Multiapproach computational modelling of tuberculosis : understanding its epidemiological dynamics for improving its control in Nigeria

Tuberculosis (TB) is an infectious disease that is considered to be the biggest killer of mankind in the history of infectious diseases. There are still more than 10 million new TB cases every year and it causes more than 1.5 million deaths annually, according to World Health Organization estimates. Nigeria, with a persistent incidence of about 219 cases per 100000 inhabitants on 2019, is among the 8 countries that accounted for two-thirds of the new TB cases in 2018. The control of the disease in this country is coordinated by the National Tuberculosis and Leprosy Control Program (NTBLCP). Despite efforts, current estimated prevalence is still similar to that of 1990. Epidemiological models can be used for a more precise diagnosis of TB situation in certain territories, as well as to help on the design and evaluation of control actions. In this project, two modelling approaches have been used to these ends. First, a top-down approach at the country level (Nigeria) by means of the design, testing and fitting of several SEIR-type models, aimed to provide a global picture of the situation and to quantify some of the most relevant parameters. Second, a bottom-up approach to a smaller area (Gombe state) by means of the design, testing and fitting of an agent-based model (ABM), aimed to unravel a particular context and to help on the design and quantification of control actions. In addition, fieldwork was also carried out in order to look for the particular socio-economic factors that are responsible for the epidemiological situation of TB spread; the statistical analysis of the data obtained is the third approach of this project. SEIR-type approach confirmed a dramatic low notification rate that varies from 16 to 20% during the analysed years (2000-2010). This factor revealed to be the bottleneck for the control of the disease in this country. Model’s predictions showed no relevant effects of control actions without a previous increase in the notification rate. Fieldwork was designed in coordination with NTBLCP local authorities with the aim of analysing socio-economic factors that condition such notification rate in Gombe state. It consisted of an initial gathering of epidemiological data, followed by a set of 52 in-depth interviews with TB patients from different health centres. Obtained data and interviews’ outcomes were statistically analysed using inferential statistics and Anova analysis of mean, with the help of machine learning techniques. Results were devastating: none of the patients interviewed had any knowledge of TB symptoms and 90% had no knowledge of TB transmission mechanisms after talking to the health workers. Mean patients’ delay before going to hospital was 9.6 weeks, and only 10% of interviewed patients went to the doctor within the first month of feeling sick. The epidemiological information obtained from the top-down approach and the results derived from the fieldwork were used for adapting an agent-based model (ABM) of TB spreading in the context of Gombe state. The resulting ABM was successfully fitted to the evolution of estimated prevalence and diagnosed cases from 2007 to 2016. Then, it was used to test different interventions aimed to increase the notification rate, decrease the diagnosis delay, and increase the population’s awareness regarding TB transmissible. The multi-approach methodology used in this project revealed to be a robust way of tackling a real problem. It was capable of providing a global and detailed picture of TB situation in a certain area, relating model’s parameters and outcomes with a real socioeconomic context, and generating a useful tool for helping on the design and evaluation of TB control actions.La tuberculosi (TB) és la malaltia infecciosa que ha causat més morts a la història de la humanitat. Actualment, hi ha més de 10 milions de nous casos de TB cada any i provoca més d’1.5 milions de morts anuals, segons estimacions de l’Organització Mundial de la Salut. Nigèria, amb una incidència persistent d’uns 219 casos per 100.000 habitants l’any 2019, es troba entre els 8 països que van representar dos terços dels nous casos de tuberculosi el 2018. El control de la malaltia d’aquest país està coordinat pel Programa Nacional de