research on malaria prevalence

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities

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• Published: 06 July 2024
• Volume 123 , article number  262 , ( 2024 )

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• Ropo Ebenezer Ogunsakin 1 ,
• Bayowa Teniola Babalola 2 ,
• Johnson Adedeji Olusola 3 ,
• Ayodele Oluwasola Joshua 4 &
• Moses Okpeku 5  

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Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000–2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria’s six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

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Malaria, a significant global health threat, causes extensive illness and death worldwide, with a particularly severe impact on the African continent (Sankineni et al. 2023 ; Simon-Oke et al. 2023 ; Rodríguez et al. 2023 ). This disease has posed a persistent challenge to public health on a global scale for many years, especially in tropical regions. The primary Plasmodium species responsible for malaria are Plasmodium falciparum ,  Plasmodium vivax ,  Plasmodium ovale , and  Plasmodium malaria,  with  P. falciparum  being the deadliest and accounting for up to 95% of malaria implicated deaths in Africa (Sankineni et al. 2023 ; Simon-Oke et al. 2023 ). Despite a modest decrease in malaria prevalence in recent years, as indicated by past research, many individuals globally, particularly in Africa, continue to grapple with malaria due to insufficient socio-economic status and access to treatment resources. (Das et al. 2023 ; Mac et al. 2023 ; Sarfo et al. 2023 ; Taiwo et al. 2023 ).

Similarly, the World Malaria Report highlighted a staggering 247 million malaria cases in 2021, resulting in over 600,000 fatalities during the same year (World Health Organization 2023 ), with only four African countries accounting for more than half of all malaria deaths worldwide; Nigeria alone accounting for about 31.3%. These statistics made Nigeria the leading country with the highest proportion of malaria deaths in 2021. Despite numerous studies on malaria prevention in Nigeria, high malaria prevalence persists in many regions (Kanmiki et al. 2019 ). Strengthening prevention efforts remains crucial. Understanding transmission variations can aid in developing effective control strategies and resource allocation.

Despite government efforts at different levels to combat malaria, high prevalence persists in regions with heavy rainfall and warm temperatures (Oviedo et al. 2023 ). Rainfall creates breeding sites for mosquitoes, the vectors of malaria, while warmer temperatures accelerate the malaria parasite’s growth and prolong the mosquito’s lifespan. Consequently, malaria is particularly prevalent in areas with heavy rainfall and warm temperatures. Malaria prevalence in Nigeria differs across geopolitical regions because of the varying environmental and seasonal settings that affect the reproductive patterns of mosquito vectors. The prevalence of malaria is primarily a function of its underlying transmission intensity (Alegana et al. 2013 ), which in turn is propelled by indicators such as interventions (García et al. 2023 ), environmental and climatic factors (Ekpa et al. 2023 ; Rivera and Gutiérrez 2023 ), and socio-economic and demographic characteristics (Ogunsakin and Chen 2020 ; Pourtois et al. 2023 ; Rivera and Gutiérrez 2023 ). In malaria prevalence research, authors have linked malaria rates to environmental and socio-economic factors like population density and potential evapotranspiration (PET) (Yang et al. 2012 , 2005 ). Since the indicators affecting malaria are diverse, those considered in this study were selected based on previous studies, as acknowledged above. Hence, using malaria indicator survey data, the current research employed time-based spatial mapping of  the prevalence of Plasmodium falciparum  malaria.

Besides, these cross-sectional surveys are aimed at being comprehensive and nationally representative, where information on several indicators affecting the prevalence of Plasmodium falciparum is gathered. The essence of this survey is to provide recent estimates of fundamental demographic-related and health-related malaria indicators. It is intended to provide estimates at the national level, as well as in urban and rural areas and six geopolitical regions. They are conducted with a standardized methodology. Unlike other data types, nationally representative cross-sectional data is invulnerable to incompleteness or standards for clinical diagnosis (Arambepola et al. 2020 ; Uwemedimo et al. 2018 ). This explains why prevalence information from national health surveys is the primary source of data for mapping malaria risk in many countries has always been, particularly in Africa (Weiss et al. 2019 ; Battle et al. 2019 ).

This study aimed to determine the spatiotemporal trends of malaria distribution in Nigeria and to determine potential relationships between malaria prevalence and environmental indicators. Disease mapping is widely used in public health surveillance since it describes the spatiotemporal variation of the disease, identifies areas with unusually high risk, and formulates etiological hypotheses (Lawson 2018 ). Therefore, probing the spatiotemporal dynamics of malaria by linking GPS data to external covariates can lead to new intuitions in population processes and foster a track toward enhanced spatial management decisions. Environmental indicators were selected to establish a spatiotemporal distribution model using a geographical information system (GIS). GIS has proven to be a powerful technique for public health surveillance across various geographical areas (Musa et al. 2013 ; Kamel Boulos and Geraghty 2020 ).

The study was conducted in Nigeria, a sub-Saharan African country, between latitudes 4º16’ and 13º53’ N and longitudes 2º40’ and 14º41’ E. It shares borders with the Niger Republic in the north, the Republic of Chad in the northeast, the Republic of Cameroon in the east, and the Republic of Benin in the west. The climate and topography of Nigeria are diverse, encompassing highlands (600 to 1300 m in the North Central Zone), eastern highlands, and lowlands (less than 20 m in coastal areas). Two seasons in one year are wet and dry. The dry season runs from October to March, with a wave of freshness accompanied by the dry and dusty wind of Harmattan, mainly experienced in the north in December and January. The wet season begins in April and ends in September. The country is divided into 36 states and a Federal Capital Territory [FCT], consisting of six geopolitical zones (Fig.  1 ), and covers an area of about 923,769 square kilometres. The states are categorized into six geopolitical zones with 774 constitutionally recognized local government areas (LGAs). The country has a total surface area of approximately 910,770 square kilometers (351,650 square miles) and a population density of 246 per Km 2 (636 people per mi 2 ) ( https://www.worldometers.info/world-population/nigeria-population/ ). About 53.8 percent lived in urban centres, while 46.2 percent lived in rural areas ( https://countrymeters.info/en/Nigeria ). Administrative boundaries were also consulted through the Demographic and Health Survey (DHS) Spatial Data Repository ( https://spatialdata.dhsprogram.com/ ). Administrative boundaries are subnational regions, usually administrative level 1, and vary between survey years and countries.

Geographical setting of the study area mapping

Sampling procedures

The 2021 Nigeria Malaria Indicator Surveys (NMIS), made available on the DHS Program website, were used in this study. The DHS is the primary source of benchmarking information on women’s and young children’s health in most developing countries. It has been established that it is also helpful in investigating the connection between environmental factors and health (Boyle et al. 2020 ). The 2021 NMIS utilized a two-staged sampling procedure. In the first stage, 568 enumeration areas (EAs) were chosen with probability proportional to the EA size. In the second stage, 25 households per urban and 25 per rural cluster were selected. The size of the EA is the number of families resident in the EA. The selection of the sample was carried out to be representative of each state. This resulted in the selection of 568 clusters in the country: 195 in urban areas and 373 in rural areas. The geospatial covariates of 2021 NMIS used for the study contained data on malaria prevalence and environmental indicators. This geospatial data was measured with remote sensing within two kilometres in urban areas and 10 kms in rural areas around the site of the DHS survey cluster for 568 clusters across the country for five years (2000, 2005, 2010, 2015, and 2020).

Data sources for malaria prevalence

The Demography and Health Survey remains a valuable source that, combined with complementary information, may provide the evidence base to understand better human health resources and resource allocation (Ogunsakin and Ginindza 2022 ). This study uses secondary DHS data and is available upon request for download from the DHS Website. Geospatial covariate data from DHS and Geographic Position System (GPS) cluster points were obtained from the GDPS spatial repository. DHS spatial cluster data ( n  = 568) are GPS points captured with survey data. Most DHS surveys are now geocoded, whereas a GPS coordinate is recorded in the approximate centre of each primary sampling unit. To keep participants confidential, GPS coordinates were moved to protect the confidentiality of participants by grouping households into groups and replacing them by up to 0–2 kms for urban areas and 0–10 kms for rural regions (Bennett and Smith 2017 ), geospatial covariates often accompany the data, and it is frequently challenging to link the data with the DHS programmer’s data to determine the impact of location on health outcomes. To mitigate this challenge, the DHS geospatial team developed a set of standard geospatial covariate files that have already been used for the dataset. The covariate indicators were obtained from raster and vector data. Raster data, like images and patterned areas, are based on pixels or cells to transmit data values. On the other hand, vector data, such as dots, lines, and polygons, show a characteristic’s location or discrete limit. A full description of the DHS geospatial covariate dataset and methodology is available (Bennett and Smith 2017 ).

Data collection and preparation

Environmental indicators (Table  1 )  related to malaria prevalence were compiled from the Nigeria Demographic Health Survey (NDHS). They included the Aridity Index (AI), Enhanced Vegetation Index (EVI), insecticide-treated bed net (ITN) coverage, Maximum Temperature (MT), precipitation, rainfall, daytime and night-time land surface temperatures, and Mean annual Potential Evapotranspiration (PET). Additionally, Nigeria shape files from DIVAS-GIS ( https://www.diva-gis.org/gdata ) were utilized. These indicators were selected to establish a spatiotemporal distribution model and use a Geographical Information System (GIS). These data often come with geospatial covariates, and it is frequently challenging to link them with the DHS Programmer’s data to determine the impact of location on health outcomes. To alleviate the difficulty, the DHS Programme Geospatial Team developed a set of standardized files of the most used geospatial covariates already linked with the dataset. The prevalence of malaria was measured in the NDHS using the average Plasmodium falciparum parasite rate (PfPR). This study obtained the DHS malaria survey year from the DHS spatial data repository site ( https://spatialdata.dhsprogram.com/ ). The prevalence of malaria depends first and foremost on its underlying transmission intensity (Alegana et al. 2013 ), which is driven by indicators such as interventions and environmental/climatic, socio-economic, and demographic factors.

Statistical analysis and spatial analysis models

To increase our insight into how malaria spreads through space and how several host compartments are linked, comprehensive information was obtained from 2021 Malaria Indicator Surveys (MIS). This type of survey provides another data source for understanding the spatiotemporal trends of malaria endemicity. This study described malaria prevalence in different regions in Nigeria from years 2000 to 2020. Descriptive statistical analysis, mean difference, and the association between malaria prevalence and environmental indicators were computed using absolute and relative frequencies and Pearson correlation coefficients.

In the first stage, Kaiser–Meyer–Olkin (KMO)-Bartlett assays were performed to determine the adequacy of spatial analysis data. Initial results of the KMO sampling adequacy measurement indicated that Principal Component Analysis (PCA) would be a suitable statistic for data reduction (Farzinpour et al. 2023 ). PCA is a linear statistical process universally deployed to reduce data dimensions by extracting the most significant variations from the original data sets (Ocampo-Marulanda et al. 2022 ). It uses an orthogonal transformation to convert possibly correlated indicators into several linearly uncorrelated, independent PCs. Different co-factors affected by collinearity affect malaria transmission at various stages. PCA allowed for keeping the main environmental features without losing part of the environmental co-factors associated with malaria prevalence. Hence, a PCA with varimax rotation was applied to find this study’s explanatory indicators.

In the second stage, we performed ordinary least square (OLS) regression to test the assumption of the models according to OLS requirements. The OLS is a global model and assumes the variable relationship to be persistent throughout the study area. The ultimate assumption of a multivariate regression model is that the relation between dependent and explanatory variables is spatially constant (Yue et al. 2018 ; Mohidem et al. 2021 ). Although the OLS model is not regarded as the best technique for the statistical analysis of spatial data, it has unswervingly been the appropriate initial point for any spatial regression analyses to uncover the significant indicators (explanatory indicators) associated with the outcome variable (malaria prevalence). This model would explain if malaria hotspots occurred due to the combination of these explanatory indicators. The implication of such is that it would assist in creating a prediction map that can be used for public health resource allocations due to the spatial relationship between the dependent and explanatory indicators.

In addition, before the main spatial mapping, the purpose was to pre-process data further and assess the extent of the statistical significance between the outcome and the selected explanatory indicators from the PCA result. In this case, the independent variables were tested for normality using the Shapiro–Wilk test at 0.001. Since the  p -values were all greater than 0.001, it showed that the independent variables were normally distributed. Using all normally distributed variables, we conducted the regression analysis and developed a model for malaria prevalence. Using this model, we generated a model prevalence raster map via ArcMap’s map calculation functions and complementary datasets. Further, Variance inflation factors (VIFs) were applied to monitor multicollinearity among the indicators through a VIF function in the R statistical programming environment using the “olsrr” package. We observed that multicollinearity does not occur as all the VIF values are less than ten, and the tolerance value is higher than 0.1.

Finally, the global spatial autocorrelation was evaluated using the Global Moran’s I statistic (Moran’s I) to assess the presence of geographical clustering and variability. A positive value for Moran’s Index implies a geographical clustering for malaria. In contrast, a negative value for Moran’s Index implies a dispersion and a zero value is distributed randomly when Moran’s Index is statistically significant. The local Getis-Ord spatial statistical tool was employed to detect statically significant hotspot and cold spot regions. Hotspot refers to the occurrence of high prevalence of malaria clustered together on the map; however, cold spots refer to the occurrence of low prevalence of malaria clustered together on the map. A point density map was applied to examine the temporal pattern of malaria prevalence. The point density tool in ArcMap was used to create the point density map. The point density tool calculates the density of point features around each output raster cell. Detailed maps were built with spatial data to visualize the distribution of malaria prevalence and hot spot analysis. The PCA was performed using the FactoMineR and factoextra packages in the R project. All the spatial maps were produced in ArcMap (version 10.4).

The health sciences database offers a wide array of data, ranging in diversity, size, and complexity, which can be effectively analyzed utilizing Geographic Information System (GIS) tools. Spatial analysis techniques, such as point density, spatial autocorrelation, raster map analysis, and hot spot analysis, were applied to examine spatiotemporal patterns of malaria prevalence. Gaining a deeper understanding of malaria prevalence, particularly spatial patterns, is crucial for allocating resources effectively for malaria prevention and control efforts (Lai et al. 2015 ).

Spatiotemporal statistics of environmental indicators and malaria mean separation across five years

The trends and spatial variation of the environmental indicators from 2000 to 2020 in Nigeria were evaluated in this study. Table 2 provides descriptive characteristics of the study population. This resulted in 568 clusters in the country, 195 in urban areas, and 372 in rural areas. The 2021 NMIS geospatial covariates contained malaria prevalence information. Concerning the data set, we have 2,835 cases of malaria. Table 2 also shows the descriptive trend of the explanatory indicators utilized. As shown, malaria prevalence in Nigeria was 48%, 43%, 35%, 23%, and 27% in 2000, 2005, 2010, 2015, and 2020, respectively.

Malaria prevalence in the six regions of Nigeria between 2000–2020 is summarized in Table  3 . Malaria cases were concentrated in the northwest, northcentral, south-south, and Southwest, accounting for 71.3% of all cases. Also, malaria cases were concentrated in rural settings, accounting for 65.6% of the entire cases in the study area. At the regional level, over the five years, malaria cases reached the ultimate number in the Northwest region with 560 cases (19.75%), followed by the North-Central zone with 505 cases, South-South with 495 cases, and Southwest with 460 cases (Table  3 ).

Multivariate data analysis of environmental indicators

The KMO measure employed for the environmental indicators set with seven indicators is 0.796. Since the KMO test is > 0.50, the environmental indicators set are acceptable for PCA. Bartlett’s test of sphericity has these values: χ 2  = 22,953.58, degrees of freedom = 21, and p  < 0.0001 for α  = 0.05, which is good and indicates that we can proceed with the PCA. According to the empirical rule and the eigenvalues chart, two principal components (PCs) were chosen. The first and second main components explained 62.4% and 14.3%, respectively. Further, the PCA conducted using Kaiser’s criterion resulted in maintaining two environmental indicators that explained 76.7% of the total inertia (Fig.  2 ). Figure  2 shows the indicator factorial load chart on components and the relationship between indicators in three different ways. Each indicator is a point for which the loads on the PCs give the coordinates. If an indicator is well represented by only two principal components (F1 and F2), the sum of the cos2 on these two PCs equals one. If so, the indicators will be placed on the circle of correlations. The cos2 values serve to estimate the quality of the representation (Mebatsion et al. 2012 ). The nearer an indicator approaches the circle of correlations, the better its depiction on the factor map (and the greater the importance of interpreting these components). Indicators closed at the trace’s center are less critical for the first components. Additionally, the first principal component explained 62.4% of the total inertia. The indicators that best contributed to this were maximum temperature (13.76%, correlation coefficient r = 0.92), LST (13.40%, r = 0.87), mean temperature (13.08%, r = -0.38), and maximum temperature (10.77%; r = 0.38). The second principal component explained 14.3% of the total inertia. The indicator with the most contribution is the mean temperature (10.08%, r = 0.76).

Circle of correlations and plot of the factor loadings of the indicators with F1 and F2

Based on the Pearson correlation coefficient, the aridity (coefficient = 0.165, p  =  < 0.001), EVI (coefficient = 0.296, p  =  < 0.001), and precipitation (coefficient = 0.157, p  =  < 0.001) indicators explained a positive association with malaria prevalence. In contrast, ITNC (coefficient = -0.377, p  =  < 0.001), land surface temperature (coefficient = -0.243, p  =  < 0.001), maximum temperature (coefficient = -0.144, p  =  < 0.001), and mean temperature (coefficient = -0.171, p  =  < 0.001) explained a negative association with malaria prevalence. These findings imply some environmental indicators show a direct relationship while others indicate an indirect one.

