Prevalence and distribution pattern of malaria and soil-transmitted helminth co-endemicity in sub-Saharan Africa, 2000-2018: A geospatial analysis.

BACKGROUND: Limited understanding exists about the interactions between malaria and soil-transmitted helminths (STH), their potential geographical overlap and the factors driving it. This study characterised the geographical and co-clustered distribution patterns of malaria and STH infections among vulnerable populations in sub-Saharan Africa (SSA). METHODOLOGY/PRINCIPAL
FINDINGS: We obtained continuous estimates of malaria prevalence from the Malaria Atlas Project (MAP) and STH prevalence surveys from the WHO-driven Expanded Special Project for the Elimination of NTDs (ESPEN) from Jan 1, 2000, to Dec 31, 2018. Although, MAP provides datasets on the estimated prevalence of Plasmodium falciparum at 5km x 5km fine-scale resolution, we calculated the population-weighted prevalence of malaria for each implementation unit to ensure that both malaria and STH datasets were on the same spatial resolution. We incorporated survey data from 5,935 implementation units for STH prevalence and conducted the prevalence point estimates before and after 2003. We used the bivariate local indicator of spatial association (LISA analysis) to explore potential co-clustering of both diseases at the implementation unit levels among children aged 2-10 years for P. falciparum and 5-14 years for STH, living in SSA. Our analysis shows that prior to 2003, a greater number of SSA countries had a high prevalence of co-endemicity with P.falciparium and any STH species than during the period from 2003-2018. Similar prevalence and distribution patterns were observed for the co-endemicity involving P.falciparum-hookworm, P.falciparum-Ascaris lumbricoides and P.falciparum-Trichuris trichiura, before and after 2003. We also observed spatial variations in the estimates of the prevalence of P. falciparum-STH co-endemicity and identified hotspots across many countries in SSA with inter-and intra-country variations. High P. falciparum and high hookworm co-endemicity was more prevalent in West and Central Africa, whereas high P. falciparum with high A. lumbricoides and high P. falciparum with high T. trichiura co-endemicity were more predominant in Central Africa, compared to other sub-regions in SSA.
CONCLUSIONS/SIGNIFICANCE: Wide spatial heterogeneity exists in the prevalence of malaria and STH co-endemicity within the regions and within countries in SSA. The geographical overlap and spatial co-existence of malaria and STH could be exploited to achieve effective control and elimination agendas through the integration of the vertical control programmes designed for malaria and STH into a more comprehensive and sustainable community-based paradigm.

Introduction

Given the environmental and host factors which favour transmission of multiple parasitic infections, malaria and soil-transmitted helminths (STH), including Ascaris lumbricoides, Trichuris trichiura, and hookworms (Necator Americanus and Ancylostoma duodenale), co-exist in many parts of the world, predominantly in sub-Saharan Africa (SSA), [1] where they are most prevalent. An estimated 241 million cases of malaria were reported in 2021 in 85 malaria endemic countries, 29 of which accounted for 96% of malaria cases globally, and six SSA countries (Nigeria, Democratic Republic of the Congo, Uganda, Mozambique, Angola and Burkina Faso) carried more than half of this burden. [2] Similarly, a disproportionately high burden of STH has been reported in many countries in SSA with large concentrations of moderate-to-heavy intensity infections in Nigeria, Democratic Republic of the Congo, Ethiopia, Cameroon, Angola, Mozambique, Madagascar, Equatorial Guinea, and Gabon. [3]

In these malaria-STH co-endemic countries, estimating the national burden of the co-endemicity poses a public health challenge because national surveillance systems are often sub-optimal. [4] For example, deworming through mass drug administration (MDA) of anthelminthic drugs is widely regarded as a cost-effective strategy. However, the impact of these MDA programmes on the changing prevalence and intensity of STH in children, is often hindered by a lack of comprehensive baseline data before MDA initiation, because of financial challenges or as a result of the administration of the drugs via undocumented channels. [3] Nevertheless, recent WHO reports [2,4] indicate that considerable progress has been made in reducing the overall burden of malaria and STH in many countries including some in SSA. To sustain the gains of past decades and move the control efforts towards elimination, WHO has recommended a paradigm shift from disease-specific to an integrated approach. [4]

Empirical studies conducted across co-endemic countries have shown that variations in the prevalence of malaria and STH co-endemicity differed significantly across geographic locations. [5-9] Environmental factors such as temperature and humidity have been identified as the major driving factors for the widespread distributions of parasitic diseases such as malaria and STH. Using a combination model of temperature, rainfall and altitude, Brooker et al [1] predicted the continental distribution of each of the major STH species, which suggested that A. lumbricoides and T. trichiura were prevalent predominantly in equatorial, central and west, and southeast Africa, while the hookworm infection was ubiquitous in every African sub-region. Climate-based distribution models were also used to describe malaria transmission across SSA. [10] These spatial models were subsequently combined to estimate the co-distribution of Plasmodium falciparum and hookworm, and identified school-age children as the most vulnerable population for the co-endemicity.