Control de la Tuberculosi i la Lepra (NTBLCP). Malgrat els esforços, la prevalença estimada és la mateixa que s’estimava l’any 1990. Es poden utilitzar models per a un diagnòstic més precís de la situació de TB en determinats territoris, així com per ajudar en el disseny i avaluació de les accions de control. En aquest projecte, s’han utilitzat dues estratègies de modelització per fer-ho. En primer lloc, una estratègia top-down a nivell de país (Nigèria) mitjançant el disseny, proves i adequació de diversos models de tipus SEIR, amb l’objectiu de proporcionar una imatge global de la situació i quantificar alguns dels paràmetres més rellevants. En segon lloc, una estratègia bottom-up estudiant una àrea més petita, l’estat de Gombe, mitjançant el disseny, la prova i l’adequació d’un model basat en agents (ABM). El model ABM té per finalitat entendre en detall la situat particular de l’estat de Gombe i ajudar en el disseny i quantificació de possibles accions de control. També es va realitzar un treball de camp per buscar els factors socioeconòmics particulars responsables de la situació epidemiològica de la TB; l’anàlisi estadística de les dades obtingudes és la tercera estratègia de treball d’aquets projecte. L’aproximació mitjançant models top-down va confirmar una taxa de notificació molt baixa que varia del 16 al 20% durant els anys analitzats (2000-2010). Aquest factor es va revelar com el coll d’ampolla per al control de la malaltia en aquest país. Les prediccions del model no mostraven efectes rellevants de les accions de control sense un augment previ de la taxa de notificació. El treball de camp es va dissenyar en coordinació amb les autoritats locals NTBLCP amb l’objectiu d’analitzar els factors socioeconòmics que condicionen aquesta taxa de notificació a l’estat de Gombe. Va consistir en una recollida inicial de dades epidemiològiques, seguida d’un conjunt de 52 entrevistes amb pacients amb TB de diferents centres de salut. Les dades obtingudes i els resultats de les entrevistes es van analitzar mitjançant estadístiques inferencials i Anova, amb l’ajut de tècniques de Machine learning. Els resultats van ser devastadors: cap dels pacients entrevistats no coneixia els símptomes de la TB i el 90% no coneixia els mecanismes de transmissió de la TB després de parlar amb el personal sanitari. El retard mitjà de diagnòstic dels pacients va ser de 9.6 setmanes, i només el 10% dels entrevistats van acudir al metge durant el primer mes de sentir-se malalt. La informació obtinguda amb l’estratègia top-down i els resultats derivats del treball de camp es van utilitzar per adaptar el model ABM en el context de l’estat de Gombe. Es va ajustar amb èxit a l’evolució de la prevalença estimada i els casos diagnosticats del 2007 al 2016. A continuació, es va utilitzar per provar diferents intervencions destinades a augmentar la taxa de notificació, disminuir el retard de diagnòstic i augmentar la consciència de la població respecte a la TB transmissible. La metodologia utilitzant diferents estratègies utilitzada en aquest projecte s’ha mostrat com una forma robusta d’afrontar el problema real. Ha fet possible proporcionar una imatge global i detallada de la situació de la TB en un determinat àmbit, relacionar els paràmetres i els resultats del model amb un context socioeconòmic real i generar una eina útil per ajudar en el disseny i l’avaluació d’accions de control de la tuberculosi.La tuberculosis (TB) es la enfermedad infecciosa que más muertes ha causado en la historia de las enfermedades infecciosas, conociéndose como the big killer. Es responsable de más de mil millones de muertes en los últimos 200 años. Hoy en día, todavía hay más de 10 millones de casos nuevos de TB cada año y causa más de 1.5 millones de muertes anuales, según las estimaciones de la Organización Mundial de la Salud (OMS). Nigeria, con una incidencia persistente de aproximadamente 219 casos / 105 habitantes para el año 2019, se encuentra entre los 8 países que representaron conjuntamente dos tercios de los nuevos casos de tuberculosis en 2018. El control de la enfermedad en este país está coordinado por el Programa Nacional de Control de Tuberculosis y Lepra (NTBLCP). A pesar de los esfuerzos, que se centran principalmente en la provisión de DOTS gratuitos (tratamiento observado directamente, curso corto) a personas con TB activa que acuden al