Environmental indicators affecting malaria prevalence using the OLS model

Following the results of the correlation analysis, collinearity was carried out among the selected indicators. Since the study environmental indicators do not have a normal distribution according to the Shapiro–Wilk test (Ho: The variable is normally distributed), VIF and conditional index (CI) were calculated for the multicollinearity analysis. However, all the VIFs of the reported indicators are less than 10, indicating no collinearity. The result of the collinearity reveals Average temperature (VIF = 1.479, tolerance = 0.676); precipitation (VIF = 1.947, tolerance = 0.513); ITN Coverage (VIF = 1.122, tolerance = 0.891); enhanced vegetation index (VIF = 3.135, tolerance = 0.319) and land surface temperature (VIF = 3.578, tolerance = 0.279). Hence, the OLS model was fitted to assess the contribution of each essential indicator of malaria prevalence. The OLS model suggests that the indicators have some impact on the study area (Table  4 ). Besides, the OLS model explains the 44.2% variation in malaria prevalence by environmental indicators. This implies that unknown environmental indicators cause 55.8% of malaria prevalence. The regression coefficients for indicators significantly correlated with malaria are presented in Table  4 . It showed the regression coefficients and the robust standard error estimated by the model.

Spatiotemporal trend of malaria prevalence rates from 2000 to 2020

Figure  3 depicts the overall temporal trend of malaria between 2000 and 2020. The South-South took the lead in 2000, followed by the Northcentral and Northwest. Malaria cases decreased significantly among these three geopolitical zones, apart from the northwest, between 2000 and 2020. Over the past year, a substantial number of malaria cases have been detected in the Southwest, which could be attributed to the dense vegetation of the rural area, lack of access to adequate medical facilities, or an unsafe environment (Ekpa et al. 2023 ). Malaria prevalence has declined in the Northeast year after year from 2000 to 2020 . The decline in this region could be attributed to a recent government effort to access good health institutions, attempts by residents to make their surroundings hygienic, or appropriate civic education about the impact of illness ( Oyibo et al. 2021 ). In general, the overall prevalence of malaria over the five years had a declining trend but with inconsistencies (Fig.  3 ).

Temporal trend analysis of malaria prevalence in Nigeria between 2000 and 2020

Moreover, five malaria frequency raster maps were developed for the 5-year- intervals (Fig. 3 A–E). Consequently, the appropriate spatial information has been well condensed, including geographic locations and spatial and temporal malaria changes. The most significant time frames were identified as 2000 (Fig.  4 A) and 2020 (Fig.  4 E); the highest (0.92619) and lowest (0.687981) frequencies were recorded, respectively. The raster maps also showed the center of malaria distribution in a portion of the southern region over time (Fig. 4 A–E).

A five-year-period malaria frequency raster map for Nigeria during 2000 ( A ), 2005 ( B ), 2010 ( C ), 2015 ( D ), and 2020 ( E )

Spatial distributions of malaria prevalence in 2000, 2005, 2010, 2015 and 2020

The detailed count of malaria cases in different regions is shown in Fig.  5 . Spatial variations in malaria prevalence have been observed at regional levels. Malaria cases were concentrated in western, northwest, and eastern Nigeria. The lowest concentration was observed in Nigeria’s northcentral, northeastern, and south-south regions (Fig.  5 ).

Geographical locations of data points and malaria prevalence in Nigeria: 2000( a ), 2005( b ), 2010( c ), 2015( d ), and 2020( e )

Also, when considering the density map, high-density regions for malaria cases (between 77.12 and 154.24) at the 1-km spatial resolution, which is displayed in red and orange, have been located at the junctions between South-East (Imo and Abia State), and Southwest (Osun State) (Fig.  6 ).

Point density map showing the distribution of malaria prevalence in the study areas from 2000–2020

Spatial autocorrelation analysis (Moran’s I) of malaria

This section presents the results of methods used to analyze malaria cases. We used the Moran Global Index to estimate the overall degree of spatial autocorrelation. As Fig.  7 shows, the Moran Index value was positive, indicating statistically significant malaria within the study area. Global analysis of the spatial autocorrelation of individual surveys disclosed that there were substantial clustered trends of malaria across the country: Global Moran’s I = 1.312, Z-score = 41.35, p -value < 0.001 in NDHS 2000; Global Moran’s I = 1.098, Z-score = 34.62, p -value < 0.001 in NDHS 2005; Global Moran’s I = 0.786, Z-score = 24.78, p -value < 0.001 in NDHS 2010; Global Moran’s I = 0.686, Z-score = 21.66, p -value < 0.001 in NDHS 2015, and Global Moran’s I = 0.732, Z-score = 23.09, p -value < 0.001 in NDHS 2020 (Fig.  7 a–e). In each output, the Z-score is primarily high and positive with a highly significant p -value, which showed 99% confidence for clustering malaria across Nigeria regions. The bright red (right side) and blue (left side) colors specified increased significance levels for which the probability of clustered patterns occurring by chance was less than 1%.

Spatial patterns of malaria prevalence in Nigeria: 2000( a ), 2005( b ), 2010( c ), 2015( d ), and 2020( e ). The clustered patterns showed that the likelihood of occurrence by random chance is less than 1%

Hot spot analysis of malaria prevalence between 2000–2020

Figure  8 presents the hot spot analysis using Getis-Ord Gi*. From Fig.  8 a–d, red and yellow disclosed significant clusters of high-risk (hotspot) malaria zones, while green and blue disclosed substantial clusters of low-risk (cold spot areas). From the findings, in 2000, the hot spot zones of malaria prevalence were seen in parts of South-South, South-east, Southwest, Northcentral, and Northwest (Fig.  8 a). On the other hand, in 2005, the hot spot zones of malaria prevalence were observed in regions of South-South, South-east, Southwest, Northcentral, while the cold spot zones were well pronounced in the Northeast, parts of the Northwest and South-South (Fig.  8 b). Likewise, in 2010, the hot spot zones of malaria prevalence in Nigeria were identified in the Southwest, part of the Northwest, Northcentral, and Northeast, while the cold spot zones were more significant in South-South and part of Southwest regions (Fig.  8 c). During NDHS 2015, statistically significant hot spot zones were seen in the Northern part of Nigeria. The statistically substantial cold spot zones were seen in the country’s southern region (Fig.  8 d). Similarly, during NDHS 2020, statistically significant hot spot zones were seen in the southern part of Nigeria. The country’s Northern region saw statistically substantial cold spot zones, excluding North-Central (Fig.  8 e).

Hot spot analysis of malaria prevalence in Nigeria: 2000( a ), 2005( b ), 2010( c ), 2015( d ), and 2020( e )

Malaria is a severe menace to global health and is more critical in all regions of Nigeria, considering the country’s population. Previous findings on malaria modeling in Nigeria have reported higher malaria prevalence across various areas of Nigeria (Dawaki et al. 2016 ; Makinde et al. 2021 ; Beargie et al. 2019 ). The current study presents a spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities in Nigeria for five years, ranging from 2000 to 2020. This study’s findings revealed that most malaria cases during the year investigated were substantially more extreme in the Northern region than in the Southern part of Nigeria. Conversely, the North Central region was more prevalent than all the Southern regions. However, the overall findings showed spatial discrepancies in the prevalence of the disease, indicating the northwest as the most affected area in the country. Also, the conclusions of this study identified the environmental indicators significant to malaria and determined their association with malaria prevalence using OLS regression. It was shown that enhanced vegetation index, annual land surface temperature, insecticide-treated bed net coverage, and mean temperature are significant indicators explaining the prevalence of malaria. Besides, precipitation also affects malaria prevalence in Nigeria. This is not surprising since variations in precipitation patterns in northern and southern Nigeria can affect the spread of malaria differently. An observation that suggests various parts of Nigeria may affect malaria diffusion differently. More precipitation occurs in southern Nigeria than in the Northern region. As a result, the spread is generally extreme early in the rainy and dry seasons.

The distribution of malaria spates shifted from the Northcentral and South-South region between 2000 and 2005 (Fig.  4 A–B) towards the Northwest and Southwest region by the end of 2020 (Figs.  3 C–E). However, the malaria distribution center turned to the Northern and part of the Southern region after 2005 (Fig.  3 B), a finding that can be due to many reasons. One justification may be due to the movement from rural areas to the cities because of terrorist activities in some regions, and this may have contributed to the population increase within the towns in this region where the urban infrastructure became inadequate (Joshua et al. 2014 ; Eme et al. 2018 ). Besides, malaria cases had almost been eradicated in the Northeast region by the end of 2020 (Fig.  4 E). This finding was consistent with the one conducted by Houben (Houben et al. 2013 ) in Northeastern Nigeria. The justification for these findings might align with the result of the Nigeria Malaria Indicator Survey 2021 (MIS), which established that mosquito nets are accessible to most of the states in the northeast region of Nigeria. For instance, the latest MIS report shows that bed nets are available in 68% of households in the Northeast of Adamawa (Nigeria Malaria Indicator Survey (NMIS) 2021 ).

Furthermore, the research findings indicated that environmental factors contribute to the increased malaria prevalence in the study area. A previous study by Sadoine et al. ( 2022 ) and Arhin et al. ( 2023 ) suggested that higher temperatures may lead to elevated malaria levels, potentially due to changes in mosquito populations. This underscores the importance of considering climate change in developing early warning systems and response strategies. Moreover, our study revealed similar positive correlations between malaria cases and environmental indicators. Supporting our findings, Tangena et al. ( 2023 ) reported a negative relationship between Insecticide-Treated Net Coverage (ITNC) and malaria cases. However, our results did not determine whether areas with high access to mosquito nets had a lower prevalence of malaria than areas with low access (Lindblade et al. 2015 ). Hence, this suggests that a cost-effective response program would consider promoting household access to local mass media, where household members receive frequent medical advice, particularly in rural areas with limited access to medical care.

Consequently, malaria prevalence was spatially clustered at the regional level during the separate investigation period of the study. The spatial distribution of the high prevalence of malaria during the respective survey period was in the western, northwest, and eastern parts of Nigeria. These variations could be due to climate change and declining precipitation distribution in these areas at different times. Spatial spreading has further revealed the spatial difference between malaria and other regions of Nigeria. For example, in the 2000 survey, statistically significant sensitive areas of malaria prevalence were found in the South-South, South-East, Southwest, Northcentral, and Northwest regions, while in the 2005 survey, malaria hotspots were observed in South-South, South-east, Southwest, Northcentral. This finding might be related to the variation in rainfall patterns. In 2011, the malaria hot spot was observed primarily in the Southwest, a portion of the northwest, Northcentral, and Northeast, while in the 2016 survey, statistically significant sensitive areas were observed in the northern parts of Nigeria (Ekpa et al. 2023 ). Likewise 2020, statistically substantial sensitive regions were found in southern Nigeria. This may be due to changing precipitation patterns in northern and southern Nigeria, which can affect malaria transmission dynamics differently (Okunlola and Oyeyemi 2019 ).

The study is limited by potential issues with data quality and gaps, particularly regarding the accuracy and accessibility of malaria cases and environmental data. While the temporal analysis might overlook short-term fluctuations in malaria rates, the regional spatial analysis could mask localized transmission patterns. Additionally, although the study identifies associations between environmental factors and malaria prevalence, it does not establish causation, thus limiting the generalizability of its findings to other locations or nations.

This study utilized GIS to analyze malaria spatial patterns and environmental indicators across six regions in Nigeria. Malaria cases were concentrated in the western, northwest, and eastern areas, correlating positively with aridity, EVI, and precipitation. Despite an overall decline in malaria cases over time, particularly in the northwest region, there are still challenges in meeting the Sustainable Development Goal (SDG) target in the short term. Recommendations include focusing on rural areas with low socio-economic status and high malaria incidence, as well as scaling up interventions in areas with concentrated malaria prevalence. The study’s model equation, incorporating factors like EVI, ITNC, LST, mean temperature, and precipitation, provides evidence-based guidance for public health professionals and policymakers. Ultimately, the findings offer valuable statistical insights and inform policymaking decisions to address malaria and improve public health outcomes in Nigeria.

Data availability

Data supporting study findings are available for download from the DHS MEASURE website, conditional on approval from DHS, and will be made available upon request from the first author.

Alegana VA, Atkinson PM, Wright JA, Kamwi R, Uusiku P, Katokele S, Snow RW, Noor AM (2013) Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial–temporal models. Spat Spatiotemporal Epidemiol 7:25–36

Article   PubMed   PubMed Central   Google Scholar  

Arambepola R, Keddie SH, Collins EL, Twohig KA, Amratia P, Bertozzi-Villa A, Chestnutt EG, Harris J, Millar J, Rozier J, Rumisha SF (2020) Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data. Sci Rep 10(1):18129

Article   CAS   PubMed   PubMed Central   Google Scholar  

Arhin K, Frimpong AO, Boso R, Acheampong K (2023) A double bootstrap data envelopment analysis model for evaluating malaria spending efficiency in Sub-Saharan Africa. Health Care Anal 3:100137

Article   Google Scholar  

Battle KE, Lucas TC, Nguyen M, Howes RE, Nandi AK, Twohig KA, Pfeffer DA, Cameron E, Rao PC, Casey D, Gibson HS (2019) Mapping the global endemicity and clinical burden of plasmodium vivax, 2000–17: a spatial and temporal modelling study. The Lancet 394(10195):332–343

Beargie SM, Higgins CR, Evans DR, Laing SK, Erim D, Ozawa S (2019) The economic impact of substandard and falsified antimalarial medications in Nigeria. PLoS ONE 14(8):e0217910

Bennett MM, Smith LC (2017) Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor. Remote Sens Environ 192:176–197

Boyle EH, King ML, Garcia S, Culver C, Bourdeaux J (2020) Contextual data in IPUMS DHS: physical and social environment variables linked to the demographic and health surveys. Popul Environ 41:529–549

Das A, Pathak K, Pathak MP, Saikia R, Gogoi U, Acharya NS (2023) Potential of herbal drug delivery in treating malaria. In: Shegokar R, Pathak Y (eds) Malarial drug delivery systems: advances in treatment of infectious diseases. Springer International Publishing, Cham, pp 333–357

Chapter   Google Scholar  

Dawaki S, Al-Mekhlafi HM, Ithoi I, Ibrahim J, Atroosh WM, Abdulsalam AM, Sady H, Elyana FN, Adamu AU, Yelwa SI, Ahmed A (2016) Is Nigeria winning the battle against malaria? Prevalence, risk factors and KAP assessment among Hausa communities in Kano State. Malar J 15:1–14

Ekpa DE, Salubi EA, Olusola JA, Akintade D (2023) Spatio-temporal analysis of environmental and climatic factors impacts on malaria morbidity in Ondo State, Nigeria. Heliyon 9(3)

Eme OI, Azuakor PO, Mba CC (2018) Boko Haram and population displacement in Nigeria: a case for psychological input. Practicum Psychologia 8(1)

Farzinpour A, Dehcheshmeh EM, Broujerdian V, Esfahani SN, Gandomi AH (2023) Efficient boosting-based algorithms for shear strength prediction of squat RC walls. Case Stud Constr Mat 18:e01928

Google Scholar  

García GA, Janko M, Hergott DE, Donfack OT, Smith JM, Mba Eyono JN, DeBoer KR, Nguema Avue RM, Phiri WP, Aldrich EM, Schwabe C (2023) Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions. Malar J 22(1):1–13

Houben CH, Fleischmann H, Gückel M (2013) Malaria prevalence in north–eastern Nigeria: a cross–sectional study. Asian Pac J Trop Med 6(11):865–868

Article   CAS   PubMed   Google Scholar  

https://countrymeters.info/en/Nigeria . Accessed 5 March 2023

Joshua IA, Makama JG, Joshua WI, Audu O, Nmadu AG (2014) Disasters in Nigeria: A public health perspective. J Community Med Prim Health Care (JCMPHC) 26(1):59–75

Kamel Boulos MN, Geraghty EM (2020) Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19(1):1–12

Kanmiki EW, Awoonor-Williams JK, Phillips JF, Kachur SP, Achana SF, Akazili J, Bawah AA (2019) Socio-economic and demographic disparities in ownership and use of insecticide-treated bed nets for preventing malaria among rural reproductive-aged women in northern Ghana. PLoS ONE 14(1):e0211365

Lai S, Huang Z, Zhou H, Anders KL, Perkins TA, Yin W, Li Y, Mu D, Chen Q, Zhang Z, Qiu Y (2015) The changing epidemiology of dengue in China, 1990–2014: a descriptive analysis of 25 years of nationwide surveillance data. BMC Med 13(1):1–12

Article   CAS   Google Scholar  

Lawson AB (2018) Bayesian disease mapping: hierarchical modeling in spatial epidemiology. CRC Press

Book   Google Scholar  

Lindblade KA, Mwandama D, Mzilahowa T, Steinhardt L, Gimnig J, Shah M, Bauleni A, Wong J, Wiegand R, Howell P, Zoya J (2015) A cohort study of the effectiveness of insecticide-treated bed nets to prevent malaria in an area of moderate pyrethroid resistance. Malawi Malar J 14:1–15

Mac PA, Kroeger A, Daehne T, Anyaike C, Velayudhan R, Panning M (2023) Zika, Flavivirus and Malaria Antibody Cocirculation in Nigeria. Trop Med Infect 8(3):171

Makinde OS, Abiodun GJ, Ojo OT (2021) Modelling of malaria incidence in Akure, Nigeria: negative binomial approach. GeoJournal 86:1327–1336

Mebatsion HK, Paliwal J, Jayas DS (2012) Evaluation of variations in the shape of grain types using principal components analysis of the elliptic fourier descriptors. Comput Electron Agric 80:63–70

Mohidem NA, Osman M, Hashim Z, Muharam FM, Mohd Elias S, Shaharudin R (2021) Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor. Malaysia Plos One 16(6):e0252146

