Unraveling the 'community effects' of interventions against malaria endemicity: a systematic scoping review

Abstract

Objectives: There is an urgent need to maximise the effectiveness of existing malaria interventions and optimise the deployment of novel countermeasures. When assessing the effects of interventions against malaria, it is imperative to consider the interdependence of people and the resulting indirect effects. Without proper consideration of the effects, the interventions' impact on health outcomes and their cost-effectiveness may be miscalculated. We aimed to summarise how the indirect effects of malaria interventions were analysed and reported.

Design: We conducted a scoping review.

Data sources: We searched PubMed, Web of Science and EMBASE.

Eligibility criteria: We included studies that were conducted to quantify the indirect effects of any interventions for all species of Plasmodium infection.

Data extraction and synthesis: We used a standardised data collection form to obtain the following information from each record: title, name of authors, year of publication, region, country, study type, malaria parasite species, type of interventions, type of outcomes, separate estimated indirect effect for different conditions, pre-specified to measure indirect effect, secondary analysis of previous study, methods of indirect effects estimation, terms of indirect effects, and if positive or negative indirect effects observed.

Results: We retrieved 32 articles and observed a recent increase in both the number of reports and the variety of terms used to denote the indirect effects. We further classified nine categories of methods to identify the indirect effects in the existing literature and proposed making comparisons conditional on distance to account for mosquito flight range or intervention density within that range. Furthermore, we proposed using the words community effects or spillover effects as standardised terms for indirect effects and highlighted the potential benefits of mathematical models in estimating indirect effects.

Conclusions: Incorporating assessment of indirect effects in future trials and studies may provide insights to optimise the deployment of existing and new interventions, a critical pillar in the current fight against malaria globally.

Keywords: Disease Vectors; Epidemiology; Public Health.

Figure 1. PRISMA flowchart of study selection. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analysis.

Figure 2. Time trend of study characteristics. (a) Study type. (b) Intervention type. (c) Term used to describe the concept of indirect effects. Note that for (c), the total number of terms in the graph does not correspond to the total number of studies (n=32), as multiple terms can be used in a single paper. CRT, cluster randomised trial; Ento, entomological survey; IPT, intermittent preventive treatment; IRS, indoor residual spray; ITN, insecticide-treated net; LLIN, long-lasting insecticide-treated net; MDA, mass drug administration. For the study type, ‘Others’ included analysis of passive case detection using surveillance data. For intervention type, ‘Others’ included access to free antimalarials, target subsidies of ITNs and reactive-focal chemoprevention with IRS. Regarding indirect effect terminology, ‘Others’ included assembly effects, population effects, group-level effects, positive externality and dependent happenings.

Figure 3. Categories of indirect effect analysis methods. [1] comparison between non-treatment recipients in the treatment community and the control group, (1) comparison not conditional on treatment density nor geographical distance, (2) pre-post comparisons among non-recipients, [2] Comparison conditional on treatment coverage or geographical distance, (3) comparisons among non-recipients according to distance to the treatment cluster, (4) comparisons within the treatment area according to the coverage among those who received the treatment, (5) comparisons within the treatment area according to the coverage, including both those who received treatment and those who did not, (6) comparisons within the treatment area according to the coverage among non-recipients, (7) comparisons among non-recipients according to the coverage of the nearest treatment clusters, (8) comparisons among non-recipients according to the types of treatment of the nearest treatment clusters, [3] (9) comparisons conditional on other factors such as the repellent and killing effects of ITNs, pre-erythrocytic or blood-stage vaccine efficacy, endemicity of study area and the connectedness between different areas. Type [3] only applies to mathematical modelling studies. If one of these did not apply, it was recorded as ‘Others’.