Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets-ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

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.

Complete sources of cluster variation on the risk of under-five malaria in Uganda: a multilevel-weighted mixed effects logistic regression model approach

BACKGROUND

Malaria, a major cause of mortality worldwide is linked to a web of determinants ranging from individual to contextual factors. This calls for examining the magnitude of the effect of clustering within malaria data. Regrettably, researchers usually ignore cluster variation on the risk of malaria and also apply final survey weights in multilevel modelling instead of multilevel weights. This most likely produces biased estimates, misleads inference and lowers study power. The objective of this study was to determine the complete sources of cluster variation on the risk of under-five malaria and risk factors associated with under-five malaria in Uganda.

METHODS

This study applied a multilevel-weighted mixed effects logistic regression model to account for both individual and contextual factors.

RESULTS

Every additional year in a child's age was positively associated with malaria infection (AOR = 1.42; 95% CI 1.33-1.52). Children whose mothers had at least a secondary school education were less likely to suffer from malaria infection (AOR = 0.53; 95% CI 0.30-0.95) as well as those who dwelled in households in the two highest wealth quintiles (AOR = 0.42; 95% CI 0.27-0.64). An increase in altitude by 1 m was negatively associated with malaria infection (AOR = 0.98; 95% CI 0.97-0.99). About 77% of the total variation in the positive testing for malaria was attributable to differences between enumeration areas (ICC = 0.77; p < 0.001).

CONCLUSIONS

Interventions towards reducing the burden of under-five malaria should be prioritized to improve individual-level characteristics compared to household-level features. Enumeration area (EA) specific interventions may be more effective compared to household specific interventions.

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.

What are the pathways between poverty and malaria in sub-Saharan Africa? A systematic review of mediation studies

BACKGROUND

Malaria remains a major burden in sub-Saharan Africa (SSA). While an association between poverty and malaria has been demonstrated, a clearer understanding of explicit mechanisms through which socioeconomic position (SEP) influences malaria risk is needed to guide the design of more comprehensive interventions for malaria risk mitigation. This systematic review provides an overview of the current evidence on the mediators of socioeconomic disparities in malaria in SSA.

METHODS

We searched PubMed and Web of Science for randomised controlled trials, cohort, case-control and cross-sectional studies published in English between January 1, 2000 to May 31, 2022. Further studies were identified following reviews of reference lists of the studies included. We included studies that either (1) conducted a formal mediation analysis of risk factors on the causal pathway between SEP and malaria infections or (2) adjusted for these potential mediators as confounders on the association between SEP and malaria using standard regression models. At least two independent reviewers appraised the studies, conducted data extraction, and assessed risk of bias. A systematic overview is presented for the included studies.

RESULTS

We identified 41 articles from 20 countries in SSA for inclusion in the final review. Of these, 30 studies used cross-sectional design, and 26 found socioeconomic inequalities in malaria risk. Three formal mediation analyses showed limited evidence of mediation of food security, housing quality, and previous antimalarial use. Housing, education, insecticide-treated nets, and nutrition were highlighted in the remaining studies as being protective against malaria independent of SEP, suggesting potential for mediation. However, methodological limitations included the use of cross-sectional data, insufficient confounder adjustment, heterogeneity in measuring both SEP and malaria, and generally low or moderate-quality studies. No studies considered exposure mediator interactions or considered identifiability assumptions.

CONCLUSIONS

Few studies have conducted formal mediation analyses to elucidate pathways between SEP and malaria. Findings indicate that food security and housing could be more feasible (structural) intervention targets. Further research using well-designed longitudinal studies and improved analysis would illuminate the current sparse evidence into the pathways between SEP and malaria and adduce evidence for more potential targets for effective intervention.

Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions

INTRODUCTION

Africa has a higher burden of malaria-related cases and deaths globally. Children under five accounted for over two-thirds of all malaria deaths in sub-Saharan Africa (SSA). This scoping review aims to map evidence of the prevalence, contextual factors and health education interventions of malaria amongst children under 5 years (UN5) in SSA.

METHOD

Four main databases (PubMed, Central, Dimensions and JSTOR) produced 27,841 records of literature. Additional searches in Google, Google Scholar and institutional repositories produced 37 records. Finally, 255 full-text records were further screened, and 100 records were used for this review.