Musa GJ, Chiang PH, Sylk T, Bavley R, Keating W, Lakew B, Tsou HC, Hoven CW (2013) Use of GIS mapping as a public health tool–-from cholera to cancer. Health serv insights 6:HSI-S10471

Nigeria Malaria Indicator Survey (NMIS) (2021). https://dhsprogram.com/data/dataset/Nigeria_MIS_2021.cfm?flag=1

Ocampo-Marulanda C, Fernández-Álvarez C, Cerón WL, Canchala T, Carvajal-Escobar Y, Alfonso-Morales W (2022) A spatiotemporal assessment of the high-resolution CHIRPS rainfall dataset in southwestern Colombia using combined principal component analysis. Ain Shams Eng J 13(5):101739

Ogunsakin RE, Ginindza TG (2022) Bayesian spatial modeling of diabetes and hypertension: results from the south africa general household survey. Int J Environ Res Public Health 19(15):8886. Published 22 Jul 2022. https://doi.org/10.3390/ijerph19158886

Ogunsakin RE, Chen DG (2020) Bayesian spatial-temporal disease modeling with application to malaria. Statistical methods for global health and epidemiology: principles, methods and applications. pp 323–343

Okunlola OA, Oyeyemi OT (2019) Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria. Sci Rep 9(1):1–11

Oviedo A, Abubakar A, Uhomoibhi P, Maire M, Inyang U, Audu B, Iriemenam NC, Ogunniyi A, Ssekitooleko J, Kalambo JA, Greby SM (2023) Plasmodium falciparum infection prevalence among children aged 6–59 months from independent DHS and HIV surveys: Nigeria, 2018. Sci Rep 13(1):1998

Oyibo W, Ntadom G, Uhomoibhi P, Oresanya O, Ogbulafor N, Ajumobi O, Conway D (2021) Geographical and temporal variation in reduction of malaria infection among children under 5 years of age throughout Nigeria. BMJ Glob Health 6(2):e004250

Pourtois JD, Tallam K, Jones I, Hyde E, Chamberlin AJ, Evans MV, Ihantamalala FA, Cordier LF, Razafinjato BR, Rakotonanahary RJ, Tsirinomen’ny Aina, A. (2023) Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: evidence from rural Madagascar. PLOS Glob Public Health 3(2):e0001607

Rivera JT, Gutiérrez JD (2023) Environmental and socio-economic determinants of the occurrence of malaria clusters in Colombia. Acta Tropica 241:106892

Rodríguez ZJ, Melo FL, Torres A, Agrawal N, Cortés-Vecino JA, Lozano JM (2023) Natural plasmodium falciparum infection stimulates human antibodies to msp1 epitopes identified in mice infection models upon non-natural modified peptidomimetic vaccination. Molecules 28(6):2527

Sadoine ML, Smargiassi A, Liu Y, Gachon P, Dueymes G, Dorsey G, Fournier M, Nankabirwa JI, Rek J, Zinszer K (2022) The influence of the environment and indoor residual spraying on malaria risk in a cohort of children in Uganda. Sci Rep 12(1):11537

Sankineni S, Chauhan S, Shegokar R, Pathak Y (2023) Global Health and Malaria: Past and Present. In: Shegokar R, Pathak Y (eds) Malarial Drug Delivery Systems: Advances in Treatment of Infectious Diseases. Springer International Publishing, Cham, pp 1–16

Sarfo JO, Amoadu M, Kordorwu PY, Adams AK, Gyan TB, Osman AG, Asiedu I, Ansah EW (2023) Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions. Eur J Med Res 28(1):1–14

Simon-Oke AI, Awosolu OB, Odeyemi O (2023) Prevalence of Malaria and COVID-19 Infection in Akure North Local Government Area of Ondo State, Nigeria. J Parasitol 2023:1–7

Taiwo AB, Fatunla OA, Ogundare OE, Oluwayemi OI, Babatola AO, Ajite AB, Ajibola AE, Olajuyin A, Sola-Oniyide B, Olatunya OS (2023) Households health care financing methods: social status differences, economic implications and clinical outcomes among patients admitted in a pediatric emergency unit of a tertiary hospital in South West Nigeria. Glob Pediatr Health 10:2333794X231159792

Tangena JAA, Mategula D, Sedda L, Atkinson PM (2023) Unravelling the impact of insecticide-treated bed nets on childhood malaria in Malawi. Malar J 22(1):1–12

Uwemedimo OT, Lewis TP, Essien EA, Chan GJ, Nsona H, Kruk ME, Leslie HH (2018) Distribution and determinants of pneumonia diagnosis using integrated management of childhood illness guidelines: a nationally representative study in Malawi. BMJ Glob Health 3(2):e000506

Weiss DJ, Lucas TC, Nguyen M, Nandi AK, Bisanzio D, Battle KE, Cameron E, Twohig KA, Pfeffer DA, Rozier JA, Gibson HS (2019) Mapping the global prevalence, incidence, and mortality of plasmodium falciparum, 2000–17: a spatial and temporal modelling study. The Lancet 394(10195):322–331

World Health Organization. https://www.who.int/news-room/fact-sheets/detail/malaria . Accessed 22 March

Worldometer. https://www.worldometers.info/world-population/nigeria-population/ . Accessed 21 Apr 2023

Yang GJ, Vounatsou P, Xiao-Nong Z, Utzinger J, Tanner M (2005) A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Trop 96(2–3):117–129

Article   PubMed   Google Scholar  

Yang K, LeJeune J, Alsdorf D, Lu B, Shum CK, Liang S (2012) Global distribution of outbreaks of water-associated infectious diseases. PLoS Negl Trop Dis 6(2):e1483

Yue Y, Sun J, Liu X, Ren D, Liu Q, Xiao X, Lu L (2018) Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: a case study in five districts of Guangzhou City, China, 2014. Int J Infect Dis 75:39–48

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Acknowledgements

We thank the Demographic and Health Survey for supplying us with the environmental indicators’ dataset used for this study.

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Ropo Ebenezer Ogunsakin

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Ropo Ebenezer Ogunsakin: Conceived and designed the study, compiled the malaria data, conducted spatial analyses, developed the malaria maps, and analyzed and modelled malaria prevalence, methodology, Writing - original draft, and was the primary author of the manuscript. Johnson Adedeji Olusola: Designed the study, Writing – review & editing. Bayowa Teniola Babalola: Assisted with the analysis, Writing – review & editing. Ayodele Oluwasola Joshua: Assisted with the analysis, Writing & editing. Moses Okpeku: Review & editing the final version.

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Ogunsakin, R.E., Babalola, B.T., Olusola, J.A. et al. GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities. Parasitol Res 123 , 262 (2024). https://doi.org/10.1007/s00436-024-08276-0

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Introduction, prevalence trends, associations with prevalence of malaria, meta regression, quality assessment, data availability statement, author contribution, funding statement, competing interest, ethical standard, malaria in pregnancy: meta-analyses of prevalence and associated complications.

Published online by Cambridge University Press:  13 February 2024

• Supplementary materials

This review aims to assess the prevalence of malaria in pregnancy during antenatal visits and delivery, species-specific burden together with regional variation in the burden of disease. It also aims to estimate the proportions of adverse pregnancy outcomes in malaria-positive women. Based on the PRISMA guidelines, a thorough and systematic search was conducted in July 2023 across two electronic databases (including PubMed and CENTRAL). Forest plots were constructed for each outcome of interest highlighting the effect measure, confidence interval, sample size, and its associated weightage. All the statistical meta-analysis were conducted using R-Studio version 2022.07. Sensitivity analyses, publication bias assessment, and meta-regression analyses were also performed to ensure robustness of the review. According to the pooled estimates of 253 studies, the overall prevalence of malaria was 18.95% (95% CI: 16.95–21.11), during antenatal visits was 20.09% (95% CI: 17.43–23.06), and at delivery was 17.32% (95% CI: 14.47–20.61). The highest proportion of malarial infection was observed in Africa approximating 21.50% (95% CI: 18.52–24.81) during ANC and 20.41% (95% CI: 17.04–24.24) at the time of delivery. Our analysis also revealed that the odds of having anaemia were 2.40 times (95% CI: 1.87–3.06), having low birthweight were 1.99 times (95% CI: 1.60–2.48), having preterm birth were 1.65 times (95% CI: 1.29–2.10), and having stillbirths were 1.40 times (95% CI: 1.15–1.71) in pregnant women with malaria.

Malaria during pregnancy is a significant source of concern in public health because of the negative repercussions it can have, not only on the mother but also on the developing foetus [ Reference Adam, Ibrahim and Elhardello 1 ]. According to the World Malaria Report by World Health Organization (WHO), there were 241 million cases of malaria in the year 2020 in 85 malaria endemic countries, an increase from the 227 million cases in 2019 [ Reference Aleem and Bhutta 2 ]). Concurrently, around 33.8 million pregnancies occurred during the same duration, with 34 percent of women accounting to 11.6 million being exposed to malaria infection during pregnancy [ Reference Aleem and Bhutta 2 ]).

According to literature, there are two types of malaria that can occur during pregnancy: placental malaria (PM) and gestational malaria (GM), both of which are diagnosed by demonstrating the presence of Plasmodium spp. in the placenta or the mother’s peripheral blood using a thick blood smear (TBS), polymerase chain reaction (PCR), or rapid diagnostic tests [ Reference Almaw 3 ]. Simple, quick, and more convenient, rapid diagnostic techniques have great potential in malaria detection. They may be of great utility as helpful instruments in the global delivery of health services by improving overall diagnosis of malaria infections. However, the testing procedure must be improved further to overcome the shortcomings of the present implementation. In spite of its drawbacks, such as time and expense, PCR remains the gold standard for identification of malaria parasites [ Reference Balduzzi, Rücker and Schwarzer 4 ].

Several unfavourable effects have been reported to occur after parasite sequestration, including maternal anaemia, foetal growth restriction, abortion or stillbirth, premature delivery, and low birthweight (LBW) [ Reference Cardona-Arias and Carmona-Fonseca 5 ]. Malaria contributes to up to 26% of cases of severe anaemia during pregnancy in endemic regions, and it is responsible for between 0.5 and 23% of all maternal fatalities caused by malaria [ Reference De Beaudrap 6 ]. In sub-Saharan Africa, malaria during pregnancy is responsible for up to 20% of LBW, or 35% of all avoidable LBW [ Reference Dellicour 7 , Reference Desai 8 ]. Successful malaria preventive measures during pregnancy have been shown to reduce perinatal death by 27% [ Reference Dellicour 7 ].

In malaria-endemic regions, pregnancy and the disease have been shown to worsen each other, especially for first-time mothers and individuals who were previously resistant to malaria. Though it has been previously reported that multigravida bear the heaviest burden of malaria in pregnancy both in terms of prevalence and outcome, it is now widely acknowledged that women with greater gravidities, even in areas of low transmission, are also susceptible [ Reference Dellicour 7 ].

About 125 million pregnant women worldwide are at risk of contracting malaria caused by either Plasmodium falciparum or Plasmodium vivax each year [ Reference Dosoo 9 ]. While Plasmodium falciparum malaria is responsible for most of the malaria-related morbidity, Plasmodium vivax may also play a crucial role in certain regions of South America and Southeast Asia [ Reference Falade 10 ]. A systematic review of sub-Saharan Africa concluded that the prevalence of Plasmodium falciparum was (22.1%, 95% CI: 17.1–27.2 %), followed by Plasmodium vivax 3% (95%CI: 0–5%), Plasmodium malariae 0.8% (95%CI: 0.3–0.13%), and Plasmodium ovale 0.2% (95%CI: −0.01–0.5) [ Reference Furuya-Kanamori, Barendregt and Doi 11 ]. Similarly, another meta-analysis has shown a significant incidence of malaria in pregnancy in Colombia, which emphasizes the urgent need for researchers, research funding organizations, government agencies, and health authorities to pay more attention to its research and intervention [ Reference Guyatt and Snow 12 ].

Based on the significant burden of malaria on the pregnancy outcomes and the health of pregnant women, marked variation in the available evidence is recorded due to diagnostic technique variability, heterogeneity in the enormity of disease, low sample size in some studies, lack of solid meta-analysis of relevant literature, and a substantial lack of understanding on the prevalence of malaria associated in pregnancy, which highlights the significance of a systematic review to quantify the prevalence of disease and understand the underpinnings pertaining to the causality and the burden of outcomes associated. Thus, the current review aims to assess the overall prevalence of malaria in pregnancy along with time-specific burden, that is, during antenatal visits and during delivery and to deduce the specie-specific and regional prevalence of infection. Secondarily, the review also aims to estimate the proportions of adverse pregnancy outcomes and its association with the presence of malarial infection.

Study design

Using the guidelines provided by ‘Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)’, a systematic review was conducted. Comprising of a 27-component checklist, the PRISMA guidelines aids in producing a transparent and coherent review which can be easily understood and interpretated globally [ Reference Guyatt and Snow 13 ].

Data source and searches

To find relevant articles, a thorough and systematic search was conducted on 31 July 2023 across two electronic databases (including PubMed and CENTRAL) using precise and accurate search strategies. Publications from the year 2000 to 2023 were searched using database specific strategies. To ensure completeness and entirety, manual searches were also conducted in addition to cross-referencing of source articles to avoid missing out any important source of evidence.

Search strategies

Based on the MeSH terminologies specific to the objectives and aims of the study, the following search strategy was developed to retrieve studies from databases.

(“Malaria”[Mesh] OR “Malaria, Vivax”[Mesh] OR “Malaria, Falciparum”[Mesh] OR “P. vivax malaria” OR “P. falciparum” OR “maternal malarial” OR “congenital malaria” OR “foetal malaria” OR “malaria in pregnancy” OR “malaria in pregnant”) AND ("Pregnancy"[Mesh:NoExp] OR pregnancy OR pregnant OR “malaria in pregnancy” OR pregnant women OR pregnant woman) AND (parasite densities OR diagnostic test* OR diagnostic* OR endemicity OR Intermittent Preventive Treatment OR IPT OR Intermittent Preventive Therapy OR Insecticide Treated Nets OR drug therapies)

Eligibility criteria

All the studies quantifying the burden of malaria in pregnancy along with the impact of Plasmodium falciparum and vivax on maternal and child adverse outcomes were taken into consideration. The studies considered eligible were those that were published after the year 2000, were in English language, and catered human subjects only.

The exclusion criteria involved: (1) Clinical trials in which the randomization was done on a predefined criterion; (2) Cohort studies in which the exposure of interest was malaria cases; (3) Case control studies in which the cases were malaria patients as this would not enumerate the burden; (4) Study designs including case reports, case series, commentaries, editorials, narrative reviews, and systematic reviews; (5) Studies using data from previous publications of the author.

To avoid double-counting/the same data being pooled more than once, data reported from different studies, such as those by the same authors, were checked to ensure patient cohorts were non-overlapping.

Study selection and data extraction

Articles retrieved from the databases were screened by two independent reviewers at a title and abstract level. Articles not immediately ruled out as irrelevant were then reviewed in a similar manner on a full-text basis. Where the relevance of an article was deemed ambiguous, or reviewer decisions conflicted, consensus was reached amongst the authors. Data were then extracted from each included article by a reviewer.

Extracted parameters included author names, publication year, location of study, diagnostic test used for malaria, malaria case count, strain of organism involved, time point in pregnancy at which diagnosis was made, sample size, and calculated prevalence. Additionally, where reported, data were extracted on complications and adverse outcomes for the pregnant women and their foetuses/offspring, for both test-positive and test-negative pregnant women. These data were used to perform secondary analyses to evaluate the association between malaria and maternal and infant morbidity.

Some studies reported adjusted odds ratios but not dichotomized data. Due to the non-uniformity in the method by which these odds ratios were computed, pooling them was deemed invalid and they were not extracted for meta-analysis.

Studies using multiple diagnostic modalities

Certain included studies tested the same subjects at the time time point for malaria using multiple diagnostic tools. Based on the evidence, a hierarchy of selection was determined to prefer PCR data, followed by microscopy, and then rapid diagnostic tests [ Reference Guyatt and Snow 13 , Reference Kaforau 14 ]. In this manner, the most reliable data for a cohort at a given time point were pooled in the analysis without double or triple counting.

Studies reporting prevalence of multiple strains or at multiple time points

Some included studies did not explicitly state an overall prevalence of malaria but reported prevalence in a strain-wise fashion. In these cases, it was evaluated if the reported patient positive for different strains of malaria were non-overlapping groups. Where this condition was met, the groups were combined, and the overall prevalence was calculated and utilized in the analysis.

Similarly, some studies reported prevalence data for a cohort during ANC and then again during delivery. Given that these estimates were taken at distinct points in time, they were considered separate datapoints and pooled in overall estimates of prevalence.

Peripheral and placental malaria

Where studies clearly reported overall prevalence data, the data were extracted and analysed simply. However, some studies reported results having tested participants for both peripheral and placental malaria. In such cases, data on peripheral infection were pooled and analysed and placental infection data were only used if that on peripheral infection was not reported.

Data analysis

The proportions of pregnant women who tested positive for malaria using any diagnostic technique were tabulated. Similarly, the proportions of pregnant women with adverse pregnancy outcomes were also recorded for both test-positive and test-negative women.

Along with confidence intervals of 95%, the following quantitative assessments of malaria were deduced:

1. Overall prevalence of malaria in pregnancy irrespective of the diagnostic test used, period of pregnancy and organism involved.

