The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda

Process and Methodological Considerations for Observational Analyses of Vector Control Interventions in Sub-Saharan Africa Using Routine Malaria Data

Progress in malaria control has stalled in recent years. With growing resistance to existing malaria vector control insecticides and the introduction of new vector control products, national malaria control programs (NMCPs) increasingly need to make data-driven, subnational decisions to inform vector control deployment. As NMCPs are increasingly conducting subnational stratification of malaria control interventions, including malaria vector control, country-specific frameworks and platforms are increasingly needed to guide data use for vector control deployment. Integration of routine health systems data, entomological data, and vector control program data in observational longitudinal analyses offers an opportunity for NMCPs and research institutions to conduct evaluations of existing and novel vector control interventions. Drawing on the experience of implementing 22 vector control evaluations across 14 countries in sub-Saharan Africa, as well as published and gray literature on vector control impact evaluations using routine health information system data, this article provides practical guidance on the design of these evaluations, makes recommendations for key variables and data sources, and proposes methods to address challenges in data quality. Key recommendations include appropriate parameterization of impact and coverage indicators, incorporating explanatory covariates and contextual factors from multiple sources (including rapid diagnostic testing stockouts; insecticide susceptibility; vector density measures; vector control coverage, use, and durability; climate and other malaria and non-malaria health programs), and assessing data quality before the evaluation through either on-the-ground or remote data quality assessments. These recommendations may increase the frequency, rigor, and utilization of routine data sources to inform national program decision-making for vector control.

Comparison of fine-scale malaria strata derived from population survey data collected using RDTs, microscopy and qPCR in South-Eastern Tanzania

BACKGROUND

Malaria-endemic countries are increasingly adopting data-driven risk stratification, often at district or higher regional levels, to guide their intervention strategies. The data typically comes from population-level surveys collected by rapid diagnostic tests (RDTs), which unfortunately perform poorly in low transmission settings. Here, a high-resolution survey of Plasmodium falciparum prevalence rate (PfPR) was conducted in two Tanzanian districts using rapid diagnostic tests (RDTs), microscopy, and quantitative polymerase chain reaction (qPCR) assays, enabling the comparison of fine-scale strata derived from these different diagnostic methods.

METHODS

A cross-sectional survey was conducted in 35 villages in Ulanga and Kilombero districts, south-eastern Tanzania between 2022 and 2023. A total of 7,628 individuals were screened using RDTs (SD-BIOLINE) and microscopy, with two thirds of the samples further analysed by qPCR. The data was used to categorize each district and village as having very low (PfPR < 1%), low (1%≤PfPR < 5%), moderate (5%≤PfPR < 30%), or high (PfPR ≥ 30%) parasite prevalence. A generalized linear mixed model was used to analyse infection risk factors. Other metrics, including positive predictive value (PPV), sensitivity, specificity, parasite densities, and Kappa statistics were computed for RDTs or microscopy and compared to qPCR as reference.

RESULTS

Significant fine-scale variations in malaria risk were observed within and between the districts, with village prevalence ranging from 0% to > 50%. Prevalence varied by testing method: Kilombero was low risk by RDTs (PfPR = 3%) and microscopy (PfPR = 2%) but moderate by qPCR (PfPR = 9%); Ulanga was high risk by RDTs (PfPR = 39%) and qPCR (PfPR = 54%) but moderate by microscopy (PfPR = 26%). RDTs and microscopy classified majority of the 35 villages as very low to low risk (18-21 villages). In contrast, qPCR classified most villages as moderate to high risk (29 villages). Using qPCR as the reference, PPV for RDTs and microscopy ranged from as low as < 20% in very low transmission villages to > 80% in moderate and high transmission villages. Sensitivity was 62% for RDTs and 41% for microscopy; specificity was 93% and 96%, respectively. Kappa values were 0.7 for RDTs and 0.5 for microscopy. School-age children (5-15 years) had higher malaria prevalence and parasite densities than adults (P < 0.001). High-prevalence villages also had higher parasite densities (Spearman r = 0.77, P < 0.001 for qPCR; r = 0.55, P = 0.003 for microscopy).

