Impact of home-based management of malaria on health outcomes in Africa: a systematic review of the evidence

BACKGROUND: Home-based management of malaria (HMM) is promoted as a major strategy to improve prompt delivery of effective malaria treatment in Africa. HMM involves presumptively treating febrile children with pre-packaged antimalarial drugs distributed by members of the community. HMM has been implemented in several African countries, and artemisinin-based combination therapies (ACTs) will likely be introduced into these programmes on a wide scale. CASE PRESENTATIONS: The published literature was searched for studies that evaluated the health impact of community- and home-based treatment for malaria in Africa. Criteria for inclusion were: 1) the intervention consisted of antimalarial treatment administered presumptively for febrile illness; 2) the treatment was administered by local community members who had no formal education in health care; 3) measured outcomes included specific health indicators such as malaria morbidity (incidence, severity, parasite rates) and/or mortality; and 4) the study was conducted in Africa. Of 1,069 potentially relevant publications identified, only six studies, carried out over 18 years, were identified as meeting inclusion criteria. Heterogeneity of the evaluations, including variability in study design, precluded meta-analysis. DISCUSSION AND EVALUATION: All trials evaluated presumptive treatment with chloroquine and were conducted in rural areas, and most were done in settings with seasonal malaria transmission. Conclusions regarding the impact of HMM on morbidity and mortality endpoints were mixed. Two studies showed no health impact, while another showed a decrease in malaria prevalence and incidence, but no impact on mortality. One study in Burkina Faso suggested that HMM decreased the proportion of severe malaria cases, while another study from the same country showed a decrease in the risk of progression to severe malaria. Of the four studies with mortality endpoints only one from Ethiopia showed a positive impact, with a reduction in the under-5 mortality rate of 40.6% (95% CI 29.2 - 50.6). CONCLUSION: Currently the evidence base for HMM in Africa, particularly regarding use of ACTs, is narrow and priorities for further research are discussed. To optimize treatment and maximize health benefits, drug regimens and delivery strategies in HMM programmes may need to be tailored to local conditions. Additional research could help guide programme development, policy decision-making, and implementation

An Economic Evaluation of Home Management of Malaria in Uganda: An Interactive Markov Model

BACKGROUND: Home management of malaria (HMM), promoting presumptive treatment of febrile children in the community, is advocated to improve prompt appropriate treatment of malaria in Africa. The cost-effectiveness of HMM is likely to vary widely in different settings and with the antimalarial drugs used. However, no data on the cost-effectiveness of HMM programmes are available. METHODS/PRINCIPAL FINDINGS: A Markov model was constructed to estimate the cost-effectiveness of HMM as compared to conventional care for febrile illnesses in children without HMM. The model was populated with data from Uganda, but is designed to be interactive, allowing the user to adjust certain parameters, including the antimalarials distributed. The model calculates the cost per disability adjusted life year averted and presents the incremental cost-effectiveness ratio compared to a threshold value. Model output is stratified by level of malaria transmission and the probability that a child would receive appropriate care from a health facility, to indicate the circumstances in which HMM is likely to be cost-effective. The model output suggests that the cost-effectiveness of HMM varies with malaria transmission, the probability of appropriate care, and the drug distributed. Where transmission is high and the probability of appropriate care is limited, HMM is likely to be cost-effective from a provider perspective. Even with the most effective antimalarials, HMM remains an attractive intervention only in areas of high malaria transmission and in medium transmission areas with a lower probability of appropriate care. HMM is generally not cost-effective in low transmission areas, regardless of which antimalarial is distributed. Considering the analysis from the societal perspective decreases the attractiveness of HMM. CONCLUSION: Syndromic HMM for children with fever may be a useful strategy for higher transmission settings with limited health care and diagnosis, but is not appropriate for all settings. HMM may need to be tailored to specific settings, accounting for local malaria transmission intensity and availability of health services

An Epidemiologic Investigation of Potential Risk Factors for Nodding Syndrome in Kitgum District, Uganda

<div><p>Introduction</p><p>Nodding Syndrome (NS), an unexplained illness characterized by spells of head bobbing, has been reported in Sudan and Tanzania, perhaps as early as 1962. Hypothesized causes include sorghum consumption, measles, and onchocerciasis infection. In 2009, a couple thousand cases were reportedly in Northern Uganda.</p><p>Methods</p><p>In December 2009, we identified cases in Kitgum District. The case definition included persons who were previously developmentally normal who had nodding. Cases, further defined as 5- to 15-years-old with an additional neurological deficit, were matched to village controls to assess risk factors and test biological specimens. Logistic regression models were used to evaluate associations.</p><p>Results</p><p>Surveillance identified 224 cases; most (95%) were 5–15-years-old (range = 2–27). Cases were reported in Uganda since 1997. The overall prevalence was 12 cases per 1,000 (range by parish = 0·6–46). The case-control investigation (n = 49 case/village control pairs) showed no association between NS and previously reported measles; sorghum was consumed by most subjects. Positive onchocerciasis serology [age-adjusted odds ratio (AOR₁) = 14·4 (2·7, 78·3)], exposure to munitions [AOR₁ = 13·9 (1·4, 135·3)], and consumption of crushed roots [AOR₁ = 5·4 (1·3, 22·1)] were more likely in cases. Vitamin B6 deficiency was present in the majority of cases (84%) and controls (75%).</p><p>Conclusion</p><p>NS appears to be increasing in Uganda since 2000 with 2009 parish prevalence as high as 46 cases per 1,000 5- to 15-year old children. Our results found no supporting evidence for many proposed NS risk factors, revealed association with onchocerciasis, which for the first time was examined with serologic testing, and raised nutritional deficiencies and toxic exposures as possible etiologies.</p></div

Frequency of nodding syndrome cases and village controls with positive exposures or presence of clinical findings.

<p>Statistically significant values are in bold.</p><p>CI: Confidence interval. OR: odds ratio.</p>*<p>Percent with exposure is calculated by number of cases with a positive exposure divided by number of cases, or number of controls exposed divided by number of controls.</p>‡<p>Odds ratio calculated as odds of positive exposure in cases versus odds of exposure in controls.</p>†<p>AOR₁: Odds ratio adjusted for age. Note: additional models 2 (adjusted for age, munitions, roots) and 3 (adjusted for age, measles, sorghum, onchocerciasis skin snip positive) are available in an online appendix Table 5.</p>?<p>Missing data existed for the following exposure variables: malaria, malnutrition, pneumonia, diarrhea, head injury, crushed leaves, roots, flowers, inhaled medicine (number of cases responding to question = 50); swimming in the river or pond, visual or auditory hallucinations (cases = 49); skin nodules (cases = 48); low height for age, low BMI for age (cases = 45, controls = 48); all data used for frequencies, data from available matched pairs used for matched analyses.</p>+<p>Firth’s correction.</p>**<p>low BMI-for-age z-score: <−2 SD, an indicator of acute malnutrition; low height-for-age z-score: <−2SD, chronic malnutrition.</p><p>Unadjusted and adjusted odds of positive exposure or clinical finding in a case versus control.</p

Epidemic curve depicting time course of disease onset: cases identified by surveillence and case-control studies in Kitgum District plotted by reported NS onset year.*.

<p>Epidemic curve depicting time course of disease onset: cases identified by surveillence and case-control studies in Kitgum District plotted by reported NS onset year.*.</p