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. 2024 Aug 18;22:21. doi: 10.1186/s12963-024-00341-1

Prevalence of asymptomatic malaria at the communal level in Burkina Faso: an application of the small area estimation approach

Hervé Bassinga 1,, Mady Ouedraogo 2, Kadari Cisse 3, Parfait Yira 2, Sibiri Clément Ouedraogo 2, Abdou Nombré 2, Wofom Lydie Marie-Bernard Bance 4, Mathias Kuepie 5, Toussaint Rouamba 6,7
PMCID: PMC11330607  PMID: 39155384

Abstract

Background

In malaria-endemic countries, asymptomatic carriers of plasmodium represent an important reservoir for malaria transmission. Estimating the burden at a fine scale and identifying areas at high risk of asymptomatic carriage are important to guide malaria control strategies. This study aimed to estimate the prevalence of asymptomatic carriage at the communal level in Burkina Faso, the smallest geographical entity from which a local development policy can be driven.

Methods

The data used in this study came from several open sources: the 2018 Multiple Indicator Cluster Survey on Malaria and the 2019 general census of the population data and environmental. The analysis involved a total of 5489 children under 5 from the malaria survey and 293,715 children under 5 from the census. The Elbers Langjouw and Langjouw (ELL) approach is used to estimate the prevalence. This approach consists of including data from several sources (mainly census and survey data) in a statistical model to obtain predictive indicators at a sub-geographical level, which are not measured in the population census. The method achieves this by finding correlations between common census variables and survey data.

Findings

The findings suggest that the spatial distribution of the prevalence of asymptomatic carriage is very heterogeneous across the communes. It varies from a minimum of 5.1% (95% CI 3.6–6.5) in the commune of Bobo-Dioulasso to a maximum of 41.4% (95% CI 33.5–49.4) in the commune of Djigoué. Of the 341 communes, 208 (61%) had prevalences above the national average of 20.3% (95% CI 18.8–21.2).

Contributions

This analysis provided commune-level estimates of the prevalence of asymptomatic carriage of plasmodium in Burkina Faso. The results of this analysis should help to improve planning of malaria control at the communal level in Burkina Faso.

Keywords: Small area estimates, Communes, Malaria, Spatial analysis, Burkina Faso

Introduction

Numerous efforts have been made worldwide to fight malaria. These efforts have led to a significant reduction in malaria-related morbidity and mortality, especially among children under 5. However, morbidity and mortality remain below expectations. Indeed, severe malaria remains one of the main causes of mortality, contributing to 6% of malaria deaths in sub-Saharan Africa (SSA) [1].

In 2022, the West African sub-region had approximately 121 million estimated cases and approximately 324,000 estimated deaths: an increase of 2% and a decrease of 15% respectively compared to 2010 [2]. Five countries accounted for more than 80% of the estimated cases, including Burkina Faso with 7% of cases [2]. Globally, Burkina Faso is among the ten countries most affected by malaria (3.4% of cases and 3.2% of deaths worldwide in 2020) [3]. In Burkina Faso, several initiatives such as the distribution of long-acting insecticide-treated mosquito nets (LLINs), seasonal malaria chemoprevention (SMC), indoor residual spraying (IRS) and the use of artemisinin-based combination therapies (ACTs) have been implemented to reduce the incidence and mortality of malaria. However, as in other SSA countries, malaria remains a major public health problem in the country. In 2017, Ministry of Health statistics show that malaria was the main reason for consultations (53%), hospitalization (48%) and 66% of deaths of children under 5 in hospitals and health facilities [4].

In view of the persistence of high morbidity and mortality due to malaria, several studies have focused on different aspects of the malaria disease process [1, 49] including factors associated with transmission and spatio-temporal inequalities in morbidity. Most of this research is based on survey or routine data, sometimes geographically targeted [5, 10] giving rise to only a partial analysis of the national situation or to an analysis on a relatively large geographical scale [1, 46] or unstructured [7]. For example, Ouédraogo and al. [4] using data from the baseline survey on "Assessing the impact of results-based financing in Burkina Faso", identified districts at higher risk of asymptomatic malaria infection in children in 24 districts of Burkina Faso. Along the same line, Rouamba and al. [5] used a hierarchical Bayesian spatio-temporal modeling to explore spatio-temporal patterns to identify health districts with probably of severe malaria incidence during pregnancy and high rates of mortality from routine data between 2013 and 2018.

Current guidelines on malaria elimination are based on the principle of "High burden to high impact: A targeted malaria response". In other words, interventions should target localities or entire towns where the incidence of malaria is higher, until only individual episodes of malaria remain. [10]. To contribute to optimize the elimination/control program by targeting the high risk area, the aim of this study was therefore to estimate malaria prevalence at commune level, using survey data designed to be representative at regional level.

Materials and methods

Study setting

A landlocked country of 274,200 km2, Burkina Faso is located in the heart of West Africa. The country shares borders with Côte d’Ivoire, Ghana, Togo and Benin to the south, Mali to the north and Niger to the northwest. Its total population is estimated at 20 million (RGPH 2019). Burkina Faso has a dry, tropical climate of the Sudano-Sahelian type, characterized by highly variable rainfall ranging from 350 mm in the northern part of the country to over 1000 mm in its southwestern part [11]. There are two very distinct seasons. The first, the rainy season, lasts around 5 months (generally between mid-May and September), with a relatively shorter duration in the north of the country. The second season, the dry season, is the longest and is characterized by the Harmattan, a hot, dry, dust-laden wind from the Sahara desert. Based on rainfall and temperature, there are three main climatic bands in Burkina Faso [12]. Firstly, there's the Sahelian strip, which covers the north of the country, with its highly capricious rainfall of less than 600 mm per year and its extreme thermal oscillations (15 to 45 degrees). Then we have the Sudano-Sahelian band, a median zone for temperatures and rainfall that covers the central strip of the country. Finally, we have the Sudanian band covering the southern part of the country, the wettest with over 900 mm of rain per year and relatively low average temperatures. Rainfall thus decreases from the south-west to the north of the country.

This research complements these numerous studies to propose estimates of malaria infection at the scale of the three hundred and forty-two (342) communes covered by the census in 2019. The last administrative entity in the country, after the region and the province, the commune is a grouping of localities that are geographically close, often with cultural and economic ties. It is the only administrative entity managed by an elected official, the mayor. The management of communes is partly the responsibility of the local population, who contribute to their management through the payment of communal taxes. The commune is therefore the smallest geographical administrative entity from which a local development policy can be driven and coordinated by the community, under the watchful eye of the central administration. The choice of the commune is also justified by the fact that spatial disparities become more pronounced as the scale of analysis moves down to a finer level [13, 14]. This choice is also in line with one of the recommendations of the Sustainable Development Goals (SDGs), which call for the results of sustainable development actions to be assessed at finer geographical scales for greater effectiveness. [15, 16].

Sources of data

Three main data sources were used in this study: the general population and housing census (RGPH) carried out in 2019, the Malaria indicator survey carried out in 2018 and environmental data downloaded from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) website and the MOD11C3.006 module [17].