Changes in temperature, humidity, rainfall, and other climatic conditions have an impact on key factors that shape the transmission of malaria, such as the mosquito lifespan and the development of malaria parasites in the vector. [11] Similarly, the interactions between the climatic parameters affect rates of desiccation and death of the eggs of A. lumbricoides and T. trichiura, and hookworm egg hatching success, thereby affecting the overall survival of the STH parasites and development of infective larval stages. In addition, socio-economic conditions such as agricultural practices and other human behaviours also alter ecological landscape, soil larval loads, vector survival, food security and overall human health and wellbeing, thereby providing favourable interface for transmission and co-distribution of malaria and STH. [12,13] The roles of biological factors driven by immune modulation were also reported to be responsible for the co-existence of the mixed infections. [14]

Given the interplay of several complex factors promoting malaria-STH co-endemicity, limited understanding exists about the spatial distribution of this co-endemicity in vulnerable populations. Majority of the published studies targeting malaria-STH co-endemicity have focused on describing the transmission and burden among affected populations, [5,6,9,15] and have paid little attention to their concurrent spatial distribution. Previous systematic reviews have also reported an over-estimation of the relationship between malaria and helminths making it difficult to establish conclusively the burden of malaria-STH dual infections. [16,17] Consistent with findings of these reviews, a recently conducted systemic review and meta-analysis of 55 studies which enrolled 37,559 children across low and middle- income countries (LMIC) found a wide variation in the prevalence of malaria-helminth co-endemicity at country level, ranging from 7–76% across LMICs. [18] These findings may be due to the low sensitivity of the diagnostic methods employed for the detection of malaria-helminth co-endemicity in the primary studies included in the systematic review.

The WHO road map for the control and elimination of NTDs for the 2021–2030 period defines STH elimination as a public health problem (EPHP) when there is a prevalence of less than 2% of moderate-to-heavy intensity infections. [4] However, as STH control accelerates and new detection tools are developed, a significant number of people with clinical features outside high intensity infection hotspots are now being seen. [19] The 2020 Population raster of individual countries in SSA from the WorldPop database [20] showed that an estimated 165 million people were in high-risk clusters of P.falciparum, hookworm and A.lumbricoides. For malaria-STH co-endemicity, about 73 million, 21 million and 118 million people were in high risk clusters of co-endemicity involving P.falciparum and hookworm; P.falciparum and T. trichiura; P.falciparum and A.lumbricoides respectively. Given that the WorldPop estimates are tied typically to high-risk clusters in a single year, reliable estimates of the transmission dynamics of malaria-STH morbidity hotspots are needed to achieve the NTD roadmap goal of intensifying cross-cutting approaches which integrate STH control with common delivery platforms for diseases that share similar epidemiology such as malaria. Also, empirical evidence shows that obtaining reliable estimates of the distribution of STH-malaria co-endemicity would help address the critical gaps in planning and implementation of integration of STH control strategies with other interventions. [21,22]

We undertook a geospatial analysis of the datasets describing the prevalence of malaria and STH infections among children, obtained from open-access data generated from the Malaria Atlas Project (MAP) [23] and the WHO-driven Expanded Special Project to Eliminate NTDs (ESPEN) [24], to characterise the geographical and co-clustered distribution patterns for malaria and STH co-endemicity among the vulnerable populations in SSA.

Methods

Based on the classifications of the United Nations Geoscheme for Africa, [25] we categorised SSA into west, east, central and southern African sub-regions. These include 46 of Africa’s 54 countries and territories that are fully or partially south of the Sahara, excluding Algeria, Djibouti, Egypt, Libya, Morocco, Somalia, Sudan and Tunisia.

Data curation

We obtained datasets on the prevalence of P. falciparum from the MAP database (last accessed September 2, 2021). MAP is a WHO collaborating platform for geospatial disease modelling which was created forthe purpose of determining spatial limits, prevalence and endemicity of P. falciparum and P. vivax. MAP provides a database to obtain data on the estimated prevalence of P. falciparum and P. vivax at 5km resolution for each year from 2000 onwards.[23] The MAP initiative was established in 2006 to project the expected prevalence and endemicity of malaria in all locations around the world, by modelling the available prevalence data. The MAP described the intensity of malaria transmission or endemicity based on the prevalence of peripheral malaria parasitaemia in children aged 2 to 10 years (PfPR2–10). This is because the index has been found to closely correlate with the entomological inoculation rate (EIR) which refers to the number of infectious bites per person per day. Due to the ability of PfPR2–10 to mirror the EIR, the MAP generated models to predict the PfPR2–10 for a given set of environmental conditions. In high endemicity areas, parasite rate (PR) samples are often restricted to children aged 2–10 years, but in areas of low endemicity, surveys are usually extended to include all age groups. [26,27]