2. Prevalence of infection during antenatal care and at delivery.

3. Regional disparities of malaria proportions according to UNICEF regions.

4. Association of malaria with adverse pregnancy outcomes.

Due to heterogeneity caused by experimental differences between the included articles, all reported results were computed using a random-effects model meta-analysis. Point estimates and 95% confidence intervals are reported, while heterogeneity was evaluated using the Tau-squared and I-squared metrics, which represent the variance of the distribution of estimates reported by included studies and the percentage of that variation not attributable to sampling error, respectively. Forest plots were constructed for each outcome of interest highlighting the effect measure, confidence interval, sample size, and its associated weightage. Both pooled estimates and sub-groups estimates were illustrated using effective plots.

Publication biases were assessed using DOI plots and LFK index [ Reference Kaforau 14 ]. The sensitivity analysis was conducted through the leave-one-out method. This method recalculates the effect sizes and heterogeneity by removing one study each time [ Reference Kattenberg 15 ]. Additionally, meta regression analyses were conducted to evaluate differences in proportions within subgroups of region, species, and diagnostic test.

R-Studio version 2022.07.1 was used to carry out the meta-analysis using the package ‘meta’ (version 6.1.0) [ Reference Lawn 16 ], and a p -value of less than 0.05 was taken as benchmark of significance.

Each study included in the systematic review underwent a quality assessment to evaluate the research methodology employed in each study to ensure the reliability and validity of its findings. The Joanna Briggs Institute (JBI) critical appraisal tools, widely acknowledged and reliable for quality assessment, were used to investigate each study [ Reference Mabrouk 17 ]. It covers variations of study designs including analytical cross-sectional analysis, case–control, and cohort studies which were used to report the quality of studies in this systematic review. This tool aims to understand the extent to which the study has considered the potential bias in its design and implementation. An overview of the results has been provided in the tables.

Figure 1 below depicts the selection process of the studies included in the review. Initially, 7824 studies were retrieved out of which only 253 qualified for the final inclusion.

Figure 1. PRISMA diagram of included studies.

The characteristics of the included studies including the author and the year, title, study design, region, sample size, point of pregnancy at which the data were recorded, and diagnostic test used are summarized in Table 1 below.

Table 1. Characteristics of included studies

Supplementary Figure 2 shows overall trends of prevalence of malaria in an ascending order of years, estimated from 253 studies. As evident, the proportions have remained relatively persistent with the passing years and no significant reduction has been observed from the year 2000 to year 2023.

According to the pooled estimates, the prevalence of malaria was 18.95% (95% CI: 16.95–21.11, n=375,792) based on random-effects model. Similarly, when bifurcated on the time of reporting, the prevalence of malaria during antenatal visits was 20.09% (95% CI: 17.43–23.06, n =282,169, studies = 182) and during delivery was 17.32% (95% CI: 14.47–20.61, n = 93,623, studies = 121) using the same random-effects model. The heterogeneity was deduced using I-squared test, which was reported to be 99% in each model. Sensitivity analysis showed no change in the heterogeneity ( Supplementary Appendix Figure 1a ). The DOI plot was symmetrical indicating no publication bias ( Supplementary Appendix Figure 1b ).

Specie-specific prevalence

During the antenatal period, the prevalence of malaria caused by Plasmodium falciparum alone was 17.76% (95% CI: 15.04–20.85, n = 269,537, studies = 166) using random-effects model. This was followed by Plasmodium vivax caused infections accounting to 4.41% (95% CI: 2.80–6.89, n = 164,008, studies = 26) prevalence. In about 1.69% (95% CI: 0.80–3.52, n = 109,497, studies = 16) pregnant women, traces of both Plasmodium falciparum and vivax species were found as shown in Supplementary Figure 3a and Figures 2 and 3 .

Figure 2. Forest Plot depicting Plasmodium vivax pooled estimates of prevalence of malaria with 95% CIs.

Figure 3. Forest Plot depicting Plasmodium falciparum and vivax pooled estimates of prevalence of malaria with 95% CIs.

A similar pattern of infection was observed during delivery. Approximately 16.55% (95% CI: 13.57–20.04, n= 73,417, studies = 113) pregnant women were infected by Plasmodium falciparum and 5.18% (95% CI: 3.10–8.54, n= 21,928 studies = 17) by Plasmodium vivax, and 0.73% (95% CI: 0.19–2.75, n = 8149, studies = 7) were infected by both Plasmodium falciparum and vivax. The sensitivity analysis showed no change in heterogeneity ( Supplementary Appendix Figure 3a–c ). The DOI plots showed no asymmetry for Plasmodium falciparum but for Plasmodium vivax alone and combined vivax and falciparum thus concluding positive publication bias ( Supplementary Appendix Figure 2a–c ).

Regional distribution of malarial infection

The meta-analysis revealed that the highest proportion of malarial infection during ANC was observed in Africa approximating 21.50% (95% CI: 18.52–24.81, n = 110,012, studies = 143). This was followed East Asia and Pacific region accounting to 17.28% (95% CI: 9.29–29.86, n = 157,986, studies = 18). The lowest prevalence was observed in South Asia 8.66% (95% CI: 3.06–22.17, n = 8,513, studies = 9) followed by Latin America and Caribbean region 14.20% (95% CI: 6.31–28.91, n = 3,929, studies = 7) as shown in Supplementary Figure 4a . Sensitivity analysis revealed no significant difference. A symmetrical DOI plot was also indicative of no publication bias ( Supplementary Appendix Figures 4a and 5a ).

A similar random-effects meta-analysis at the time of delivery revealed that the prevalence of malaria in Africa was 20.41% (95% CI: 17.04–24.24, n = 46,925, studies = 95), in East Asia in Pacific Region was 16.33% (95% CI: 8.46–29.19, n = 22,214, studies =12), in Latin America and Caribbean region was 5.28% (95% CI: 2.68–10.12, n = 4,834, studies = 7), and in South Asia was 4.14% (95% CI: 1.52–10.80, n = 19,071, studies = 6) as shown in Supplementary Figure 4b . Sensitivity analysis revealed no significant difference. On the other hand, DOI for delivery showed minor asymmetry favouring positive publication bias ( Supplementary Appendix Figure 5b ).

Adverse pregnancy outcomes have shown mild-to-moderate associations with the prevalence of malarial infection in pregnancy.

A statistically significant association was observed between anaemia and malaria presence in 62 studies as shown in Figure 4 . The odds of having anaemia were 2.40 times (95% CI: 1.87–3.06) in malaria-positive women as compared to malaria-negative women. The heterogeneity of the studies as calculated with I-squared value was 86%. Sensitivity analysis revealed that the effect size of meta-analysis was deviating significantly due to two studies; hence, they were excluded ( Supplementary Appendix Figure 6 ). The DOI plot showed minor asymmetry thus depicting minimal publication bias ( Supplementary Appendix Figure 7 ).

Figure 4. Forest plot confirming association of malaria in pregnancy and anaemia.

Low birthweight

A significant association of low birthweight of the babies and malaria-positive women was also observed after pooling estimates from 42 studies as shown in Figure 5 . The overall odds ratio deduced was 1.99 (95% CI: 1.60–2.48). Sensitivity analyses revealed that two studies were responsible for major deviation in the effect size; hence, they were excluded. Absence of publication bias was confirmed by symmetrical DOI plot ( Supplementary Appendix Figure 9 ).

Figure 5. Forest plot confirming association of malaria in pregnancy and LBW.

Pre-term birth

A positive relation between malaria in pregnancy and preterm births was observed in 24 studies with an overall odds ratio of 1.65 (95% CI: 1.29–2.10) as shown in Figure 6 . The random-effects model took into consideration the heterogeneity of 49% as calculated by I-squared value. Sensitivity analysis revealed that the effect size of meta-analysis was deviating significantly due to one study; hence, it was excluded. The DOI plot showed major asymmetry, thus indicating positive publication bias ( Supplementary Appendix Figure 11 ).

Figure 6. (a) Forest plot confirming association of malaria in pregnancy and preterm births. (b) Forest plot confirming association of malaria in pregnancy and stillbirths. (c) Forest plot confirming association of malaria in pregnancy and SGA.

A statistically significant association was observed between stillbirths amongst malaria test-positive pregnant women with and odds ratio of 1.40 (95% CI: 1.15–1.71) based on ten studies as shown in Figure 6b . Sensitivity analyses revealed that one study was responsible for major deviation in the effect size; hence, it was excluded. The DOI plot showed major asymmetry, thus indicating positive publication bias ( Supplementary Appendix Figure 13 ).

Small for gestational age (SGA)

A significant association has been observed between SGA and pregnancy malaria with an overall odds ratio of 1.50 (95% CI: 1.42–1.59) 1.39 (95% CI: 0.99–1.96) using estimates of six studies as shown in Figure 6c . Sensitivity analysis revealed that the effect size of meta-analysis was deviating significantly due to one study; hence, it was excluded. The DOI plot shows minor asymmetry, thus depicting minimal publication bias ( Supplementary Appendix Figure 15 ).

An insignificant statistical association was observed in abortion and malaria in pregnancy with an odds ratio of 0.85 (95% CI: 0.21–3.48) using estimates from five studies ( Supplementary Appendix Figure 16 ). Sensitivity analyses revealed that two studies were responsible for major deviation in the effect size; hence, they were excluded. The DOI plot showed major asymmetry, thus confirming negative publication bias ( Supplementary Appendix Figure 17 ).

Preeclampsia

A statistically insignificant association was seen with pre-eclampsia using the estimates from three studies with an odds ratio of 0.82 (95% CI: 0.16–4.34). Sensitivity analyses revealed that one study was responsible for major deviation in the effect size; hence, it was excluded ( Supplementary Appendix Figure 18 ). The DOI showed no asymmetry, thus confirming absence of publication bias ( Supplementary Appendix Figure 19 ).

Growth restriction

A statistically insignificant association was seen with growth restriction using the estimates from two studies with an odds ratio of 1.21 (95% CI: 0.04–35.52, n= 508). There was no change in effect observed during sensitivity analysis ( Supplementary Appendix Figure 20 ). The DOI showed major asymmetry, thus confirming negative publication bias ( Supplementary Appendix Figure 21 ).

Results of meta regression analyses for region, diagnostic test, and specie variables are displayed in Table 2 Test of moderators were found significant in both region ( p < 0.001) and specie ( p -value < 0.01), indicating a significant influence on the effect sizes. The R-squared for region showed that 10.45% of the difference in the true effect sizes can be explained by the region, and 3.67% by the specie, and 1.22% by the diagnostic variable.

Table 2. Meta regression analysis of effect size with respect to region, diagnostic tests, and specie

For meta-regression analysis by region, South America had the highest effect sizes when compared with South Asia (b=1.92, p < 0.001) which was followed by Africa (b=1.35, p < 0.001). Conversely, the effect sizes for the East Asia and Pacific were relatively lower (b=1.07, p < 0.01).

None of the diagnostic tests showed a significant difference in effect sizes when compared with histopathology, as evident. With respect to specie, Plasmodium falciparum was the only specie with significantly higher effect size when compared to Plasmodium vivax in the meta regression analysis by specie.

All studies were included in the review after quality assessment. The JBI checklists for case–control, cohort, and cross-sectional studies were used according to the study designs ( Table 3 ). Each study was scored out of the number of questions included in the checklist. The highest score was 10 for case–control studies, 11 for cohort studies, and 8 for cross-sectional studies.

Table 3. JBI appraisal checklist for included studies

Abbreviations: Y, Yes; N, No; U, Unclear; N/A, Not Applicable.

Out of the 8 case–control studies, three studies scored 10/10, one study scored 8/10, and four studies scored 7/10. Of the 71 cohort studies, one study scored 11/11, twenty-two studies scored 10/11, seventeen studies scored 8/11, nineteen studies scored 7/11, one study scored 6/11, and two studies scored 5/11. Of the 174 cross-sectional studies, seventy-one studies scored 8/8, fifteen studies scored 7/8, sixty-three studies scored 6/8, nineteen studies scored 5/8, five studies scored 4/8, and one study scored 3/8.

The most common problems that came across overall were the identification of confounding factors and strategies to deal with confounding factors were not mentioned clearly. In the cohort studies, the most common problem was that the subjects were not free of the outcome at the start of the study and strategies to deal with incomplete follow-up were not clearly mentioned.

Malaria in pregnancy is a cause of extensive morbidity and mortality globally, both among infectious diseases and overall. While numerous studies have estimated the rate of infection in different regions, this meta-analysis synthesizes an immense volume of data to describe the overall prevalence and distribution of the disease. The findings of our study highlight that prevalence of malaria varies geographically, temporally, and species specifically. Amongst the many virulent species, Plasmodium falciparum has been the cause of highest incidence of infection. Similarly, African region has shown highest regional prevalence amongst the other regions. In addition, prevalence was higher during the antenatal visits as opposed to at delivery.

In addition, we have secondarily analysed and demonstrated that several morbid disease states and outcomes, such as anaemia, low birthweight, preterm birth, and stillbirth, may be significantly associated with malaria during pregnancy. These detrimental factors to the well-being and survival of mothers and their infants may influence maldevelopment and poor health in individuals throughout the life-course if left unaddressed.

As estimated by our study, Africa presents with the highest burden of malaria in pregnancy. This is in line with studies conducted earlier in the region and the report presented by the World Health Organization [ Reference Aleem and Bhutta 2 , Reference Matteelli 18 – Reference Moya-Alvarez, Abellana and Cot 20 ]. This may be due to malarial endemicity of the region as it is considered as the most tropical continent, coupled with higher transmissibility of the infection. This endemicity is the product of a complex interplay of environmental, biological, and socio-economic factors. Tropical climates with appropriate temperature, humidity, and rainfall conditions encourage endemicity of the disease as they are conducive to the reproduction of the parasite within the anopheles’ mosquito, which is itself native to these environments [ Reference Mabrouk 17 ].

However, this natural localization of malaria is compounded by a lack of robust and resilient health systems in many of the affected countries, where poverty, conflict, and natural disasters often further limit the impact of concerted public health efforts to tackle the disease [ Reference Kaforau 14 , Reference Kattenberg 15 ]. To counter, preventive measures and immunogenicity of the population play a very significant role in combatting the pathogenesis of disease in any geographical region. Thus, the prevalence has reduced within Africa but is still the highest amongst other regions [ Reference Munn 21 ]. Even though the studies of Africa have shown a significant reduction in the prevalence of malaria, it is worth noting that these measures have not accounted for all the countries in the region, hence limiting its generalizability [ Reference Furuya-Kanamori, Barendregt and Doi 11 ].

In this study, we also observed that Plasmodium falciparum was responsible for the pathogenicity of the majority of infections. Several systematic reviews have confirmed that P. falciparum is the highest inhabited organism in pregnancy to cause the infection [ Reference Dellicour 7 , Reference Ndifreke Edem, Okon Mbong and Hussain 22 ]. Our study’s findings of a disproportionately high prevalence of this organism of malaria underscore the importance of taking strong measures to prevent and manage the disease, especially among pregnant women. While the WHO malaria 2016 report found that over 99% of malaria cases were attributable to P. falciparum, our analysis found a smaller proportion of P. falciparum-causing illnesses [ Reference Otten 23 ]). Extreme seasonal, interannual, and geographical fluctuation may be responsible for these shifts. Possible causes include dissimilarities in development and housing patterns, population migration, as well as climatic (temperature, precipitation, and relative humidity) factors.

The study assessment also revealed that malaria-positive women were more prone to encounter anaemia. Several meta-analyses support our findings as the overall odds of malaria of anaemia are higher amongst pregnant women with malaria [ Reference Page, McKenzie, Bossuyt, Boutron, Hoffmann, Mulrow, Shamseer, Tetzlaff, Akl, Brennan, Chou, Glanville, Grimshaw, Hróbjartsson, Lalu, Li, Loder, Mayo-Wilson, McDonald, McGuinness, Stewart, Thomas, Tricco, Welch, Whiting and Moher 24 ]. According to a review, malaria is responsible for an estimated 26% of the severe anaemia experienced by pregnant women of all gravities (population attributable fraction) [ Reference Dellicour 7 ]. Anaemia is strongly linked to malaria, although the underlying pathophysiology is poorly understood. Nonetheless, illness-related inadequate food intake, haemolysis, and a lack of micronutrients are all viable justifications for anaemia and malaria.

Association of low birthweight with the presence of maternal malaria was amongst the deductions from our study. This is validated by other reviews conducted that suggest the same statistically significant association between malaria in pregnancy and low birthweight of the baby [ Reference Rayco-Solon, Fulford and Prentice 25 ]. Around 19% of LBWs and 6% of LBW-related infant fatalities are attributed to malaria in regions where the disease is endemic. According to these estimates, over 100,000 infants die each year in parts of Africa where malaria is common because to LBW [ Reference Rogerson and Unger 26 ].

Augmenting with the findings of our study related to preterm babies and malaria exposure, several reviews have reported malaria to be the primary infection in pregnancy that can be associated with the PTB [ Reference Willis and Riley 27 ]. Moreover, PTB seasonality patterns were also observed in some studies to be paralleling those of malaria infection, with its peak occurring with periods of high malaria infection [ 28 ].

Our study also revealed that proportions of stillbirths were higher with women with malaria in pregnancy. This has been validated by other reviews conducted earlier that have reported a widespread effect of malaria and risk of stillbirths [ Reference Falade 10 , 29 ]. Amongst the major modifiable risk factors of stillbirths, risk attributed to malaria is approximately 8% which can be prevented if exposure minimized [ Reference Yimam, Nateghpour, Mohebali and Afshar 30 ].

Amongst the major strengths of the review, the inclusion of 253 studies determining the burden of malaria in pregnancy creates a substantial mark. It gives us a holistic global standpoint of prevalence of the disease and its association with adverse pregnancy outcomes on both the maternal and neonatal health. To further strengthen the robustness of the review, sensitivity analyses were performed which refined the effect sizes of the meta-analyses eliminating the influential studies. In addition, assessment of publication bias was also undertaken to identify the presence of biases via relevant plots.