hospital, la prevalencia estimada es de alrededor de 330 / 105 habitantes, que es aproximadamente igual a la prevalencia estimada de TB en 1990 (323 / 100,000). Los modelos epidemiológicos se pueden utilizar para un diagnóstico más preciso de la situación de TB en ciertos territorios, así como para ayudar en el diseño y evaluación de acciones de control. En este proyecto, se han utilizado dos enfoques de modelización para estos fines. Primero, un enfoque de arriba hacia abajo (top-down) a nivel de país (Nigeria) mediante el diseño, prueba y ajuste de varios modelos tipo SEIR, con el objectivo de proporcionar una imagen global de la situación y cuantificar algunos de los parámetros más relevantes. En segundo lugar, un enfoque de abajo a arriba (bottom-up) en una área más pequeña (estado de Gombe, en el noreste del país) mediante el diseño, prueba y ajuste de un modelo basado en agentes, con el objetivo de desentrañar un contexto particular y ayudar en el diseño y cuantificación de las acciones de control. Además, también se realizó un Trabajo de campo para investigar los factores socioeconómicos particulares que son responsables de la situación epidemiológica de la propagación de la tuberculosis. El análisis estadístico de los datos obtenidos es el tercer enfoque de este proyecto. Se construyeron varios modelos de tipo SEIR en un intento de caracterizar y comprender progresivamente la dinámica de la TB en diferentes contextos. Estos modelos se ajustaron a algunos países seleccionados de alta carga y baja carga en todo el mundo, y en particular a la situación epidemiológica en Nigeria. La división de la población infecciosa (enferma) en dos subpoblaciones, la de personas que se diagnostican (y se tratan) y la de las que no, confirman una tasa de notificación baja dramática que varía entre el 16 y el 20 % durante los años analizados (2000-2010). Este factor reveló ser el cuello de botella para el control de la enfermedad en este país. Las predicciones del modelo no mostraron efectos relevantes de las acciones de control sin un aumento previo en la tasa de notificación. El trabajo de campo se diseñó en coordinación con las autoridades locales de NTBLCP con el objetivo de analizar los factores socioeconómicos que condicionan dicha tasa de notificación en el estado de Gombe. Consistió en una recopilación inicial de datos de la sucursal del estado de NTBLCP Gombe, la Junta de Administración de Hospitales del Estado de Gombe y varios hospitales en el estado de Gombe, seguida de un conjunto de 52 entrevistas en profundidad con pacientes con tuberculosis de diferentes centros de salud. Los datos obtenidos y los resultados de las entrevistas se analizaron estadísticamente utilizando estadísticas inferenciales y análisis de medias de Anova, con la ayuda de técnicas de aprendizaje automático. Los resultados fueron devastadores: ninguno de los pacientes entrevistados conocía los síntomas de la tuberculosis y el 90 % no conocía los mecanismos de transmisión de la tuberculosis después de hablar con el personal sanitario. El retraso medio de los pacientes en acudir al centro médico fue de 9.6 semanas con una desviación estándar de 4.8; solo el 10 % de los pacientes entrevistados acudió al médico dentro del primer mes de sentirse enfermo, el 30 % dentro de 1-2 meses, el 20 % dentro de 2-3 meses y el 40 % después de sentirse enfermo durante más de 3 meses. La información epidemiológica obtenida del enfoque bottom-up y los resultados derivados del trabajo de campo se utilizaron para adaptar un modelo basado en agentes (ABM) de propagación de TB en el contexto del estado de Gombe. El ABM resultante se ajustó con éxito a la evolución de la prevalencia estimada y los casos diagnosticados de 2007 a 2016. Luego, se usó para probar diferentes intervenciones destinadas a aumentar la tasa de notificación, disminuir el retraso del diagnóstico y aumentar la conciencia de la población sobre la TB transmisible. La metodología de enfoque múltiple utilizada en este proyecto reveló ser una forma sólida de abordar un problema real. Fue capaz de proporcionar una imagen global y detallada de la situación de la TB en un área determinada, relacionar los parámetros y resultados del modelo con un contexto socioeconómico real, y generar una herramienta útil para ayudar en el diseño y la evaluación de acciones de control de TB.Postprint (published version

