Evidence-informed policy making at country level: lessons learned from the South African Tuberculosis Think Tank

White, Richard G.; Charalambous, Salome; Cárdenas, Vicky; Hippner, Piotr; Sumner, Tom; Bozzani, Fiammetta; Mudzengi, Don; Houben, R M G J; Collier, D; Kimerling, Michael E.; Vassall, Anna; Pillay, Yogan; Churchyard, Gavin J.

doi:10.5588/ijtld.17.0485

Cited by 23 publications

(26 citation statements)

References 4 publications

(12 reference statements)

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“…This publication described a unique example of an iterative modelling process resulting from a close collaboration between the NMCP in Tanzania, a modelling team at the Swiss TPH and other stakeholders. Similar experiences and challenges were identified previously in health policy and decision-making research [14,[59][60][61].…”

Section: Discussionsupporting

confidence: 66%

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

Runge

Molteni

Mandike

et al. 2020

Malar J

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Background: More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned. Case description: In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed. Evaluation: In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context. Conclusion: Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized.

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Section: Discussionsupporting

confidence: 66%

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

Runge

Molteni

Mandike

et al. 2020

Malar J

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“…In 2015, Intensified Case Finding (ICF) for TB was adopted in South Africa for detecting TB cases among HIV-infected health facility attendees, who are screened for TB at each facility visit. During the previous year, the South African TB Think Tank was established with the purpose of advising the National Department of Health (NDoH) on TB policy and strategic planning [ 18 ]. As part of this effort, an economic analysis to identify the optimal ICF and TB diagnostic strategy for South Africa was conducted.…”

Section: Methodsmentioning

confidence: 99%

Empirical estimation of resource constraints for use in model-based economic evaluation: an example of TB services in South Africa

Bozzani

Mudzengi

Sumner

et al. 2018

Cost Eff Resour Alloc

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BackgroundEvidence on the relative costs and effects of interventions that do not consider ‘real-world’ constraints on implementation may be misleading. However, in many low- and middle-income countries, time and data scarcity mean that incorporating health system constraints in priority setting can be challenging.MethodsWe developed a ‘proof of concept’ method to empirically estimate health system constraints for inclusion in model-based economic evaluations, using intensified case-finding strategies (ICF) for tuberculosis (TB) in South Africa as an example. As part of a strategic planning process, we quantified the resources (fiscal and human) needed to scale up different ICF strategies (cough triage and WHO symptom screening). We identified and characterised three constraints through discussions with local stakeholders: (1) financial constraint: potential maximum increase in public TB financing available for new TB interventions; (2) human resource constraint: maximum current and future capacity among public sector nurses that could be dedicated to TB services; and (3) diagnostic supplies constraint: maximum ratio of Xpert MTB/RIF tests to TB notifications. We assessed the impact of these constraints on the costs of different ICF strategies.ResultsIt would not be possible to reach the target coverage of ICF (as defined by policy makers) without addressing financial, human resource and diagnostic supplies constraints. The costs of addressing human resource constraints is substantial, increasing total TB programme costs during the period 2016–2035 by between 7% and 37% compared to assuming the expansion of ICF is unconstrained, depending on the ICF strategy chosen.ConclusionsFailure to include the costs of relaxing constraints may provide misleading estimates of costs, and therefore cost-effectiveness. In turn, these could impact the local relevance and credibility of analyses, thereby increasing the risk of sub-optimal investments.Electronic supplementary materialThe online version of this article (10.1186/s12962-018-0113-z) contains supplementary material, which is available to authorized users.

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“…The fact that participants felt that the workshops improved their competencies and skills in research priority setting, evidence generation, and communication not just for the control of endemic diseases but for the holistic evidence-based improvements of the health system shows the potential for increasing evidence-informed decision making with appropriate interventions as shown in previous studies [22][23][24][25]. This is a very timely project in Nigeria and some other African countries, where it has been found that the capacity for research and knowledge translation activities, although growing, still remains largely uncoordinated and remains as small-scale activities, and primarily driven from outside Africa [26].…”

Section: Discussionmentioning

confidence: 61%

Impact of capacity building interventions on individual and organizational competency for HPSR in endemic disease control in Nigeria: a qualitative study

et al. 2020

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Background: The need to build capacity for health policy and systems research (HPSR) in low-and middle-income countries has been underscored as this encompasses the processes of decision-making at all levels of the health system. This implementation research project was undertaken in Southeast Nigeria to evaluate whether the capacity-building intervention improves the capacity to produce and use research evidence for decision making in endemic disease control. Methods: Three training workshops were organized for purposively selected participants comprising "producers of evidence" such as health research scientists in three universities and "users of evidence" such as policy makers, program managers, and implementers in the public health sector. Participants also held step-down workshops in their organizations. The last workshop was used to facilitate the formation of knowledge networks comprising of both producers and users, which is a critical step for getting research into policy and practice (GRIPP). Three months after the workshops, a subset, 40, of workshop participants was selected for in-depth interviews. Information was collected on (i) perceptions of usefulness of capacity-building workshops, (ii) progress with proposed research and research uptake activities, (iii) effects of these activities on evidence-informed decision making, and (iv) constraints and enablers to implementation of proposed activities.

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Evidence-informed policy making at country level: lessons learned from the South African Tuberculosis Think Tank

Cited by 23 publications

References 4 publications

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

Empirical estimation of resource constraints for use in model-based economic evaluation: an example of TB services in South Africa

Impact of capacity building interventions on individual and organizational competency for HPSR in endemic disease control in Nigeria: a qualitative study

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