The limitations of the review include the non-uniformity of diagnostic test used. Multiple approaches, varying in sensitivity and specificity, were used to detect malaria during pregnancy. Not all studies utilize PCR for logistical reasons, and microscopy and rapid diagnostic tests are vulnerable to errors depending on reagents, personnel, mutant strains, and other factors. It is also pertinent to note that we lacked access to individual patient data from the studies that yielded adjusted estimates; thus, we were unable to account for this variation. Since the factors adjusted were not uniform in all studies, dichotomous data were preferred as a measure of reported and studies that failed to report dichotomous data were excluded. Further, confounding was also not taken into consideration when deducing associations with adverse outcomes and we also could not conduct the association analysis by strain due to paucity and diversity of data, which did not allow us to do a sub-group analysis.

Despite significant work being done to control the spread of the disease, the burden of malaria persists. A substantial impact of unfavourable pregnancy outcome also adds up to the seriousness of the issue and requires urgent attention and concern. Large-scale interventional studies are the need of the time to address this public health issue along with global level policy formulations to target the vulnerable populations living with such elevated burden of disease.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1017/S0950268824000177 .

Data are available upon reasonable request. All data relevant to the study is included in the article.

Conceptualization: S.L., J.K.D., S.K., Z.A.P., M.A.B.; Data curation: S.L., F.S., J.K.D., S.K.N., Z.R.; Formal analysis: S.L.; Investigation: S.L., F.S., J.K.D., Z.A.P., Z.R., M.A.B.; Methodology: S.L., F.S., J.K.D., A.R.R., Z.A.P.; Project administration: S.L., J.K.D., Z.A.P., M.A.B.; Writing – original draft: S.L., H.J., O.M.; Writing – review & editing: S.L., H.A.N., J.K.D., S.K., Z.A.P., M.A.B.; Supervision: J.K.D., Z.A.P., M.A.B.; Validation: J.K.D., S.K., M.A.B.; Resources: A.R.R.; Software: A.R.R.

There was no funding available for the review.

There is no competing interest declared.

Ethical approvals were acquired from the Ethics Review Committee of the Aga Khan University Hospital and the Institution Review Board of the Jinnah Postgraduate Medical Center. Patient privacy and confidentiality were maintained at every stage of the study.

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• Jai K. Das (a1) , Sohail Lakhani (a2) , Abdu R. Rahman (a1) , Faareha Siddiqui (a1) , Zahra Ali Padhani (a3) , Zainab Rashid (a1) , Omar Mahmud (a4) , Syeda Kanza Naqvi (a1) , Hamna Amir Naseem (a1) , Hamzah Jehanzeb (a4) , Suresh Kumar (a5) and Mohammad Asim Beg (a6)
• DOI: https://doi.org/10.1017/S0950268824000177

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• Published: 26 January 2022

Six-year trend analysis of malaria prevalence at University of Gondar Specialized Referral Hospital, Northwest Ethiopia, from 2014 to 2019

• Amanuel Mulugeta 1 ,
• Atsede Assefa 1 ,
• Atsede Eshetie 1 ,
• Birhanie Asmare 1 ,
• Meseret Birhanie 1 &
• Yemataw Gelaw 1  

Scientific Reports volume  12 , Article number:  1411 ( 2022 ) Cite this article

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• Microbiology
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• Molecular medicine

Globally, malaria is the major public health disease caused by plasmodium species and transmitted by the bite of the female anopheles mosquito. Assessment of the trend of malaria prevalence is important in the control and prevention of the disease. Therefore, the objective of this study was to assess the six year trend of malaria prevalence at the University of Gondar Comprehensive Specialized Hospital, northwest Ethiopia, from 2014 to 2019. A retrospective laboratory registration logbook review study was conducted on the malaria blood film examination results at the University of Gondar Comprehensive Specialized Hospital. The data was collected by using a data extraction tool and entered into SPSS version 20 for analysis. Descriptive statistics were used to summarize the socio-demographic characteristics of study participants and presented by graphs, tables and texts. The binary logistic regression was also used to test the association the trend of malaria prevalence and different factors like sex, age, year, and season. From a total of 17,500 malaria blood film examinations, 1341 (7.7%) were confirmed for malaria parasites. Of the confirmed malaria cases, 47.2%, 45.6% and 7.2% were P. vivax, P. falciparum and mixed infection , respectively. The proportion of P. vivax was the predominant species in the first three study years (2014–2016) and P. falciparum became the predominant species in the last three study years (2017–2019). The odds of malaria prevalence was lower by 68%, 60% and 69% in the year 2017, 2018 and 2019 compared to 2014, respectively. It was also 1.41 times higher in males than in females. Moreover, the odds of malaria prevalence were 1.60, 1.64, 2.45 and 1.82 times higher in the age group of < 5, 5–14, 15–24 and 25–54 years old compared to the older age groups (> 54 years old), respectively. Even there was a significant declining in prevalence trend; malaria is still a major public health problem. The study showed that there was high seasonal fluctuation from year to year. Moreover, males and the younger age groups were more affected than females and old age groups, respectively. Therefore, malaria prevention and control activities should be strengthened and require extra efforts by considering these variability.

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Introduction.

Malaria is one of the protozoan blood parasite that cause morbidity and mortality globally 1 . It is a major public health problem throughout human history, particularly in the tropical and subtropical parts of the world.

According to records from the Ethiopian Federal Ministry of Health, 75% of the country is malarious at which 68% of the total population is living 2 . Malaria is very severe and leading cause of morbidity and mortality for many years in Ethiopia 2 , 3 . There are two peaks seasonal transmissions of malaria in Ethiopia; the months of September to December (autumn) and March to May (spring) 3 , 4 .

In Ethiopia, including the Amhara region, prevention and control activities of the malaria have been implemented as guided by the National Strategic Plan. These prevention and control activities uses a combination intervention strategy including early diagnosis and prompt treatment, selective vector control that involved use of indoor residual spraying (IRS), insecticide-treated mosquito nets (ITNs) and environmental management 4 .

However, malaria control in the country as a whole and in the region particularly continued to experience many problems. Studies have shown that the Plasmodium species compositions and the number of malaria cases vary over time due to different factors, such as change in weather conditions, intervention measures, environmental or human behavioral risk factors 3 , 5 . Some studies in Ethiopia revealed that there was a decrement of Plasmodium species over period of years 5 , 6 . On the other hand, another trend studies showed that there were fluctuation of malaria cases 4 , 7 , 8 . So, it is crucial to assess the current trend of malaria prevalence in the country as well as the study area.

Assessment of the pattern of the current malaria prevalence and understanding how malaria varies in the community as a result of seasonal, environmental, geographical or year-to-year changes will help to evaluate the effectiveness of proven control interventions of the disease in a locality 5 , 9 .

It also gives essential information about achievements of national malaria programs and identifies malaria hot spots. Additionally, it gives important insight into the changing malaria situation, which might guide adjustments of malaria program activities and the prioritization of malaria research and the changing malaria situation requires an updating description of malaria trends 10 , 11 . Therefore, the objectives of this study were to analyze trends of malaria prevalence at University of Gondar Specialized Referral Hospital, northwest Ethiopia to identify trends of Plasmodium species over the time-period.

Study area and study population

The study was conducted at University of Gondar Comprehensive Specialized Hospital located in Gondar town. Gondar is ancient city which is located Northwest direction of Ethiopia, 727 km away from Addis Ababa, the capital city of Ethiopia and 175 km from Bahir Dar, the capital city of Amhara regional state. The town has latitude and longitude 12°361 N 37°281E with an elevation of 2133 m above sea level. According to Central Statistical Agency of Ethiopia 2015 report, it has twelve sub city and 22 urban and 11 rural kebeles with a projected population of 323,900 12 . The city has 8 public health centers and 1 public comprehensive specialized hospital (University of Gondar Comprehensive Specialized Hospital), more than 13 private clinics and 1 general hospital providing health services like diagnosis, treatment, prevention and control of diseases 13 . All malaria examined blood films at the University of Gondar Comprehensive Specialized Hospital and registered at laboratory registration logbook were source of population. On the other hand, the study population in this study were all malaria examined blood films (including both sexes and any age groups) at the University of Gondar Comprehensive Specialized Hospital for the past 6 years (from 2014 to 2019). All registered malaria blood films, except incomplete data and illegible (unreadable) documents, were included from the study.

Study design

A retrospective laboratory registration logbook review study was conducted to determine the 6 years trend of malaria prevalence by reviewing malaria blood film examination laboratory registration logbook at laboratory registration log book of University of Gondar Comprehensive Specialized Hospital (2014–2019).

Sample size and sampling technique

All malaria examined blood films and register at the University of Gondar Comprehensive Specialized Hospital laboratory registration logbook from 2014 to 2019 were the sample size of study. A total of 17,500 malaria examined blood films were included. The malaria examined blood films were selected by the censuses sampling technique.

Data collection

The six years (2014–2019) malaria blood film examination laboratory registration logbook data was extracted from March to June 2020, at the University of Gondar comprehensive Specialized Hospital laboratory registration log book. The data was collected by laboratory personnel by using data extraction sheet. The data extraction sheet includes result of blood film (Negative and Positive), type of plasmodium species ( P. falciparum , P. vivax and mixed), year of examination, month of examination, season of examination, sex and age of the patient. Data on both negative and positive microscopically confirmed malaria cases were included in the study. At the University of Gondar comprehensive Specialized Hospital, patients presented sign and symptom of malaria (clinical presentation of malaria) were requested by physicians and internists. In Ethiopia, microscopy is the major diagnostic method for malaria, especially in health centers and hospitals 10 . A well-prepared Gimsa stained blood film (both thick and thin smear) was used to diagnose malaria parasites in the laboratory. Unfortunately, complete data regarding clinical presentation of patient, major interventions done against malaria and other environmental factors were not collected.

Data analysis and interpretation

The data were entered into SPSS version 20 for analysis. Descriptive statistics were used to summarize the socio-demographic of study participants and the frequency of malaria on different independent variables and presented by tables, figures and texts. Multivariable binary logistic regression analyses were performed to determine the association between the dependent (malaria prevalence and independent variables (age, sex, and year and season as categorical variable). The multivariable binary logistic regression model was analyzed with enter method and a p value < 0.05 in the multivariable regression model was considered as statistically significant. The model fitness of the final multivariable logistic regression was checked using Hosmer and Lemeshow test.

Data quality assurance

The data were checked for completeness, cleaned, and sorted daily. Moreover, the data quality was assured by following standard operation procedures, double entry. In addition, the quality of blood film staining reagents (Gimsa) was checked for its expiration date and by running the known blood sample. Moreover, the blood film examination was done by laboratory technologist and Medical parasitologist who had taken training on malaria blood film examination and malaria parasite identification. The laboratory personals are also participated in proficiency test.

Ethics approval and consent to participate

All methods were performed following the relevant guidelines and regulations. The University of Gondar has an ethical and review committee in each study field to approve the study on humans. Therefore, the ethical clearance of this study was obtained from the Ethical and Review Committee of the School of Biomedical and Laboratory Sciences, College of Medicine and Health Science, University of Gondar. After discussing the purpose and method of the study, verbal consent was obtained from the Medical Director of the University of Gondar Specialized Referral Hospital before the data collection. Since the study was used secondary data from the registration logbook informed consent for the participants was waived by the Ethical and Review Committee of School of Biomedical and Laboratory Sciences, College of Medicine and Health Science, University of Gondar.

Characteristics of study participants

During 2014 to 2019, a total of 17,500 malaria blood films (in average 2917 blood films per year) were examined microscopically for malaria diagnosis. More than half of the cases were males, 9542 (55.5%) and this was more or less consistent throughout the six years. In the six trends, the most malaria suspected and examined cases were in the age group of 25–54 (7040 (40.2%)) followed by age group of 15–24 (5540 (31.7%)) and the lowest suspected case was examined in the older age groups (> 54 years old) (1485 (8.5)). The trend of suspected cases (malaria blood film examination) was highly fluctuated. The highest blood film examination was performed in the year of 2015 (2789 (24.1%)) followed by year of 2017 (3348 (19.1%)) (Table 1 ).

Annual trends of malaria prevalence and proportion of plasmodium species

Among a total of 17,500 examined blood films, 1341 (7.7%; 95% CI 7.3–8.1) were positive for plasmodium species during the six year period. There were significant fluctuations and reduction trends of overall malaria during the past 6 years, with a maximum of 11.2% and a minimum of 3.7% of cases in 2016 and 2019, respectively. P. vivax was the predominant plasmodium species. However, the proportion of the plasmodium species was significantly fluctuated in the six years period (chi squared = 62.58, p value < 0.001). In the first 3 study years, the proportion of P. vivax was the predominant plasmodium species and in the last 3 study years P. falcifarum was the predominant plasmodium species Moreover, mixed infection ( P. vivax and P. falcifarum ) showed a significant fluctuating increment trend in the area in the 6 years, with a maximum of 10.9% and a minimum of 3.6% of cases in 2017 and 3 in 2014, respectively (Table 2 , Fig.  1 ).

Annual trend of malaria prevalence and proportion of plasmodium species at University of Gondar specialized referral hospital from 2014 to 2019.

Sex, age and seasonal variations of malaria prevalence

Despite the apparent fluctuation of total malaria trends over 6 years in the study area, malaria cases occurred throughout the year. However, there was a significant variation between the sexes and different age groups. The odds of malaria prevalence among the male was 1.41(95%CI 1.26–1.59) times higher than females. The prevalence of malaria was also higher in lower age groups compare to the older age groups. The odds of malaria prevalence was 1.60 (95%CI 1.14–2.23), 1.64 (95%CI 1.20–2.26), 2.45 (95%CI 1.86–3.22) and 1.82 (95%CI 1.39–2.40) in the age group of < 5 years, 5–14 years, 15–24 years and 25–54 years, respectively compare to age group of > 54 years old. Controlling of the confounding factors of sex and age, the prevalence of malaria also showed significant reduction in the last 3 study years (2017–2019) compare to the first study year (2014). It was decreased by 68% (95%CI 60–75), 60% (95%CI 51–68) and 69% (95%CI 59–77) in the year of 2017, 2018 and 2019, respectively. Moreover, there was a significant seasonal variation in malaria cases. The highest peak of total malaria positivity rate was observed during autumn, (September, October and November; just after the main rainy season) and the minimum positivity rate was observed during winter (the dry season in the months of December, January and February) and showed significant variation. However, controlling of the sex and age group variation in the season, the highest peak of total malaria positivity rate was observed during summer (June, July and August; main rainy season). Moreover, the seasonal variation was not consistent and highly fluctuated in the six years. Even it was the season where the highest malaria case was reported in over all seasonal malaria prevalence, autumn was the season where lowest malaria case was report in 2014 and 2017 (Table 3 , Figs.  2 , 3 ).

Seasonal variations of malaria prevalence among blood smear microscopy at University of Gondar Specialized Referral Hospital from 2014 to 2019.

Seasonal variations of malaria prevalence in each year among patients requested for malaria examination at University of Gondar Specialized Referral Hospital from 2014 to 2019.

The highest prevalence of malaria was seen in August (9.6%) followed by September and November (9.3%) whereas, the lowest prevalence was seen in January (6.1%). The proportion of Plasmodium species highly fluctuated throughout the 12 months. Plasmodium vivax was predominantly high in the winter months (December, January and February), spring months (March, April and May), and the two autumn months (September and November) whereas, Plasmodium falciparum was predominantly high in the summer months (June, July and August) and one of the autumn month (October). The mixed infection was also shoed monthly fluctuation in which the highest peak was observed in March and the lowest peak was observed in December (Table 4 ).

The present study revealed that the average annual malaria prevalence was 7.7% (95% CI 7.3–8.1). This finding was markedly lower than the study conducted elsewhere in Kola Diba, North Gondar, Northwest Ethiopia (39.6%) 4 , Adi Arkay, North Gondar, Northwest Ethiopia (36.1%) 14 , Abeshge, south-central Ethiopia (33.8%) 5 , Woreta Health Center, Northwest Ethiopia (32.6%) 14 , Dembecha Health Center, West Gojjam Zone, Northwest Ethiopia (16.34%) 8 and Halaba special district, Southern Ethiopia (9.5%) 15 . However, the current malaria prevalence was higher than other study finding conducted at Felegehiwot Referral Hospital catchment areas, Bahir Dar, northwest-Ethiopia Ethiopia (5%) 7 . The difference might be due to variations in malaria diagnosis quality and the skills of the laboratory personnel to detect and identify malaria parasites. Moreover, the implementation of malaria prevention and control activities might differ from one area to another. Besides, there might be a difference in demographic characteristics (sex, age), geographic location (altitude, temperature, rainfall) and economical activities differences that also had an effect on the prevalence of malaria. The population awareness about malaria bed net application, its transmission, and health seeking behavior might be also different.

The average annual trend of malaria prevalence revealed that there were slight increments in malaria prevalence in the first two years of the study (2015 and 2016) compared to the year 2014, but statistically, it was insignificant. However, in the last three study years (2017, 2018 and 2019) the trend showed a significant reduction in malaria prevalence. The odds of malaria prevalences were reduced by 68%, 60% and 69% in the year 2017, 2018 and 2019, respectively. The possible reasons for malaria reduction during thes study periods (2017–2019) might be due to the increased attention to malaria control and preventive activities by different responsible bodies, increased awareness of the community on the use of ITNs, IRS, the drainage system of mosquito breeding sites and climate change at national and international level. Integrated control strategies are underway in the local area as part of the nationwide malaria control activities 16 . The finding was similar to the 5-year malaria prevalence trend analysis at Dembecha Health Center, West Gojjam Zone, Northwest Ethiopia which reported that there was fluctuated decline of malaria prevalence 8 . However, the observed prevalence in this study was still considerable and public health problem.

