1
|
Albert H, Rupani S, Masini E, Ogoro J, Kamene M, Geocaniga-Gaviola D, Sistoso E, Garfin C, Chadha S, Kumar N, Kao K, Katz Z. Optimizing diagnostic networks to increase patient access to TB diagnostic services: Development of the diagnostic network optimization (DNO) approach and learnings from its application in Kenya, India and the Philippines. PLoS One 2023; 18:e0279677. [PMID: 38033120 PMCID: PMC10688908 DOI: 10.1371/journal.pone.0279677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 09/12/2023] [Indexed: 12/02/2023] Open
Abstract
Diagnostic network optimization (DNO) is an analytical approach that enables use of available country data to inform evidence-based decision-making to optimize access to diagnostic services. A DNO methodology was developed using available data sources and a commercial supply chain optimization software. In collaboration with Ministries of Health and partners, the approach was applied in Kenya, India and the Philippines to map TB diagnostic networks, identify misalignments, and determine optimal network design to increase patient access to TB diagnostic services and improve device utilization. The DNO analysis was successfully applied to evaluate and inform TB diagnostic services in Kenya, India and the Philippines as part of national strategic planning for TB. The analysis was tailored to each country's specific objectives and allowed evaluation of factors such as the number and placement of different TB diagnostics, design of sample referral networks and integration of early infant diagnosis for HIV at national and sub-national levels and across public and private sectors. Our work demonstrates the value of DNO as an innovative approach to analysing and modelling diagnostic networks, particularly suited for use in low-resource settings, as an open-access approach that can be applied to optimize networks for any disease.
Collapse
Affiliation(s)
| | - Sidharth Rupani
- LLamasoft, Inc., Ann Arbor, Michigan, United States of America
| | | | - Jeremiah Ogoro
- National Tuberculosis Leprosy and Lung Disease Programme, Nairobi, Kenya
| | - Maureen Kamene
- National Tuberculosis Leprosy and Lung Disease Programme, Nairobi, Kenya
| | | | - Eddie Sistoso
- National Tuberculosis Reference Laboratory, Metro Manila, Filinvest, Philippines
| | - Celina Garfin
- National TB Control Program, Department of Health, Manila, Philippines
| | | | - Nishant Kumar
- Central TB Division, Ministry of Health and Family Welfare, Government of India, New Delhi, India
| | | | | |
Collapse
|
2
|
Brown LK, Van Schalkwyk C, De Villiers AK, Marx FM. Impact of interventions for tuberculosis prevention and care in South Africa - a systematic review of mathematical modelling studies. S Afr Med J 2023; 113:125-134. [PMID: 36876352 DOI: 10.7196/samj.2023.v113i3.16812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Substantial additional efforts are needed to prevent, find and successfully treat tuberculosis (TB) in South Africa (SA). In thepast decade, an increasing body of mathematical modelling research has investigated the population-level impact of TB prevention and careinterventions. To date, this evidence has not been assessed in the SA context. OBJECTIVE To systematically review mathematical modelling studies that estimated the impact of interventions towards the World HealthOrganization's End TB Strategy targets for TB incidence, TB deaths and catastrophic costs due to TB in SA. METHODS We searched the PubMed, Web of Science and Scopus databases for studies that used transmission-dynamic models of TB in SAand reported on at least one of the End TB Strategy targets at population level. We described study populations, type of interventions andtheir target groups, and estimates of impact and other key findings. For studies of country-level interventions, we estimated average annualpercentage declines (AAPDs) in TB incidence and mortality attributable to the intervention. RESULTS We identified 29 studies that met our inclusion criteria, of which 7 modelled TB preventive interventions (vaccination,antiretroviral treatment (ART) for HIV, TB preventive treatment (TPT)), 12 considered interventions along the care cascade for TB(screening/case finding, reducing initial loss to follow-up, diagnostic and treatment interventions), and 10 modelled combinationsof preventive and care-cascade interventions. Only one study focused on reducing catastrophic costs due to TB. The highest impactof a single intervention was estimated in studies of TB vaccination, TPT among people living with HIV, and scale-up of ART. Forpreventive interventions, AAPDs for TB incidence varied between 0.06% and 7.07%, and for care-cascade interventions between 0.05%and 3.27%. CONCLUSION We describe a body of mathematical modelling research with a focus on TB prevention and care in SA. We found higherestimates of impact reported in studies of preventive interventions, highlighting the need to invest in TB prevention in SA. However, studyheterogeneity and inconsistent baseline scenarios limit the ability to compare impact estimates between studies. Combinations, rather thansingle interventions, are likely needed to reach the End TB Strategy targets in SA.
