To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions

Abstract

Background: Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field studies have yet to develop consensus guidelines. Moreover, there is often no way for decision-makers to directly interact with these data and/or models, incorporate local knowledge and expertise, and re-fit parameters to guide their specific goals.

Methods: We propose a general framework for comparing costs associated with mass drug administrations and mass screen and treat based on the possible outcomes of each intervention and the costs associated with each outcome. We then used publicly available data from six countries in western Africa to develop spatial-explicit probabilistic models to estimate intervention costs based on baseline malaria prevalence, diagnostic performance, and sociodemographic factors (age and urbanicity). In addition to comparing specific scenarios, we also develop interactive web applications which allow managers to select data sources and model parameters, and directly input their own cost values.

Results: The regional-level models revealed substantial spatial heterogeneity in malaria prevalence and diagnostic test sensitivity and specificity, indicating that a "one-size-fits-all" approach is unlikely to maximize resource allocation. For instance, urban communities in Burkina Faso typically had lower prevalence rates compared to rural communities (0.151 versus 0.383, respectively) as well as lower diagnostic sensitivity (0.699 versus 0.862, respectively); however, there was still substantial regional variation. Adjusting the cost associated with false negative diagnostic results to included additional costs, such as delayed treated and potential lost wages, undermined the overall costs associated with MSAT.

Conclusions: The observed spatial variability and dependence on specified cost values support not only the need for location-specific intervention approaches but also the need to move beyond standard modeling approaches and towards interactive tools which allow implementers to engage directly with data and models. We believe that the framework demonstrated in this article will help connect modeling efforts and stakeholders in order to promote data-driven decision-making for the effective management of malaria, as well as other diseases.

Keywords: Data-driven decision-making; Decision support; Malaria; Mass drug administration; Mass screen and treat; Resource allocation.

Fig. 1

Conceptual framework for costs of mass drug administration (MDA) and mass screen and treat (MSAT). Flow diagram based on the potential outcomes and associated costs for each intervention. Testing and outcome costs are shown in blue and red, respectively. FP and FN stand for false positive and false negative, respectively

Fig. 2

Costs of mass drug administration (MDA) and mass screen and treat (MSAT) based on malaria prevalence. Each panel depicts a different scenario relative to the costs associated with false positive (FP) and false negative (FN) outcomes, as specified in the legends. The lower estimated cost (y-axis) indicates which strategy will have lower associated costs for a given prevalence rate (x-axis). RDT sensitivity and specificity ranged from 0.82 to 0.96 and 0.80 to 0.90, respectively, and the cost of treatment and RDT were set to $2.40 and $0.60, based on a WHO report [26]. The gray-shaded region indicates overlap in expected cost, where the more favorable strategy is unclear due to the range of possible values for RDT sensitivity and specificity

Fig. 3

Value added from screen then treat among rural communities in Burkina Faso. Regional maps of the mean value added (i.e., MDA costs minus MSAT costs) and boxplots of value added estimates are shown on the left and right panels, respectively. Positive values (blue) indicate regions where MSAT is favored whereas negative values (red) indicate regions where MDA is favored. Whiskers in boxplots indicate 95% credible intervals. When these intervals contain both positive and negative values, there is no significant difference regarding costs between strategies (gray). Cost of diagnostic test (RDT) and treatment were set at $0.60 and $2.55, respectively. a Added value estimates ignoring any potential costs associated with false negative results. b Value added estimates when cost associated with false negative results is set to the cost of receiving delayed treatment. c Value added estimates when cost associated with false negative results includes the cost of receiving delayed treatment and 1 day of lost wage (based on minimum wage [39]). In all of these scenarios, we assume no cost associated with false positive outcomes

Fig. 4

Comparison of regional-level differences of malaria prevalence and RDT sensitivity and specificity in Burkina Faso for urban and rural areas. Mean value estimate (circles) and 95% credible interval (vertical bars) for each region are based on 1000 posterior draws from each model. Each region has a unique shade and is connected by a dotted line to depict differences in parameter estimates between urban and rural communities

Fig. 5

Urban versus rural comparison of value added from screen then treat in Burkina Faso. Comparison of regional value added (per individual) from diagnostic screening (MDA costs minus MSAT costs) between urban (left plots) and rural (right plots) communities. Positive values (blue) favor MSAT, negative values (red) favor MDA, and 95% interval ranges that contain both positive and negative values indicate no significant difference (gray). Cost of diagnostic test (RDT) and treatment were set at $0.60 and $2.55, respectively. a Added value estimates ignoring any potential costs associated with false negative results. b Value added estimates when cost associated with false negative results is set to the cost of receiving delayed treatment. c Value added estimates when cost associated with false negative results includes the cost of receiving delayed treatment and 1 day of lost wage (based on minimum wage [39]). In all of these scenarios, we assume no cost associated with false positive outcomes

Fig. 6

Regional breakpoints for cost of treatment and diagnostic test (RDT) in Burkina Faso. Cost points above the line favor MSAT in that region, whereas cost points below the line favor MDA. Rural and urban communities are depicted on the left and right columns, respectively. a No cost associated with false negative results. b A cost associated with false negative results which includes the cost of 1 day of lost wages (based on minimum wage [39]). Each line represents a different region, and the blue line emphasizes the Hauts-Bassins region. In all of these scenarios, we assume no cost associated with false positive outcomes