This study demonstrated that on average of the six years of study periods, P. vivax was the predominant species, although there was a species fluctuation from year to year. The proportion of P. vivax , P. falciparum and mixed infections was 47.2%, 45.6%, and 7.2%, respectively. This finding was consistent with the study conducted in Adama City, East Shoa Zone, Oromia, Ethiopia 16 , Halaba health center Southern Ethiopia 15 and Southwest Ethiopia, around Gilgel gibe dam and 10 kilo Metter far from Gilgel gibe dam 3 . The predominance of P. vivax might be due to relapse of dormant liver stages or increased treatment pressure against P. falciparum 17 . However, this finding was in disagreement with the study conducted at two health centers Gorgora and Chuahit in Dembia district 18 , catchment areas of Felegehiwot Referral Hospital 7 and Kola Diba, North Gondar, Northwest Ethiopia 4 which reported that P. falciparum was the predominant species. Moreover, the trend of P. vivax showed reduction whereas, P. falciparum showed an increment trend. In the last three years of the study periods, P. falciparum had become the predominant Plasmodium species. The fluctuated proportion of plasmodium species might be attributed to heterogeneous parasite species and disease distribution include differences in genetic polymorphisms underlying parasite drug resistance and host susceptibility, mosquito vector ecology and transmission seasonality. Plasmodium species interact might have geographical differences and these interactions may even change from year to year in a given locale 19 . The finding also revealed that there was fluctuated increment in the proportion of mixed infection.

The prevalence of malaria was varied among different seasons ranging from 6.6 to 8.8%, and these variations were statistically significant. The highest peak was observed in autumn (8.8%) and the lowest peak was observed in the winter season (6.6%). The malaria prevalence was reduced by 16% in the winter. However, where the sex and age were adjusted, the peak prevalence was observed in summer rather than autumn, in which the prevalence was increased by 32%. The reason might be due to climate change from year to year. In Ethiopia, summer is the season when heavy rainfall is observed and it is not a favorable season for vector spreading 16 . However, there is rainfall variation from year to year 20 . Changes in temperature, rainfall, and relative humidity due to climate change are estimated to influence malaria directly by modifying the behavior and geographical distribution of malaria vectors and by changing the length of the life cycle of the parasite. Climate change is also expected to affect malaria indirectly by changing ecological relationships that are important to the organisms involved in malaria transmission (the vector, parasite, and host) 21 .

The current study revealed that males were more affected by malaria infection than females. The odds of malaria positivity rate among males were 1.41 times higher than females. Similar studies showed that males were more affected than females 22 , 23 , 24 , 25 . The reason behind the high malaria cases in males might be due to the fact that males are involved in outdoor activities. A study conducted in Dembia district, northwest Ethiopia revealed that individuals involved in outdoor activities were more at risk for malaria infection 25 . The other possible reason might be that males are mobile to malaria-endemic areas seeking temporary employment, whereas females do not perform field activities rather they are cookers and stay at home which might reduce the risk of infection.

Age was also contributing factor to the prevalence of malaria. It was higher in younger age groups than the older age groups. The odds of malaria positivity rate among less than five years old children and 5–14 years old were 1.60 and 1.64 times higher than the age group of greater than 55 years old, respectively. The reason might be these age groups may be less immune to commutate than the older age groups (> 55 years old). This was supported by the world health organization report 26 . The study also showed that the odds of malaria positivity rate among the early working groups (15–24) and primarily working groups (25–54) were, 2.45 and 1.82 times higher than the age group of greater than 55 years old, respectively. Another study, conducted on pregnant women in Sherkole district, Benshangul Gumuz regional state, West Ethiopia also revealed that the older age groups were less likely to have malaria infection 27 . The reason behind the high malaria cases in the mentioned age group of 15–24 and 25–54 years old might be the fact that this age group might be involved in outdoor activities and are mobile to malaria-endemic areas seeking temporary employment, whereas the older age group do not perform field activities rather they are staying at home which might reduce the risk of infection. Moreover, the older age groups might frequently expose to malaria previously, which might develop immunity to malaria infection. It was known that natural infection elicits a robust immune response against the blood stage of the parasite, protecting against malaria 28 . However, according to the studies conducted in rural surroundings of Arba Minch Town, south Ethiopia 29 , and Sudan 30 , age had no significant association with malaria infection. Indeed, these studies were focused on a specific study population; under-five children and pregnant women, respectively.

The finding of the current study had its strengths; one it had enough sample size which increased the power of the study; second, it included all age segments of the populations (from children up to the old age groups). However, this study might suffer from the fact that it is secondary data; the reliability of the recorded data could not be ascertained. Moreover, the collected data relayed on the laboratory logbook which lacks participants’ body temperature, clinical presentations and residence. It also lacks information regarding the weather conditions of the month, seasons and years.

The current finding showed that there was a significant declining trend of the of malaria prevalence in the study area. However, the overall prevalence was still a major public health problem and requires extra efforts for further reduction. On average, the highest peak of malaria cases was observed during the autumn seasons. However, there was high fluctuation from year to year. Moreover, males, under-five children and the younger age groups were more affected compare to the older age groups. In addition, even P. vivax was the predominant Plasmodium species in the allover trend, there was a high fluctuation of Plasmodium species from year to year and season to season. Therefore, prevention and control activities should be continued and strengthened in the study area considering these variabilities.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

Adjusted odds ratio

Confidence interval

Plasmodium Falciparum

Plasmodium Vivax

Indoor residual spraying

Insecticide-treated mosquito nets

Kassa, A. & Beyene, B. B. Climate variability and malaria transmission—Fogera district, Ethiopia, 2003–2011. Sci. J. Public Health 2 (3), 234–7 (2014).

Article   Google Scholar  

Ayele, D. G., Zewotir, T. T. & Mwambi, H. G. Prevalence and risk factors of malaria in Ethiopia. Malar. J. 11 , 195 (2012).

Sena, L. D., Deressa, W. A. & Ali, A. A. Analysis of trend of malaria prevalence in south-west Ethiopia: A retrospective comparative study. Malar. J. 13 , 188 (2014).

Alemu, A., Muluye, D., Mihret, M., Adugna, M. & Gebeyaw, M. Ten year trend analysis of malaria prevalence in Kola Diba, North Gondar, Northwest Ethiopia. Parasites Vectors 5 , 173 (2012).

Yimer, F., Animut, A., Erko, B. & Mamo, H. Past five-year trend, current prevalence and household knowledge, attitude and practice of malaria in Abeshge, south-central Ethiopia. Malar. J. 14 , 230 (2015).

Tesfa, H., Bayih, A. G. & Zeleke, A. J. A 17-year trend analysis of malaria at Adi Arkay, north Gondar zone, Northwest Ethiopia. Malar. J. 17 (1), 155 (2018).

Yimer, M., Hailu, T., Mulu, W., Abera, B. & Ayalew, W. A 5 year trend analysis of malaria prevalence with in the catchment areas of Felegehiwot referral Hospital, Bahir Dar city, northwest-Ethiopia: A retrospective study. BMC. Res. Notes 10 (1), 239 (2017).

Haile, D., Ferede, A., Kassie, B., Abebaw, A. & Million, Y. Five-year trend analysis of malaria prevalence in Dembecha Health Center, West Gojjam Zone, Northwest Ethiopia: A retrospective study. J. Parasitol. Res. 2020 , 8828670 (2020).

Alelign, A., Tekeste, Z. & Petros, B. Prevalence of malaria in Woreta town, Amhara region, Northwest Ethiopia over eight years. BMC Public Health 18 (1), 1–6 (2018).

Federal Democratic Republic of Ethiopia Ministry of Health, National Malaria Guidelines, fourth edition, Federal Democratic Republic of Ethiopia Ministry of Health. https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/documents/files/eth_national_malaria_guidline_4th_edition.pdf . Accessed 1 June 2021.

United State Agency International Development, PRESIDENT’S MALARIA INITIATIVE ETHIOPIA Malaria Operational Plan FY 2018, USA PRESIDENT’S MALARIA INITIATIVE collaboration with Department of Health and Human Science, Center for Disease controll and prevention, United State of America Department of State . https://www.pmi.gov/docs/default-source/default-document-library/malaria-operational-plans/fy-2018/fy-2018-ethiopia-malaria-operational-plan.pdf?sfvrsn=5 . Accessed 1 June 2021.

Getaneh, A., Tegene, B. & Belachew, T. Knowledge, attitude and practices on cervical cancer screening among undergraduate female students in University of Gondar, Northwest Ethiopia: An institution based cross sectional study. BMC Public Health 21 (1), 1–9 (2021).

Levin, K. A. Study design III: Cross-sectional studies. Evid. Based Dent. 7 (1), 24 (2006).

Derbie, A. & Alemu, M. Five years malaria trend analysis in Woreta Health Center, Northwest Ethiopia. Ethiop. J. Health Sci. 27 (5), 465–472 (2017).

Shamebo, T. & Petros, B. Trend analysis of malaria prevalence in Halaba special district, Southern Ethiopia. BMC. Res. Notes 12 (1), 190 (2019).

File, T., Dinka, H. & Golassa, L. A retrospective analysis on the transmission of Plasmodium falciparum and Plasmodium vivax: The case of Adama City, East Shoa Zone, Oromia, Ethiopia. Malar. J. 18 (1), 193 (2019).

Dedgeba, S. & Mamo, H. Malaria trends in Silt’i district from 2009–2015 and current childhood malaria in K’ibbet hospital, south-central Ethiopia. Malar. World J. 8 (22), 1–5 (2017).

Google Scholar  

Addisu, A., Tegegne, Y., Mihiret, Y., Setegn, A. & Zeleke, A. J. A 7-year trend of malaria at primary health facilities in Northwest Ethiopia. J. Parasitol. Res. 2020 , 4204987 (2020).

Zimmerman, P. A., Mehlotra, R. K., Kasehagen, L. J. & Kazura, J. W. Why do we need to know more about mixed Plasmodium species infections in humans?. Trends Parasitol. 20 (9), 440–447 (2004).

Simane, B. et al. Review of climate change and health in Ethiopia: Status and gap analysis. Ethiop. J. Health Dev. = Ya’Ityopya tena lemat mashet 30 (1), 28–41 (2016).

Alemu, A., Abebe, G., Tsegaye, W. & Golassa, L. Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia. Parasit. Vectors 4 (1), 30 (2011).

Tarekegn, M., Tekie, H., Dugassa, S. & Wolde-Hawariat, Y. Malaria prevalence and associated risk factors in Dembiya district, North-western Ethiopia. Malar. J. 20 (1), 372 (2021).

Tesfahunegn, A., Berhe, G. & Gebregziabher, E. Risk factors associated with malaria outbreak in Laelay Adyabo district northern Ethiopia, 2017: Case-control study design. BMC Public Health 19 (1), 484 (2019).

Alemu, K., Worku, A., Berhane, Y. & Kumie, A. Men traveling away from home are more likely to bring malaria into high altitude villages, Northwest Ethiopia. PLoS ONE 9 (4), e95341 (2014).

Article   ADS   Google Scholar  

Agegnehu, F., Shimeka, A., Berihun, F. & Tamir, M. Determinants of malaria infection in Dembia district, Northwest Ethiopia: A case-control study. BMC Public Health 18 (1), 1–8 (2018).

WHO, malaria fact sheet report; World Health Organization. https://www.who.int/news-room/fact-sheets/detail/malaria , Accessed 18 June 2021.

Gontie, G. B., Wolde, H. F. & Baraki, A. G. Prevalence and associated factors of malaria among pregnant women in Sherkole district, Benishangul Gumuz regional state, West Ethiopia. BMC Infect. Dis. 20 (1), 573 (2020).

Article   CAS   Google Scholar  

Gonzales, S. J. et al. Naturally acquired humoral immunity against plasmodium falciparum malaria. Front. Immunol. 11 (2809), 594653 (2020).

Abossie, A., Yohanes, T., Nedu, A. & Tafesse, W. Prevalence of malaria and associated risk factors among febrile children under five years: A Cross-sectional study in Arba Minch Zuria District, South Ethiopia. Infect. Drug Resist. 7 (13), 363–372 (2020).

Adam, I., Khamis, A. H. & Elbashir, M. I. Prevalence and risk factors for Plasmodium falciparum malaria in pregnant women of eastern Sudan. Malar. J. 4 (1), 1–4 (2005).

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Acknowledgements

We are grateful to the University of Gondar Specialized Referral Hospital managers and laboratory personnel.

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Amanuel Mulugeta, Atsede Assefa, Atsede Eshetie, Birhanie Asmare, Meseret Birhanie & Yemataw Gelaw

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A.M., A.A., A.E. and B.A. participated in the study design, undertook the data collection, analyzed the data. Y.G. analyzed the data, wrote the manuscript and participated on the revision of the manuscript. M.B. participated in the study design, analyzed the data and on the revision of the manuscript. All authors have read the manuscript and approved it to submit for publication.

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Mulugeta, A., Assefa, A., Eshetie, A. et al. Six-year trend analysis of malaria prevalence at University of Gondar Specialized Referral Hospital, Northwest Ethiopia, from 2014 to 2019. Sci Rep 12 , 1411 (2022). https://doi.org/10.1038/s41598-022-05530-2

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Prevalence and associated factors of malaria among pregnant women in Sherkole district, Benishangul Gumuz regional state, West Ethiopia

• Girma Bekele Gontie 1 ,
• Haileab Fekadu Wolde 2 &
• Adhanom Gebreegziabher Baraki 2  

BMC Infectious Diseases volume  20 , Article number:  573 ( 2020 ) Cite this article

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Malaria during pregnancy leads to serious adverse effects on mothers and the fetus. Approximately 25 million pregnant women in sub-Saharan Africa live at risk of malaria. This study would help to achieve Sustainable Development Goals (SDGs) by improving programs that deal with the prevention of malaria. Therefore, this study aimed to assess the prevalence and associated factors of malaria among pregnant women.

A community-based cross-sectional study was conducted from July to August 2018 in Sherkole district, West Ethiopia. A multi-stage sampling technique was used to select 504 pregnant women. The interviewer-administered semi-structured questionnaire was used for data collection. Malaria was also diagnosed using a rapid diagnostic test. The data was entered using EPI info version 7.2.2.2 and transferred to SPSS version 20 for analysis. Descriptive statistics were done using frequency and percentages. Both bivariable and multivariable logistic regression models were employed. Variables having p -value < 0.2 were included in the final multivariable model. Variables having p -values < 0.05 from the multivariable model were considered to be significantly associated with the dependent variable. The adjusted odds ratio with its 95% confidence interval (CI) was used as a measure of association.

Of the total 498 pregnant women who participated in this study, 51(10.2, 95% CI: 7.72–13.24) were found to have malaria. Of these, 46 (90.2%) and 5 (9.8%) were caused by Plasmodium falciparum and Plasmodium vivax, respectively. Decreasing Age (Adjusted Odds Ratio (AOR) 0.78; 95% CI 0.67–0.911), not using insecticide-treated bed net (ITN) (AOR 12.5; 95% CI 4.86–32.21), lack of consultation and health education about malaria prevention (AOR 7.18; 95% CI 2.74–18.81), being on second-trimester pregnancy (AOR 7.58; 95% CI 2.84–20.2), gravidae II (AOR 5.99; 95% CI 1.68–21.44) were found to be significantly associated with malaria during pregnancy.

Malaria is still a public health problem among pregnant women in the Sherkole district. Age, ITN use, gravidity, gestational age, and health education had a significant association with malaria. Screening pregnant women for asymptomatic malaria infection and educating and consulting on the appropriate malaria preventive methods shall be provided.

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Malaria is caused by parasites of the genus Plasmodium and transmitted by female Anopheles mosquitoes. There are five different human malaria species such as P. falciparum , P. vivax , P. malariae, P. knowlesi and P. ovale . In 2016, an estimated 216 million cases of malaria and 445,000 deaths occurred worldwide [ 1 ]. Most, (90%), malaria cases and 91% of all malaria death in 2015 and 2016 were reported from the WHO African Region. Of the 91 countries reporting indigenous malaria cases worldwide, around 80% of the total cases were from sub-Saharan African countries [ 1 , 2 ].

Malaria during pregnancy is a serious public health problem in sub-Saharan Africa. It is estimated that each year approximately 25 million pregnant women in sub-Saharan Africa live at risk of P. falciparum malaria infection [ 3 ]. Two institution-based studies done among pregnant women attending antenatal care (ANC) in Nigeria showed the prevalence of malaria to be 41.6% [ 4 ] and 7.7% [ 5 ]. Another institution based study in Eastern Sudan showed 13.7% of pregnant women were infected with P. falciparum [ 6 ]. Studies conducted in Burkina Faso [ 7 ], and Malawi [ 8 ] also showed the prevalence to be 18.1%, and 19.% respectively. Besides, two institution and one community-based studies conducted in different parts of Ethiopia also showed the prevalence of malaria among pregnant women to be between 2.83 and 16.3% [ 9 , 10 , 11 ].

Malaria infection during pregnancy causes an enormous risk to the mother, fetus, and neonates [ 12 ]. Indeed although malaria during pregnancy might be asymptomatic due to a high level of acquired immunity in mothers residing in high transmission areas, it is still associated with an increased risk of maternal anemia, spontaneous abortion, stillbirth, prematurity, and low birth weight [ 3 , 13 , 14 ]. Moreover, severe maternal anemia increases the mother’s risk of death. Malaria-related anemia is estimated to cause as many as 10,000 maternal deaths each year in Africa [ 15 ].

Different risk factors for malaria among pregnant women were identified by previous studies. These include educational status [ 7 , 16 ], age [ 5 , 17 ], ANC visit, gestational age [ 18 ], parity [ 7 , 18 ], gravidity, and ITN utilization [ 11 ].