Collapse
Affiliation(s)
- L K Brown
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Cape Town, South Africa.
| | - C Van Schalkwyk
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Cape Town, South Africa.
| | - A K De Villiers
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Cape Town, South Africa; Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
| | - F M Marx
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Cape Town, South Africa; Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Division of Infectious Disease and Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany.
| |
Collapse
|
3
|
Tuberculosis control at a South African correctional centre: Diagnosis, treatment and strain characterisation. PLoS One 2022; 17:e0277459. [DOI: 10.1371/journal.pone.0277459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 10/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Correctional centres provide ideal conditions for tuberculosis (TB) transmission and disease progression. Despite the high TB incidence and incarceration rate in South Africa, data from South African correctional centres are scarce. Thus, the study evaluated TB diagnosis, treatment initiation and completion, and identified prevalent Mycobacterium tuberculosis strains among detainees entering a South African correctional centre.
Methods
This study was a prospective observational study that enrolled participants between February and September 2017 from a correctional centre located in the Western Cape, South Africa. All adult male detainees who tested positive for TB during admission screening were eligible to participate in the study. Sputum samples from enrolled participants underwent smear microscopy and culture. Strain typing was performed on culture-positive samples. The time between specimen collection and diagnosis, the time between diagnosis and treatment initiation, and the proportion of detainees completing TB treatment at the correctional centre were calculated.
Results
During the study period, 130 TB cases were detected through routine admission screening (126 male, 2 female, 2 juvenile). Out of the 126 eligible male detainees, 102 were enrolled in the study (81%, 102/126). All TB cases were detected within 30 hrs of admission screening. The majority (78%, 80/102) of participants started treatment within 48 hrs of TB diagnosis. However, only 8% (9/102) of participants completed treatment at the correction centre. Sputa from 90 of the 102 participants were available for smear and culture. There was a high smear positivity, with 49% (44/90) of isolates being smear positive. The Beijing family was the most frequent lineage (55.2%) in the study.
Conclusion
The strengths of the current TB control efforts at the correctional centre include rapid detection of cases through admission screening and prompt treatment initiation. However, a high number of detainees exiting before treatment completion highlights the need to strengthen links between correctional TB services and community TB services to ensure detainees complete TB treatment after release and prevent TB transmission.
Collapse
|
4
|
Ricks S, Denkinger CM, Schumacher SG, Hallett TB, Arinaminpathy N. The potential impact of urine-LAM diagnostics on tuberculosis incidence and mortality: A modelling analysis. PLoS Med 2020; 17:e1003466. [PMID: 33306694 PMCID: PMC7732057 DOI: 10.1371/journal.pmed.1003466] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 11/13/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Lateral flow urine lipoarabinomannan (LAM) tests could offer important new opportunities for the early detection of tuberculosis (TB). The currently licensed LAM test, Alere Determine TB LAM Ag ('LF-LAM'), performs best in the sickest people living with HIV (PLHIV). However, the technology continues to improve, with newer LAM tests, such as Fujifilm SILVAMP TB LAM ('SILVAMP-LAM') showing improved sensitivity, including amongst HIV-negative patients. It is important to anticipate the epidemiological impact that current and future LAM tests may have on TB incidence and mortality. METHODS AND FINDINGS Concentrating on South Africa, we examined the impact that widening LAM test eligibility would have on TB incidence and mortality. We developed a mathematical model of TB transmission to project the impact of LAM tests, distinguishing 'current' tests (with sensitivity consistent with LF-LAM), from hypothetical 'future' tests (having sensitivity consistent with SILVAMP-LAM). We modelled the impact of both tests, assuming full adoption of the 2019 WHO guidelines for the use of these tests amongst those receiving HIV care. We also simulated the hypothetical deployment of future LAM tests for all people presenting to care with TB symptoms, not restricted to PLHIV. Our model projects that 2,700,000 (95% credible interval [CrI] 2,000,000-3,600,000) and 420,000 (95% CrI 350,000-520,000) cumulative TB incident cases and deaths, respectively, would occur between 2020 and 2035 if the status quo is maintained. Relative to this comparator, current and future LAM tests would respectively