MAP collated survey data that are typically clustered at village level and recorded data on parasite positivity rates. MAP used data points generated from malaria surveys for its estimation by combining these data points with Geographic Information System (GIS) data. MAP was updated in 2010 to include new malariometric data extending to 13,449 administrative units in 43 endemic countries and 22,212 P. falciparum parasite rate surveys were used to define spatial limits of malaria transmission. To generate continuous global maps, MAP matched spatially referenced data, e.g., altitude, temperature, rainfall, and vegetation extent with the PR-survey positions, to establish uni-and/or multivariate statistical relationships between malaria endemicity and the environmental factors that affect the distribution of the mosquito vectors responsible for malaria transmission. [23] We focused on P. falciparum in this analysis because it is the predominant causative agent for more than 95% cases of clinical malaria in SSA. [2]

We also obtained the datasets on the prevalence of STH (A. lumbricoides, T. trichiuria and hookworms) from the WHO-driven ESPEN portal. ESPEN portal contains survey datasets on NTDs in Africa.[24] Sartorius and colleagues [3] developed a Bayesian spatio-temporal hierarchical model to analyse the STH datasets from 2000 to 2018; and provided the prevalence estimates at implementation unit (IU) covering the entire period. We used these estimates to determine the hotspots of P.falciparum-STH co-endemicity. Given that many control programmes implemented during the period under review could have changed the infection risk for each of the STH species, we took the temporal effects of control programmes into consideration to reflect the transmission hotspots before and after the scale-up of the control programmes. As many control programmes were scaled up around 2003, we conducted the point prevalence estimates before 2003 and from 2003–2018.

To ensure that both the malaria and the STH datasets were on the same spatial resolution, we calculated the population-weighted prevalence of malaria for each IU using the population density data obtained from the WorldPop database. [20] WHO consider the IU as the geographical area over which a particular treatment strategy or intervention is applied. This geographical area could be a district, local government area, county, or province. In most SSA countries, the IU is the second administrative level and there were a total of 5,935 IUs in the study areas reported in this paper. [23]

Statistical analysis

Spatial autocorrelation

To examine the spatial autocorrelation between malaria and STH, we used the bivariate Moran’s I statistics (https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/spatial-autocorrelation.htm). We showed via a scatter plot the degree to which the prevalence of a given variable at a given IU is correlated with its neighbours for a different variable. Hence, we plotted the prevalence of P. falciparum on the x-axis against the spatial lag of other variables including hookworm, T. trichiura and A. lumbricoides on the y-axis. The variables were standardised before generating the Moran’s I scatter plot using the z-scores. The z-scores enabled us to compare the prevalence estimates of any two variables as they might have different mean and a standard deviation. Z-score value equal to zero was interpreted as the mean prevalence; above zero as higher than the mean prevalence and below zero as lower than the mean prevalence. Therefore, on the scatter plots, all points with values above the mean (i.e. above zero) were IUs with higher than mean prevalence while all points with values below the mean (i.e. below zero) were IUs with lower than mean prevalence. The scatter plot was divided into four quadrants; upper-right, lower-left, upper-left and lower-right. The values of P. falciparum and the spatial lag of any of hookworm, T. trichiura and A.lumbricoides that fell in the upper-right quadrant and the lower-left quadrant were regarded as an evidence of positive spatial autocorrelation while those in other quadrants indicate negative spatial autocorrelation. The overall conclusion of positive, negative or no spatial autocorrelation depends on the dominant quadrant and the overall Moran’s I estimates. A positive value of Moran’s I estimate indicates positive spatial autocorrelation while a negative value indicates negative spatial autocorrelation (https://arcview-gis.software.informer.com/10.3/).

Bivariate spatial clustering

To identify the spatial clustering of the IUs, we used local Moran’s I algorithm in Geoda software (https://geodacenter.github.io/) to implement the bivariate local indicator of spatial association (LISA). [28] Bivariate LISA is a method that uses local spatial autocorrelation to identify localised IUs where prevalence values of one variable are strongly positively or negatively associated with spatial lag of another variable. It gives an indication of the extent of significant spatial clustering of similar values around that observation. We used LISA to identify the hotspots and coldspots, and areas endemic to malaria-STH co-endemicity across the study areas. We considered alpha level ≤0.001 as statistically significant hotspots.

Results

Overall, a high prevalence of P.falciparum and hookworm co-endemicity was reported in more countries, compared to co-endemicity involving P. falciparum and A. lumbricoides; or P. falciparum and T. trichiura, which were found in lesser number of countries. Similarly, a high prevalence of co-endemicity and significant hotspot locations were generally predominant in more countries pre-2003 compared to 2003–2018 (S1–S3 Tables).