Multiapproach computational modelling of tuberculosis : understanding its epidemiological dynamics for improving its control in Nigeria

Tuberculosis (TB) is an infectious disease that is considered to be the biggest killer of mankind in the history of infectious diseases. There are still more than 10 million new TB cases every year and it causes more than 1.5 million deaths annually, according to World Health Organization estimates. Nigeria, with a persistent incidence of about 219 cases per 100000 inhabitants on 2019, is among the 8 countries that accounted for two-thirds of the new TB cases in 2018. The control of the disease in this country is coordinated by the National Tuberculosis and Leprosy Control Program (NTBLCP). Despite efforts, current estimated prevalence is still similar to that of 1990. Epidemiological models can be used for a more precise diagnosis of TB situation in certain territories, as well as to help on the design and evaluation of control actions. In this project, two modelling approaches have been used to these ends. First, a top-down approach at the country level (Nigeria) by means of the design, testing and fitting of several SEIR-type models, aimed to provide a global picture of the situation and to quantify some of the most relevant parameters. Second, a bottom-up approach to a smaller area (Gombe state) by means of the design, testing and fitting of an agent-based model (ABM), aimed to unravel a particular context and to help on the design and quantification of control actions. In addition, fieldwork was also carried out in order to look for the particular socio-economic factors that are responsible for the epidemiological situation of TB spread; the statistical analysis of the data obtained is the third approach of this project. SEIR-type approach confirmed a dramatic low notification rate that varies from 16 to 20% during the analysed years (2000-2010). This factor revealed to be the bottleneck for the control of the disease in this country. Model’s predictions showed no relevant effects of control actions without a previous increase in the notification rate. Fieldwork was designed in coordination with NTBLCP local authorities with the aim of analysing socio-economic factors that condition such notification rate in Gombe state. It consisted of an initial gathering of epidemiological data, followed by a set of 52 in-depth interviews with TB patients from different health centres. Obtained data and interviews’ outcomes were statistically analysed using inferential statistics and Anova analysis of mean, with the help of machine learning techniques. Results were devastating: none of the patients interviewed had any knowledge of TB symptoms and 90% had no knowledge of TB transmission mechanisms after talking to the health workers. Mean patients’ delay before going to hospital was 9.6 weeks, and only 10% of interviewed patients went to the doctor within the first month of feeling sick. The epidemiological information obtained from the top-down approach and the results derived from the fieldwork were used for adapting an agent-based model (ABM) of TB spreading in the context of Gombe state. The resulting ABM was successfully fitted to the evolution of estimated prevalence and diagnosed cases from 2007 to 2016. Then, it was used to test different interventions aimed to increase the notification rate, decrease the diagnosis delay, and increase the population’s awareness regarding TB transmissible. The multi-approach methodology used in this project revealed to be a robust way of tackling a real problem. It was capable of providing a global and detailed picture of TB situation in a certain area, relating model’s parameters and outcomes with a real socioeconomic context, and generating a useful tool for helping on the design and evaluation of TB control actions.La tuberculosi (TB) és la malaltia infecciosa que ha causat més morts a la història de la humanitat. Actualment, hi ha més de 10 milions de nous casos de TB cada any i provoca més d’1.5 milions de morts anuals, segons estimacions de l’Organització Mundial de la Salut. Nigèria, amb una incidència persistent d’uns 219 casos per 100.000 habitants l’any 2019, es troba entre els 8 països que van representar dos terços dels nous casos de tuberculosi el 2018. El control de la malaltia d’aquest país està coordinat pel Programa Nacional de Control de la Tuberculosi i la Lepra (NTBLCP). Malgrat els esforços, la prevalença estimada és la mateixa que s’estimava l’any 1990. Es poden utilitzar models per a un diagnòstic més precís de la situació de TB en determinats territoris, així com per ajudar en el disseny i avaluació de les accions de control. En aquest projecte, s’han utilitzat dues estratègies de modelització per fer-ho. En primer lloc, una estratègia top-down a nivell de país (Nigèria) mitjançant el disseny, proves i adequació de diversos models de tipus SEIR, amb l’objectiu de proporcionar una imatge global de la situació i quantificar alguns dels paràmetres més rellevants. En segon lloc, una estratègia bottom-up estudiant una àrea més petita, l’estat de Gombe, mitjançant el disseny, la prova i l’adequació d’un model basat en agents (ABM). El model ABM té per finalitat entendre en detall la situat particular de l’estat de Gombe i ajudar en el disseny i quantificació de possibles accions de control. També es va realitzar un treball de camp per buscar els factors socioeconòmics particulars responsables de la situació epidemiològica de la TB; l’anàlisi estadística de les dades obtingudes és la tercera estratègia de treball d’aquets projecte. L’aproximació mitjançant models top-down va confirmar una taxa de notificació molt baixa que varia del 16 al 20% durant els anys analitzats (2000-2010). Aquest factor es va revelar com el coll d’ampolla per al control de la malaltia en aquest país. Les prediccions del model no mostraven efectes rellevants de les accions de control sense un augment previ de la taxa de notificació. El treball de camp es va dissenyar en coordinació amb les autoritats locals NTBLCP amb l’objectiu d’analitzar els factors socioeconòmics que condicionen aquesta taxa de notificació a l’estat de Gombe. Va consistir en una recollida inicial de dades epidemiològiques, seguida d’un conjunt de 52 entrevistes amb pacients amb TB de diferents centres de salut. Les dades obtingudes i els resultats de les entrevistes es van analitzar mitjançant estadístiques inferencials i Anova, amb l’ajut de tècniques de Machine learning. Els resultats van ser devastadors: cap dels pacients entrevistats no coneixia els símptomes de la TB i el 90% no coneixia els mecanismes de transmissió de la TB després de parlar amb el personal sanitari. El retard mitjà de diagnòstic dels pacients va ser de 9.6 setmanes, i només el 10% dels entrevistats van acudir al metge durant el primer mes de sentir-se malalt. La informació obtinguda amb l’estratègia top-down i els resultats derivats del treball de camp es van utilitzar per adaptar el model ABM en el context de l’estat de Gombe. Es va ajustar amb èxit a l’evolució de la prevalença estimada i els casos diagnosticats del 2007 al 2016. A continuació, es va utilitzar per provar diferents intervencions destinades a augmentar la taxa de notificació, disminuir el retard de diagnòstic i augmentar la consciència de la població respecte a la TB transmissible. La metodologia utilitzant diferents estratègies utilitzada en aquest projecte s’ha mostrat com una forma robusta d’afrontar el problema real. Ha fet possible proporcionar una imatge global i detallada de la situació de la TB en un determinat àmbit, relacionar els paràmetres i els resultats del model amb un context socioeconòmic real i generar una eina útil per ajudar en el disseny i l’avaluació d’accions de control de la tuberculosi.La tuberculosis (TB) es la enfermedad infecciosa que más muertes ha causado en la historia de las enfermedades infecciosas, conociéndose como the big killer. Es responsable de más de mil millones de muertes en los últimos 200 años. Hoy en día, todavía hay más de 10 millones de casos nuevos de TB cada año y causa más de 1.5 millones de muertes anuales, según las estimaciones de la Organización Mundial de la Salud (OMS). Nigeria, con una incidencia persistente de aproximadamente 219 casos / 105 habitantes para el año 2019, se encuentra entre los 8 países que representaron conjuntamente dos tercios de los nuevos casos de tuberculosis en 2018. El control de la enfermedad en este país está coordinado por el Programa Nacional de Control de Tuberculosis y Lepra (NTBLCP). A pesar de los esfuerzos, que se centran principalmente en la provisión de DOTS gratuitos (tratamiento observado directamente, curso corto) a personas con TB activa que acuden al hospital, la prevalencia estimada es de alrededor de 330 / 105 habitantes, que es aproximadamente igual a la prevalencia estimada de TB en 1990 (323 / 100,000). Los modelos epidemiológicos se pueden utilizar para un diagnóstico más preciso de la situación de TB en ciertos territorios, así como para ayudar en el diseño y evaluación de acciones de control. En este proyecto, se han utilizado dos enfoques de modelización para estos fines. Primero, un enfoque de arriba hacia abajo (top-down) a nivel de país (Nigeria) mediante el diseño, prueba y ajuste de varios modelos tipo SEIR, con el objectivo de proporcionar una imagen global de la situación y cuantificar algunos de los parámetros más relevantes. En segundo lugar, un enfoque de abajo a arriba (bottom-up) en una área más pequeña (estado de Gombe, en el noreste del país) mediante el diseño, prueba y ajuste de un modelo basado en agentes, con el objetivo de desentrañar un contexto particular y ayudar en el diseño y cuantificación de las acciones de control. Además, también se realizó un Trabajo de campo para investigar los factores socioeconómicos particulares que son responsables de la situación epidemiológica de la propagación de la tuberculosis. El análisis estadístico de los datos obtenidos es el tercer enfoque de este proyecto. Se construyeron varios modelos de tipo SEIR en un intento de caracterizar y comprender progresivamente la dinámica de la TB en diferentes contextos. Estos modelos se ajustaron a algunos países seleccionados de alta carga y baja carga en todo el mundo, y en particular a la situación epidemiológica en Nigeria. La división de la población infecciosa (enferma) en dos subpoblaciones, la de personas que se diagnostican (y se tratan) y la de las que no, confirman una tasa de notificación baja dramática que varía entre el 16 y el 20 % durante los años analizados (2000-2010). Este factor reveló ser el cuello de botella para el control de la enfermedad en este país. Las predicciones del modelo no mostraron efectos relevantes de las acciones de control sin un aumento previo en la tasa de notificación. El trabajo de campo se diseñó en coordinación con las autoridades locales de NTBLCP con el objetivo de analizar los factores socioeconómicos que condicionan dicha tasa de notificación en el estado de Gombe. Consistió en una recopilación inicial de datos de la sucursal del estado de NTBLCP Gombe, la Junta de Administración de Hospitales del Estado de Gombe y varios hospitales en el estado de Gombe, seguida de un conjunto de 52 entrevistas en profundidad con pacientes con tuberculosis de diferentes centros de salud. Los datos obtenidos y los resultados de las entrevistas se analizaron estadísticamente utilizando estadísticas inferenciales y análisis de medias de Anova, con la ayuda de técnicas de aprendizaje automático. Los resultados fueron devastadores: ninguno de los pacientes entrevistados conocía los síntomas de la tuberculosis y el 90 % no conocía los mecanismos de transmisión de la tuberculosis después de hablar con el personal sanitario. El retraso medio de los pacientes en acudir al centro médico fue de 9.6 semanas con una desviación estándar de 4.8; solo el 10 % de los pacientes entrevistados acudió al médico dentro del primer mes de sentirse enfermo, el 30 % dentro de 1-2 meses, el 20 % dentro de 2-3 meses y el 40 % después de sentirse enfermo durante más de 3 meses. La información epidemiológica obtenida del enfoque bottom-up y los resultados derivados del trabajo de campo se utilizaron para adaptar un modelo basado en agentes (ABM) de propagación de TB en el contexto del estado de Gombe. El ABM resultante se ajustó con éxito a la evolución de la prevalencia estimada y los casos diagnosticados de 2007 a 2016. Luego, se usó para probar diferentes intervenciones destinadas a aumentar la tasa de notificación, disminuir el retraso del diagnóstico y aumentar la conciencia de la población sobre la TB transmisible. La metodología de enfoque múltiple utilizada en este proyecto reveló ser una forma sólida de abordar un problema real. Fue capaz de proporcionar una imagen global y detallada de la situación de la TB en un área determinada, relacionar los parámetros y resultados del modelo con un contexto socioeconómico real, y generar una herramienta útil para ayudar en el diseño y la evaluación de acciones de control de TB

The impact of novel diagnostics on infectious disease epidemics

Diagnostic tests play a crucial role in the control and surveillance of infectious diseases, and to ensure effective clinical management. Novel diagnostic tests are traditionally evaluated in terms of their accuracy (sensitivity and specificity). Using mathematical models, I examine the impact of different novel diagnostic tests on the tuberculosis (“TB”) and severe acute respiratory syndrome coronavirus 2 (“SARS-CoV-2”) epidemics, and investigate how the context in which these tests are used may affect this impact. In chapter 3, I evaluate the use of a hypothetical biomarker test that can detect individuals at imminent risk of progressing to active TB disease (incipient TB) and subsequent TB preventive treatment (“TPT”) initiation in a high TB burden setting. I demonstrate that biomarker-led TPT can have a significant impact on TB incidence in a high TB burden setting; however, the cost of implementing such a strategy is likely to be prohibitive given the testing effort needed to identify those with incipient TB, even if testing is targeted to populations at high risk of TB. Next, in chapter 4, I evaluate the use of urine-based tests for active TB disease, in a high TB and HIV burden setting. Although current urine-based tests for TB suffer from poor sensitivity, these tests are continuously improving, and are essential for TB diagnosis amongst patient subgroups who struggle to produce quality sputum and who are therefore missed by traditional methods of TB diagnosis. I demonstrate that although urine-based diagnostic tests reduce mortality amongst people living with HIV, population-level epidemiological impact is not seen unless the tests are deployed outside of HIV care and into routine TB care. In chapter 5, I investigate the cost and epidemiological impact of expanding different TB publicprivate sector engagement services. My results reveal that services involving the use of Xpert, a highly accurate but costly test for diagnosing active TB disease are epidemiologically impactful, but costly, and thus have the highest cost per TB case or TB death averted than other services. Finally, in chapter 6, I explore the context under when cheap but less accurate rapid antigen diagnostic tests (“Ag-RDTs”) offer greater public health value than more accurate but costly nucleic acid amplification tests (“NAATs”) that often have high turnaround times. My results highlight that Ag-RDTs-led strategies, despite their imperfect sensitivity and specificity, are more impactful at a lower cost than NAATs under different use-cases. Overall, the context in which diagnostic tests are used is crucial in anticipating their impact. Factors, including but not limited to testing eligibility, levels of current testing and turnaround time, can affect 6 the potential epidemiological impact of a diagnostic test. Thus, future evaluation of diagnostic tests should move away from focussing exclusively on accuracy and move towards clearly defining different use-cases and investigating which factors other than accuracy, may affect the epidemiological impact of a diagnostic test.Open Acces

Drivers of Tuberculosis Transmission.

Measuring tuberculosis transmission is exceedingly difficult, given the remarkable variability in the timing of clinical disease after Mycobacterium tuberculosis infection; incident disease can result from either a recent (ie, weeks to months) or a remote (ie, several years to decades) infection event. Although we cannot identify with certainty the timing and location of tuberculosis transmission for individuals, approaches for estimating the individual probability of recent transmission and for estimating the fraction of tuberculosis cases due to recent transmission in populations have been developed. Data used to estimate the probable burden of recent transmission include tuberculosis case notifications in young children and trends in tuberculin skin test and interferon γ-release assays. More recently, M. tuberculosis whole-genome sequencing has been used to estimate population levels of recent transmission, identify the distribution of specific strains within communities, and decipher chains of transmission among culture-positive tuberculosis cases. The factors that drive the transmission of tuberculosis in communities depend on the burden of prevalent tuberculosis; the ways in which individuals live, work, and interact (eg, congregate settings); and the capacity of healthcare and public health systems to identify and effectively treat individuals with infectious forms of tuberculosis. Here we provide an overview of these factors, describe tools for measurement of ongoing transmission, and highlight knowledge gaps that must be addressed

Structure and agency in the economics of public policy for TB control

Globally, Tuberculosis remains a devastating disease, despite the availability of treatment. The disease is associated with poverty, and those with the disease incur a high cost of accessing care, while simultaneously experiencing income loss due to a loss in productivity. A key challenge in TB programmes remains the accurate diagnosis of the disease, especially in people who are HIV positive. Diagnosing TB can be very resource intensive and the accuracy of diagnosis is dependent on a range of disease, health service organisation and provider behaviour factors. This thesis seeks to enhance understanding of how the behaviour of healthcare workers mediates the value of TB diagnostic algorithms, and how this may affect the costs, outcomes as well as the economic burden associated with the disease in South Africa. The work presented is based on empirical work done alongside a pragmatic cluster randomized control trial. Empirically, it examines the longitudinal economic burden of TB diagnosis and treatment in South Africa. The discrepancies between the time at which patients incur the greatest cost and income loss, and the available social protection are highlighted. Based on empirical work, a purpose-built state-transition mathematical model of TB diagnosis and treatment was developed to estimate the cost-effectiveness, from the perspective of the health service and the patient, of health systems interventions to strengthen TB diagnosis. Recognising healthcare workers as those who ultimately express policies, the behaviour of healthcare workers was included in the cost-effectiveness analysis by 1) using data from a pragmatic trial reflecting routine practice and clinical decision-making at the time of the study; 2) developing a conceptual framework of the relationship between behaviour at decision points and disease outcomes; and 3) investigating how these interactions may influence the value of the diagnostic algorithm. Possible public policy levers to improve TB diagnosis in healthcare facilities, as well as the potential mediators of costs and effects were explored. The thesis concludes with recommendations for further methodological work to expand on the approach explored in this thesis to improve how heterogeneity in estimates of cost-effectiveness is presented to decision-makers

Epidemiologic impact of treatment interventions for tuberculosis control

Background Once thought to be on its way to elimination, tuberculosis (TB) has resurged in recent decades and is now the leading cause of death among infectious diseases globally. Effective TB control will require optimal implementation of treatment interventions in order to maximize the potential benefits not only for individual patients but also at the population level. Methods We conducted three studies using dynamic compartmental models of TB transmission to project the potential impact of shortened duration of first-line TB therapy on TB incidence and mortality (Chapter II), the effect of re-using pyrazinamide in both first- and second-line treatment on the emergence of extensive drug resistance (Chapter III), and the value of treatment scale-up and programmatic improvements in the control of multidrug-resistant (MDR) TB (Chapter IV). Results Contrary to previous studies, we find that shortening the duration of first-line TB therapy is unlikely to yield major reductions in incidence over a time span of 15 years (projected reduction 1.9% with 4-month vs. 6-month treatment). We then demonstrate how the routine use of pyrazinamide in both first- and second-line TB treatment may promote the emergence of extensively drug-resistant TB. In the last study, we find that although scaling up treatment of MDR TB may substantially reduce future prevalence (median reduction in MDR TB prevalence 28.1% over 20 years), combining scale-up with programmatic interventions that improve linkage to care and treatment completion maximizes impact (median reduction 74.5%). Conclusions This work provides valuable guidance in optimizing treatment interventions to achieve population-level impact in global TB control

Healthcare seeking behaviour as a link between tuberculosis and socioeconomic factors

Socioeconomic barriers to tuberculosis care-seeking and costs due to care-seeking lead to unfavourable treatment, epidemiological and economic outcomes. Especially in the post-2015 era, socioeconomic interventions for tuberculosis control are receiving increasing attention. In Taiwan, the National Health Insurance programme minimises out-of-pocket expenses for patients, but important delays to tuberculosis treatment still exist. Based on the population and tuberculosis epidemiology in Taiwan, I develop an analysis for profiling the efficacy of tuberculosis care provision and patients' care-seeking pathways. The results highlight that the interrupted tuberculosis evaluation processes and low diagnostic capacity in small local hospitals stands as key causes of extended delays to treatment, unfavourable outcomes, and costs. I analyse socioeconomic status (SES) of employment, vulnerability, and residential contexts, to identify risk factors for different aspects of care-seeking. To link the care-seeking pathways to the nationwide tuberculosis epidemiology, I develop a data-driven hybrid simulation model. The model integrates the advantages of agent-based approaches in representing detail, and equation-based approaches in simplicity and low computational cost. This approach makes feasible Monte-Carlo experiments for robust inferences without over-simplifying the care-seeking details of interest. By comparing the hybrid model simulations with a corresponding equation-based comparator, I confirm its validity. I considered interventions to improve universal health coverage by decentralising tuberculosis diagnostic capacity. I modelled specific interventions increasing the coverage of tuberculosis diagnostic capacity using various SES-targeted scale-up strategies. These show potential benefits in terms of reducing dropouts and reducing the tuberculosis burden, without significant increases in the inequality of care-seeking costs. I suggest considering additional SES variables such as education, health illiteracy, and social segregation to find other care-seeking barriers. Further investigations of SES-related interventions against tuberculosis, including formal impact and health economic evaluation, should be pursued in collaboration with policymakers able to advise on feasibility and patients able to advise on acceptability

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis.

Global tuberculosis incidence has declined marginally over the past decade, and tuberculosis remains out of control in several parts of the world including Africa and Asia. Although tuberculosis control has been effective in some regions of the world, these gains are threatened by the increasing burden of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis. XDR tuberculosis has evolved in several tuberculosis-endemic countries to drug-incurable or programmatically incurable tuberculosis (totally drug-resistant tuberculosis). This poses several challenges similar to those encountered in the pre-chemotherapy era, including the inability to cure tuberculosis, high mortality, and the need for alternative methods to prevent disease transmission. This phenomenon mirrors the worldwide increase in antimicrobial resistance and the emergence of other MDR pathogens, such as malaria, HIV, and Gram-negative bacteria. MDR and XDR tuberculosis are associated with high morbidity and substantial mortality, are a threat to health-care workers, prohibitively expensive to treat, and are therefore a serious public health problem. In this Commission, we examine several aspects of drug-resistant tuberculosis. The traditional view that acquired resistance to antituberculous drugs is driven by poor compliance and programmatic failure is now being questioned, and several lines of evidence suggest that alternative mechanisms-including pharmacokinetic variability, induction of efflux pumps that transport the drug out of cells, and suboptimal drug penetration into tuberculosis lesions-are likely crucial to the pathogenesis of drug-resistant tuberculosis. These factors have implications for the design of new interventions, drug delivery and dosing mechanisms, and public health policy. We discuss epidemiology and transmission dynamics, including new insights into the fundamental biology of transmission, and we review the utility of newer diagnostic tools, including molecular tests and next-generation whole-genome sequencing, and their potential for clinical effectiveness. Relevant research priorities are highlighted, including optimal medical and surgical management, the role of newer and repurposed drugs (including bedaquiline, delamanid, and linezolid), pharmacokinetic and pharmacodynamic considerations, preventive strategies (such as prophylaxis in MDR and XDR contacts), palliative and patient-orientated care aspects, and medicolegal and ethical issues

Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review

Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the 'feasibility' of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease. We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints. A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget). The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models