RESULTS

Low or no formal education, poverty or low income and rural areas are risk factors for malaria amongst UN5. Evidence on age and malnutrition as risk factors for malaria in UN5 is inconsistent and inconclusive. Furthermore, the poor housing system in SSA and the unavailability of electricity in rural areas and unclean water make UN5 more susceptible to malaria. Health education and promotion interventions have significantly reduced the malaria burden on UN5 in SSA.

CONCLUSION

Well-planned and resourced health education and promotion interventions that focus on prevention, testing and treatment of malaria could reduce malaria burden amongst UN5 in SSA.

You cannot find what you are not looking for! detecting malaria outbreaks in Uganda: a case study

Malaria is the leading cause of morbidity and mortality in Uganda, with nearly half of the population becoming infected in any given year. Uganda relies on analyzing high-quality surveillance data to help detect outbreaks, determine which areas or population groups are most affected, and help target resources to where they are most needed. In March 2019, over 300 health facilities from different districts in Uganda reported substantially higher malaria cases than usual. In 13 districts, health facilities reported that the number of malaria cases was so high that they were experiencing stock outs of antimalarial drugs. Although seasonal increases in cases had been expected, districts reported that the number of cases being identified were overwhelming the capacity of the health facilities. Uganda´s National Malaria Control Division tasked a team of epidemiologists to investigate this unprecedented increase in malaria cases. National Malaria Control Division were interested in how malaria epidemiology had been changing in recent years, and whether they had missed something that would have predicted the situation they were facing in 2019. This case study describes the steps taken to conduct a descriptive analysis of routine malaria surveillance data and demonstrates how to detect malaria outbreaks using historical data. It is useful for training Field Epidemiologists and public health officers involved in analysis of surveillance data.

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.

Burden of malaria in children under five and caregivers' health-seeking behaviour for malaria-related symptoms in artisanal mining communities in Ghana

Background

Artisanal mining creates enabling breeding ground for the vector of malaria parasites. There is paucity of data on the effects of artisanal mining on malaria. This study assessed burden of malaria and caregivers’ health-seeking behaviour for children under five in artisanal mining communities in East Akim District in Ghana.

Methods

A cross-sectional study involving caregivers and their children under five was conducted in three artisanal mining communities in the East Akim District in Ghana. Caregivers were interviewed using a structured questionnaire. Finger prick blood samples were collected and analysed for haemoglobin concentration using a rapid diagnostic test, and thick and thin blood smears were analysed to confirm the presence of malaria parasites.

Results

Of the 372 children under 5 years included in the study, 197 (53.1%) were male, with a mean age (± SD) of 23.0 ± 12.7 months. The proportion of children with malaria (Plasmodium falciparum and P. malariae) was 98.1% and 1.9%, respectively, whilst the proportion with anaemia (Hb < 11.0 g/dl) was 39.5% (n = 147). Almost all caregivers were female (98.9%), and 28.6% (n = 106) did not have access to any malaria control information. Caregivers associated malaria infection with mosquito bites (68.3%, n = 254) and poor sanitation (21.2%, n = 79). Malaria in children under five was significantly associated with anaemia (OR 11.07, 95% CI 6.59–18.68, n = 111/160, 69.4%; P < 0.0001), residing close to stagnant water (≤ 25 m) from an artisanal mining site (AOR 2.91, 95% CI 1.47–5.76, P = 0.002) and caregiver age younger than 30 years (OR 0.44, 95% CI 0.208–0.917, n = 162, 43.55%, P = 0.001).

Conclusions

There is a high burden of malaria and anaemia among children under five in artisanal mining communities of the East Akim District, and far higher than in non-artisanal mining sites. Interventions are needed to effectively regulate mining activities in these communities, and strengthen malaria control and health education campaigns to curtail the high malaria burden and improve health-seeking behaviour.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04919-8.

Sustainable Surveillance of Neglected Tropical Diseases for the Post-Elimination Era

The World Health Organization’s (WHO’s) 2030 road map for neglected tropical diseases (NTDs) emphasizes the importance of strengthened, institutionalized “post-elimination” surveillance. The required shift from disease-siloed, campaign-based programming to routine, integrated surveillance and response activities presents epidemiological, logistical, and financial challenges, yet practical guidance on implementation is lacking. Nationally representative survey programs, such as demographic and health surveys (DHS), may offer a platform for the integration of NTD surveillance within national health systems and health information systems. Here, we describe characteristics of DHS and other surveys conducted within the WHO Africa region in terms of frequency, target populations, and sample types and discuss applicability for post-validation and post-elimination surveillance. Maximizing utility depends not only on the availability of improved diagnostics but also on better understanding of the spatial and temporal dynamics of transmission at low prevalence. To this end, we outline priorities for obtaining additional data to better characterize optimal post-elimination surveillance platforms.