P. falciparum and co-endemicity with any species of soil-transmitted helminths

A high prevalence of co-endemicity of P. falciparum and any STH species was observed in 20 sub-Saharan African countries pre-2003, in contrast to only 12 countries from 2003–2018. These were mostly in West and Central Africa, pre and post 2003. The countries with locations with a high prevalence of co-endemicity of P. falciparum and any STH species pre-2003 were Benin, Cote d’Ivoire, Sierra Leone, Liberia, Guinea, Togo, Nigeria, Cameroon, Central Africa Republic, Equatorial Guinea, Gabon, Congo, Angola, Tanzania, Democratic Republic of the Congo (DRC), Burundi, Uganda, Zambia, Mozambique, Madagascar. Countries with locations with a high prevalence of co-endemicity of P. falciparum and any STH species from 2003–2018 were Benin, Cote d’Ivoire, Guinea, Liberia, Nigeria, DRC, Cameroon, Equatorial Guinea, Gabon, Congo, Angola, Mozambique (Fig 1 and S1 Table).

Fig 1

The prevalence of P. falciparum and any soil-transmitted helminth co-endemicity, and significant hotspots of P. falciparum and any soil-transmitted co-endemicity in sub-Saharan Africa.

The density description next to the colour-coded box refers to the prevalence of P. falciparum, whilst the column further to the right refers to density of any STH. Link to the base layer of the map: https://gadm.org/index.html.

There were significant (p<0.001) hotspot locations of P. falciparum and any STH co-endemicity in six countries pre-2003, and 2003–2018 respectively, with positive spatial autocorrelation showing Moran’s I = 0.241 (pre-2003), and 0.256 (2003–2018), respectively (S1 Fig). The six countries with significant hotspot locations pre-2003 were Cameroon, Equatorial Guinea, Gabon, and Angola in central African, Tanzania in east Africa, and Madagascar in southern Africa. The six countries with significant hotspot locations from 2003–2018 were Liberia and Nigeria in west Africa and Cameroon, Equatorial Guinea, Gabon, and Congo in central Africa (Fig 1 and S1 Table).

P. falciparum and hookworm co-endemicity

There was a high prevalence of co-endemicity of P. falciparum and hookworm in 19 sub-Saharan African countries pre-2003, in contrast to 16 countries from 2003–2018. Pre-2003, 52% of the 19 countries with a high prevalence of co-endemicity of P. falciparum and hookworm were in west Africa, while from 2003–2018, there were 43% (7/16) in west Africa and 31.3% (5/16) in central Africa. The 19 countries with locations with a high prevalence of co-endemicity of P. falciparum and hookworm pre-2003 were Benin, Cote d’Ivoire, Ghana, Guinea, Guinea Bissau, Liberia, Nigeria, Togo, Sierra Leone, Cameroon, DRC, Equatorial Guinea, Gabon, Uganda, Tanzania, Angola, Mozambique, Madagascar, and Zambia (Fig 2 and S1 Table). Of these 19 countries pre-2003, there were significant (p<0.001) hotspot locations of P. falciparum and hookworm co-endemicity in seven countries, with positive spatial autocorrelation, and Moran’s I = 0.390 (S2 Fig). The seven countries with significant hotspot locations were Liberia, Nigeria, Ghana, Sierra Leone, Angola, Uganda and Zambia.

Fig 2

The prevalence of P. falciparum and hookworm co-endemicity, and significant hotspots of P.falciparum and hookworm co-endemicity in sub-Saharan Africa.

’The density description next to the colour coded box refers to the prevalence of P. falciparum, whilst the column further to the right refers to density of hookworm. Link to the base layer of the map: https://gadm.org/index.html.

The 16 countries with locations of high prevalence of co-endemicity of P. falciparum and hookworm in the period 2003–2018 were Benin, Cote d’Ivoire, Nigeria, Togo, Ghana, Liberia, Guinea, Angola, Cameroon, DRC, Equatorial Guinea, Gabon, Uganda, Tanzania, Zambia and Mozambique. Among these 16 countries, there were significant (p<0.001) hotspot locations of P. falciparum and hookworm co-endemicity in Guinea and Nigeria, both in west Africa (Fig 2 and S1 Table), with positive spatial autocorrelation and Moran’s I = 0.245 (S2 Fig).

P. falciparum and A. lumbricoides co-endemicity

There was a high prevalence of co-endemicity of P.falciparum and A.lumbricoides in 12 sub-Saharan African countries pre-2003, in contrast to 11 countries from 2003–2018. Pre-2003, 58% of the 12 countries with high prevalence of co-endemicity of P. falciparum and A.lumbricoides were in central Africa, while from 2003–2018, 63.6% (7/11) of the countries were in central Africa. The 12 countries with locations of high prevalence of P. falciparum and A. lumbricoides co-endemicity pre-2003 were Liberia, Nigeria, Cameroon, Equatorial Guinea, Gabon, Congo, Angola, Burundi, DRC, Kenya, Mozambique and Madagascar. The 11 countries with locations with a high prevalence of co-endemicity of P. falciparum and A.lumbricoides in the period 2003–2018 were Liberia, Nigeria, Cameroon, DRC, Equatorial Guinea, Gabon, Congo, Angola, Burundi, Kenya, Mozambique (Fig 3 and S1 Table).