The RGPH is a complex operation carried out in 2019 that involved enumerating the Burkinabe population and its characteristics using a digitized questionnaire. It began on November 15, 2019 and officially ended on December 31, 2019. This nationwide operation was carried out against a backdrop of security crisis that led to partial coverage of the national territory. Of the country's 351 communes, 52 were only partially covered, and nine (9) were not covered at all. [18]. Estimates will therefore not include the nine (9) communes not covered by the census.

DHS program malaria indicator survey is a household survey based on a stratified 2-stage random sample selection. The primary sampling unit is the Enumeration Area (EA). Each area was subdivided into urban and rural parts to build the sampling strata, and the sample was drawn independently in each stratum. Overall, twenty-six strata were created. In the first stage, 252 EAs were drawn (52 in urban areas and 200 in rural areas)1 with probability proportional to size. In the second stratum, 26 households were systematically selected with equal probability from each of the EA drawn in the first stratum. In all, 6552 households were selected, including 1352 in urban areas and 5500 in rural areas. This survey, unlike the census, was conducted on paper and took place between November 2017 and March 2018. The Survey involved a representative sample of 6500 households and 7600 women aged 15–49. Blood samples were taken from 50% of selected households, for malaria screening. All children aged 6–59 months living in these households were eligible for malaria screening. Parental or guardian consent was required for their children's participation.

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MOD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5600 m at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7200 columns and 3600 rows representing the entire globe. Climate Hazards Group In-fraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Span-ning 50°S–50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

For the purposes of our analysis, precipitation and temperature were used. Monthly data for each geographical entity in 2019 were downloaded and then aggregated into annual values.

Study population

Target population study is children under five. The study population is represented by 5482 children from DHS program malaria survey in 2018 and 293,715 children from a random sample 10% Census (2019) database.

Variables of interest

Outcome variable

Response or the main outcome variable in this study was asymptomatic malaria infection (asymptomatic carriage) in children under 5 detected by rapid diagnostic test (RDT) during the survey. Malaria was diagnosed using serological biomarkers, SD Bioline Pan/Pf which is based on the detection of the HRP-2 antigen and specific pLDH for the five species of Plasmodium. The antibodies directed against Plasmodium antigens are sensitive biomarkers of malaria exposure to detect malaria in the community and to monitor variations over time or the impact of interventions, and to confirm malaria elimination. RDT requires 5 μL of blood drawn using a loop from the same finger prick taken for the hemoglobin test. The lancets included in the SD Bioline Pan/Pf kit have not been used and have been destroyed with other biohazardous waste.

Interpretation test is done after 15 min and the result and its interpretation have been communicated to the parents/adults responsible for the children who have taken the test.

Independent variables

The choice of variables is based on a review of the literature which highlighted factors associated with the prevalence of malaria in SSA. Commonly cited factors include:

Socio-demographic and residential factors: child's age, gender (male/female), household standard of living (very poor, poor, average, rich, very rich), mother's education, measured here as the proportion of educated women aged 15–49 in the region (for the survey) and in the commune (for the census), head of household's gender (male/female), head of household's age (15–34, 35–49, 50–64, 65 and over), religion of head of household (Muslim, Christian, Traditional), place of residence (Urban/Rural), region of residence (Boucle du Mouhoun, Cascades, Centre, Centre Est, Centre Nord, Centre Ouest, Centre Sud, Est, Hauts-Bassins, Nord, Plateau Central, Sahel, Sud-Ouest) and climatic factors such as temperature and rainfall [4, 7, 13, 14, 19].

Factors related to malaria control interventions: possession of LLINs (LLINs) (Yes/No), use of LLINs (Yes/No).

Environmental factors: cumulative monthly rainfall by commune and average monthly temperatures by commune for 2019.

Data processing and analysis

For the covariates retained in the two databases, the names and coding were harmonized before the two databases were assembled. Since differences in the distribution of the variables retained in the two databases could be a source of estimation bias, a consistency analysis was carried out (see Appendix A) to exclude variables with large distribution deviations. In addition, we ensured that the co-variables in the two data sources were comparable by examining the data collection methods and the definitions of the various concepts.

Ultimately, the variables retained at individual level are region and area of residence, age and sex of the head of household, age and sex of the child and household standard of living. At communal level, the proportion of educated women aged 15–49, annual rainfall and annual temperature were used in this secondary analysis. The distribution of these variables is shown in Appendix A.

Modeling approach

The estimation approach is that proposes by Elbers Langjouw and Langjouw (ELL),2 which consists of combining data from several sources in an econometric model. In this study, we assembled data from the census and the malaria survey [13]. The variable of interest (here RDT positivity) was present only for survey participants. We conducted a binary logistic regression and estimated the regression coefficients from the survey data, then predicted the value of the variable of interest using the census data. Confidence intervals are calculated using the Delta method.3 The procedure is described as follows.

A logistic regression model was used to predict the probability of child i testing positive for asymptomatic malaria infection using data from the malaria survey. The logistic regression model is expressed as follows:

yiBernoulli(pi)logpi1-pi=β0+p=1Pβpxpi(1) 1

where pi is the probability that a child i has asymptomatic malaria infection. xpi are the predictors variables included in the model. The coefficients βp are the coefficients of each of the predictor variables included in the model. β0 is the intercept.

The probability of a child under 5 years of age testing positive for asymptomatic malaria is defined as follows:

pi=eβ0+p=1Pβpxpi1+eβ0+p=1Pβpxpi 2

A stepwise regression was applied and Akaike's information criterion (AIC) [20] was used to select the best model to explain asymptomatic malaria infection in children under 5. Thus, the model with the lowest AIC was selected. We also used the ROC (Receiver Operating Characteristic) curve to assess model quality. This assessment is based on the predictive power of the model. The ROC curve is recognized as one of the best tools for evaluating the predictive power of a logistic model [21].

In addition to these econometric evaluations, we compared direct estimates of the prevalence of asymptomatic malaria infection from the survey with predicted estimates at regional level. Furthermore, to refine the t-model, we ensured that a replication of the estimates from the census co-variates offered relevant results. This check on the model's consistency and relevance is carried out at regional level, where the actual values from the officially published survey report are available [22].

For the model selected, coefficients are applied to the same covariates in the census data to predict the probabilities of a child under 5 testing positive for asymptomatic malaria infection. These individual probabilities are then aggregated to obtain estimates of the prevalence of asymptomatic malaria at communal, regional and national levels (Appendix D). After estimation at different geographical levels, an important challenge is to assess the uncertainty associated with the estimates. As these estimates are averages of predictions, confidence intervals can be estimated using the Delta [23, 24]. In this study, we used the STATA post estimation "margins" which produce both the average of the predictive margins [25] and calculate the associated standard errors by the Delta method.

Results

Analysis of the consistency of the results

Evaluation of the final model gives the Area Under the ROC curve (AUC) of 69.0% (Fig. 1). This value shows that the model provides non-random estimates.

Fig. 1.