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.

Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

Background

Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy.

Methods

Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods.

Results

Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26; 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = − 0.01–0.02) and slope (β = 0.12; 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation.

Conclusions

BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.

Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model

Background:

Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates.

Methods:

The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR).

Results:

The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island.

Conclusion:

Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.

Improving national level spatial mapping of malaria through alternative spatial and spatio-temporal models

The most common approach to create spatial prediction of malaria in the literature is to approximate a Gaussian process model using stochastic partial differential equation (SPDE). We compared SPDE to computationally faster alternatives, generalized additive model (GAM) and state-of-the-art machine learning method gradient boosted trees (GBM), with respect to their predictive skill for country-level malaria prevalence mapping. We also evaluated the intuition that incorporation of past data and the use of spatio-temporal models may improve predictive accuracy of present spatial distribution of malaria. Model performances varied among the countries and setting with SPDE and GAM performed well generally. The inclusion of past data is beneficial for GAM and GBM, but not for SPDE. We further investigated the weaknesses of SPDE at spatio-temporal setting and GAM at the edges of the countries. Taken together, we believe that spatial/spatio-temporal SPDE models should be evaluated alongside with the alternatives or at least GAM.

Prevalence of Asymptomatic Malaria Infections in Seemingly Healthy Children, the Rural Dzanga Sangha Region, Central African Republic

According to the World Health Organization 94% of global malaria cases and 94% of global malaria deaths have been reported from Africa. Unfortunately, it is difficult to determine the exact prevalence of disease in some African countries due to a large number of asymptomatic cases. The aim of this study was to assess the prevalence of malaria infections in seemingly healthy children living in the Central African Republic (CAR). CareStart^TM Malaria HRP2 rapid diagnostic test (RDT) targeting Plasmodium falciparum was used to test a group of 500 asymptomatic children aged 1-15 years old (330 settled Bantu and 170 semi-nomadic BaAka Pygmies) inhabiting the villages in the Dzanga Sangha region (south-west CAR) in March 2020. In total, 32.4% of asymptomatic Bantu and 40.6% of asymptomatic Pygmy children had a positive result of malaria RDT. Our findings allowed us to demonstrate the high prevalence of asymptomatic malaria infections in south-west CAR. RDTs seem to be a useful tool for the detection of Plasmodium falciparum in areas with limited possibilities of using other diagnostic methods, such as light microscopy and molecular biology.

Geostatistical analysis and mapping of malaria risk in children of Mozambique

Malaria remains one of the most prevalent infectious diseases in the tropics and subtropics, and Mozambique is not an exception. To design geographically targeted and effective intervention mechanisms of malaria, an up-to-date map that shows the spatial distribution of malaria is needed. This study analyzed 2018 Mozambique Malaria Indicator Survey using geostatistical methods to: i) explore individual, household, and community-level determinants of malaria in under-five children, ii) prepare a malaria prevalence map in Mozambique, and iii) produce prediction prevalence maps and exceedence probability across the country. The results show the overall weighted prevalence of malaria was 38.9% (N = 4347, with 95% CI: 36.9%–40.8%). Across different provinces of Mozambique, the prevalence of malaria ranges from 1% in Maputo city to 57.3% in Cabo Delgado province. Malaria prevalence was found to be higher in rural areas, increased with child’s age, and decreased with household wealth index and mother’s level of education. Given the high prevalence of childhood malaria observed in Mozambique there is an urgent need for effective public health interventions in malaria hot spot areas. The household determinants of malaria infection that are identified in this study as well as the maps of parasitaemia risk could be used by malaria control program implementers to define priority intervention areas.