Fig 3

The prevalence of P. falciparum and Ascaris lumbricoides, and significant hotspots of P. falciparum and A.lumbricoides co-endemicity in sub-Saharan Africa.

The density description next to the colour-coded box refers, to the prevalence of P. falciparum whilst the column further to the right refers to density of A. lumbricoides. Link to the base layer of the map: https://gadm.org/index.html.

Pre-2003, there were significant (p<0.001, positive spatial autocorrelation, with Moran’s I = 0.059) hotspot locations of P. falciparum and A.lumbricoides co-endemicity in six countries, compared to only three countries during the period 2003–2018 (p<0.001, Moran’s I = 0.184) (S3 Fig). The six countries pre-2003 were Liberia, Cameroon, Equatorial Guinea, Angola, Burundi and Madagascar. The three countries with significant hotspots from 2003–2018 were Cameroon, Equatorial Guinea, and Angola, all located in central African region (Fig 3 and S1 Table).

P. falciparum and T. trichiura co-endemicity

Pre-2003, there was a high prevalence of co-endemicity of P. falciparum and T. trichiura in 10 sub-Saharan African countries, mainly located in central Africa (70%; 7/10). The 10 central African countries with locations of high co-endemicity of P. falciparum and T.trichiura pre-2003 were Liberia, Cameroon, Equatorial Guinea, Gabon, Congo, DRC, Angola, Burundi, Kenya, and Madagascar. Among these 10 countries, there were significant hotspot locations (p<0.001, with positive spatial autocorrelation, Moran’s I = 0.037) of P. falciparum and T. trichiura, (S4 Fig) in Cameroon, Equatorial Guinea, Gabon, and Madagascar, (Fig 4 and S1 Table).

Fig 4

The prevalence of P. falciparum and T.trichiura, and significant hotspots of P,falciparum and T. trichiura co-endemicity in sub-Saharan Africa.

The density description next to the colour-coded box refers, to the prevalence of P. falciparum whilst the column further to the right refers to density of T.trichiura. Link to the base layer of the map: https://gadm.org/index.html.

From 2003–2018, there was a high prevalence of co-endemicity of P. falciparum and T. trichiura in only five sub-Saharan African countries (mainly in central Africa). The five central African countries with locations of high co-endemicity of P. falciparum and T. trichiura from 2003–2018 were Cameroon, Equatorial Guinea, Gabon, Congo, DRC. Among these five countries, there were significant hotspot locations (p<0.001, with positive spatial autocorrelation, Moran’s I = 0.069) of P. falciparum and T. trichiura (S4 Fig) in Cameroon, Equatorial Guinea, Gabon (Fig 4 and S1 Table).

Discussion

Our analysis shows spatial variations in the estimates of prevalence of P.falciparum-STH co-endemicity and identified hotspots across many countries in SSA where the co-endemicity were high. Before the scale-up of specific control programmes for malaria and STH in 2003, our findings showed that a greater number of countries in SSA had a high prevalence of co-endemicity with P.falciparium and any STH species than during the period from 2003–2018 when the control programmes were expanded. Similar prevalence and distribution patterns were observed for the co-endemicity involving P.falciparum-hookworm, P.falciparum-A.lumbricoides and P.falciparum-T.trichiura, during these periods.

Consistent with previous studies [3,17,29,30], our findings also highlight inter- and intra-country variations in the prevalence of malaria-helminth co-endemicity. Across Central and West Africa sub-regions, a high prevalence of P.falciparum and high hookworm co-endemicity was predominant, while the burden of P. falciparum-A. lumbricoides and P.falciparum-T. trichiura co-endemicity was higher in Central Africa than in neighbouring sub-regions. Although, recent reports have shown that a considerable gain has been made in the control of malaria and STH in the last two decades, [1,19] our findings suggest that a high burden of falciparum malaria and moderate-to-heavy infections for at least one STH species are still occurring in the southern part of West Africa and extending to Central Africa including Cameroon, Burundi and DRC. We observed similar patterns and trends in the hotspot distributions for P. falciparum and any of the STH species. While programmatic implementation of preventive chemotherapy and intermittent preventive treatment of malaria in children (IPTc) which later became seasonal malaria chemoprevention (SMC) have contributed to a significant reduction in the burden of STH and malaria in many countries, inherent challenges such as logistical burden, medication adherence, and incomplete coverage have been reported to be responsible for the persistently high burden of malaria and STH in the spatial areas identified in our study. [19,21]

Taken together the geographical overlap and spatial co-existence of malaria and STH, favoured largely by environmental factors [1,3], could be exploited to achieve effective control and elimination agendas through integration of the vertical programmes designed for malaria and STH into a more comprehensive and sustainable community-based paradigm, that includes other preventive care such as hand hygiene and water sanitation. The WHO 2030 NTD road map recommends concrete actions within integrated platforms for delivery of the interventions needed to improve the cost–effectiveness, coverage and geographical reach of these integrated programmes. [4] A successful integration of the vertical control programmes aligns well with the global trend towards integrated, non-disease specific approaches which have become an increasingly recognised strategy recommended by WHO. [22] However, limited evidence is currently available on the feasibility and effectiveness of integrating malaria with STH control at community level and evidence that this can be done effectively is needed.