In Benishangul Gumuz regional state, almost all districts (98%) of the landmass are malarious areas and 97% of the population are at risk for malaria infection. Despite the high risk of malaria transmission in the area, there is limited evidence about the burden and risk factors of malaria among pregnant women which can be used for reducing maternal and child mortality due to the disease. Therefore, this study aimed to assess the prevalence of malaria and its associated factors among pregnant women in Sherkole District, Benishangul Gumuz regional state, West Ethiopia.

Study area and period

The study was conducted in Sherkole district, Benishangul Gumuz Regional state (BGRS) from July 20 to August 30, 2018. Sherkole district is one of the 21 BGRS administration districts which is found 756 km to the West of Addis Ababa, the capital city of the country, and 96 km far away from region city, Assosa. The district is found at a latitude of 13.169308 and longitude of 39.987117 and the altitude of the district is 680–800 m above sea level. The climatic condition of the district is hot and the annual temperature is estimated to be between 25 °C and 41 °C. The Annual range of rainfall in the district is 900–1200 mm. In this district, all kebeles are malarious with 39,373 populations at risk of the disease. In 2016/2017, the annual malaria incidence rate in the district was 263 cases per 1000 population. There were 1243 pregnant women, 1196 under 1 year, and 6370 under 5 years old of children in the district (Fig.  1 ).

Location of the study area

Study design and population

A community-based cross-sectional study was conducted. The source population for this study was all pregnant women at any gestational age living in the district. The study population was those pregnant women in the selected kebeles and who were available during the data collection period. Pregnant women with mental illness and severely debilitating diseases were excluded from the study.

Sample size determination and sampling procedure

The sample size was determined using a single proportion formula using a 50% prevalence of malaria among pregnant women, 95% confidence level, 5% margin of error, and design effect of 2. To compensate for the non-response rate, 10% of the determined sample size was added. Finally, finite population correction was done to adjust the final sample size which gives a total sample size of 504. A multi-stage sampling technique was used to select the determined sample size. At the first stage, from a total of 20 kebeles in the district, 8 kebeles with a a total of 1243 pregnant women were selected by using a simple random sampling technique. In the second stage, the sample size was distributed proportionally for the 8 kebeles based on the number of pregnant women in the kebeles with a range of 41 to 79 housholds for each kebele and then households were selected using a simple random sampling technique. Finally, pregnant women in the household were taken and in the presence of more than one eligible woman in a single household, a lottery method was used to select one.

Variable measurement and data collection procedure

The outcome variable for this study was malaria infection which was assessed using RDT and pregnant mother with any type of Plasmodium species from the test were considered as having malaria infection. The independent variables include socio-demographic factors (age, marital status, educational status, and occupational status); obstetric factors (gravidity, parity, trimester of pregnancy, history of abortion); malaria prevention measures (ITN ownership, indoor residual spraying (IRS) use, personal protective measures, and ITN utilization); health service use (accessibility of ANC, gestational age at the first visit, number of ANC visit, place of delivery for the previous child, previous history of malaria infection during pregnancy, and health education about malaria prevention methods during ANC follow up).

The interviewer-administered Semi-structured questionnaire was used to collect the required information. For those pregnant women who were on ANC followup, the data collector reviewed their antenatal followup cards to cross-check the information given by them. Card information checked includes; gravidity, parity, and gestational age at first ANC visit. Following the interviews, blood was obtained from the third finger of women’s left hand. First, the tip of the finger was wiped with a piece of cotton wool lightly soaked in alcohol. Then piercing with sterile lancet was done and the blood allowed to flow freely without squeezing the finger. Then, 5 μl (μl) blood was collected and a single small drop was added on the CareStart RDT to examine the presence or absence of malaria and to differentiate its species. The RDT read and determine the species qualitatively after 15–20 min of putting the blood to the kit. Ten percent of the randomly selected negative slides were rechecked and reread. Eight trained diploma nurses and midwives collected the data and they were supervised by two health professionals with a qualification of BSc degree. The questioner was pretested and one-day training was given for supervisors and data collectors on the basic technique of the data collection.

Data processing and analysis

The data were entered using EPI-Info 7.2.2 and then transferred to SPSS version 20 statistical package for further analysis. Data cleaning and management were done. Descriptive statistics (frequencies, mean, SD, and percentage) were done to explain the study population in relation to relevant variables. The Chi-square assumption was checked for all categorical independent variables and multicollinearity was also checked using the Variance inflation factor (VIF). Both bi-variable and multi-variable logistic regressions were used to assess the association between outcome and explanatory variables. Factors with p -value ≤0.2 from the bi-variable model were included in the final model. Variables having a p -value < 0.05 from the multivariable model were considered as having a statistically significant association with the outcome. Adjusted Odds ratio with 95% CI was used as a measure of association. The model goodness of fit was assessed using the Hosmer lemisho test.

Socio-demographic characteristics, obstetric characteristics, and malaria prevention methods adopted by pregnant women

A total of 498 pregnant women participated in this study with a response rate of 98.8%. The majority, 208(41.8%), of the pregnant women were in the age group of 25–29 years. Concerning the educational status, more than three fourth, 384(77.1%), of the mothers had no formal education. Almost all, 482 (96.8%), study participants were farmers and traditional gold miners. About 478 (96%) of respondents owned at least one mosquito bed net, and 405 (81.3%) of them sleep under mosquito nets in the previous night. Almost all, 485 (97.8%), of the households had Indoor Residual Spray (IRS) in the last 12 months. All women 431 (86.5%) who had ANC follow-up were given health education about the prevention methods of malaria infection during their ANC follow-up. The majority, 323 (64.9%), of the study participants were multi-gravida, and more than half, 292 (58.6%), of the study participants were in their third trimester of pregnancy (Table  1 ).

Prevalence of malaria infection among pregnant women

In our study, the prevalence of malaria was found to be 10.2% (95% CI: 7.72–13.24). Of these, 46(90.2%) were P. falciparum cases and 5 (9.8%) were P. vivax cases. From the total confirmed cases, the majority, 35 (68.8%) were asymptomatic.

Factors affecting malaria infection among pregnant women

From the bi-variable logistic regression, malaria was significantly associated with all of the variables at a significance level of 0.2. However, from the multivariable logistic regression model only age, ITN utilization, consultation about malaria prevention methods during ANC, trimester of pregnancy, and gravidity were significantly associated with malaria infection during pregnancy. For 1 year increase in the age of the pregnant women, the odds of malaria infection was decreased by 22%(AOR = 0.78, 95% CI: 0.67, 0.91). The odds of malaria infection was 14.98 times higher among pregnant women who did not utilize ITN compared to their counterparts (AOR = 14.98, 95% CI: 5.24, 42.27). Pregnant women who had no education about malaria prevention methods during their ANC follow up had 7.15 times increased odds of malaria infection compared to their counterparts (AOR = 7.15, 95% CI: 2.44, 20.96). Women who were in their first trimester of pregnancy had 23.33 times increased odds of having malaria infection compared to mothers on their third trimester (AOR = 23.33, 95% CI: 1.90, 28.20). Women who are in their second trimester of pregnancy also had 7.78 times increased odds of having malaria infection compared to mothers on their third trimester (AOR = 7.78, 95% CI:2.77, 21.87). The odds of malaria infection was 5.87 times higher among women who had their second pregnancy compared to multi gravid women (AOR = 5.87, 95% CI: 1.61, 21.37) (Table  2 ).

This study assessed the prevalence of malaria infection and associated factors among pregnant women in Sherkole district, Benishangul Gumuz regional state, West Ethiopia. Different studies reported different factors that affect the rate of malaria infection among pregnant women. Our study also assessed socio-demographic, obstetric, and ITN ownership and utilization factors. As a result, Age the woman, ITN utilization, health education about prevention methods during pregnancy, gestational age, and gravidity were found to be significantly associated with malaria infection.

In this study, the prevalence of malaria was found to be 10.2%. This result was higher than studies conducted in Felege Hiwot referral hospital and Addis Zemen health center, Ethiopia (2.83%) [ 9 ], rural district surrounding Arbaminch town, Ethiopia (9.1%) [ 11 ], coastal Ghana (5%) [ 19 ], South-West Nigeria (7.7%) [ 5 ], Southern Laos (8.3%) [ 20 ] and India (5.4%) [ 21 ]. This difference might be attributed to the difference in geographical location among the study areas. For instance, our study was conducted in a malaria-endemic area with a high rate of transmission. Therefore, individuals living in malaria-endemic areas have a greater chance of developing asymptomatic malaria, while those living in low transmission areas have a low chance of being infected, which can lead to a low prevalence of the diseases in such areas. Another reason for the difference could be the inclusion criteria used by the studies because our study included both symptomatic and asymptomatic pregnant women which might increase the prevalence but most of the other studies included only asymptomatic pregnant women. On the other hand, the prevalence in our study was found to be lower than studies conducted in Pawe hospital, Ethiopia (16.3%) [ 10 ], Sudan (13.7%) [ 6 ], Nigeria (41.6%) [ 4 ], Malawi (19.6%) [ 8 ] Burkinafaso (18.1%) [ 7 ] and a systematic review and meta-analysis in Ethiopia 12.7% [ 22 ]. It is also found to be much lower than the findings from two studies conducted in Nigeria which showed the prevalence to be 58% [ 23 ] and 59.9% [ 24 ]. This difference may be due to better implementation of improved malaria interventions including increased coverage in the distribution of Long Lasting Insecticide Treated Nets (LLINs), and indoor residual spraying in our study area which showed 96 and 81.3% of respondents own and utilize ITN, respectively. Almost all (98%) of participants in our study area also lived in residual sprayed households. Therefore, these interventions might reduce the malaria burden in the study area. Another possible reason for the low prevalence in our study could be, the study was done during the low malaria transmission season (July – August). However, the major transmission for malaria occurs between September and December.

In this study, 90.2% of the cases were caused by P.falciparum species. This result was in line with the study conducted in tropical Africa which showed 80–95% of malaria infections are caused by P. falciparum [ 19 ]. However, our result was higher than the national prevalence reports of the species which was 60–70% [ 25 ]. This high proportion of this malaria species in our study is a clear implication that there is a need for aggressive prevention and control of the diseases, especially among pregnant women. Because P. falciparum causes the most severe form of the disease and it can cause devastating complications not only for the mother but also for the fetus. This result also implies that there is a need for early screening of pregnant women for early detection and treatment of the cases to prevent possible complications. On the other hand, the proportion of malaria cases caused by P.falciparum in our study was lower than the WHO malaria 2017 report which revealed over 99% of malaria cases were due to P.falciparum [ 1 ]. The possible reason for these variations might be due to marked seasonal, inter-annual, and spatial variability. It may also be due to large differences in climate (temperature, rainfall, and relative humidity), human settlement, and population movement patterns.

In this study mothers with an increased age were found to have lower odds of developing malaria infection. This is in line with studies conducted in different tropical African countries [ 5 , 17 ] which reported pregnant women of young age are at the greatest risk of malaria infection, as well as having the highest parasite densities. This may be attributed to mothers with increased age have better exposure to health services and gain a good awareness about the disease and the ways of prevention. Also, due to previous frequent malaria exposures, older aged mothers might develop immunity to malaria. However, according to the studies conducted in rural surroundings of Arbaminch Town, Ethiopia [ 11 ], and Sudan [ 6 ], age had no significant association with malaria infection.

According to our study, pregnant women who were in the second trimester of pregnancy were at increased odds of developing malaria infection compared to mothers in the third trimester. Besides, and women who were gravidae II have increased odds of malaria infection compared to the multi gravid. Similar results were found from studies done in sub-Saharan Africa countries [ 7 , 11 , 18 ], which showed a higher risk of malaria infection among primigravidae and gravida two than multigravidae. Low risk of malaria among multigravidae mothers may be associated with the development of pre-immunity to malaria with increased gravidity and previous exposures. It might be also linked to infection-specific immunological factors. Some Plasmodium -infected erythrocytes sequester/arrest in the maternal placenta by producing surface antigens mainly variant surface antigens that adhere to chondroitin sulphate-A (CSA) receptors expressed by syncytiotrophoblasts in the placenta. These antibodies are associated with protection against placental infection. Therefore, primigravidae and secundigravidae mothers lack these anti-adhesion antibodies against CSA binding parasites, which develop only after successive pregnancies and this makes them more susceptible to infection [ 26 ].

In our study, getting a consultation and health education about malaria preventive methods during ANC follow up significantly decreased the odds of developing malaria infection during pregnancy. A similar association was found in studies conducted different parts of Ethiopia [ 27 , 28 ]. Health education and consultation specifically on prevention and control program of malaria during pregnancy ensures the use of antimalarials and other intervention measures effectively.

In this study, not using ITN increases the odds of developing malaria infection during pregnancy. Indeed, WHO, MoH, and presidents malaria initiatives (PMI) have advocated for a three-pronged approach to tackle malaria and one of the strategies is the use of ITN [ 1 , 25 , 29 ]. This study’s finding was also in agreement with the study conducted in Malawi [ 8 ], Nigeria [ 30 ], and Arbaminch, Ethiopia [ 11 ], which showed that the use of bed nets has a significant impact on decreasing malaria infection. The possible explanation for this association could be ITNs effectively reduce human-mosquito contact which can prevent diseases.

Since our study used a cross-sectional study design, it does not show a direct temporal relationship. Though using PCR and blood film microscopy may have higher sensitivity, we could not do these tests because the study is done in rural areas and there is no electricity in the area. Therefore, the result of this study could be affected by the inherent performance of the RDT utilized.

The prevalence of malaria infection among pregnant women was relatively low in Sherkole district and P. falciparum is the most predominant Plasmodium species in the area. Age of respondents, ITN use, gravidity, gestational age, and health education about malaria prevention methods during ANC had a significant association with malaria infection. Health professionals should give health education about malaria prevention methods during ANC and they should also give special attention to those pregnant women with the identified risk factors. Besides, further research is recommended by using more sensitive diagnostic methods like PCR and blood film microscopy for the diagnosis of malaria.

Availability of data and materials

The data upon which the result based could be accessed a reasonable request.

Abbreviations

Adjusted Odds Ratio

Antenatal Care

Benishangul Gumuz Regional state

Chondroitin Sulphate-A

Confidence Interval

Insecticide Treated Net

Long Lasting Insecticide Treated Nets

Ministry of Health

Presidents Malaria Initiatives

Rapid Diagnostic Test

World Health Organization

WHO. World malaria report 2017 World health organization 2017.

WHO. World malaria report 2016. 2016.

Steketee RWNB, Parise MEMC. The burden of malaria in pregnancy in malaria-endemic areas. Am J Trop Med Hyg. 2001;64:28–35.

Article   CAS   PubMed   Google Scholar  

Kagu M, Kawuwa M, Gadzama G. Anaemia in pregnancy: a cross-sectional study of pregnant women in a Sahelian tertiary hospital in northeastern Nigeria. J Obstet Gynaecol. 2007;27(7):676–9.

Agomo CO, Oyibo WA, Anorlu RI, Agomo PU. Prevalence of malaria in pregnant women in Lagos, south-West Nigeria. Korean J Parasitol. 2009;47(2):179.

Article   PubMed   PubMed Central   Google Scholar  

Adam I, Khamis AH, Elbashir MI. Prevalence and risk factors for Plasmodium falciparum malaria in pregnant women of eastern Sudan. Malar J. 2005;4(1):18.

Cisse M, Sangare I, Lougue G, Bamba S, Bayane D, Guiguemde RT. Prevalence and risk factors for Plasmodium falciparum malaria in pregnant women attending antenatal clinic in Bobo-Dioulasso (Burkina Faso). BMC Infect Dis. 2014;14(1):631.

Boudová S, Divala T, Mawindo P, Cohee L, Kalilani-Phiri L, Thesing P, et al. The prevalence of malaria at first antenatal visit in Blantyre, Malawi declined following a universal bed net campaign. Malar J. 2015;14(1):422.

Asmamaw T, Alemu A, Alemu A, Unakal C. Prevalence of malaria and HIV among pregnant women attending antenatal clinics at felege hiwot referral hospital and Addis zemen health center. Int J Life Sci Biotechnol Pharma Res. 2013;2(1–13).

Geleta G, Ketema T. Prevalence of malaria and frequency of severe symptoms among pregnant women in Pawe hospital, North Western Ethiopia. Ann Clin Pathol. 2017;5:1109.

Google Scholar  

Nega D, Dana D, Tefera T, Eshetu T. Prevalence and predictors of asymptomatic malaria parasitemia among pregnant women in the rural surroundings of Arbaminch town, South Ethiopia. PLoS One. 2015;10(4):e0123630.

Gajida A, Iliyasu Z, Zoakah A. Malaria among antenatal clients attending primary health care facilities in Kano state, Nigeria. Annals of African medicine. 2010;9(3)..

BJ B. An analysis of malaria in pregnancy in Africa. Bull World Health Organ 1983; 61:1005–1016.

WHOAFRG. African Region 2010 malaria report. 2010.

WHO. A Strategic Framework for Malaria Prevention and Control During Pregnancy in the African Region, In World Health Organization RegionalOffice for Africa. AFR/MAL/04/01. 2004.

Fana SA, Bunza MD, Anka SA, Imam AU, Nataala SU. Prevalence and risk factors associated with malaria infection among pregnant women in a semi-urban community of North-Western Nigeria. Infect Dis Poverty. 2015;4:24.

Jäckle MJ, Blumentrath CG, Zoleko RM, Akerey-Diop D, Mackanga J-R, Adegnika AA, et al. Malaria in pregnancy in rural Gabon: a cross-sectional survey on the impact of seasonality in high-risk groups. Malar J. 2013;12(1):412.