Prevalence and Risk Factors Associated with Malaria among Children Aged Six Months to 14 Years Old in Rwanda: Evidence from 2017 Rwanda Malaria Indicator Survey

Malaria is a major public health risk in Rwanda where children and pregnant women are most vulnerable. This infectious disease remains the main cause of morbidity and mortality among children in Rwanda. The main objectives of this study were to assess the prevalence of malaria among children aged six months to 14 years old in Rwanda and to identify the factors associated with malaria in this age group. This study used data from the 2017 Rwanda Malaria Indicator Survey. Due to the complex design used in sampling, a survey logistic regression model was used to fit the data and the outcome variable was the presence or absence of malaria. This study considered 8209 children in the analysis and the prevalence of malaria was 14.0%. This rate was higher among children aged 5-9 years old (15.6%), compared to other age groups. Evidently, the prevalence of malaria was also higher among children from poor families (19.4%) compared to children from the richest families (4.3%). The prevalence of malaria was higher among children from rural households (16.2%) compared to children from urban households (3.4%). The results revealed that other significant factors associated with malaria were: the gender of the child, the number of household members, whether the household had mosquito bed nets for sleeping, whether the dwelling had undergone indoor residual spraying in the 12 months prior to the survey, the location of the household's source of drinking water, the main wall materials of the dwelling, and the age of the head of the household. The prevalence of malaria was also high among children living in houses with walls built from poorly suited materials; this suggests the need for intervention in construction materials. Further, it was found that the Eastern Province also needs special consideration in malaria control due to the higher prevalence of the disease among its residents, compared to those in other provinces.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

BACKGROUND

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies.

METHODS

Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded.

RESULTS

We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure.

CONCLUSIONS

The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

Evaluating the Effects of Climate and Environmental Factors on Under-5 Children Malaria Spatial Distribution Using Generalized Additive Models (GAMs)

Although malaria burden has declined globally following scale up of intervention, the disease has remained a leading cause of hospitalization and deaths among children aged under-5 years in Nigeria. Malaria is known to be related to climate and environmental conditions. Previous research has usually studied the effects of these factors, neglecting possible correlation between them, high correlation among variables is a source of multicollinearity that induces overfitting in regression modelling. In this paper, a factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria. The GAM incorporated the complexity of the survey data, while simultaneously modelling the nonlinear and spatial random effects to allow a more precise identification of the major malaria risk factors that influence the geographical distribution of the disease. From our findings, the three latent factor components (constituted by humidity, precipitation, potential evapotranspiration, and wet days/maximum and minimum temperature/proximity to permanent waters, respectively) were significantly associated with malaria prevalence. Our analysis also detected statistically significant and nonlinear effect of altitude: the risk of malaria increased with lower values but declined sharply with higher values. A significant spatial variability in under-5 malaria prevalence across the survey clusters was also observed; malaria burden was higher in the northern part of Nigeria. Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015–2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context.

Decision-to-delivery interval of emergency cesarean section in Uganda: a retrospective cohort study

Background

In many low and medium human development index countries, the rate of maternal and neonatal morbidity and mortality is high. One factor which may influence this is the decision-to-delivery interval of emergency cesarean section. We aimed to investigate the maternal risk factors, indications and decision-to-delivery interval of emergency cesarean section in a large, under-resourced obstetric setting in Uganda.

Methods

Records of 344 singleton pregnancies delivered at ≥24 weeks throughout June 2017 at Mulago National Referral Hospital were analysed using Cox proportional hazards models and multivariate logistic regression models.

Results

An emergency cesarean section was performed every 104 min and the median decision-to-delivery interval was 5.5 h. Longer interval was associated with preeclampsia and premature rupture of membranes/oligohydramnios. Fetal distress was associated with a shorter interval (p < 0.001). There was no association between decision-to-delivery interval and adverse perinatal outcomes (p > 0.05). Mothers waited on average 6 h longer for deliveries between 00:00–08:00 compared to those between 12:00–20:00 (p < 0.01). The risk of perinatal death was higher in neonates where the decision to deliver was made between 20:00–02:00 compared to 08:00–12:00 (p < 0.01).