Our study has some limitations. First, our study used secondary data obtained from two different sources. The datasets on P.falciparum obtained from Malaria Atlas Project were generated from statistical approaches to modelling the prevalence of malaria on a global scale using Bayesian model-based geostatistics, while STH datasets obtained from ESPEN were direct estimates of the prevalence of STH. We overcame the uncertainty posed by this challenge by ensuring that both malaria and STH datasets were on the same spatial resolution by calculating the population-weighted prevalence of malaria for each implementation unit using the population density data obtained from the WorldPop database. Furthermore, it is a common occurrence in spatial epidemiology that very limited number of measurements were done in large parts of the geographical region of interest, thereby reporting error-prone or incomplete measurements. Nevertheless, the use of Bayesian model-based geostatistics addressed the spatial variations in the level of uncertainty associated with the mapped surfaces.

Another limitation of our study is that the STH datasets focused largely on school-age children while the malaria datasets concentrated predominantly on children aged 2–10 years because of the ability of prevalence of peripheral malaria parasitaemia in children aged 2–10 years (PfPR2–10) to mirror the entomological inoculation rate, making the MAP to generate models to predict the PfPR2–10 for a given set of environmental conditions. [27] While the prevalence of P.falciparum-STH co-endemicity is known to vary in a relatively predictable fashion in space and time, the observed prevalence depends heavily on individual’s age and intervention coverage. The disparity in the ages of children and relatively higher focus on school age children may explain the inconsistencies of our findings with empirical studies, especially for STH prevalence. [31,32] For example, we observed high uncertainty around estimates in Central Africa and some parts of West Africa, which corresponds to areas with a high burden of infection. Lack of survey data for corresponding age groups is a plausible reason for this observation. [3]

More importantly, the absence of survey data or inadequacy in definition of space-time that contributes to the uncertainty observed in our findings may have also introduced bias leading to over-estimation or under-estimation of the prevalence of STH and malaria co-endemicity. Contributing further to the likely over or under-estimation of the burden of STH and malaria could be empirical treatments of malaria and STH outside the control programmes. Our findings could also be affected by the diagnostic approaches involving malaria rapid testing [33] and Kato-Katz method [34] used to generate the data, as these have been widely reported to be less sensitive than molecular methods such as PCR, especially in low-transmission settings.

In conclusion, we have demonstrated wide spatial heterogeneity within the SSA and within countries, on the prevalence of malaria and STH confections. To consolidate on the encouraging progress made in the reduction of the prevalence of malaria and STH in some SSA countries, it is crucial that integrated control programmes target the regions with high prevalence of malaria-STH co-endemicity and areas of hotspot transmission. Our study may also have critical implications for policy-making and resource allocations based on the needs of each region, as well as for the future of the implementation of integrated malaria-helminth control programmes. Nevertheless, it is important that our findings are confirmed by further empirical studies as this will provide the platform for research agenda that will lead to the establishment of the much-needed platform for the implementation of the integrated programmes that are cost-effective, and make optimum use of limited human resources frequently found in SSA.

Moran’s I statistic plot showing spatial auto-correlation for P. falciparum vs any soil-transmitted helminth co-endemicity in sub-Saharan Africa, pre-2003 and 2003–2018.

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Moran’s I statistic plot showing spatial auto-correlation for P. falciparum vs hookworm co-endemicity in sub-Saharan Africa, pre-2003 and 2003–2018.

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Moran’s I statistic plot showing spatial auto-correlation for P. falciparum vs Ascaris lumbricoides co-endemicity in sub-Saharan Africa, pre-2003 and 2003–2018.

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Moran’s I statistic plot showing spatial auto-correlation for P. falciparum vs T.trichiura co-endemicity in sub-Saharan Africa, pre-2003 and 2003–2018.

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Click here for additional data file.

The prevalence of P. falciparum and soil-transmitted helminth (Hookworm, Ascaris lumbricoides, Trichiuris trichiura) co-endemicity, and their corresponding significant hotspots locations (ADM1, ADM2, and ADM3) in sub-Saharan Africa, pre-2003 and 2003–2018.

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Summary table of STH prevalence estimates by country and frequency of prevalence surveys per country, sub-Saharan Africa, 2000–2018.

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Summary table of P.falciparum prevalence estimates by country and frequency of prevalence surveys per country, sub-Saharan Africa, 2000–2018.