Fig. 1

The ROC curve: overall assessment of model performance by plotting sensitivity against specificity 1

Estimation of the prevalence of asymptomatic malaria in children under 5 years of age at regional level, using data from the malaria survey, provides estimates that are more or less equal to those observed, i.e. those derived from the survey analysis report [22]. In fact, for all 13 administrative regions of Burkina Faso, the confidence intervals derived from estimates based on survey data contain the prevalence of asymptomatic malaria observed in each region (cf. Table 1 and Fig. 2).

Table 1.

Regional prediction of malaria prevalence and values observed in the survey report

Region Observed 2018 Malaria survey 2018 Census data 2019
Boucle du Mouhoun 25.0 22.3 (18.7, 26.0) 22.5 (18.8, 26.1)
Cascades 13.9 14.7 (10.3, 19.2) 18.6 (13.1, 24.0)
Centre 13.2 14.8 (10.8, 18.7) 15.2 (11.0, 19.4)
Centre Est 18.8 20.9 (16.8, 25.0) 22.0 (17.7, 26.4)
Centre Nord 19.8 22.7 (18.2, 27.1) 25.0 (20.1, 29.8)
Centre Ouest 34.7 34.7 (30.8, 38.6) 33.4 (29.5, 37.3)
Centre Sud 23.3 23.3 (17.1, 29.6) 23.1 (16.8, 29.4)
Est 19.7 19.7 (16.4, 22.9) 19.3 (16.1, 22.5)
Hauts-Bassins 13.5 11.4 (9.1, 13.6) 11.2 (8.9, 13.5)
Nord 16.6 16.7 (13.3, 20.1) 15.8 (12.5, 19.2)
Plateau Central 13.3 11.5 (7.3, 15.6) 11.4 (7.2, 15.5)
Sahel 19.2 19.1 (14.8, 23.3) 18.4 (14.3, 22.6)
Sud-Ouest 32.4 32.9 (26.7, 39.1) 32.6 (26.3, 38.8)
Total (National average) 20.02 20.02 (19.1, 21.3) 20.03 (18.8, 21.2)

Fig. 2.

Fig. 2

Regional predictions of malaria prevalence from the two sources and values observed in the survey report

Verification of the consistency of estimates derived from census data also attests to better regional estimates. Indeed, cross-analysis of the confidence intervals of regional estimates from the two sources (Table 1) shows that estimates of asymptomatic malaria prevalence from census data are significantly the same as those observed and those derived from the survey.

Results description: exploring geographical heterogeneity

Figure 3 shows the spatial distribution of the prevalence of asymptomatic malaria in children under 5. It shows that the prevalence of asymptomatic malaria infection in children is similar across regions, whatever the data source considered. The regions with the highest prevalences were Centre-Ouest (33.4; 95% CI [29.5; 37.3]) and Sud-Ouest (32.6 with 95% CI [26.3; 38.8]), while Centre (15.2 with 95% CI [11.0; 19.4]), Hauts-Bassins (11.2 with 95% CI [8.9; 13.5]) and Plateau-central (11.4 with 95% CI [7.2; 15.5]) have the lowest levels of prevalence of asymptomatic malaria infection.

Fig. 3.

Fig. 3

Mapping of prevalence predictions for asymptomatic malaria in children under 5 at the communal level.

Source of the data: Map created by Bassinga et al. (2024)

The consistency of estimates at regional level supports the idea that the model is suitable for predicting reliable estimates at communal level, as communal and regional results are the result of aggregating individual malaria infection probabilities.

Thus, analysis of estimates at commune level reveals heterogeneity in the prevalence of asymptomatic malaria in children between these entities (Fig. 3).

Of the 341 communes in which estimates were made, 208 had prevalences higher than the national average of 20.3% (95% CI [18.8; 21.2]). The ten communes (Fig. 4) with the highest prevalences were Djigoué (41.4% with 95% CI [33.5; 49.4]), Périgban (40.9% with 95% CI [33; 48.8]), Bougnounou (40.1% with 95% CI [35.5; 44.8]), Loropéni (39.9% with 95% CI [32.3; 47.5]), Siglé (39.7% with 95% CI [35.2; 44.2]), Kpuéré (39.7% with 95% CI [32.2; 47.2]), Zamo (39% with 95% CI [34.5; 43.4]), Nébiélianayou (38.7% with 95% CI [34.2; 43.1]), Silly (38.7% with 95% CI [34.3; 43.0]), and Zawara (38.6% with 95% CI [34.3; 42.9]). At the other end of the scale in terms of prevalence of asymptomatCI malaria, the communes of Bobo-Dioulasso (5.1% with 95% CI [3.6; 6.5]), Houndé (8.4% with 95% CI [6.4; 10.4]), Ziniaré (8.7% with 95% CI [5.4; 11.9]), Zorgho (8.9% with 95% CI [5.5; 12.3]), Oula, Ouagadougou, Boussé, Orodara, Djibo and Pouytenga are the ten communes where malaria prevalence is relatively low.

Fig. 4.

Fig. 4

Malaria prevalence in the ten communes with the highest and ten with the lowest indicator values

When we look at heterogeneity within regions, we generally find that rural communes have the highest prevalence of asymptomatic malaria infection, compared to the urban communes. For instance, in the Centre region, the regional level prevalence of asymptomatic malaria is 15.3%, while this varies considerably between the urban commune of Ouagadougou (9,7%) and the region's rural communes of Komki-Ipala (36.1%), Komsilga (31.5%), Koubri (34.9%), Pabré (35.4%), Saaba (30.7%) and Tanghuin-Dassouri (34.6%), with an average malaria prevalence of 33%. The same is true for the Haut-Bassins region (11.2%), where the urban communes of Bobo-Dioulasso (5.1%), Houndé (8.2%) and Orodara (10.0%) have the lowest levels of malaria infection in children under five, compared with the other communes in the region, where prevalence varies from 14.0 to 19.0%. In the Centre-West and South-West regions, where levels of the indicator are the highest in the country, there are also strong communal disparities in malaria infection.

Discussion

Geographical identification of health problems is an important element of efforts control as it facilitates better allocation of the limited resources, improved health management and better targeting of interventions to maximize risk reduction [8, 23, 26, 27]. Analysis of the geographical distribution of the prevalence of asymptomatic malaria infection across the communes of Burkina Faso has highlighted sub-regional inequalities or heterogeneity that are often overlooked and difficult to highlight using survey data. The most perceptible communal differences are found between urban and rural communes within the same region, which is in itself a very useful result.

Geostatistical modeling of malaria risk among children in Burkina Faso using 2010 DHS data showed that low-risk areas were mainly concentrated in large urban centers such Ouagadougou the capital city [8]. This variation in malaria relative risk between localities in endemic regions is not surprising. It has always been recognized in other contexts [28, 29]. Differences between communes may be the result of heterogeneous ecological conditions that sustain larval breeding sites and thus facilitate the proliferation of mosquitoes, the vectors of malaria [30]. These vectors mainly determine the distribution and intensity of the disease [30]. In Kenya, for example, researchers noted that exposure to malaria could not be homogeneous, as malaria incidence did not follow a Poisson distribution, a phenomenon they described as over-dispersion [28]. Hence, the heterogeneity of malaria distribution at commune level could be explained mainly by socio-economic, health, hygiene and sanitation inequalities between communes, inequalities that are more prevalent between urban and rural communes [23]. These factors generally depend on the level of development of the various localities, since malaria and poverty are closely linked [30].