Conclusion

In this setting, the average decision-to-delivery interval is longer than targets adopted in high development index countries. Decision-to-delivery interval varies diurnally, with decisions and deliveries made at night carrying a higher risk of adverse perinatal outcomes. This suggests a need for targeting the improvement of service provision overnight.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

Characterising malaria connectivity using malaria indicator survey data

Malaria connectivity describes the flow of parasites among transmission sources and sinks within a given landscape. Because of the spatial and temporal scales at which parasites are transported by their hosts, malaria sub-populations are largely defined by mosquito movement and malaria connectivity among them is largely driven by human movement. Characterising malaria connectivity thus requires characterising human travel between areas with differing levels of exposure to malaria. Whilst understanding malaria connectivity is fundamental for optimising interventions, particularly in areas seeking or sustaining elimination, there is a dearth of human movement data required to achieve this goal. Malaria indicator surveys (MIS) are a generally under utilised but potentially rich source of travel data that provide a unique opportunity to study simple associations between malaria infection and human travel in large population samples. This paper shares the experience working with MIS data from Bioko Island that revealed programmatically useful information regarding malaria importation through human travel. Simple additions to MIS questionnaires greatly augmented the level of detail of the travel data, which can be used to characterise human travel patterns and malaria connectivity to assist targeting interventions. It is argued that MIS potentially represent very important and timely sources of travel data that need to be further exploited.

The potential of pregnant women as a sentinel population for malaria surveillance

Background

With increasing spatial heterogeneity of malaria transmission and a shift of the disease burden towards older children and adults, pregnant women attending antenatal care (ANC) have been proposed as a pragmatic sentinel population for malaria surveillance. However, the representativeness of routine ANC malaria test-positivity and its relationship with prevalence in other population subgroups are yet to be investigated.

Methods

Monthly ANC malaria test-positivity data from all Tanzanian health facilities for January 2014 to May 2016 was compared to prevalence data from the School Malaria Parasitaemia Survey 2015, the Malaria Indicator Survey (MIS) 2015/16, the Malaria Atlas Project 2015, and a Bayesian model fitted to MIS data. Linear regression was used to describe the difference between malaria test-positivity in pregnant women and respective comparison groups as a function of ANC test-positivity and potential covariates.

Results

The relationship between ANC test-positivity and survey prevalence in children follows spatially and biologically meaningful patterns. However, the uncertainty of the relationship was substantial, particularly in areas with high or perennial transmission. In comparison, modelled data estimated higher prevalence in children at low transmission intensities and lower prevalence at higher transmission intensities.

Conclusions

Pregnant women attending ANC are a pragmatic sentinel population to assess heterogeneity and trends in malaria prevalence in Tanzania. Yet, since ANC malaria test-positivity cannot be used to directly predict the prevalence in other population subgroups, complementary community-level measurements remain highly relevant.

How far is the journey before malaria is knocked out in Zimbabwe: results of the malaria indicator survey 2016 [corrected]

BACKGROUND

Zimbabwe conducts Malaria Indicator Surveys after 3 years and Demographic and Health Surveys to track the impact of malaria interventions. The last one to be conducted was in 2016 and had set an aim aimed to collect data to track malaria indicators as well as to save as the baseline source for the Malaria Strategic Plan (2016-2020).

METHODS

Malaria Indicator Survey-2016 utilized the frame of enumeration areas (EAs) from the Zimbabwe Master Sample (ZMS12) created after the 2012 population census for each of the survey districts. The design for the survey was a representative probability sample to produce estimates at national level for the respective domains, which are the forty-four malaria-endemic districts. Survey teams comprised of Ministry of Health personnel who administered the standard questionnaire (adapted to country setting) to respondents within sampled EAs, performed RDT, anaemia test, prepared microscopic slide and collected DBS and data analysis of collected information was analysed. Microscopic slides examined centrally at the National Institute of Health Research.

RESULTS

The overall protection coverage by at least one major vector control measure, IRS and/or Nets, was 82.5%. Use of nets among high-risk groups 32.5% For children under five and 24.5% for pregnant women. LLIN utilization quite low taking into consideration the net ownership per household, which was 58% for the general population. Moreover, IPTp coverage has remained almost unchanged since the 2012 MIS, with only a third of pregnant women receiving at least two doses of IPTp. Malaria prevalence appears to be on the decline with 2016 MIS recording 0.2% compared to 0.4% as of 2012 MIS. Plasmodium falciparum remains the predominant parasite species in the country at 98%.

CONCLUSION

The results indicated that some progress has been made in malaria control although there is still subsequent low malaria risk perception that comes with the reduced prevalence. It has been shown that there is low use of interventions shown by the low use of LLINs by vulnerable groups like pregnant women and children under five.

