Evidence for optimal allocation of malaria interventions in Africa
Authors: Fabrizio Tediosi, Melissa Penny
Journal: The Lancet Global Health (2016)

In recent decades, malaria burden has been significantly reduced through scale-up of effective preventive interventions such as insecticide-treated nets (ITNs), indoor residual spraying (IRS), and via increased and improved treatment of clinical cases.1Bhatt S Weiss DJ Cameron E et al.The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015.Nature. 2015; 526: 207-211Crossref PubMed Scopus (1563) Google Scholar Yet a significant increase in funding will be required to achieve the goals of burden reduction and elimination set by the WHO Global Technical Strategy for Malaria 2016–2030.2World Health OrganizationGlobal technical strategy for malaria 2016–2030.http://apps.who.int/iris/bitstream/10665/176712/1/9789241564991_eng.pdf?ua=1Google Scholar Despite ITNs, IRS, and treatment having been shown to be cost-effective,3White MT Conteh L Cibulskis R Ghani AC Costs and cost-effectiveness of malaria control interventions—a systematic review.Malaria J. 2011; 10: 337Crossref PubMed Scopus (181) Google Scholar little evidence is available on which combination of interventions should be prioritised to rationally allocate resources that maximise health benefits. In The Lancet Global Health, Patrick Walker and colleagues4Walker PGT Griffin JT Ferguson NM Ghani AC Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study.Lancet Glob Health. 2016; (published online June 3.)http://dx.doi.org/10.1016/S2214-109X(16)30073-0Google Scholar attempt to fill this gap using a modelling approach to assess the efforts required and the ordering of malaria interventions to firstly reduce malaria burden and transmission in different malaria-endemic ecological settings, and secondly to achieve pre-elimination status in these settings. The intervention ranking is based on a relative efficiency criterion defining "an optimal intervention combination for a given target as the combination which achieves this target at minimum cost". The results show that high coverage of long-lasting insecticide-treated nets (LLINs) is the most efficient intervention with which to reduce burden in all settings. In settings where LLINs alone do not achieve a burden reduction of 75%, then seasonal malaria chemoprevention or IRS would be included as the second most efficient intervention depending on seasonality and vector species. The model predicts that it is not possible to achieve pre-elimination in all settings, but, where possible, high coverage of interventions will be necessary and the most efficient allocation includes implementation of IRS before seasonal malaria chemoprevention, following high coverage of LLINs. If mass drug administration is an available option, that or IRS was preferred as the second intervention depending on the level of outdoor resting mosquitos. The results also indicate that targeting policies at subnational level would be more efficient in countries with high degrees of heterogeneity in transmission, indicating that local context and heterogeneity in transmission are important in tailoring intervention packages. Although most of these results are intuitively expected, Walker and colleagues have provided systematic evidence of efficient intervention packages in different ecological settings for several malaria goals. They also show that current interventions are not enough to eliminate malaria in many settings, and development of new malaria interventions is a priority. An important merit of the manuscript is that it highlights the need for assessing impact and costs of malaria control or elimination strategies together and not only single interventions. Their attempt to estimate an "optimal intervention combination" by combining validated epidemiological and malaria intervention modelling with available costs of interventions is laudable, and Walker and colleagues have created a flexible framework for future use. In addition, the analysis at provincial and local level shows that one-size-fits-all approaches to malaria control and elimination will not work, and that the specific epidemiological conditions should be considered when designing and implementing interventions. However, there are several issues not included in Walker and colleagues' paper that may be important for tailoring future malaria intervention packages. First, the simulations assume for all countries that 80% of all clinical episodes receive appropriate and effective treatment. A sensitivity analysis assuming a lower bound of 40% did not change the ordering of intervention packages but did change the level of burden reduction and thus areas that can achieve pre-elimination. The idea behind the assumption of high treatment coverage is that it is a prerequisite to achieving pre-elimination. Yet recent evidence on the effective coverage of malaria case management shows that most malaria-endemic countries are still far from being able to ensure high levels.5Galactionova K Tediosi F de Savigny D Smith T Tanner M Effective coverage and systems effectiveness for malaria case management in sub-Saharan African countries.PLoS One. 2015; 10: e0127818Crossref Scopus (57) Google Scholar Country malaria control programmes may also be interested in when and where to focus efforts to increase treatment through routine outlets or through communities. They may also like to understand where and under which conditions it is cost-effective to add further surveillance or reactive case detection to achieve elimination. Second, while sensitivity analysis to key epidemiology parameters and intervention effectiveness was undertaken, not all levels of uncertainty were included, in particular importation or costs associated with scaling up. This may affect the recommended level of coverage of each intervention in a package to efficiently reach a particular health target. Third, the age impact of interventions was not explicitly explored. Further analysis by age may help to assess whether particular intervention combinations would lead to an age-shifting of clinical disease.6Pemberton-Ross P Smith TA Hodel EM Kay K Penny MA Age-shifting in malaria incidence as a result of induced immunological deficit: a simulation study.Malaria J. 2015; 14: 1-14Crossref PubMed Scopus (26) Google Scholar This would indicate whether additional interventions are cost-effective to reach pre-elimination if they target children older than 5–10 years who are not covered by seasonal malaria chemoprevention. Lastly, while the analyses at provincial level convey an important message about spatial scales for tailoring intervention packages, they do not take into account additional sources of heterogeneity in the local settings that may have affected the feasibility of implementing different strategies and thus the overall results. These challenges show the need for more research integrating epidemiological, economic, and health systems modelling to predict the impact of malaria intervention combinations, taking into account the specific features of malaria-endemic areas. We declare no competing interests. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling studyThese results highlight the cost–benefits of carefully tailoring malaria interventions to the ecological landscape of different areas. However, novel interventions are necessary if malaria eradication is to be achieved. Full-Text PDF Open Access