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Click here for additional data file.

22 Apr 2022

Dear Dr. Afolabi,

Thank you very much for submitting your manuscript "Estimating the burden of malaria and soil-transmitted helminth co-infection in sub-Saharan Africa: a geospatial study" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: METHODS

- Is the MAP data only individuals aged 5-14 years? Clarify. More information is needed on the prevalence estimates for MAP; provide key information on demographics, source of prevalence surveys, and concentration of data by country.

- Provide summary tables (can go in SI) for which years STH prevalence estimates were available by country and how many prevalence surveys per country were available, as the sparsity of the data will influence your estimates. Ideally, something similar for the MAP estimates should be shown too.

- You are using estimates from MAP yet direct prevalence measurements from ESPEN. Why? Please explain how the methods for MAP and methods for STH are the same or differ in the prevalence kriging or estimation. If MAP data is not from prevalence surveys then discuss what the key dominating environmental features are of that data and how those environmental features feature into STH estimation (if at all). The introduction would have already given us an idea of the importance of each feature for each set of infections.

Reviewer #2: -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? No applicable

-Is the study design appropriate to address the stated objectives? Partially

-Is the population clearly described and appropriate for the hypothesis being tested? No applicable

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? No applicable

-Were correct statistical analysis used to support conclusions? Partially

-Are there concerns about ethical or regulatory requirements being met? No

The authors estimated the overall prevalence for each species over 18 years (i.e. 2000-2018). However, due to many control programs done in this period, the infection risk for each species changed a lot over the study period, temporal effect should not be ignored. As MAP provides estimate prevalence of P. falciparum for each year, and ESPEN provides STH survey data from 2000 to 2018, the data which the analysis based includes temporal information. Advanced methods should be used to take into account temporal effect.

The authors used the inverse distance weighted kriging methods to produce STH prevalence surface. However, they did not provide any further methodology on how to validate the model performance. For example, how to assess the accuracy of the method on estimating the prevalence where no survey data exist? In addition, as the prevalence surfaces were produced from spatial interpolation instead of real observation, methods and results for prediction uncertainty should be provided.

In line 164, please explain what “spatially lagged variables” means.

In lines 165-167, the authors define high prevalence and low prevalence as values above and below the mean, respectively. What is the rationality for this definition? For example, if the prevalence for one species is very high across the study region, e.g., the prevalence of all areas is above 50%, then defining the areas with prevalence lower the mean level as “low prevalence” areas seems not reasonable. Besides, was the prevalence standardized before analysis? All prevalence should be above or equal to zero, however, the authors used the “above zero” and “below zero” in the definition.

In lines 188-189, how to define the “high-risk clusters of P. falciparum, hookworm and A. lumbricoides”? Besides, the numbers of people in these clusters should be put in the result section instead in the methodology section.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: -Does the analysis presented match the analysis plan? Partially

-Are the results clearly and completely presented? No

-Are the figures (Tables, Images) of sufficient quality for clarity? No

The authors provide results for co-infection of P. falciparum and each species of STH. Results for co-infection of malaria and any species of STH seems more meaningful for integrated control programs, as control methods for the three species of STH are similar. Such results should also be provided.

The authors put the estimated prevalence of each species in the attachment. As these results are important, they should be put in the main text and more description of these results should be provided.

In Figs 1A-1C, the prevalence below “-1” seems cut. What is the reason for this? Besides, how could prevalence below zero?

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: DISCUSSION

- A major limitation of the study is the 5km resolution, especially for STHs.

- Information is needed on the effect of key ecological features in the estimations. Discuss gradients of infection prevalence for both malaria and hookworm as one moves away from water bodies; this will affect the interpolation.

- Rerun the analysis from 2003- onwards as this period is when MDA was substantially scaled up and discuss whether there is any heterogeneity pre 2003 versus post. Also, the breakdown of data pre-post MDA and its discussion is needed.

- Discuss limitations of focusing on school-aged children, as hookworm at least is known to increase in prevalence/intensity with age.

Reviewer #2: -Are the conclusions supported by the data presented? Partially

-Are the limitations of analysis clearly described? Partially

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? Yes

-Is public health relevance addressed? Yes

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: Attentions should be paid to the writing, as following:

In many places of the manuscript, species was not written italic, and there is no space between the first and the second words of the species. For example, errors in line 231.

Presentation of the figures should also be revised. For example, Fig 1 should be a figure instead of separating it into Fig 1A, Fig 1B and Fig 1C. Please follow the standards of the journal.

The positions of the titles of figures should also be revised according to the journal standards. For example, the positions of Fig 3A should be below the figure.

Authors should be carefully check the typing errors. For example, in line 209, does “high prevalence of P. falciparum and high HW co-infection” mean “high prevalence of P. falciparum and HW co-infection”? Besides, if “HW” is the abbreviation of hookworm, it should be mentioned at the first time it appears.

