Mathematical modelling of tuberculosis in South Africa : investigating the impact of interventions on population-level incidence and mortality

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dc.contributor.advisorVan Schalkwyk, Carien_ZA
dc.contributor.advisorMarx, Florianen_ZA
dc.contributor.authorBrown, Laurenen_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Applied Mathematics.en_ZA
dc.date.accessioned2023-02-21T09:46:28Z
dc.date.accessioned2023-11-16T09:25:23Z
dc.date.available2023-02-21T09:46:28Z
dc.date.available2023-11-16T09:25:23Z
dc.date.issued2023-03
dc.descriptionThesis (MSc)--Stellenbosch University, 2023.en_ZA
dc.description.abstractENGLISH SUMMARY: Background. Tuberculosis (TB) remains a major public health threat in South Africa. Substantial additional efforts are therefore needed to prevent, find, and successfully treat the disease. An increasing number of mathematical modelling studies have investigated the population-level impact of TB prevention and care interventions; however, this evidence has not yet been assessed in the South African context. Of particular concern for TB care in South Africa is the high proportion of initial loss to follow-up (ILTFU), defined as loss to follow-up of individuals who were diagnosed with TB but who did not (yet) initiate TB treatment. The aim of this thesis was to review existing literature on TB mathematical modelling research to determine the most effective intervention strategies to reduce TB burden in South Africa, to identify potential gaps in TB modelling research, and further, to conduct a mathematical modelling study to estimate the impact of reducing ILTFU in South Africa. Methods. A systematic review of studies that used transmission-dynamic models of TB in South Africa was conducted. PubMed, Scopus, and Web of Science databases were searched. Target populations, types of interventions, and estimates of impact on outcomes related to the End TB strategy targets were summarized. For country-level studies, average annual percentage declines (AAPDs) in TB incidence and mortality were estimated to compare the impact of interventions. Additionally, an existing TB transmission-dynamic model was adapted to estimate the impact of reducing ILTFU in South Africa. Data from the LINKEDIn study, a large quasi-experimental study that was conducted in three South African provinces, were used to inform model scenarios and intervention parameter estimates. The impact of scaling-up the LINKEDIn intervention to country level was specified as the number of incident cases and deaths averted over a 13-year period (2023-2035). Results. Twenty-nine studies were identified in the systematic review, of which seven modelled TB preventive interventions, 12 considered interventions along the TB care cascade, and 10 modelled combinations of both. One study considered reductions in TB-related catastrophic costs. The highest impact of a single intervention was estimated in studies of TB vaccination, preventive treatment among people living with HIV, and scale up of antiretroviral treatment. For preventive interventions, AAPDs for incidence varied between 0.06% and 7.07%, and for care-cascade interventions between 0.05% and 3.27%. In the modelling study, reducing ILTFU by 50% in the population was projected to avert 49,812 (95% uncertainty interval [UI]: 21,258-84,644) incident TB cases and 21,479 (UI: 9,500-32,661) deaths between 2023 and 2035. Sensitivity analyses showed that population-level impact would depend on rapid implementation and maximum effect of the intervention. Conclusion. This thesis describes a body of mathematical modelling research with focus on TB prevention and care in South Africa. Higher estimates of impact reported in studies of preventive interventions were found, highlighting the need to invest in TB prevention in South Africa. The population-level impact of reducing ILTFU was projected to be modest. Combinations rather than single interventions, such as the LINKEDIn intervention, are likely needed to reach the End TB Strategy targets in South Africa.