outcomesto the actual distribution of disease risks in populations,we hope to stimulate a concerted action by the researchcommunity to optimise analytic tools. Incidence data atfiner stratification are needed for the construction of stra-tegic models that better capture heterogeneities in trans-mission. Models and targets need to take explicit accountof risk inequalities and their determinants. Meanwhile, asshown here, policies that reduce risk inequalities, whethersocial [14, 15] or biomedical [16], are expected to boostthe impact of population-wide interventions that targettuberculosis. Any disease specific control programme thatensures health interventions to work better for those atgreater risk [17], contributes to the huge task of makingpopulations more homogeneous, besides the more imme-diate returns in terms of reducing the target disease. Socialprotection and poverty alleviation actions [18, 19] canthen synergise with these programmes to increase thepower of already existing disease control tools.Additional fileAdditional file 1: Mathematical model and extended interventionimpact analysis. (PDF 422 kb)AbbreviationsWHO: World Health Organization; TB: tuberculosis.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionsMGMG carried out the analyses and drafted the manuscript. MLB, PG, GFM,LCR, JW, SBS read, revised and approved the final manuscript.AcknowledgmentsMGMG is grateful to Instituto Gulbenkian de Ciência for hosting during thedevelopment of these ideas.Fig 2 Impact of simulated vaccine decaying with the variance-to-meanratio of disease risk. In all cases the vaccine is given to all individuals tohalve susceptibility to infection and reduce reactivation rate by 90 %.Two epidemiological settings are considered: baseline incidence of 1000per 100,000 person-years (red); and 50 per 100,000 person-years (grey)Gomes et al. BMC Infectious Diseases  (2016) 16:132 Page 3 of 4FundingThe work was supported by FCT (IF/01346/2014) and CAPES (Sciencewithout Borders).Author details1Liverpool School of Tropical Medicine, Liverpool, UK. 2CIBIO-InBIO, Centro deInvestigação em Biodiversidade e Recursos Genéticos, Universidade do Porto,Vila do Conde, Portugal. 3Instituto de Matemática e Estatística, Universidadede São Paulo, São Paulo, Brazil. 4Centro de Pesquisas Gonçalo Moniz,FIOCRUZ, Salvador, Brazil. 5Instituto de Saúde Coletiva, Universidade Federalda Bahia, Salvador, Brazil. 6World Health Organization, 1211 Geneva 27,Switzerland. 7London School of Hygiene and Tropical Medicine, London, UK.8Department of Infectious Diseases Epidemiology, National Institute of PublicHealth and the Environment, PO Box 13720 BA Bilthoven, The Netherlands.9Department of Medical Statistics and Bioinformatics, Leiden UniversityMedical Center, PO Box 96002300 RC Leiden, The Netherlands.Received: 18 December 2015 Accepted: 10 March 2016References1. 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Undiagnosed tuberculosis in a communitywith high HIV prevalence. Am J Respir Crit Care Med. 2007;175:87–93.8. Dowdy DW, Golub JE, Chaisson RE, Saraceni V. Heterogeneity in tuberculosistransmission and the role of geographic hotspots in propagating epidemics.Proc Natl Acad Sci U S A. 2012;109:9557–62.9. Gomes MGM, Gjini E, Lopes JS, Souto-Maior C, Rebelo C. A theoreticalframework to identify invariant thresholds from global infectious diseasedata. J Theor Biol. 2016;395:97–102.10. Gomes MGM, Aguas R, Lopes JS, et al. How host selection governs tuberculosisreinfection. Proc Biol Sci. 2012;279:2473–8.11. Ypma RJF, Altes HK, van Soolingen D, Wallinga J, van Ballegooijen WM.A sign of superspreading in tuberculosis: highly skewed distribution ofgenotypic cluster sizes. Epidemiol. 2013;24:395–400.12. Vaupel JW, Manton KG, Stallard E. Impact of heterogeneity in individualfrailty on the dynamics of mortality. Demography. 1979;16:439–54.13. Vaupel JW, Yashin AI. Heterogeneity’s ruses: some surprising effects ofselection on population dynamics. Am Stat. 1985;39:176–85.14. Boccia D, Hargreaves J, Lönnroth K, et al. Cash transfer and microfinanceinterventions for tuberculosis control: review of the impact evidence andpolicy implications. Int J Tuberc Lung Dis. 2011;15 Suppl 2:S37–49.15. Reeves A, Basu S, McKee M, et al. Social protection and tuberculosis controlin 21 European countries, 1995–2012: a cross-national statistical modellinganalysis. Lancet Infect Dis. 2014;14:1105–12.16. Lessells RJ, Swaminathan S, Godfrey-Faussett P. HIV treatment cascade intuberculosis patients. Curr Opin HIV AIDS. 2015;10:439–46.17. Simwaka BN, Theobard S, Willets A, et al. Acceptability and effectiveness ofthe storekeeper-based TB referral system for TB suspects in sub-districts ofLilongwe in Malawi. PLoS One. 2012;7(9), e39746.18. Wingfield T, Boccia D, Tovar M, et al. Defining catastrophic costs and comparingtheir importance for adverse tuberculosis outcome with multi-drug resistance:a prospective cohort study, Peru. PLoS Med. 2014;11(7), e1001675.19. Wingfield T, Boccia D, Tovar MA, et al. Designing and implementing asocioeconomic intervention to enhance TB control: operational evidence fromthe CRESIPT project in Peru. BMC Public Health. 2015;15:810.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Gomes et al. BMC Infectious Diseases  (2016) 16:132 Page 4 of 4

End TB strategy: the need to reduce risk inequalities

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End TB strategy: the need to reduce risk inequalities

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