In fig 2B, 3B and 4B, “probability” should be revised to “P value”, as it presents P value for statistical inference instead of the real probability of the events happening.

Fig 4A and Fig 4B seem in the wrong positions.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: I read this paper with great interest. It is well written and produces maps and estimates of spatial autocorrelation for STH and malaria coinfections on the African continent. More detail is needed on the methods. The purpose of the analysis must be made clearer, as the environmental factors that might lead to overlap in unclear (given that the sets of infections have vastly different transmission routes, and this was not discussed). The limitations of the analysis need to be better described.

ABSTRACT

Lines 42-43: P. falciparum is repeated.

The abstract needs to directly state the lowest resolution (km) where coinfection estimation was possible. Also, clarify in the abstract whether STH prevalence was georeferenced when estimated or if the implementation unit for STH prevalence was used and then a point estimate was created in the middle of that implementation unit. And, directly state the age range at which these coinfection estimates are applicable.

INTRO

Throughout the introduction, provide prevalence estimates with their relevant administrative unit.

Given the vastly different transmission routes and the localized influence of soil conditions on STH infections, please expand the introduction to provide a background of exactly what environmental factors, including their directionality of influence, overlap between the two sets of infections. Reporting co-endemicity by country is too vague. Literature on the epidemiology of the two infections is needed and past empirical findings should be noted.

Lines 107-109: Clarify whether these estimates are at the country level.

Reviewer #2: Using data from MAP and ESPEN and methods of spatial cluster analysis, the authors identified high co-infection areas of malaria and each species of STH. The research topic of the manuscript is meaningful for integrated control programs for malaria and STHs. However, there are several important points regarding to the methods and the results needed to address, as described in the above “Methods” and “Results”. Besides, the manuscript seems not well organized, and concerns about the title and the writing should also be paid attention.

Regarding to the title, the “burden” in the title seems not proper as the manuscript mainly provided results for high co-infection areas, instead of the estimation of disease burden due to co-infection. For writing concerns, please refer to the above “Editorial and Data Presentation Modifications”.

--------------------

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Reviewer #1: No

Reviewer #2: No

Figure Files:

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

14 Jul 2022

Submitted filename: RESPONSE SHEET_PNTD-D-22-00324_final.doc

Click here for additional data file.

23 Aug 2022

Dear Dr. Afolabi,

Thank you very much for submitting your manuscript "Prevalence and distribution pattern of malaria and soil-transmitted helminth co-infections in sub-Saharan Africa, 2000-2018: a geospatial analysis" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Uwem Friday Ekpo, PhD

Academic Editor

PLOS Neglected Tropical Diseases

Marco Coral-Almeida

Section Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: (No Response)

Reviewer #2: (No Response)

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: (No Response)

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

Reviewer #2: (No Response)

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The authors have addressed my requests and greatly improved the manuscript; minor changes are needed for the introduction and terminology before acceptance.

Given that the authors are not looking at the same set of people for malaria and STH nor are they looking at the same age range, using the term co-infection may be misleading. Instead, the manuscript should be revised to clearly note geographical co-endemicity. Co-distributions might also be appropriate as already used in the lay summary. Choose one terminology and use throughout the manuscript.

The additional description of shared environmental or other drivers between malaria and STHs is poorly written without clear examples. Clearly state exactly what climate-related factors predicted both malaria and STHs. Also, note what ‘immune modulation’ is responsible for mixed infections; there is not an interaction between malaria and STH that affects susceptibility to infections, so this sentence is misleading. Clarify what socioeconomic factors affect both diseases. There is still no discussion here of the vastly different transmission routes or differences in climatic factors.

Reviewer #2: The authors revised the manuscript according to editorial and reviewers’ comments. It seems much better compared to the original one. Only one comment: The authors used Sartorius et al’s estimates for STH infection instead of the original kriging method. Are these estimates also based on ESPEN portal? Please clarify it.

--------------------

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Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article's retracted status in the References list and also include a citation and full reference for the retraction notice.

30 Aug 2022

Submitted filename: RESPONSE SHEET_PNTD-D-22-00324R1.doc

Click here for additional data file.

16 Sep 2022

Dear Dr. Afolabi,

We are pleased to inform you that your manuscript 'Prevalence and distribution pattern of malaria and soil-transmitted helminth co-endemicity in sub-Saharan Africa, 2000-2018: a geospatial analysis' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Uwem Friday Ekpo, PhD

Academic Editor

PLOS Neglected Tropical Diseases

Marco Coral-Almeida

Section Editor

PLOS Neglected Tropical Diseases

***********************************************************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: (No Response)

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: (No Response)

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

21 Sep 2022

Dear Dr. Afolabi,

We are delighted to inform you that your manuscript, "Prevalence and distribution pattern of malaria and soil-transmitted helminth co-endemicity in sub-Saharan Africa, 2000-2018: a geospatial analysis," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases