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

Fiammetta M Bozzani,Salome Charalambous,Gabriela B Gomez,Piotr Hippner,Tom Sumner,Richard White,Don Mudzengi,Anna Vassall,Vicky Cardenas

doi:10.1186/s12962-018-0113-z

Abstract

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.

Highlights

Evidence on the relative costs and effects of interventions that do not consider ‘real-world’ constraints on implementation may be misleading
Model-based priority setting has incorporated and adapted to local demographic and epidemiological characteristics, but to date the explicit consideration of the impact of context-specific health system constraints on the costs and cost-effectiveness of global health interventions is often absent [8]
While South Africa has demonstrated the capacity to rapidly scale up TB diagnostic volumes [26], in consultation with the National Department of Health (NDoH) we identified a constraint on the amount of TB diagnostic supplies (Xpert MTB/RIF tests) purchased annually

Summary

Introduction

Evidence on the relative costs and effects of interventions that do not consider ‘real-world’ constraints on implementation may be misleading. Several approaches have been proposed for incorporating both financial and non-financial resource constraints in model-based priority setting for infectious diseases These allow the analyst to either restrict outputs to limit the impact of interventions, or to cost the relaxation of constraints. Some have adopted an ‘integrated modelling’ approach combining disease and operational (health systems) modelling This approach has substantial data requirements as detailed knowledge of numerous processes across the health system is necessary to populate the operational model [9, 10]. Mathematical programming has the advantage of potentially dealing simultaneously with multiple constraints, such as equity and efficiency [12,13,14] Combining this approach with infectious disease models is computationally complex and data-intensive [14]. While mathematical programming has the potential to be widely applied in the presence of strengthened health information systems, the ‘black box’ nature of this approach may constitute a barrier for users and result in a process that lacks transparency for decision-makers [15]

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Conclusion