CONCLUSION

This study highlights significant fine-scale variability in malaria burden within and between the study districts and emphasizes the variable performance of the testing methods when stratifying risk at local scales. While RDTs and microscopy were effective in high-transmission areas, they performed poorly in low-transmission settings; and classified most villages as very low or low risk. In contrast, qPCR classified most villages as moderate or high risk. The findings emphasize that, where precise mapping and effective targeting of malaria are required in localized settings, tests must be both operationally feasible and highly sensitive. Furthermore, when planning microstratification efforts to guide local control measures, it is crucial to carefully consider both the strengths and limitations of the available data and the testing methods employed.

Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso

BACKGROUND

Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet).

METHODS

Monthly confirmed malaria cases of children under five during the period 2015-2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics.

RESULTS

Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones.

CONCLUSIONS

The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems.

Relative effects of climate factors and malaria control interventions on changes of parasitaemia risk in Burkina Faso from 2014 to 2017/2018

BACKGROUND

In Burkina Faso, the prevalence of malaria has decreased over the past two decades, following the scale-up of control interventions. The successful development of malaria parasites depends on several climatic factors. Intervention gains may be reversed by changes in climatic factors. In this study, we investigated the role of malaria control interventions and climatic factors in influencing changes in the risk of malaria parasitaemia.

METHODS

Bayesian logistic geostatistical models were fitted on Malaria Indicator Survey data from Burkina Faso obtained in 2014 and 2017/2018 to estimate the effects of malaria control interventions and climatic factors on the temporal changes of malaria parasite prevalence. Additionally, intervention effects were assessed at regional level, using a spatially varying coefficients model.

RESULTS

Temperature showed a statistically important negative association with the geographic distribution of parasitaemia prevalence in both surveys; however, the effects of insecticide-treated nets (ITNs) use was negative and statistically important only in 2017/2018. Overall, the estimated number of infected children under the age of 5 years decreased from 704,202 in 2014 to 290,189 in 2017/2018. The use of ITNs was related to the decline at national and regional level, but coverage with artemisinin-based combination therapy only at regional level.

CONCLUSION

Interventions contributed more than climatic factors to the observed change of parasitaemia risk in Burkina Faso during the period of 2014 to 2017/2018. Intervention effects varied in space. Longer time series analyses are warranted to determine the differential effect of a changing climate on malaria parasitaemia risk.

A roadmap for using DHIS2 data to track progress in key health indicators in the Global South: experience from sub-saharan Africa

High quality health data as collected by health management information systems (HMIS) is an important building block of national health systems. District Health Information System 2 (DHIS2) software is an innovation in data management and monitoring for strengthening HMIS that has been widely implemented in low and middle-income countries in the last decade. However, analysts and decision-makers still face significant challenges in fully utilizing the capabilities of DHIS2 data to pursue national and international health agendas. We aimed to (i) identify the most relevant health indicators captured by DHIS2 for tracking progress towards the Sustainable Development goals in sub-Saharan African countries and (ii) present a clear roadmap for improving DHIS2 data quality and consistency, with a special focus on immediately actionable solutions. We identified that key indicators in child and maternal health (e.g. vaccine coverage, maternal deaths) are currently being tracked in the DHIS2 of most countries, while other indicators (e.g. HIV/AIDS) would benefit from streamlining the number of indicators collected and standardizing case definitions. Common data issues included unreliable denominators for calculation of incidence, differences in reporting among health facilities, and programmatic differences in data quality. We proposed solutions for many common data pitfalls at the analysis level, including standardized data cleaning pipelines, k-means clustering to identify high performing health facilities in terms of data quality, and imputation methods. While we focus on immediately actionable solutions for DHIS2 analysts, improvements at the point of data collection are the most rigorous. By investing in improving data quality and monitoring, countries can leverage the current global attention on health data to strengthen HMIS and progress towards national and international health priorities.

Malaria, climate variability, and interventions: modelling transmission dynamics

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6-36.9) in Kenya, 10.6% (3.4-39.2) in mainland Tanzania, and 9.5% (4.0-48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.

