The past five years have witnessed the adoption of SUFI by many malaria-endemic African countries, deploying efficacious interventions and collecting comparable indicators that permit assessment of trends in child survival and malaria morbidity. There is remarkable consistency in impact across a diverse range of countries. Among the settings describing marked progress, three are islands (Bioko, São Tomé and Príncipe, and Zanzibar with a combined population of approximately 1.5 million), there is a three-country partnership (LSDI working with some districts in each of Mozambique, South Africa, and Swaziland); three are small countries (Eritrea, Gambia, and Rwanda with a combined population of approximately 16 million), and three are larger countries (Ethiopia, Tanzania, and Zambia with a combined population of more than 100 million). The epidemiologic and demographic breadth of these settings cannot yet be claimed to be representative of the diversity of malaria in Africa. But additional countries are progressing with malaria programme scale-up, and while it is understood that there is an inherent interval between programme scale-up and documenting programme outcomes, it is clear that a broad and representative profile of programme impact is emerging.
Attribution of malaria control to the documented improvement in child survival
With the increasing documentation of geographic expansion of malaria programme scale-up in the Africa region, it is important to assess whether the observed health benefits are simply associations in time and place, or if convincing evidence exists to assert a causal relationship between the malaria control interventions and the dramatic documented health improvements.
Based on B.A. Hill's 1965 epidemiologic framework for causal inference [28
], eight criteria need to be considered: 1) experimental evidence; 2) plausibility; 3) strength of association; 4) specificity of the association; 5) temporal congruity [or lack of temporal ambiguity]; 6) biologic gradient; 7) consistency of findings; and 8) coherence of the evidence. For the malaria control interventions, the first two criteria are addressed by the existing scientific studies that document the efficacy of interventions in controlled trials--that is, the interventions have been proven to reduce morbidity and mortality and it is fully plausible that their use in national settings can achieve the comparable results. The country data on intervention coverage scale-up and impact measures largely address criteria 3 through 8. For the strength and specificity of association and the temporal congruity (criteria 3, 4 and 5), a programme assessment may lack the most stringent criteria, however in the examples of Bioko Island, Zambia and Zanzibar, the association and time link between malaria control scale up and reductions in malaria morbidity and mortality is strong; and efforts to examine alternative explanations did not demonstrate substantial weaknesses with the associations. The timing of the intervention application and the benefit achieved is closely sequenced. The Bioko Island experience shows a dramatic and almost immediate drop in child mortality following the application of the malaria prevention and treatment package [41
]. Similarly, the experience in Zanzibar showed a dramatic response to a combination of IRS, ITNs, and aggressive diagnosis and treatment [34
]. Regarding biologic-gradient or dose-response, the sum of the multi-country information may not yet fully establish the dose-response effect where higher coverage is directly linked to higher impact. However, it is quite clear that low coverage of malaria interventions remains linked to limited improvements in child survival.
As for coherence of the evidence, it is critical to examine alternative explanations. Factors that could offer alternative explanations for the suggested link between malaria control scale-up and malaria morbidity and mortality reductions might include: 1) variations in rainfall and temperature; 2) broad socio-economic change; 3) changing HIV conditions; 4) other child health interventions discussed previously that might account for the differences; and/or 5) biologic changes in the malaria-vector-human cycle that is making malaria infection and illness less virulent. Many of the studies address and account for rainfall and temperature patterns and demonstrate that these are not plausible explanations of marked reductions in malaria during this time interval in most African countries. In Ethiopia, weather patterns are thought to have contributed to a substantial malaria epidemic from 2003 through 2005, so some of the findings there may be accounted for by this earlier period with high malaria as a comparison time for more recent scale-up and impact; however, this is not the case for other country settings. Some socio-economic change certainly occurred in Bioko Island with the growth of the oil industry, and improved copper prices in the 2005 through 2008 interval may have contributed indirectly in Zambia--but again, such socio-economic improvements occur in many countries but are not likely to explain the dramatic reduction in childhood mortality documented in the setting of SUFI. HIV rates have not dropped consistently across these countries, but improved treatment with anti-retroviral drugs may have contributed partially to the improved child survival and reductions in fever and malaria incidence and prevalence.
There are numerous challenges in a multi-country review of programme scale-up and its consequences. Largely, the study relied on national population-based survey data, which typically come from DHS, MICS and MIS, which have been repeatedly compared and updated to assure common wording and sequence of questions and standard reporting procedures; thus it is not expected that these different surveys introduced substantial bias in comparisons. For some questions, the timing of the survey is important. For example ITN use may vary substantially between the high transmission season and the hotter and dryer seasons when few or no mosquitoes prompt people to use their ITNs less; this would generally bias any results to being underestimates of actual ITN use in the peak transmission season. For counting malaria cases, however, a substantial variability is expected between and within countries as the introduction and expansion of diagnostics has increasingly excluded non-malarial fevers over time; unfortunately this leads to over-reporting of progress in reducing malaria cases and it is not possible to fully account for this in this summary review. In addition, child survival may well have been improving in some of these countries prior to intervention scale-up. The health systems must be reasonably robust to deliver the spectrum of malaria interventions through child health services, maternal health services, and community outreach and campaign distributions. These systems likely have been delivering other services (e.g., immunizations, vitamin A, and treatment of other illnesses) that improve child survival. While a clear majority alternative explanation for the improved health in these countries was not observed, each of these (and possibly additional factors) should be considered carefully by programmes examining the benefit of their malaria control scale-up.
Some of the observed measures of impact are at higher programme effectiveness levels than were predicted based on the coverage levels achieved and the known intervention efficacy data. This should not be surprising for several reasons. First, programmes are typically using multiple interventions simultaneously (ITNs, IRS, IPTp, and case management), not just one intervention that might have been tested in a controlled trial. Second, the controlled trials likely improved the services for both the intervention and comparison groups, thus making the efficacy estimate a conservative one. In contrast, programmes are being compared historically to times when much less was being done overall for malaria and possibly for other child health interventions, thus their observed gains appear large.
No single intervention can be credited with these dramatic improvements in the many countries; IRS may be credited in one country, ITNs in another, and effective drugs for case management in a third country. In fact, it is more likely that the composite package of preventive interventions (ITNs, IRS, IPTp) and treatment interventions (changing to highly effective drugs and using quality diagnostics) is responsible for the level of mortality reduction in individual countries. In some settings the benefit has been attributed to a specific intervention but this may be because countries have not clearly accounted for the role of the other interventions. For example, in most countries with marked improvement of case management, it is likely that the use of good diagnostics is responsible for much of the decline in reported malaria cases - by leading to the exclusion of non-malaria fevers from that case count. While scale-up of case management may have been the weakest of the efforts to date, recent emphasis on diagnostics may help further address this in the coming years. Of note, the overall effect of malaria control has been generated largely through the reduction of malaria transmission--both vector control and aggressive diagnosis and treatment in places like Zanzibar have contributed substantially to reduced transmission. It is inevitable that programme orientation to sustain the current gains will require an intense focus on transmission reduction.