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Agtergrond. Tuberkulose (TB) bly 'n groot bedreiging vir openbare gesondheid in Suid-Afrika. Aansienlike bykomende pogings is dus nodig om die siekte te voorkom, op te spoor en suksesvol te behandel. 'n Toenemende aantal wiskundige modelleringstudies het die bevolkingsvlakimpak van TB-voorkoming en sorgintervensies ondersoek; hierdie werk is egter nog nie in die Suid-Afrikaanse konteks beoordeel nie. Van besondere kommer vir TB-sorg in Suid-Afrika is die hoe proporsie van aanvanklike verlies tot opvolg (ILTFU), gedefinieer as verlies aan opvolg van individue wat met TB gediagnoseer is, maar wat (nog) nie TB-behandeling begin het nie. Die doel van hierdie tesis was om bestaande literatuur oor TB wiskundige modelleringsnavorsing te hersien om die mees doeltreffende intervensiestrategiee te bepaal om TB-las in Suid-Afrika te verminder, om potensiele leemtes in TB-modelleringsnavorsing te identifiseer, en verder om 'n wiskundige modelleringstudie uit te voer om die impak van die vermindering van ILTFU in Suid-Afrika te beraam. Metodes. 'n Sistematiese oorsig van studies wat oordrag-dinamiese modelle van TB in Suid-Afrika gebruik het, is uitgevoer. PubMed-, Scopus- en Web of Science-databasisse is deursoek. Teikenpopulasies, tipes intervensies en ramings van impak op uitkomste wat verband hou met die eind-TB-strategie-teikens is opgesom. Vir studies toepaslik tot die hele Suid-Afrikaanse bevolking is gemiddelde jaarlikse persentasie dalings (AAPDs) in TB-voorkoms en mortaliteit beraam om die impak van intervensies te vergelyk. Daarbenewens is 'n bestaande TB-oordrag-dinamiese model aangepas om die impak van die vermindering van ILTFU in Suid-Afrika te beraam. Data van die LINKEDIn-studie, 'n groot kwasi-eksperimentele studie wat in drie Suid-Afrikaanse provinsies uitgevoer is, is gebruik om modelscenario's en intervensie parameter beramings in te lig. Die impak van die uitbreiding van die LINKEDIn-invensie na die hele Suid-Afrika is gedefinieer as die aantal TB gevalle en sterftes wat oor 'n tydperk van 13 jaar (2023-2035) afgeweer is. Resultate. Nege-en-twintig studies is in die sistematiese oorsig geidentifiseer, waarvan sewe TB-voorkomende intervensies, 12 intervensies tot die TB-sorgkaskade, en 10 kombinasies van beide gemodelleer het. Een studie het verlagings in TB-verwante katastrofiese koste oorweeg. Die grootste impak van 'n enkele intervensie is beraam in studies van TB-inenting, TPT vir mense wat met MIV leef, en opskaal van antiretrovirale behandeling. Vir voorkomende intervensies het AAPD's vir insidensie tussen 0.06% en 7.07% gewissel, en vir sorg-kaskade intervensies tussen 0.05% en 3.27%. In die modelleringstudie, is die vermindering van ILTFU met 50% in die bevolking geprojekteer om 49 812 (95% onsekerheidsinterval [UI]: 21 258-84 644) TB-gevalle en 21 479 (UI: 9 500-32 661) TB-sterftes tussen 2023 en 2035 te voorkom. Sensitiwiteitsanalises het getoon dat bevolkingsvlak impak sal afhang van vinnige implementering en die maksimum effek van die intervensie. Afsluiting. Hierdie tesis beskryf 'n geheel van wiskundige modelleringsnavorsing met die fokus op TB-voorkoming en -sorg in Suid-Afrika. Hoer ramings van impak wat in studies van voorkomende intervensies gerapporteer is, is gevind, wat die behoefte beklemtoon om in TB-voorkoming in Suid-Afrika te bele. Die impak op bevolkingsvlak van die vermindering van ILTFU is geprojekteer om beskeie te wees. Kombinasies eerder as enkele intervensies, soos die LINKEDIn-intervensie, is waarskynlik nodig om die eind-TB-strategie-teikens in Suid-Afrika te bereik.af_ZA
dc.description.versionMasters
dc.format.extentix, 73 pages : illustrations, includes annexures
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/128715
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subject.lcshMathematical models -- Research -- South Africaen_ZA
dc.subject.lcshTuberculosis -- prevention -- South Africaen_ZA
dc.subject.lcshTuberculosis -- Transmission -- Prevention -- South Africaen_ZA
dc.subject.nameUCTD
dc.titleMathematical modelling of tuberculosis in South Africa : investigating the impact of interventions on population-level incidence and mortalityen_ZA
dc.typeThesisen_ZA
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