avert 54 (95% CrI 33-86) and 90 (95% CrI 55-145) TB deaths amongst inpatients between 2020 and 2035, i.e., reductions of 5% (95% CrI 4%-6%) and 9% (95% CrI 7%-11%) in inpatient TB mortality. This impact in absolute deaths averted doubles if testing is expanded to include outpatients, yet remains <1% of country-level TB deaths. Similar patterns apply to incidence results. However, deploying a future LAM test for all people presenting to care with TB symptoms would avert 470,000 (95% CrI 220,000-870,000) incident TB cases (18% reduction, 95% CrI 9%-29%) and 120,000 (95% CrI 69,000-210,000) deaths (30% reduction, 95% CrI 18%-44%) between 2020 and 2035. Notably, this increase in impact arises largely from diagnosis of TB amongst those with HIV who are not yet in HIV care, and who would thus be ineligible for a LAM test under current guidelines. Qualitatively similar results apply under an alternative comparator assuming expanded use of GeneXpert MTB/RIF ('Xpert') for TB diagnosis. Sensitivity analysis demonstrates qualitatively similar results in a setting like Kenya, which also has a generalised HIV epidemic, but a lower burden of HIV/TB coinfection. Amongst limitations of this analysis, we do not address the cost or cost-effectiveness of future tests. Our model neglects drug resistance and focuses on the country-level epidemic, thus ignoring subnational variations in HIV and TB burden. CONCLUSIONS These results suggest that LAM tests could have an important effect in averting TB deaths amongst PLHIV with advanced disease. However, achieving population-level impact on the TB epidemic, even in high-HIV-burden settings, will require future LAM tests to have sufficient performance to be deployed more broadly than in HIV care.
Collapse
Affiliation(s)
- Saskia Ricks
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
- * E-mail:
| | - Claudia M. Denkinger
- Center of Infectious Disease, University of Heidelberg, Heidelberg, Germany
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| | | | - Timothy B. Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Nimalan Arinaminpathy
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| |
Collapse
|
5
|
Kibachio J, Mwenda V, Ombiro O, Kamano JH, Perez‐Guzman PN, Mutai KK, Guessous I, Beran D, Kasaie P, Weir B, Beecroft B, Kilonzo N, Kupfer L, Smit M. Recommendations for the use of mathematical modelling to support decision-making on integration of non-communicable diseases into HIV care. J Int AIDS Soc 2020; 23 Suppl 1:e25505. [PMID: 32562338 PMCID: PMC7305412 DOI: 10.1002/jia2.25505] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 03/03/2020] [Accepted: 03/31/2020] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Integrating services for non-communicable diseases (NCDs) into existing primary care platforms such as HIV programmes has been recommended as a way of strengthening health systems, reducing redundancies and leveraging existing systems to rapidly scale-up underdeveloped programmes. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation of such programmes. In this study, we examine the case for NCD-HIV integration, use Kenya as a case-study to highlight how modelling has supported wider policy formulation and decision-making in healthcare and to collate stakeholders' recommendations on use of models for NCD-HIV integration decision-making. DISCUSSION Across Africa, NCDs are increasingly posing challenges for health systems, which historically focused on the care of acute and infectious conditions. Pilot programmes using integrated care services have generated advantages for both provider and user, been cost-effective, practical and achieve rapid coverage scale-up. The shared chronic nature of NCDs and HIV means that many operational approaches and infrastructure developed for HIV programmes apply to NCDs, suggesting this to be a cost-effective and sustainable policy option for countries with large HIV programmes and small, un-resourced NCD programmes. However, the vertical nature of current disease programmes, policy financing and operations operate as barriers to NCD-HIV integration. Modelling has successfully been used to inform health decision-making across a number of disease areas and in a number of ways. Examples from Kenya include (i) estimating current and future disease burden to set priorities for public health interventions, (ii) forecasting the requisite investments by government, (iii) comparing the impact of different integration approaches, (iv) performing cost-benefit analysis for integration and (v) evaluating health system capacity needs. CONCLUSIONS Modelling can and should play an integral part in the decision-making processes for health in general and NCD-HIV integration specifically. It is especially useful where little data is available. The successful use of modelling to inform decision-making will depend on several factors including policy makers' comfort with and understanding of models and their uncertainties, modellers understanding of national priorities, funding opportunities and building local modelling capacity to ensure sustainability.
Collapse
Affiliation(s)
- Joseph Kibachio
- Division of Non‐communicable DiseasesMinistry of HealthKenya
- Faculty of MedicineUniversity of GenevaSwitzerlandGeneva
| | - Valerian Mwenda
- Division of Non‐communicable DiseasesMinistry of HealthKenya
| | - Oren Ombiro
- Division of Non‐communicable DiseasesMinistry of HealthKenya
| | - Jamima H Kamano
- Department of MedicineMoi University School of MedicineKenyaEldoret
- AMPATHKenyaLondon
| | - Pablo N Perez‐Guzman
- MRC Centre for Global Infectious Disease AnalysisDepartment of Infectious Disease EpidemiologyImperial College LondonLondonUnited Kingdom
| | | | - Idris Guessous
- Division of Primary Care MedicineGeneva University Hospital and University of GenevaGenevaSwitzerland
| | - David Beran
- Division of Tropical and Humanitarian MedicineUniversity of Geneva and Geneva University HospitalsGenevaSwitzerland
| | - Paratsu Kasaie
- John Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Brian Weir
- John Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Blythe Beecroft
- Fogarty International CenterNational Institutes of HealthBethesdaMDUSA
| | | | - Linda Kupfer
- Fogarty International CenterNational Institutes of HealthBethesdaMDUSA
| | - Mikaela Smit
- MRC Centre for Global Infectious Disease AnalysisDepartment of Infectious Disease EpidemiologyImperial College LondonLondonUnited Kingdom
| |
Collapse
|
6
|
Lee Y, Raviglione MC, Flahault A. Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review. J Med Internet Res 2020; 22:e15727. [PMID: 32053111 PMCID: PMC7055857 DOI: 10.2196/15727] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 02/06/2023] Open
Abstract
Background Tuberculosis (TB) is the leading cause of death from a single infectious agent, with around 1.5 million deaths reported in 2018, and is a major contributor to suffering worldwide, with an estimated 10 million new cases every year. In the context of the World Health Organization’s End TB strategy and the quest for digital innovations, there is a need to understand what is happening around the world regarding research into the use of digital technology for better TB care and control. Objective The purpose of this scoping review was to summarize the state of research on the use of digital technology to enhance TB care and control. This study provides an overview of publications covering this subject and answers 3 main questions: (1) to what extent has the issue been addressed in the scientific literature between January 2016 and March 2019, (2) which countries have been investing in research in this field, and (3) what digital technologies were used? Methods A Web-based search was conducted on PubMed and Web of Science. Studies that describe the use of digital technology with specific reference to keywords such as TB, digital health, eHealth, and mHealth were included. Data from selected studies were synthesized into 4 functions using narrative and graphical methods. Such digital health interventions were categorized based on 2 classifications, one by function and the other by targeted user. Results A total of 145 relevant studies were identified out of the 1005 published between January 2016 and March 2019. Overall, 72.4% (105/145) of the research focused on patient care and 20.7% (30/145) on surveillance and monitoring. Other programmatic functions 4.8% (7/145) and electronic learning 2.1% (3/145) were less frequently studied. Most digital health technologies used for patient care included primarily diagnostic 59.4% (63/106) and treatment adherence tools 40.6% (43/106). On the basis of the second type of classification, 107 studies targeted health care providers (107/145, 73.8%), 20 studies targeted clients (20/145, 13.8%), 17 dealt with data services (17/145, 11.7%), and 1 study was on the health system or resource management. The first authors’ affiliations were mainly from 3 countries: the United States (30/145 studies, 20.7%), China (20/145 studies, 13.8%), and India (17/145 studies, 11.7%). The researchers from the United States conducted their research both domestically and abroad, whereas researchers from China and India conducted all studies domestically. Conclusions The majority of research conducted between January 2016 and March 2019 on digital interventions for TB focused on diagnostic tools and treatment adherence technologies, such as video-observed therapy and SMS. Only a few studies addressed interventions for data services and health system or resource management.
Collapse
Affiliation(s)
- Yejin Lee
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Global Studies Institute, University of Geneva, Geneva, Switzerland
| | - Mario C Raviglione
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Global Studies Institute, University of Geneva, Geneva, Switzerland.,Centre for Multidisciplinary Research in Health Science (MACH), Università di Milano, Milan, Italy
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Global Studies Institute, University of Geneva, Geneva, Switzerland
| |
Collapse
|