As Zhang et al. (2020) have already done in Nepal [16], we have succeeded in showing how the ELL small-area estimation model can be used to combine high-resolution census data with household survey data to produce more detailed and useful estimates. Compared with other small area estimation approaches (Bayesian models in particular), this one is relatively simple to implement and provides reasonably accurate results, provided certain precautions are taken to ensure data consistency and relevance. The steps required to implement the ELL method appear to be less complex, which represents a good opportunity to produce indicators at finer scales and adapt them to the needs of development policies, as recommended by the MDGs [18]. One of the prerequisites for this estimation approach is that the two operations (survey and census) are carried out at similar times, to avoid any major change in population and household structure. The data used in this analysis are collected at 1-year intervals, which is a major strength of this approach.

However, as is common knowledge, statistical estimates are sometimes subject to errors related to the estimation or sampling model. It is therefore important to assess the extent of these errors. In this study, the ROC curve reveals that the final model does not fully explain malaria prevalence, no doubt due to the failure to account for important and necessary variables. However, the robustness of the survey and census data helps to improve the accuracy of the estimates.

Another important fact to note, and one that remains a limitation of this study, is the failure to consider spatial autocorrelation. In fact, in the geographical analysis of a phenomenon measured in several places, it is generally observed that there is a relationship between the values of areas that are relatively close to each other [31]. As a result, other approaches to accounting for this spatial autocorrelation as a random effect in the analysis may be important. Such approaches may help to explain unmodeled variability more effectively, and would probably present a better picture of the spatial distribution of these data, albeit at the cost of reduced precision of the estimates [16].

Conclusions

The geographical inequalities in malaria prevalence highlighted among children under five suggest significant disparities between communes. Within the same region, this disparity is particularly marked between urban and rural communes. This highlights a pattern of dichotomy between urban and rural communities within the same region. Children in urban communes are relatively less exposed to malaria than those in rural communes. This situation seems to depend on the level of development of the targeted communes, especially since the factors associated with malaria prevalence easily demonstrate this.

This analysis shows that, while malaria control measures for children under 5 years of age need to be strengthened, regardless of their place of residence, rural communes need to be given greater attention in order to achieve greater gains in reducing malaria morbidity and mortality. Given the government's efforts to combat malaria, it's undeniable that we need to take account of the specific features of rural areas, where sanitation levels are sometimes poor.

Abbreviations

AUC

Area under the curve

CI

Confidence interval

CMG

Climate modeling grid

ELL

Elbers Langjouw and Langjouw

INSD

National Institute of Statistics and Demography

LST&E

Land surface temperature and emissivity

ROC

Receiver operating characteristic

SDGs

Sustainable development goals

SSA

Sub-Saharan Africa

WHO

World Health Organization

Appendix

Appendix A: frequency and mean distributions of variables

Variables Malaria survey 2018 Census 2019
Number N % Number N %
Region of residence
Boucle du Mouhoun 558 10.2 28,898 9.8
Cascades 255 4.6 13,344 4.5
Centre 313 5.7 36,483 12.4
Centre Est 441 8.0 24,757 8.4
Centre Nord 526 9.6 25,222 8.6
Centre Ouest 553 10.1 24,082 8.2
Centre Sud 167 3.1 10,807 3.7
Est 566 10.3 30,141 10.3
Hauts-Bassins 762 13.9 31,065 10.6
Nord 466 8.5 26,762 9.1
Plateau Central 255 4.6 14,931 5.1
Sahel 406 7.4 14,341 4.9
Sud-Ouest 214 3.9 12,882 4.4
Place of residence
Urban 878 16.0 64,487 22.0
Rural 4604 84.0 229,228 78.0
Child's gender
Male 2800 51.1 147,362 50.2
Female 2682 48.9 146,353 49.8
Child's age
0 an 605 11.0 51,963 17.7
1 an 1098 20.0 53,276 18.1
2 ans 1206 22.0 64,018 21.8
3 ans 1326 24.2 62,399 21.2
4 ans 1247 22.8 62,059 21.1
Gender of head of household
Male 5175 94.4 264,041 89.9
Female 307 5.6 29,674 10.1
Age of head of household
15–34 ans 1652 30.1 110,538 37.6
35–49 ans 2345 42.8 122,721 41.8
50–64 ans 1079 19.7 45,204 15.4
65 ans ou+ 406 7.4 15,252 5.2
Wealth index
Poorest 1148 20.9 63,950 21.8
Poorer 1147 20.9 65,420 22.3
Middle 1129 20.6 64,658 22.0
Richer 1102 20.1 57,306 19.5
Richest 956 17.4 42,174 14.4
Other variables Malaria survey 2018 Census 2019
Mean Mean
Proportion of women aged 15–49 with education 42.5 56.4
Net usage rate 55.2 89.4
Annual rainfall 928.8 907.6
Annual temperatures 35.9 35.9

Appendix B: ELL method

It consists in building an econometric model linking the malaria infection status of each child to a set of explanatory variables common to both the IBHS and the RGPH. The coefficients of the model's exogenous variables obtained from the survey data are fed into the census database to generate a prevalence of malaria infection per census child. Finally, the malaria prevalence is constructed for different geographical groups. The process thus comprises three stages.

First step: we begin by identifying a set of explanatory variables present in both databases that meet certain comparability criteria. We check that the wording of the questions and answers is the same in both questionnaires. From the selected questions, we then construct a series of variables whose comparability we test.

Second step: the per capita malaria prevalence model is estimated using the survey data. To maximize accuracy, the model is estimated at the lowest geographical level for which the survey remains representative. This level is usually the sampling strata. In this analysis, the geographical level considered is the region.

Third step: to complete the map, we associate the parameters estimated in the second step with the characteristics of each child in the census, to predict per capita prevalence. Individual prevalences are then aggregated at the regional and commune levels.