Spatial Modelling of Risk Factors for Malaria Prevalence in SNNP Regional State, Ethiopia

Background

Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases and about one million deaths reported to date of which 90% were from world health organization (WHO) Sub Saharan Africa (SSA) countries. The purpose of this study was to explore the spatial distribution of malaria parasite prevalence (MPP) among districts of Southern Nations Nationalities and Peoples Regional State (SNNRS) in Ethiopia by using 2011 malaria indicator survey (MIS) data collected for 76 districts and to model its relationship with different covariates.

Method

Exploratory spatial data analysis (ESDA) was conducted followed by implementation of spatial lag model (SLM) and spatial error model (SEM) in GeoDa software. Queen contiguity second order type of spatial weight matrix was applied in order to formalize spatial interaction among districts.

Results

From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Hot spot areas for MPP were found in the eastern and southeast parts of the region. Relying on specification diagnostics and measures of fit, SLM was found to be the best model for explaining the geographical variation of MPP. SLM analysis demonstrated that proportion of households living in earth/local dung plastered floor house, proportion of households living under thatched roof house, average number of rooms/person in a given district, proportion of households who used anti-malaria spray in the last 12 months before the survey, percentage household using mosquito nets and average number of mosquito nets/person in a given district have positive and statistically significant effect on spatial distribution of MPP across districts of SNNPRS. Percentage of households living without access to radio and television has negative and statistically significant effect on spatial distribution of MPP across districts of MPP.

Conclusion

Malaria is spatially clustered in space. The implication of the spatial clustering is that, in cases where the decisions on how to allocate funds for interventions needs to have spatial dimension.

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Effectiveness of a national mass distribution campaign of long-lasting insecticide-treated nets and indoor residual spraying on clinical malaria in Malawi, 2018-2020

Introduction

Malawi’s malaria burden is primarily assessed via cross-sectional national household surveys. However, malaria is spatially and temporally heterogenous and no analyses have been performed at a subdistrict level throughout the course of a year. The WHO recommends mass distribution of long-lasting insecticide-treated bed nets (LLINs) every 3 years, but a national longitudinal evaluation has never been conducted in Malawi to determine LLIN effectiveness lifespans.

Methods

Using District Health Information Software 2 (DHIS2) health facility data, available from January 2018 to June 2020, we assessed malaria risk before and after a mass distribution campaign, stratifying by age group and comparing risk differences (RDs) by LLIN type or annual application of indoor residual spraying (IRS).

Results

711 health facilities contributed 20 962 facility reports over 30 months. After national distribution of 10.7 million LLINs and IRS in limited settings, malaria risk decreased from 25.6 to 16.7 cases per 100 people from 2018 to 2019 high transmission seasons, and rebounded to 23.2 in 2020, resulting in significant RDs of −8.9 in 2019 and −2.4 in 2020 as compared with 2018. Piperonyl butoxide (PBO)-treated LLINs were more effective than pyrethroid-treated LLINs, with adjusted RDs of −2.3 (95% CI −2.7 to −1.9) and −1.5 (95% CI −2.0 to −1.0) comparing 2019 and 2020 high transmission seasons to 2018. Use of IRS sustained protection with adjusted RDs of −1.4 (95% CI −2.0 to −0.9) and −2.8% (95% CI −3.5 to −2.2) relative to pyrethroid-treated LLINs. Overall, 12 of 28 districts (42.9%) experienced increases in malaria risk in from 2018 to 2020.

Conclusion

LLINs in Malawi have a limited effectiveness lifespan and IRS and PBO-treated LLINs perform better than pyrethroid-treated LLINs, perhaps due to net repurposing and insecticide-resistance. DHIS2 provides a compelling framework in which to examine localised malaria trends and evaluate ongoing interventions.

