Background To reduce the malaria burden in Nigeria, the country is scaling up prevention and treatment interventions, especially household ownership and use of insecticide-treated nets (ITNs). Nevertheless, large gaps remain to achieve the goals of the National Malaria Strategic Plan 2014–2020 of universal access to ITNs and their increased use. To inform the targeting of intervention strategies and to maximize impact, the authors conducted a sub-national profiling of household ITN ownership and use in the general population to identify key predictors of ITN ownership and use, and the sub-groups that are at higher risk of low ITN coverage and use. Methods The authors conducted a secondary analysis of data from the 2015 Nigeria Malaria Indicator Survey. Using the Chi square automatic interaction detector (CHAID) and multiple logistic regression analysis, the authors examined the key predictors of ITN ownership and use in the general population throughout Nigeria. Results The CHAID models identified region of the country as the best predictor of household ownership of at least one ITN and its use in the general population, with higher ownership and use observed in the northern regions. The odds of a household owning an ITN were five times greater in the North West region compared with the North Central region (odds ratio [OR] = 5.47, 95% confidence interval [CI] 4.46–6.72, p < 0.001). The odds of ITN use were two times greater for those living in the North West region compared with the North Central region (OR = 2.04, 95% CI 1.73–2.41, p < 0.001). Other significant predictors were household size, head of household education level, household wealth quintile, and place of residence. The CHAID gain index results identified households in the South West, North Central and South Central regions with low ITN ownership, and the general population in the South South, South East and North Central regions with low ITN use. Conclusions This study reveals regional differences in ITN ownership and use in Nigeria. Therefore, the findings from this analysis provide evidence that could inform the NMEP to better target future campaign and routine distribution of ITNs, to achieve universal access and increased use by 2020 in Nigeria.
Background: Many countries have made substantial progress in scaling-up and sustaining malaria intervention coverage, leading to more focalized and heterogeneous transmission in many settings. Evaluation provides valuable information for programmes to understand if interventions have been implemented as planned and with quality, if the programme had the intended impact on malaria burden, and to guide programmatic decision-making. Low-, moderate-, and heterogeneous-transmission settings present unique evaluation challenges because of dynamic and targeted intervention strategies. This paper provides illustration of evaluation approaches and methodologies for these transmission settings, and suggests how to answer evaluation questions specific to the local context. Methods:The Roll Back Malaria Monitoring and Evaluation Reference Group formed a task force in October 2017 to lead development of this framework. The task force includes representatives from National Malaria Programmes, funding agencies, and malaria research and implementing partners. The framework builds on existing guidance for process and outcome evaluations and impact evaluations specifically in high transmission settings. Results:The theory of change describes how evaluation questions asked by national malaria programmes in different contexts influence evaluation design. The transmission setting, existing stratification, and data quality and availability are also key considerations. The framework is intended for adaption by countries to their local context, and use for evaluation at sub-national level. Confirmed malaria incidence is recommended as the primary impact indicator due to its sensitivity to detect changes in low-transmission settings. It is expected that process evaluations provide sufficient evidence for programme monitoring and improvement, while impact evaluations are needed following adoption of new mixes of interventions, operational strategies, tools or policies, particularly in contexts of changing malaria epidemiology. Impact evaluations in low-, moderate-, or heterogeneous-transmission settings will likely use plausibility designs, and methods highlighted by the framework include interrupted time series, district-level doseresponse analyses, and constructed control methods. Triangulating multiple data sources and analyses is important to strengthen the plausibility argument. Conclusions:This framework provides a structure to assist national malaria programmes and partners to design evaluations in low-, moderate-or heterogeneous-transmission settings. Emphasizing a continuous cycle along the causal pathway linking process evaluation to impact evaluation and then programmatic decision-making, the © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and in...
Background New national malaria strategic plans (NMSPs) should build upon the achievements and challenges identified during the implementation of previous plans, but there is limited research on the transition process between NMSPs. This study aims to fill this gap through an assessment of NMSPs across sub-Saharan Africa. Methods The study reviewed the two most recent NMSPs for selected sub-Saharan African countries. Targets for six core malaria indicators were extracted from each NMSP and compared to the coverage achieved according to corresponding population-based surveys completed near the end of the NMSP term. Implementation challenges and proposed solutions identified through the NMSP analysis were documented. The current NMSP was reviewed to determine whether proposed solutions had been integrated into the strategy. Results Twenty-two countries in sub-Saharan Africa were included in the assessment. Of the 135 verified targets, only 4 were achieved. No country reached more than one of the six targets assessed in each NMSP. Despite this low success rate, only four of the 22 countries lowered a subsequent target, with most setting the next target at an equal or greater level. Most NMSPs identified solutions to address implementation challenges faced, but the solutions were not always fully incorporated in the new strategy. Conclusions The results show a disconnect between NMSPs. Most targets were set according to global goals rather than the individual country’s previous achievements and limitations. This indicates a need to revise the NMSP development process to guide programmes in defining targets based on their country context and incorporate strategies to address challenges identified in the previous NMSP. This will allow countries to set and meet achievable targets as they work toward global goals. Electronic supplementary material The online version of this article (10.1186/s12936-019-2898-4) contains supplementary material, which is available to authorized users.
There is global interest in frequent reliable estimates of the malaria burden, especially at the country and subnational levels, to determine progress in malaria control efforts. Traditionally, these estimates were obtained mostly from national household surveys such as demographic and health surveys and malaria indicator surveys. However, routine health information systems (RHIS) are increasingly being used to refine these estimates, especially at the subnational levels and during periods between surveys. Currently, the WHO provides malaria burden estimates annually through the World Malaria Report, an important information source for the global malaria community to track efforts and progress. The 2018 estimates were generated through a model-based geostatistical framework, which converts Plasmodium falciparum parasite rate (PfPR) data to malaria incidence in high-burden countries and converts case data captured through surveillance to national incidence and PfPR estimates in lower burden countries. 1,2 Because these estimates rely on country RHIS data and modeling, there is an inherent degree of uncertainty, and the results, in some cases, are challenged by countries.In addition to these methods, other approaches including time-series analysis, such as auto-regressive integrated moving averages, 3,4 have been used on RHIS data to estimate the malaria burden and understand the effects of malaria interventions. Programs are increasingly focusing efforts on surveillance as a key data source to routinely monitor progress and inform program implementation. As RHIS improve in malaria-endemic countries, routine data are becoming more available and accessible to generate estimates with real-time data. However, these data are sensitive to several biases related to population access to health care, care-seeking behavior, malaria testing rates, and reporting completeness. The article, "A robust estimator of malaria incidence from routine health facility data" by Thwing et al. 5 in this issue, aimed to address these issues using an algebraic method that relies on a number of assumptions to improve malaria incidence estimates and quantify commodity needs. The authors applied their method on RHIS data to generate incidence estimates and estimate gaps in malaria testing and treatment needs from World Malaria Report-reported incidence rates for Guinea, Mozambique, and all of sub-Saharan Africa.The goal for developing this method was to provide national malaria control program (NMCP) staff and their partners a tool to interpret routinely reported surveillance data without needing a background in statistics to understand the results, and to provide reliable burden estimates to allow for comparison between districts or regions, and to evaluate trends over time.The authors presented their approach as a more robust method to adjust crude incidence rates for potential biases. However, to better interpret these results, the underlying assumptions need to be understood. Furthermore, although their approach is intended to help inform ...
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