Geostatistical analysis and mapping of malaria risk in children under 5 using point-referenced prevalence data in Ghana

BACKGROUND

Malaria remains a major challenge in sub-Saharan Africa and Ghana is not an exception. Effective malaria transmission control requires evidence-based targeting and utilization of resources. Disease risk mapping provides an effective and efficient tool for monitoring transmission and control efforts. The aim of this study is to analyse and map malaria risk in children under 5 years old, with the ultimate goal of identifying areas where control efforts can be targeted.

METHODS

Data collected from the 2016 Ghana demographic and health survey was analyzed. Binomial logistic regression was applied to examine the determinants of malaria risk among children. Model-based geostatistical methods were applied to analyze, predict and map malaria prevalence.

RESULTS

There is a significant association of malaria prevalence with area of residence (rural/urban), age, indoor residual spray use, social economic status and mother's education level. Overall, parasitaemia prevalence among children under 5 years old for the year 2016 is low albeit characterized by "hotspots" in specific areas.

CONCLUSION

The risk maps indicate the spatial heterogeneity of malaria prevalence. The high resolution maps can serve as an effective tool in the identification of locations that require targeted interventions by programme implementers; this is key and relevant for reducing malaria burden in Ghana.

The effects and contribution of childhood diseases on the geographical distribution of all-cause under-five mortality in Uganda

Introduction

Information on the causes of death among under-five children is key in designing and implementation of appropriate interventions. In Uganda, civil death registration is incomplete which limits the estimation of disease-related mortality burden especially at a local scale. In the absence of routine cause-specific data, we used household surveys to quantify the effects and contribution of main childhood diseases such as malaria, severe or moderate anaemia, severe or moderate malnutrition, diarrhoea and acute respiratory infections (ARIs) on all-cause under-five mortality (U5M) at national and sub-national levels. We related all-cause U5M with risks of childhood diseases after adjusting for geographical disparities in coverages of health interventions, socio-economic, environmental factors and disease co-endemicities.

Methods

Data on U5M, disease prevalence, socio-economic and intervention coverage indicators were obtained from the 2011 Demographic and Health Survey, while data on malaria prevalence were extracted from the 2009 Malaria Indicator Survey. Bayesian geostatistical Weibull proportional hazards models with spatially varying disease effects at sub-national scales were fitted to quantify the associations between childhood diseases and the U5M. Spatial correlation between clusters was incorporated via locational random effects while region-specific random effects with conditional autoregressive prior distributions modeled the geographical variation in the effects of childhood diseases. The models addressed geographical misalignment in the locations of the two surveys. The contribution of childhood diseases to under-five mortality was estimated using population attributable fractions.

Results

The overall U5M rate was 90 deaths per 1000 live births. Large regional variations in U5M rates were observed, lowest in Kampala at 56 and highest in the North-East at 152 per 1000 live births. National malaria parasitemia prevalence was 42%, with Kampala experiencing the lowest of 5% and the Mid-North the highest of 62%. About 27% of Ugandan children aged 6–59 months were severely or moderately anaemic; lowest in South-West (8%) and highest in East-Central (46%). Overall, 17% of children were either severely or moderately malnourished. The percentage of moderately/severely malnourished children varied by region with Kampala having the lowest (8%) and North-East the highest (45%). Nearly a quarter of the children under-five years were reported to have diarrhoea at national level, and this proportion was highest in East-Central (32%) and Mid-Eastern (33%) and lowest in South-West (14%). Overall, ARIs in the two weeks before the survey was 15%; highest in Mid-North (22%) and lowest in Central 1 (9%). At national level, the U5M was associated with prevalence of malaria (hazard ratio (HR) = 1.74; 95% BCI: 1.42, 2.16), severe or moderate anaemia (HR =1.37; 95% BCI: 1.20, 1.75), severe or moderate malnutrition (HR = 1.49; 95% BCI: 1.25, 1.66) and diarrhoea (HR = 1.61; 95% BCI: 1.31, 2.05). The relationship between malaria and U5M was important in the regions of Central 2, East-Central, Mid-North, North-East and West-Nile. Diarrhoea was associated with under-five deaths in Central 2, East-central, Mid-Eastern and Mid-Western. Moderate/severe malnutrition was associated with U5M in East-Central, Mid-Eastern and North-East. Moderate/severe anaemia was associated with deaths in Central 1, Kampala, Mid-North, Mid-Western, North-East, South-West and West-Nile.