Laboratory Detection of Malaria Antigens: a Strong Tool for Malaria Research, Diagnosis, and Epidemiology

The identification and characterization of proteins produced during human infection with Plasmodium spp. have guided the malaria community in research, diagnosis, epidemiology, and other efforts. Recently developed methods for the detection of these proteins (antigens) in the laboratory have provided new types of data that can inform the evaluation of malaria diagnostics, epidemiological investigations, and overall malaria control strategies. Here, the focus is primarily on antigens that are currently known to be detectable in human specimens and on their impact on the understanding of malaria in human populations. We highlight historical and contemporary laboratory assays for malaria antigen detection, the concept of an antigen profile for a biospecimen, and ways in which binary results for a panel of antigens could be interpreted and utilized for different analyses. Particular emphasis is given to the direct comparison of field-level malaria diagnostics and laboratory antigen detection for the development of an external evaluation scheme. The current limitations of laboratory antigen detection are considered, and the future of this developing field is discussed.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Impact of 1.5 oC and 2 oC global warming scenarios on malaria transmission in East Africa

Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 ^oC and 2.0 ^oC global warming levels (hereinafter GWL1.5 and GWL2.0, respectively).

Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios.

Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 ^oC and 1.5 ^oC and 1.0 ^oC to 2.0 ^oC under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region.

Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.

Impact of 1.5 oC and 2 oC global warming scenarios on malaria transmission in East Africa

Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 oC and 2.0 oC global warming levels (hereinafter GWL1.5 and GWL2.0, respectively). Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios. Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 oC and 1.5 oC and 1.0 oC to 2.0 oC under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region. Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.

Malaria in Cambodia: A Retrospective Analysis of a Changing Epidemiology 2006-2019

Background: In Cambodia, malaria persists with changing epidemiology and resistance to antimalarials. This study aimed to describe how malaria has evolved spatially from 2006 to 2019 in Cambodia. Methods: We undertook a secondary analysis of existing malaria data from all government healthcare facilities in Cambodia. The epidemiology of malaria was described by sex, age, seasonality, and species. Spatial clusters at the district level were identified with a Poisson model. Results: Overall, incidence decreased from 7.4 cases/1000 population in 2006 to 1.9 in 2019. The decrease has been drastic for females, from 6.7 to 0.6/1000. Adults aged 15–49 years had the highest malaria incidence among all age groups. The proportion of Plasmodium (P.) falciparum + Mixed among confirmed cases declined from 87.9% (n = 67,489) in 2006 to 16.6% (n = 5290) in 2019. Clusters of P. falciparum + Mixed and P. vivax + Mixed were detected in forested provinces along all national borders. Conclusions: There has been a noted decrease in P. falciparum cases in 2019, suggesting that an intensification plan should be maintained. A decline in P. vivax cases was also noted, although less pronounced. Interventions aimed at preventing new infections of P. vivax and relapses should be prioritized. All detected malaria cases should be captured by the national surveillance system to avoid misleading trends.

Routine data for malaria morbidity estimation in Africa: challenges and prospects

BACKGROUND

The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented.

CONCLUSION

Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens.

Practical Implications of a Relationship between Health Management Information System and Community Cohort-Based Malaria Incidence Rates

Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, their strong predictive power of unbiased malaria burden when improved highlights the important role they could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

Rapid shifts in the age-specific burden of malaria following successful control interventions in four regions of Uganda

Background

Malaria control using long-lasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) has been associated with reduced transmission throughout Africa. However, the impact of transmission reduction on the age distribution of malaria cases remains unclear.

Methods

Over a 10-year period (January 2009 to July 2018), outpatient surveillance data from four health facilities in Uganda were used to estimate the impact of control interventions on temporal changes in the age distribution of malaria cases using multinomial regression. Interventions included mass distribution of LLINs at all sites and IRS at two sites.

Results

Overall, 896,550 patient visits were included in the study; 211,632 aged < 5 years, 171,166 aged 5–15 years and 513,752 > 15 years. Over time, the age distribution of patients not suspected of malaria and those malaria negative either declined or remained the same across all sites. In contrast, the age distribution of suspected and confirmed malaria cases increased across all four sites. In the two LLINs-only sites, the proportion of malaria cases in < 5 years decreased from 31 to 16% and 35 to 25%, respectively. In the two sites receiving LLINs plus IRS, these proportions decreased from 58 to 30% and 64 to 47%, respectively. Similarly, in the LLINs-only sites, the proportion of malaria cases > 15 years increased from 40 to 61% and 29 to 39%, respectively. In the sites receiving LLINs plus IRS, these proportions increased from 19 to 44% and 18 to 31%, respectively.

Conclusions

These findings demonstrate a shift in the burden of malaria from younger to older individuals following implementation of successful control interventions, which has important implications for malaria prevention, surveillance, case management and control strategies.

Malaria Case Fatality Rate among Children under Five in Burkina Faso: An Assessment of the Spatiotemporal Trends Following the Implementation of Control Programs

Reducing the 2015 level of malaria mortality by 90% by 2030 is a goal set by the World Health Organization (WHO). In Burkina Faso, several malaria control programs proven to be effective were implemented over the last decade. In parallel, the progressive strengthening of the health surveillance system is generating valuable data, which represents a great opportunity for analyzing the trends in malaria burden and assessing the effect of these control programs. Complementary programs were rolled out at different time points and paces, and the present work aims at investigating both the spatial and temporal pattern of malaria case fatality rate (mCFR) by considering the effect of combining specific and unspecific malaria control programs. To this end, data on severe malaria cases and malaria deaths, aggregated at health district level between January 2013 and December 2018, were extracted from the national health data repository (ENDOS-BF). A Bayesian spatiotemporal zero-inflated Poisson model was fitted to quantify the strength of the association of malaria control programs with monthly mCFR trends at health district level. The model was adjusted for contextual variables. We found that monthly mCFR decreased from 2.0 (95% IC 1.9-2.1%) to 0.9 (95% IC 0.8-1.0%) deaths for 100 severe malaria cases in 2013 and 2018, respectively. Health districts with high mCFR were identified in the northern, northwestern and southwestern parts of the country. The availability of malaria rapid diagnosis tests (IRR: 0.54; CrI: 0.47, 0.62) and treatment (IRR: 0.50; CrI: 0.41, 0.61) were significantly associated with a reduction in the mCFR. The risk of dying from malaria was lower in the period after the free healthcare policy compared with the period before (IRR: 0.47; CrI: 0.38, 0.58). Our findings highlighted locations that are most in need of targeted interventions and the necessity to sustain and strengthen the launched health programs to further reduce the malaria deaths in Burkina Faso.

Effect of Free Healthcare Policy for Children under Five Years Old on the Incidence of Reported Malaria Cases in Burkina Faso by Bayesian Modelling: "Not only the Ears but also the Head of the Hippopotamus"

Burkina Faso has recently implemented an additional strategy, the free healthcare policy, to further improve maternal and child health. This policy targets children under five who bear the brunt of the malaria scourge. The effects of the free-of-charge healthcare were previously assessed in women but not in children. The present study aims at filling this gap by assessing the effect of this policy in children under five with a focus on the induced spatial and temporal changes in malaria morbidity. We used a Bayesian spatiotemporal negative binomial model to investigate the space–time variation in malaria incidence in relation to the implementation of the policy. The analysis relied on malaria routine surveillance data extracted from the national health data repository and spanning the period from January 2013 to December 2018. The model was adjusted for meteorological and contextual confounders. We found that the number of presumed and confirmed malaria cases per 1000 children per month increased between 2013 and 2018. We further found that the implementation of the free healthcare policy was significantly associated with a two-fold increase in the number of tested and confirmed malaria cases compared with the period before the policy rollout. This effect was, however, heterogeneous across the health districts. We attributed the rise in malaria incidence following the policy rollout to an increased use of health services combined with an increased availability of rapid tests and a higher compliance to the “test and treat” policy. The observed heterogeneity in the policy effect was attributed to parallel control interventions, some of which were rolled out at different paces and scales. Our findings call for a sustained and reinforced effort to test all suspected cases so that, alongside an improved case treatment, the true picture of the malaria scourge in children under five emerges clearly (see the hippopotamus almost entirely).