Appendix C: logistic regression result of the final model

Predictor Odds ratio (95% CI)
Regions
Boucle du Mouhoun 0.713 (0.474, 1.074)
Cascades 0.461 (0.281, 0.756)
Centre 1.324 (0.781, 2.244)
Centre Est 0.747 (0.484, 1.152)
Centre Nord 0.927 (0.572, 1.503)
Centre Ouest 1.262 (0.873, 1.824)
Centre Sud 0.673 (0.412, 1.099)
Est 0.555 (0.363, 0.849)
Hauts-Bassins 0.348 (0.236, 0.513)
Nord 0.494 (0.322, 0.758)
Plateau Central 0.336 (0.191, 0.592)
Sahel 0.634 (0.369, 1.090)
Sud-Ouest 1
Place of residence
Rural 3.331 (2.289, 4.845)
Urban 1
Child's age
0 year 1
1 year 1.764 (1.259, 2.473)
2 years 2.282 (1.646, 3.164)
3 years 2.624 (1.904, 3.616)
4 ans 3.312 (2.404, 4.564)
Age of head of household
15–34 ans 0.747 (0.625, 0.892)
35–49 ans 0.945 (0.765, 1.169)
50–64 ans 1.230 (0.929, 1.629)
65 ans et+ 1
Wealth index
Poorest 1
Poorer 0.882 (0.708, 1.097)
Middle 0.624 (0.494, 0.786)
Richer 0.757 (0.601, 0.954)
Richest 0.443 (0.311, 0.631)
Temperature
Mean 0.854 (0.766, 0.952)

Appendix D: results from estimation at commune level using census and survey data