At the national level, 97% (PAF = 96.9; 95%BCI: 94.4, 98.0), 91% (PAF = 90.9; 95%BCI: 84.4, 95.3), 89% (PAF = 89.3; 95%BCI: 76.0,93.8) and 93% (PAF = 93.3 95%BCI: 87.7,96.0) of the deaths among children less than five years in Uganda were attributable to malaria, severe/moderate anaemia, severe/moderate malnutrition and diarrhoea respectively. The attribution of malaria was comparable in Central 2, East-Central, Mid-North, North-East and West-Nile while severe/moderate anaemia was more common in all regions except Central 2, East-Central and Mid-Eastern. The attribution of diarrhoea in Central 2, East-Central, Mid-Eastern and Mid-Western was similar. The attribution of severe/moderate malnutrition was common in East-Central, Mid-Eastern and North-East.

Conclusion

In Uganda, the contribution and effects of childhood diseases on U5M vary by region. Majority of the under-five deaths are due to malaria, followed by diarrhoea, severe/moderate anaemia and severe/moderate malnutrition. Thus, strengthening disease-specific interventions especially in the affected regions may be an important strategy to accelerate progress towards the reduction of the U5M as per the SDG target by 2030. In particular, Indoor Residual Spraying, iron supplementation, deworming, exclusive breastfeeding, investment in nutrition and education in nutrition practices, oral rehydration therapy or recommended home fluid, improved sanitation facilities should be improved.

Measuring health facility readiness and its effects on severe malaria outcomes in Uganda

There is paucity of evidence for the role of health service delivery to the malaria decline in Uganda We developed a methodology to quantify health facility readiness and assessed its role on severe malaria outcomes among lower-level facilities (HCIIIs and HCIIs) in the country. Malaria data was extracted from the Health Management Information System (HMIS). General service and malaria-specific readiness indicators were obtained from the 2013 Uganda service delivery indicator survey. Multiple correspondence analysis (MCA) was used to construct a composite facility readiness score based on multiple factorial axes. Geostatistical models assessed the effect of facility readiness on malaria deaths and severe cases. Malaria readiness was achieved in one-quarter of the facilities. The composite readiness score explained 48% and 46% of the variation in the original indicators compared to 23% and 27%, explained by the first axis alone for HCIIIs and HCIIs, respectively. Mortality rate was 64% (IRR = 0.36, 95% BCI: 0.14–0.61) and 68% (IRR = 0.32, 95% BCI: 0.12–0.54) lower in the medium and high compared to low readiness groups, respectively. A composite readiness index is more informative and consistent than the one based on the first MCA factorial axis. In Uganda, higher facility readiness is associated with a reduced risk of severe malaria outcomes.

Spatial distribution and determinants of asymptomatic malaria risk among children under 5 years in 24 districts in Burkina Faso

BACKGROUND

In malaria endemic countries, asymptomatic cases constitute an important reservoir of infections sustaining transmission. Estimating the burden of the asymptomatic population and identifying areas with elevated risk is important for malaria control in Burkina Faso. This study analysed the spatial distribution of asymptomatic malaria infection among children under 5 in 24 health districts in Burkina Faso and identified the determinants of this distribution.

METHODS

The data used in this study were collected in a baseline survey on "evaluation of the impact of pay for performance on the quality of care" conducted in 24 health districts in Burkina Faso, between October 2013 and March 2014. This survey involved 7844 households and 1387 community health workers. A Bayesian hierarchical logistic model that included spatial dependence and covariates was implemented to identify the determinants of asymptomatic malaria infection. The posterior probability distribution of a parameter from the model was summarized using odds ratio (OR) and 95% credible interval (95% CI).

RESULTS

The overall prevalence of asymptomatic malaria infection in children under 5 years of age was estimated at 38.2%. However, significant variation was observed between districts ranging from 11.1% in the district of Barsalgho to 77.8% in the district of Gaoua. Older children (48-59 vs < 6 months: OR: 6.79 [5.62, 8.22]), children from very poor households (Richest vs poorest: OR: 0.85 [0.74-0.96]), households located more than 5 km from a health facility (< 5 km vs  ≥ 5 km: OR: 1.14 [1.04-1.25]), in localities with inadequate number of nurses (< 3 vs  ≥ 3: 0.72 [0.62, 0.82], from rural areas (OR: 1.67 [1.39-2.01]) and those surveyed in high transmission period of asymptomatic malaria (OR: 1.27 [1.10-1.46]) were most at risk for asymptomatic malaria infection. In addition, the spatial analysis identified the following nine districts that reported significantly higher risks: Batié, Boromo, Dano, Diébougou, Gaoua, Ouahigouya, Ouargaye, Sapouy and Toma. The district of Zabré reported the lowest risk.