Region Province Commune Prevalence (95% CI)
Boucle du mouhoun Bale Bagassi 25.1 (20.8, 29.4)
Boucle du mouhoun Bale Bana 24.4 (20.2, 28.7)
Boucle du mouhoun Bale Boromo 17.6 (14.0, 21.2)
Boucle du mouhoun Bale Fara 20.7 (17.0, 24.4)
Boucle du mouhoun Bale Oury 23.6 (19.6, 27.6)
Boucle du mouhoun Bale Pa 25.8 (21.2, 30.3)
Boucle du mouhoun Bale Pompoi 24.6 (20.4, 28.7)
Boucle du mouhoun Bale Poura 19.6 (16.0, 23.2)
Boucle du mouhoun Bale Siby 25.1 (20.6, 29.6)
Boucle du mouhoun Bale Yaho 26.1 (21.7, 30.5)
Boucle du mouhoun Banwa Balave 22.1 (18.4, 25.8)
Boucle du mouhoun Banwa Kouka 24.7 (20.3, 29.1)
Boucle du mouhoun Banwa Sami 26.5 (22.0, 30.9)
Boucle du mouhoun Banwa Sanaba 24.0 (20.1, 28.0)
Boucle du mouhoun Banwa Solenzo 23.1 (19.2, 27.0)
Boucle du mouhoun Banwa Tansila 24.8 (20.7, 28.9)
Boucle du mouhoun Kossi Barani 24.7 (20.5, 28.9)
Boucle du mouhoun Kossi Bomborokuy 21.2 (16.8, 25.6)
Boucle du mouhoun Kossi Bourasso 22.6 (18.6, 26.5)
Boucle du mouhoun Kossi Djibasso 23.8 (19.8, 27.8)
Boucle du mouhoun Kossi Dokuy 23.7 (19.7, 27.7)
Boucle du mouhoun Kossi Doumbala 23.1 (19.1, 27.1)
Boucle du mouhoun Kossi Kombori 22.9 (18.9, 26.9)
Boucle du mouhoun Kossi Madouba 21.6 (17.9, 25.3)
Boucle du mouhoun Kossi Nouna 17.2 (13.9, 20.6)
Boucle du mouhoun Kossi Sono 25.0 (20.1, 30.0)
Boucle du mouhoun Mouhoun Bondokuy 25.4 (21.1, 29.6)
Boucle du mouhoun Mouhoun Dedougou 14.5 (11.7, 17.2)
Boucle du mouhoun Mouhoun Douroula 22.3 (18.6, 26.1)
Boucle du mouhoun Mouhoun Kona 23.5 (19.6, 27.4)
Boucle du mouhoun Mouhoun Ouarkoye 24.6 (20.6, 28.7)
Boucle du mouhoun Mouhoun Safane 23.0 (19.2, 26.8)
Boucle du mouhoun Mouhoun Tcheriba 25.0 (20.9, 29.1)
Boucle du mouhoun Nayala Gassan 19.7 (16.0, 23.5)
Boucle du mouhoun Nayala Gossina 24.5 (20.5, 28.5)
Boucle du mouhoun Nayala Kougny 20.7 (16.7, 24.6)
Boucle du mouhoun Nayala Toma 18.0 (14.7, 21.3)
Boucle du mouhoun Nayala Yaba 24.2 (20.1, 28.3)
Boucle du mouhoun Nayala Ye 22.8 (19.0, 26.7)
Boucle du mouhoun Sourou Di 24.2 (19.9, 28.4)
Boucle du mouhoun Sourou Gomboro 23.2 (19.3, 27.1)
Boucle du mouhoun Sourou Kassoum 21.5 (17.3, 25.6)
Boucle du mouhoun Sourou Kiembara 24.4 (20.3, 28.4)
Boucle du mouhoun Sourou Lanfiera 22.5 (18.7, 26.2)
Boucle du mouhoun Sourou Lankoue 24.5 (20.4, 28.6)
Boucle du mouhoun Sourou Toeni 25.0 (20.5, 29.5)
Boucle du mouhoun Sourou Tougan 20.5 (16.9, 24.1)
Cascades Comoe Banfora 10.3 (6.9, 13.6)
Cascades Comoe Beregadougou 20.6 (14.3, 26.9)
Cascades Comoe Mangodara 20.8 (14.7, 26.9)
Cascades Comoe Moussodougou 22.9 (15.9, 29.9)
Cascades Comoe Niangoloko 17.7 (12.4, 23.0)
Cascades Comoe Ouo 21.3 (15.1, 27.6)
Cascades Comoe Sideradougou 20.5 (14.5, 26.5)
Cascades Comoe Soubakaniedougou 19.6 (13.7, 25.4)
Cascades Comoe Tiefora 20.8 (14.7, 27.0)
Cascades Leraba Dakoro 19.7 (13.8, 25.6)
Cascades Leraba Douna 20.9 (14.6, 27.1)
Cascades Leraba Kankalaba 20.7 (14.6, 26.9)
Cascades Leraba Loumana 19.8 (13.9, 25.6)
Cascades Leraba Niankorodougou 18.7 (13.0, 24.4)
Cascades Leraba Oueleni 21.7 (15.3, 28.2)
Cascades Leraba Sindou 17.1 (12.0, 22.2)
Cascades Leraba Wolonkoto 22.8 (16.0, 29.6)
Centre Kadiogo Komki-Ipala 36.1 (27.0, 45.3)
Centre Kadiogo Komsilga 31.5 (23.0, 40.0)
Centre Kadiogo Koubri 34.9 (26.1, 43.8)
Centre Kadiogo Ouagadougou 9.7 (6.3, 13.1)
Centre Kadiogo Pabre 35.4 (26.5, 44.4)
Centre Kadiogo Saaba 30.7 (22.3, 39.1)
Centre Kadiogo Tanghin Dassouri 34.6 (25.7, 43.5)
Centre est Boulgou Bagre 28.2 (22.5, 33.9)
Centre est Boulgou Bane 28.4 (22.6, 34.1)
Centre est Boulgou Beguedo 23.3 (18.3, 28.2)
Centre est Boulgou Bissiga 26.2 (21.1, 31.3)
Centre est Boulgou Bittou 22.5 (18.0, 27.0)
Centre est Boulgou Boussouma 27.0 (21.2, 32.8)
Centre est Boulgou Boussouma 26.2 (21.1, 31.3)
Centre est Boulgou Garango 17.5 (13.7, 21.2)
Centre est Boulgou Komtoega 22.4 (17.8, 27.1)
Centre est Boulgou Niaogho 26.9 (21.5, 32.4)
Centre est Boulgou Tenkodogo 18.0 (14.3, 21.6)
Centre est Boulgou Zabre 24.7 (19.8, 29.6)
Centre est Boulgou Zoaga 29.6 (23.8, 35.4)
Centre est Boulgou Zonse 23.1 (18.4, 27.8)
Centre est Koulpelogo Comin-Yanga 23.5 (18.7, 28.4)
Centre est Koulpelogo Dourtenga 23.5 (18.7, 28.2)
Centre est Koulpelogo Lalgaye 27.5 (21.9, 33.1)
Centre est Koulpelogo Ouargaye 21.2 (17.0, 25.4)
Centre est Koulpelogo Sanga 26.7 (21.5, 31.9)
Centre est Koulpelogo Soudougui 26.5 (21.4, 31.6)
Centre est Koulpelogo Yargatenga 24.0 (19.2, 28.8)
Centre est Koulpelogo Yonde 24.6 (19.8, 29.4)
Centre est Kouritenga Andemtenga 24.2 (19.4, 29.0)
Centre est Kouritenga Baskoure 25.4 (20.3, 30.4)
Centre est Kouritenga Dialgaye 23.1 (18.4, 27.8)
Centre est Kouritenga Gounghin 24.4 (19.6, 29.3)
Centre est Kouritenga Kando 24.2 (19.4, 29.0)
Centre est Kouritenga Koupela 15.2 (11.8, 18.5)
Centre est Kouritenga Pouytenga 10.3 (7.4, 13.2)
Centre est Kouritenga Tensobentenga 23.6 (18.9, 28.3)
Centre est Kouritenga Yargo 23.9 (19.1, 28.7)
Centre nord Bam Bourzanga 27.3 (22.0, 32.7)
Centre nord Bam Guibare 26.8 (21.5, 32.1)
Centre nord Bam Kongoussi 20.8 (16.4, 25.2)
Centre nord Bam Rollo 27.1 (21.8, 32.5)
Centre nord Bam Rouko 27.9 (22.5, 33.3)
Centre nord Bam Sabce 28.2 (22.5, 33.9)
Centre nord Bam Tikare 26.1 (21.1, 31.2)
Centre nord Namentenga Boala 27.1 (21.7, 32.4)
Centre nord Namentenga Boulsa 25.5 (20.5, 30.5)
Centre nord Namentenga Bouroum 26.2 (20.9, 31.6)
Centre nord Namentenga Dargo 29.4 (23.6, 35.2)
Centre nord Namentenga Nagbingou 26.2 (20.9, 31.5)
Centre nord Namentenga Tougouri 25.3 (19.9, 30.7)
Centre nord Namentenga Yalgo 24.7 (19.7, 29.7)
Centre nord Namentenga Zeguedeguin 26.6 (21.2, 32.1)
Centre nord Sanmatenga Kaya 16.5 (12.9, 20.1)
Centre nord Sanmatenga Korsimoro 23.7 (18.8, 28.6)
Centre nord Sanmatenga Mane 27.3 (22.0, 32.6)
Centre nord Sanmatenga Pibaore 28.5 (23.0, 33.9)
Centre nord Sanmatenga Pissila 27.4 (22.0, 32.7)
Centre nord Sanmatenga Ziga 27.5 (22.3, 32.8)
Centre ouest Boulkiemde Bingo 35.7 (31.4, 40.0)
Centre ouest Boulkiemde Imasgho 36.5 (32.2, 40.9)
Centre ouest Boulkiemde Kindi 37.5 (33.0, 42.0)
Centre ouest Boulkiemde Kokoloko 34.7 (30.7, 38.8)
Centre ouest Boulkiemde Koudougou 18.6 (15.3, 21.8)
Centre ouest Boulkiemde Nandiala 35.1 (30.6, 39.6)
Centre ouest Boulkiemde Nanoro 35.7 (31.4, 40.0)
Centre ouest Boulkiemde Pella 35.7 (31.4, 40.1)
Centre ouest Boulkiemde Poa 33.1 (28.9, 37.3)
Centre ouest Boulkiemde Ramongo 38.3 (34.0, 42.6)
Centre ouest Boulkiemde Sabou 33.8 (29.4, 38.2)
Centre ouest Boulkiemde Sigle 39.7 (35.2, 44.2)
Centre ouest Boulkiemde Soaw 36.5 (32.0, 41.0)
Centre ouest Boulkiemde Sourgou 35.6 (31.5, 39.7)
Centre ouest Boulkiemde Thyou 33.8 (29.6, 37.9)
Centre ouest Sanguie Dassa 33.7 (29.4, 37.9)
Centre ouest Sanguie Didyr 34.3 (29.6, 39.0)
Centre ouest Sanguie Godyr 35.4 (30.6, 40.2)
Centre ouest Sanguie Kordie 34.8 (29.9, 39.7)
Centre ouest Sanguie Kyon 34.9 (30.0, 39.7)
Centre ouest Sanguie Pouni 35.4 (30.8, 40.0)
Centre ouest Sanguie Reo 24.7 (20.8, 28.5)
Centre ouest Sanguie Tenado 37.1 (32.7, 41.5)
Centre ouest Sanguie Zamo 39.0 (34.5, 43.4)
Centre ouest Sanguie Zawara 38.6 (34.3, 42.9)
Centre ouest Sissili Bieha 37.4 (33.1, 41.7)
Centre ouest Sissili Boura 38.0 (33.7, 42.2)
Centre ouest Sissili Leo 22.2 (18.7, 25.7)
Centre ouest Sissili Nebielianayou 38.7 (34.2, 43.1)
Centre ouest Sissili Niabouri 37.4 (33.1, 41.7)
Centre ouest Sissili Silly 38.7 (34.3, 43.0)
Centre ouest Sissili To 36.5 (32.3, 40.6)
Centre ouest Ziro Bakata 38.5 (34.0, 42.9)
Centre ouest Ziro Bougnounou 40.1 (35.5, 44.8)
Centre ouest Ziro Cassou 38.4 (34.1, 42.8)
Centre ouest Ziro Dalo 38.2 (33.7, 42.7)
Centre ouest Ziro Gao 36.8 (32.6, 41.0)
Centre ouest Ziro Sapouy 33.5 (29.4, 37.5)
Centre sud Bazega Doulougou 24.7 (18.0, 31.5)
Centre sud Bazega Gaongo 21.5 (15.1, 27.9)
Centre sud Bazega Ipelce 23.5 (16.9, 30.0)
Centre sud Bazega Kayao 25.5 (18.7, 32.4)
Centre sud Bazega Kombissiri 17.3 (12.3, 22.3)
Centre sud Bazega Sapone 23.6 (17.1, 30.0)
Centre sud Bazega Toece 24.1 (17.5, 30.6)
Centre sud Nahouri Guiaro 26.3 (19.2, 33.3)
Centre sud Nahouri 19.4 (13.9, 25.0)
Centre sud Nahouri Tiebele 25.7 (18.9, 32.5)
Centre sud Nahouri Zecco 23.0 (16.6, 29.4)
Centre sud Nahouri Ziou 25.5 (18.7, 32.4)
Centre sud Zoundweogo Bere 22.8 (16.4, 29.1)
Centre sud Zoundweogo Binde 21.4 (15.1, 27.6)
Centre sud Zoundweogo Gogo 26.9 (19.7, 34.1)
Centre sud Zoundweogo Gomboussougou 27.6 (19.7, 35.6)
Centre sud Zoundweogo Guiba 24.0 (17.4, 30.6)
Centre sud Zoundweogo Manga 11.6 (7.8, 15.3)
Centre sud Zoundweogo Nobere 24.4 (17.7, 31.1)
Est Gnagna Bilanga 19.6 (16.2, 23.0)
Est Gnagna Bogande 17.3 (14.1, 20.4)
Est Gnagna Coalla 16.1 (12.8, 19.3)
Est Gnagna Liptougou 18.4 (15.0, 21.9)
Est Gnagna Mani 16.9 (13.4, 20.3)
Est Gnagna Piela 19.3 (16.0, 22.5)
Est Gnagna Thion 18.0 (14.3, 21.7)
Est Gourma Diabo 19.2 (15.8, 22.6)
Est Gourma Diapangou 20.6 (17.0, 24.2)
Est Gourma Fada N'Gourma 13.6 (11.2, 16.0)
Est Gourma Matiacoali 22.6 (18.7, 26.5)
Est Gourma Tibga 19.4 (16.2, 22.7)
Est Gourma Yamba 19.6 (16.3, 22.9)
Est Komandjoari Bartibougou 20.5 (17.0, 24.1)
Est Komandjoari Foutouri 19.7 (16.4, 23.0)
Est Komandjoari Gayeri 17.6 (14.4, 20.8)
Est Kompienga Kompienga 25.1 (19.4, 30.9)
Est Kompienga Madjoari 26.7 (21.3, 32.1)
Est Kompienga Pama 15.2 (12.6, 17.9)
Est Tapoa Botou 22.8 (18.8, 26.8)
Est Tapoa Diapaga 19.0 (15.8, 22.3)
Est Tapoa Kantchari 22.1 (18.3, 25.8)
Est Tapoa Logobou 24.0 (19.6, 28.4)
Est Tapoa Namounou 20.4 (17.0, 23.8)
Est Tapoa Partiaga 22.8 (18.9, 26.7)
Est Tapoa Tambaga 23.9 (19.5, 28.4)
Est Tapoa Tansarga 21.6 (18.0, 25.1)
Hauts-bassins Houet Bama 16.0 (12.8, 19.2)
Hauts-bassins Houet Bobo-Dioulasso 5.1 (3.6, 6.5)
Hauts-bassins Houet Dande 16.8 (13.4, 20.2)
Hauts-bassins Houet Faramana 15.8 (12.4, 19.2)
Hauts-bassins Houet Fo 15.2 (12.1, 18.4)
Hauts-bassins Houet Karankasso Sambla 16.9 (13.2, 20.5)
Hauts-bassins Houet Karankasso-Vigue 15.0 (11.9, 18.1)
Hauts-bassins Houet Koundougou 16.3 (12.9, 19.6)
Hauts-bassins Houet Lena 17.6 (14.1, 21.2)
Hauts-bassins Houet Padema 14.6 (11.5, 17.7)
Hauts-bassins Houet Peni 16.8 (13.4, 20.3)
Hauts-bassins Houet Satiri 16.4 (13.1, 19.7)
Hauts-bassins Houet Toussiana 17.6 (13.5, 21.7)
Hauts-bassins Kenedougou Banzon 16.9 (13.1, 20.8)
Hauts-bassins Kenedougou Djigouera 17.4 (13.5, 21.3)
Hauts-bassins Kenedougou Kangala 19.0 (14.1, 23.9)
Hauts-bassins Kenedougou Kayan 15.0 (11.9, 18.2)
Hauts-bassins Kenedougou Koloko 16.2 (12.6, 19.9)
Hauts-bassins Kenedougou Kourignon 18.7 (14.0, 23.3)
Hauts-bassins Kenedougou Kourouma 15.5 (12.4, 18.7)
Hauts-bassins Kenedougou Morolaba 14.2 (11.0, 17.3)
Hauts-bassins Kenedougou N'Dorola 14.6 (11.5, 17.7)
Hauts-bassins Kenedougou Orodara 10.0 (7.0, 13.1)
Hauts-bassins Kenedougou Samogohiri 19.0 (14.2, 23.8)
Hauts-bassins Kenedougou Samorogouan 14.7 (11.7, 17.8)
Hauts-bassins Kenedougou Sindo 14.0 (10.9, 17.1)
Hauts-bassins Tuy Bekuy 16.3 (13.0, 19.7)
Hauts-bassins Tuy Bereba 16.0 (12.7, 19.2)
Hauts-bassins Tuy Bony 14.7 (11.1, 18.3)
Hauts-bassins Tuy Founzan 14.3 (10.9, 17.6)
Hauts-bassins Tuy Hounde 8.4 (6.4, 10.4)
Hauts-bassins Tuy Koti 14.4 (11.0, 17.8)
Hauts-bassins Tuy Koumbia 15.1 (12.0, 18.2)
Nord Loroum Banh 18.7 (14.7, 22.7)
Nord Loroum Ouindigui 17.9 (14.1, 21.7)
Nord Loroum Solle 19.5 (15.3, 23.6)
Nord Loroum Titao 12.2 (9.4, 15.0)
Nord Passore Arbole 17.9 (14.2, 21.6)
Nord Passore Bagare 19.5 (15.5, 23.5)
Nord Passore Bokin 17.9 (14.2, 21.6)
Nord Passore Gomponsom 23.4 (17.6, 29.3)
Nord Passore Kirsi 16.0 (12.4, 19.6)
Nord Passore La-Todin 17.4 (13.7, 21.0)
Nord Passore Pilimpikou 17.8 (14.0, 21.5)
Nord Passore Samba 18.2 (14.4, 22.0)
Nord Passore Yako 14.5 (11.5, 17.6)
Nord Yatenga Barga 15.2 (11.5, 18.9)
Nord Yatenga Kain 18.3 (14.2, 22.4)
Nord Yatenga Kalsaka 18.0 (14.3, 21.7)
Nord Yatenga Kossouka 15.6 (12.1, 19.2)
Nord Yatenga Koumbri 16.4 (12.7, 20.1)
Nord Yatenga Ouahigouya 16.3 (12.7, 19.8)
Nord Yatenga Oula 9.7 (7.5, 11.9)
Nord Yatenga Rambo 15.8 (12.2, 19.4)
Nord Yatenga Seguenega 17.0 (13.5, 20.6)
Nord Yatenga Tangaye 16.0 (12.5, 19.5)
Nord Yatenga Thiou 16.9 (13.2, 20.7)
Nord Yatenga Zogore 18.7 (14.8, 22.5)
Nord Zondoma Bassi 15.7 (12.2, 19.3)
Nord Zondoma Boussou 19.8 (15.8, 23.8)
Nord Zondoma Gourcy 14.0 (11.0, 17.0)
Nord Zondoma Leba 17.4 (13.8, 21.0)
Nord Zondoma Tougo 17.7 (14.1, 21.4)
Plateau central Ganzourgou Boudry 11.5 (7.2, 15.7)
Plateau central Ganzourgou Kogho 14.1 (9.0, 19.3)
Plateau central Ganzourgou Meguet 11.4 (7.2, 15.6)
Plateau central Ganzourgou Mogtedo 10.4 (6.4, 14.3)
Plateau central Ganzourgou Salogo 13.1 (8.3, 17.9)
Plateau central Ganzourgou Zam 10.6 (6.5, 14.7)
Plateau central Ganzourgou Zorgho 8.9 (5.5, 12.3)
Plateau central Ganzourgou Zoungou 11.2 (7.0, 15.4)
Plateau central Kourweogo Bousse 10.0 (6.2, 13.8)
Plateau central Kourweogo Laye 13.8 (8.6, 19.1)
Plateau central Kourweogo Niou 13.0 (8.2, 17.7)
Plateau central Kourweogo Sourgoubila 14.9 (9.4, 20.4)
Plateau central Kourweogo Toeghin 12.9 (8.2, 17.6)
Plateau central Oubritenga Absouya 11.9 (7.5, 16.2)
Plateau central Oubritenga Dapelogo 13.0 (8.3, 17.8)
Plateau central Oubritenga Loumbila 12.4 (7.7, 17.1)
Plateau central Oubritenga Nagreongo 12.5 (7.9, 17.0)
Plateau central Oubritenga Ourgou-Manega 13.0 (8.3, 17.8)
Plateau central Oubritenga Ziniare 8.7 (5.4, 11.9)
Plateau central Oubritenga Zitenga 11.2 (6.9, 15.4)
Sahel Oudalan Deou 19.6 (15.1, 24.0)
Sahel Oudalan Gorom-Gorom 17.7 (13.6, 21.8)
Sahel Oudalan Markoye 17.7 (13.2, 22.1)
Sahel Oudalan Oursi 20.3 (15.7, 24.9)
Sahel Oudalan Tin-Akoff 19.4 (14.8, 23.9)
Sahel Seno Bani 19.7 (15.2, 24.1)
Sahel Seno Dori 17.6 (13.7, 21.6)
Sahel Seno Falagountou 18.9 (14.5, 23.3)
Sahel Seno Gorgadji 20.8 (16.1, 25.4)
Sahel Seno Sampelga 20.8 (16.2, 25.5)
Sahel Seno Seytenga 20.5 (15.9, 25.0)
Sahel Soum Arbinda 20.0 (15.5, 24.5)
Sahel Soum Baraboule 22.6 (17.5, 27.6)
Sahel Soum Diguel 23.4 (18.1, 28.7)
Sahel Soum Djibo 10.2 (7.3, 13.1)
Sahel Soum Kelbo 19.6 (14.9, 24.3)
Sahel Soum Nassoumbou 22.0 (17.1, 26.9)
Sahel Soum Pobe-Mengao 20.8 (15.6, 25.9)
Sahel Soum Tongomayel 21.8 (16.9, 26.7)
Sahel Yagha Sebba 15.6 (12.0, 19.2)
Sahel Yagha Solhan 20.6 (15.9, 25.2)
Sahel Yagha Tankougounadie 19.9 (15.4, 24.4)
Sahel Yagha Titabe 21.4 (16.7, 26.2)
Sud ouest Bougouriba Bondigui 35.5 (28.8, 42.3)
Sud ouest Bougouriba Diebougou 24.2 (18.9, 29.4)
Sud ouest Bougouriba Dolo 34.7 (28.0, 41.4)
Sud ouest Bougouriba Iolonioro 37.2 (30.2, 44.2)
Sud ouest Bougouriba Tiankoura 38.0 (31.0, 44.9)
Sud ouest Ioba Dano 23.7 (18.0, 29.4)
Sud ouest Ioba Dissin 30.7 (23.8, 37.5)
Sud ouest Ioba Gueguere 31.9 (25.1, 38.6)
Sud ouest Ioba Koper 32.0 (24.5, 39.4)
Sud ouest Ioba Niego 31.3 (23.6, 39.1)
Sud ouest Ioba Oronkua 32.1 (25.1, 39.1)
Sud ouest Ioba Ouessa 27.6 (20.2, 35.0)
Sud ouest Ioba Zambo 32.3 (25.3, 39.2)
Sud ouest Noumbiel Batie 27.7 (22.1, 33.4)
Sud ouest Noumbiel Boussoukoula 37.7 (30.7, 44.7)
Sud ouest Noumbiel Kpuere 39.7 (32.2, 47.2)
Sud ouest Noumbiel Legmoin 37.4 (30.5, 44.3)
Sud ouest Noumbiel Midebdo 38.0 (30.9, 45.2)
Sud ouest Poni Bouroum-Bouroum 34.8 (28.2, 41.5)
Sud ouest Poni Boussera 35.3 (28.0, 42.6)
Sud ouest Poni Djigoue 41.4 (33.5, 49.4)
Sud ouest Poni Gaoua 21.7 (16.9, 26.6)
Sud ouest Poni Gbomblora 36.7 (29.9, 43.5)
Sud ouest Poni Kampti 38.5 (31.1, 46.0)
Sud ouest Poni Loropeni 39.9 (32.3, 47.5)
Sud ouest Poni Malba 34.7 (27.6, 41.7)
Sud ouest Poni Nako 34.6 (27.7, 41.4)
Sud ouest Poni Perigban 40.9 (33.0, 48.8)