CONCLUSION

The analysis of spatial distribution of infectious reservoir allowed the identification of risk areas as well as the identification of individual and contextual factors. Such national spatial analysis should help to prioritize areas for increased malaria control activities.

A cross-sectional study of asymptomatic Plasmodium falciparum infection burden and risk factors in general population children in 12 villages in northern Uganda

BACKGROUND

Plasmodium falciparum malaria is an important cause of morbidity in northern Uganda. This study was undertaken to assess village-, household-, and individual-level risk factors of asymptomatic falciparum malaria in children in 12 villages in northern Uganda.

METHODS

Between 10/2011 and 02/2014, 1006 apparently healthy children under 16 years old were enrolled in 12 villages using a stratified, multi-stage, cluster survey design and assessed for P. falciparum malaria infection using the rapid diagnostic test (RDT) and thick film microscopy (TFM), and structured interviewer-administered questionnaires. Associations between weighted P. falciparum malaria prevalence (pfPR), based on RDT, and covariates were estimated as odds ratios and 95% confidence intervals (ORs, 95% CIs) using logistic models accounting for the survey design.

RESULTS

Among 942 (93.5%) children successfully tested, pfPR was 52.4% by RDT and 32.7% by TFM. Overall pfPR was lower in villages where indoor residual insecticide spray (IRS) was, versus not, implemented (18.4% versus 75.2%, P < 0.0001). However, pfPR was heterogeneous both within IRS (10.6-34.8%) and non-IRS villages (63.6-86.2%). Elevated pfPR was associated with having a sibling who was RDT positive (OR 5.39, 95% CI 2.94-9.90, P = 0.0006) and reporting a fever at enrollment (aOR 4.80, 95% CI 1.94-11.9, P = 0.0094). Decreased pfPR was associated with living in an IRS village (adjusted OR 0.06, 95% CI 0.04-0.07, P < 0.0001), in a household with one (aOR 0.48, 95% CI 0.30-0.76) or more than one child below 5 years (aOR 0.23, 95% CI 0.12-0.44, Ptrend = 0.014), and reporting keeping a goat inside or near the house (aOR 0.42, 95% CI 0.29-0.62, P = 0.0021).

CONCLUSIONS

The results show high but heterogeneous pfPR in villages in northern Uganda, confirm significantly decreased pfPR associated with IRS implementation, and suggest significant associations with some household characteristics. Further research is needed to elucidate the factors influencing malaria heterogeneity in villages in Uganda.

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

BACKGROUND

Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016.

METHODS

Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission.

RESULTS

The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children < 5 years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals ≥ 5 years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children < 5 years and individuals ≥ 5 years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children < 5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15.

CONCLUSION

The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.

Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon

BACKGROUND

In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator cluster survey. Malaria parasitological data were collected, but the survey period did not overlap with the high malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite risk and of the effects of interventions obtained from the DHS and MIS survey data.

METHODS

Bayesian geostatistical models were applied on DHS and MIS data to obtain georeferenced estimates of the malaria parasite prevalence and to assess the effects of interventions. Climatic predictors were retrieved from satellite sources. Geostatistical variable selection was used to identify the most important climatic predictors and indicators of malaria interventions.

RESULTS

The overall observed malaria parasite risk among children was 33 and 30% in the DHS and MIS data, respectively. Both datasets identified the Normalized Difference Vegetation Index and the altitude as important predictors of the geographical distribution of the disease. However, MIS selected additional climatic factors as important disease predictors. The magnitude of the estimated malaria parasite risk at national level was similar in both surveys. Nevertheless, DHS estimates lower risk in the North and Coastal areas. MIS did not find any important intervention effects, although DHS revealed that the proportion of population with an insecticide-treated nets access in their household was statistically important. An important negative relationship between malaria parasitaemia and socioeconomic factors, such as the level of mother's education, place of residence and the household welfare were captured by both surveys.

CONCLUSION

Timing of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high risk areas. A continuous MIS or a combination of MIS, health information system data and data from sentinel sites may be able to capture the disease risk distribution in space across different seasons.