Author contributions

Conceptualization: BH, MO, RT; methodology: BH, MO.; validation: MK, OM, formal analysis: BH, MO; data curation: BH, MOYP, OC, NA; writing—original draft: BH; writing—review and editing: BH, OM, OC, CK, RT; funding acquisition: this research received no external funding; project administration: BH.; supervision: MK. All authors read and approved the final manuscript.

Funding

This research received no external funding.

Availability of data and materials

Malaria Indicators Survey dataset is available on the dhs program (https://dhsprogram.com) website and the General Population and Housing Census dataset is available at the National Institute of Statistics and Demography (INSD) in Burkina Faso.

Declarations

Ethics approval

Permission to access the data was obtained from the measure DHS program (http://www.dhsprogram.com) via online request. The website and the data used were publicly available with no personal identifier. All methods were carried out in accordance with relevant guidelines and regulations.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Competing interests

The authors declare that they have no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Footnotes

1

ZDs were allocated to strata in proportion to the number of ZDs in each stratum.

2

See Appendix A2 for more details.

3

In probability and statistics, the delta method is a method for approximating the asymptotic distribution of the transform of an asymptotically normal random variable.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Malaria Indicators Survey dataset is available on the dhs program (https://dhsprogram.com) website and the General Population and Housing Census dataset is available at the National Institute of Statistics and Demography (INSD) in Burkina Faso.


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