Mario J Jäckle CGB, Rella M Zoleko, Daisy Akerey-Diop, Jean-Rodolphe Mackanga,, Ayôla A Adegnika BL, Pierre-Blaise Matsiegui, Peter G Kremsner, Ramharter GM-NaM Malaria in pregnancy in rural Gabon: a cross-sectional survey on the impact of seasonality in high-risk groups malaria journal 2013;12:412.

Steffen R. debernardis C, Baños A. Travel epidemiology—a global perspective. Int J Antimicrob Agents. 2003;21(2):89–95.

Briand V, Le Hesran JY, Mayxay M, Newton PN, Bertin G, Houze S, et al. Prevalence of malaria in pregnancy in southern Laos: a cross-sectional survey. Malar J. 2016;15(1):436.

Sohail M, Shakeel S, Kumari S, Bharti A, Zahid F, Anwar S, et al. Prevalence of malaria infection and risk factors associated with Anaemia among pregnant women in Semiurban Community of Hazaribag, Jharkhand, India. Biomed Res Int. 2015;2015:740512.

Tegegne Y, Asmelash D, Ambachew S, Eshetie S, Addisu A, Jejaw ZA. The prevalence of malaria among pregnant women in Ethiopia: a systematic review and meta-analysis. J Parasitol Res. 2019;8396091.

Ifeanyi U, Onyebuchi V, Ukamaka T, Ugochukwu B. Asymptomatic Plasmodium parasitaemia in pregnant Nigerian women : almost a decade after roll Back malaria. Malar J. 2009;24:16–20.

Ogbodo S, Nwagha, U., Okaka, A., Ogenyi, S., Okoko, R., & Nwagha, T. . Malaria parasitaemia among pregnant women in a rural community of eastern Nigeria; need for combined measures. Nigerian Journal of Physiological Science. (2009);24(2)..

MOH. Four years NMSP of Ethiopia from2014/2015–2019/2020. 2014.

FriedM N, Brockman A, Brabin BJ, Duffy PE. Maternalantibodiesblockmalaria. Nature. 1998;95:852.

Deribew A, Alemseged F, Birhanu Z, Sena L, Tegegn A, Zeynudin A, et al. Effect of training on the use of long-lasting insecticide-treated bed nets on the burden of malaria among vulnerable groups, south-West Ethiopia: baseline results of a cluster randomized trial. Malar J. 2010;9(1):1303–13.

Belay M DW. Use of insecticide treated nets by pregnant women and associated factors in a pre-dominantly rural population in northern Ethiopia. Trop Med Int Health. 2008;13(10).

INITIATIVE PSM, ETHIOPIA. Malaria Operational Plan FY 2017 2017.

Oyibo COAaWA. Factors associated with risk of malaria infection among pregnant women in Lagos. Nigeria: Infect Dis Poverty; 2013.

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Acknowledgments

We would like to express our deepest thanks to the University of Gondar College of Medicine and Health Sciences and Health Officer Department, for facilitating the research work. We also want to thank all pregnant women who participated in this study for their contribution.

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All authors actively participate in the conception and design, acquisition of data, or analysis and interpretation of data. GBG, HFW, and AGB critically revise the manuscript. All authors read and approved the final version of the manuscript.

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Ethical clearance was obtained from the Ethical review board of the University of Gondar and the permission letter was also obtained from Benishangul Gumuz regional state administration Health Bureau. Then this letter was delivered to Sherkole district Health office and the respected villages. The purpose and importance of the study were explained to the participants and since the majority of our study participants cannot read and write, verbal consent was obtained from each participant above the age of 18. Assent was also obtained for participants below the age of 18 from their parents or guardian. Pregnant women who tested positive were linked to the nearby health center for treatment. Confidentiality of the information was maintained by omitting their names and personal identification.

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Gontie, G.B., Wolde, H.F. & Baraki, A.G. Prevalence and associated factors of malaria among pregnant women in Sherkole district, Benishangul Gumuz regional state, West Ethiopia. BMC Infect Dis 20 , 573 (2020). https://doi.org/10.1186/s12879-020-05289-9

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Prevalence and associated factors of malaria in children under the age of five years in Wogera district, northwest Ethiopia: A cross-sectional study

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Supervision, Writing – original draft

Affiliation Department of Epidemiology and Biostatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft

Affiliation Wogera District Health Office, North Gondar Zone, Gondar, Ethiopia

Roles Data curation, Formal analysis, Methodology, Software, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Nursing and Midwifery, Haramaya University, Harar, Ethiopia

• Adino Tesfahun Tsegaye, 
• Andualem Ayele, 
• Simon Birhanu

• Published: October 11, 2021
• https://doi.org/10.1371/journal.pone.0257944
• Reader Comments

Malaria is a major public health problem in sub-Saharan Africa, and children are especially vulnerable. In 2019, an estimated 409,000 people died of malaria, most (274,000) were young children and 94% of the cases and deaths were in Africa. Prior studies in Ethiopia focused on the adult population and high transmission areas. Hence, this study aimed to determine the prevalence and associated factors of malaria in children under five years in low transmission areas.

A facility-based cross-sectional study was conducted among 585 under-five children who attended public health facilities in the Wogera district from September to October, 2017. Health facilities were selected by stratified cluster sampling, and systematic random sampling was held to select study participants from the selected facilities. Multivariable logistic regression was used to identify correlates of malaria.

Of 585 children who provided blood samples, 51 (8.7%) had malaria. The predominant Plasmodium species were P . falciparum 33 (65%) and P . vivax 18 (35%). Regularly sleeping under long-lasting insecticide treated nets (LLIN) was associated with decreased odds of malaria (AOR = 0.08, 95% CI: 0.01–0.09), and an increased odds of malaria was observed among children who live in households with stagnant water in the compound (AOR = 6.7, 95% CI: 3.6–12.6) and children who stay outdoors during the night (AOR = 5.5, 95% CI: 2.7–11.1).

The prevalence of malaria in the study population was high. Environmental and behavioral factors related to LLIN use remain potential determinants of malaria. Continued public health interventions targeting proper utilization of bed nets, drainage of stagnant water, and improved public awareness about reducing the risk of insect bites have the potential to minimize the prevalence of malaria and improve the health of children.

Citation: Tsegaye AT, Ayele A, Birhanu S (2021) Prevalence and associated factors of malaria in children under the age of five years in Wogera district, northwest Ethiopia: A cross-sectional study. PLoS ONE 16(10): e0257944. https://doi.org/10.1371/journal.pone.0257944

Editor: Benedikt Ley, Menzies School of Health Research: Charles Darwin University, AUSTRALIA

Received: December 10, 2020; Accepted: September 14, 2021; Published: October 11, 2021

Copyright: © 2021 Tsegaye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data generated or analyzed during this study is included in this published article.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AIDS, Acquired Immune Deficiency Syndrome; AOR, Adjusted Odds Ratio; API, Annual Parasite Incidence; CI, Confidence interval; DRC, Democratic Republic of Congo; EMIS, Ethiopia Malaria Indicator Survey; HIV, Human Immune Virus; IRS, Indoor Residual Spraying; LLITN, Long Lasting Insecticide Treated Nets; OPD, Outpatient Department; RDT, Rapid Diagnostic Test; SNNPR, Southern Nation Nationalities and People Region

In sub-Saharan Africa, infectious diseases remain the primary public health threat [ 1 ]. Malaria is one of the commonest infections, disproportionately affecting children and pregnant women. In 2019, an estimated 409,000 people died of malaria. Most (274,000) were young children, and 94% of the infections and deaths occurred in Africa [ 2 , 3 ]. Although several Plasmodium species are responsible for malaria, only a few of them cause most infections.

In 2018, Plasmodium falciparum accounted for 99.7% of estimated malaria cases in the World Health Organization (WHO) African Region, 50% in the WHO South-East Asia Region, 71% in the Eastern Mediterranean, and 65% in the Western Pacific. P . vivax is the predominant parasite in the WHO Region of the Americas, representing 75% of malaria cases [ 3 ]. In Ethiopia, peak malaria transmission occurs between September and December in most parts, following the rainy season from June to August, mainly affecting young children, and P . falciparum and P . vivax are the major malaria parasites [ 4 , 5 ].

Children under five years are one of the most vulnerable groups affected by malaria. Severe anemia, hypoglycemia and cerebral malaria are features of severe malaria more commonly seen in children than in adults [ 6 ]. Children’s susceptibility to diarrhea, respiratory infections, and other illnesses increases when they develop repeated malaria infections [ 7 ]. An estimated 2% of children who recover from cerebral malaria develop learning impairments and disabilities, including epilepsy and spasticity, resulting from the brain damage caused by the infection [ 8 ]. In general, malaria could cause severe outcomes in children in three major ways: First, since children do not usually have acquired immunity, they are more likely to develop severe malaria manifested by seizures or coma (cerebral malaria), which can cause emergency death. Second, through complications related to repeated infections such as anemia. Finally, it causes low birth weight when it happens during pregnancy and increases the risk of death in the first month of life [ 4 ].

According to the WHO 2016 report, the global prevalence of malaria among under-five children was 16% [ 9 ]. In the same year, the prevalence in Ethiopia was 0.6% [ 5 ].

The Ethiopian government developed a National Malaria Control Strategy (NMSP) for the years 2017–2020 that was envisioned to be aligned with the country’s four-year health sector transformation plan (HSTP) 2015/16–2019/20. The proposed goals for the 2017–2020 NMSP include: maintaining near-zero malaria deaths (< = 1 death per 100,000) by 2020, reducing malaria cases by 40% by 2020, and eliminating malaria from Ethiopia by 2030 [ 2 , 5 ].

Even though malaria is one of the leading causes of under-five morbidity and mortality in Ethiopia, prior studies focused only on the adult population and were done in malaria-endemic transmission areas. Nevertheless, it is a potential threat in non-endemic regions [ 5 ]. There has been limited information on the epidemiology of malaria among under-five children living in low malaria transmission areas [ 10 ]. This study aimed to close a critical knowledge gap by assessing the prevalence and determinants of malaria among under-five years old children living in low malaria transmission areas. The findings from this study will inform public health and clinical decision-making and will initiate further investigations.

Methods and materials

Study setting and design.

A health facility-based cross-sectional study was conducted from September to October, 2017 in the Wogera district. Wogera is one of the districts in the North Gondar zone. It has an average altitude of greater than 2050 meters above sea level, with an estimated total population of 274,384, of which 37,152 (13.5%) are children under five years old. The district has 42 rural and one city kebeles (the smallest administrative unit ), of which 15 kebeles (35.7%) are malaria-endemic. In the Wogera district, there was 1 hospital, 10 health centers, 42 health posts, and 4 private health institutions. It shares borders with Dabat and Tach-Armacho in the North, Misrak-Belesa and Janamora in the West, Merab Belesa in the South and Lay-Armacho in the East [ 11 ]. According to the new stratification of malaria risk in the country, the district is under the classification of low transmission areas with expected sporadic epidemics every five years [ 5 ]. Despite that, the report of the district health office indicates that malaria is one of the leading causes of morbidity both in adults and under-five children.

Study participants

All children whose age was five years or below visiting the selected health facilities during the study period were the source population.

Sample size estimation

The calculated sample size was 266 using a single population proportion formula as well as a power approach using a double proportion formula based on previous studies [ 12 ]. Adding a 10% non-response rate and a design effect of two, the final sample size was 585.

Sampling procedure

First, we stratified the health facilities as malaria-endemic and non-endemic based on their altitude. Then, we randomly selected five health centers (Ambagiorgis HC, Gedebgie HC, Selarie HC, Tirgosgia HC, and Chichiki HC) and one hospital (Wogera hospital) from the non-endemic clusters by using a lottery method. The calculated 585 sample size was proportionally allocated to the selected health facilities. Finally, a systematic random sampling technique was used to reach under-five clients who attended the selected health facilities.

Data collection tools and procedures

A structured questionnaire was used for data collection. The tool contained socio-demographic, environmental, and malaria prevention related questions. The questionnaire was initially developed in English and translated into Amharic for data collection. A face-to-face interview of the parents/guardians of the under-five children was conducted to collect the data.

After the interview was completed, following the Federal Democratic Republic of Ethiopia Ministry of Health National Malaria Guidelines, blood was taken from a finger prick to prepare thick and thin blood film smears [ 13 ]. Using a sterile lancet, a finger prick was performed, and 5 micro liters of whole blood was drawn from each child included in the sampling regardless of signs and symptoms of malaria using a capillary tube. The blood smears were prepared on microscope slides and stained using 10% Giemsa to be examined under 100x microscopes for the presence of malaria parasites. The thick smear was used to determine whether the malaria parasites were present or absent and the thin smear was used to identify the type of Plasmodium species. A positive result was defined as the presence of one or more asexual stages (trophozoite, ring stage, merozoite, or gametocyte) of plasmodium [ 14 ].

Data quality assurance

Six laboratory technicians (1 from each health facility) and two supervisors from the district health office were trained for two days by the investigators. Each filled questionnaire was checked thoroughly for completeness and consistency, and the necessary feedback was given to data collectors. Recruitment was preceded by obtaining informed written consent from parents or caregivers of the children. To assure the quality of the microscopic examinations, all positive and randomly selected five percent of the negative slides were checked blindly by another experienced medical laboratory technologist.

Operational definitions

Bed net utilization: was self-reported ownership and regular use of bed nets. A 15-day recall period was used to measure whether each child regularly slept under long lasting insecticide treated nets (LLIN) or not.

Malaria : was defined as a positive thin or thick blood film for the Plasmodium parasite.

Data processing and analysis

After data collection, data were entered using Epi info version 7 and then exported to SPSS version 20 for analysis. The correlates of malaria were identified using bivariate and multivariate logistic regression models. Variables which had a P-value of <0.2 in the bivariable regression were included in the multivariable logistic regression analysis. A P-value <0.05 was considered to determine statistical significance. Finally, adjusted odds ratios (AOR) with a 95% confidence interval (CI) were used to determine the strength of association of variables.

Ethical approval and consent to participate

Ethical approval was obtained from the ethical review committee of the Institute of Public Health, College of Medicine and Health Science, University of Gondar, Ethiopia. Permission was gained from the Amhara Regional Health Bureau, North Gondar health department, and Wogera health office. The caregivers were given detailed explanations about the study’s objectives, procedures, and potential risks and benefits, and written consent was obtained following that. The interview of each study participant took place in a separate room after the children gave blood samples. Appropriate treatment was given to children who tested positive.

Socio-demographic characteristics of study participants

In this study, 585 children from five health centers and one district hospital participated: Gedebgie health center (HC) 178 (30.4%), Ambagiorgis HC 114 (19.5%), Tirgosgia HC 111 (19%), Selarie HC 98 (16.8%), Ambagiorgis hospital 37 (6.3%) and Chichiki HC 47 (8%). Three hundred twenty-three (55.2%) were males and 218 (37.3%) were below 12 months. About 370 (63%) of the respondents live in rural areas, and 305 (54%) of the caregivers can not read and write ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0257944.t001

Indoor Residual Spraying (IRS), Long Lasting Insecticide Treated Nets (LLIN), and environmental characteristics of study participants

Only 131 (22.4%) of the respondents had LLIN. Of the respondents who possessed LLIN, 90% of respondents reported that their children had regularly slept under LLIN in the last 15 days ( Table 2 ).

https://doi.org/10.1371/journal.pone.0257944.t002

Magnitude of Malaria

In this study, the prevalence of malaria by microscopy among under-five children was 8.7% (51). There was a considerable variation in the prevalence rate between the health facilities, ranging from 0% at Wogera hospital to 21% at Selarie health center ( Table 3 ).

https://doi.org/10.1371/journal.pone.0257944.t003

Factors associated with malaria infection

Both bivariable and multivariable binary logistic regression analyses were done to identify the determinants of malaria infection. In bivariate analysis, factors with a P-value of <0.2 were: place of residence, stagnant water around the home, staying outside during the night, possession of an LLIN and regularly sleeping under an LLIN for the last 2 weeks. However, place of residence, sex of the child, age of the child, age of the mother/guardian, educational status of the mother/guardian, presence of radio/television, child having a regular sleeping area, construction material of the house and incidence of IRS within six months had a P-value of >0.2 in the bivariate analysis and were not included in the final model.

In the final adjusted model, children who stayed outside at night had 5.5 times higher odds of malaria infection than children who did not stay outside at night (AOR = 5.5, 95% CI: 2.7–11.1). Children who regularly slept under a LLIN had 92% lower odds of infection than those who did not sleep regularly (AOR = 0.08, 95% CI: 0.08, 0.09). Children who lived in households with close to stagnant water had—4 times higher odds of malaria infection than children who did not live in those homes with nearby stagnant water (AOR = 4, 95% CI: 1.9, 8.1) ( Table 4 ).

https://doi.org/10.1371/journal.pone.0257944.t004

In this study, we estimated the prevalence of malaria among under-five children in the low-risk area and its determinant factors, and the results showed that the malaria prevalence in under-five children was 8.7%, which is in line with the study conducted in Dilla, Southern Ethiopia, where the prevalence of malaria in under-five children was identified to be 7.1% [ 15 ] and a study of analysis of the five-year trend of malaria at Bichena primary hospital, Amhara Region, Ethiopia, where the overall prevalence of malaria was 9.28% [ 16 ].

This finding is much higher when compared to the national malaria indicator survey in 2015 that identified a prevalence of 0.6% among under-five children [ 5 ] and another study conducted in four regional states in Ethiopia, where the prevalence was 4.6% [ 17 ]. This could be due to the difference in methodology used, and also, it might be due to the season when the studies were conducted. Malaria increases from September to December (major transmission season). However, this finding is lower when compared to the global magnitude of malaria among under-five children, which is about 16% [ 9 ] and studies conducted in East Shewa 18.9% [ 18 ], Tanzania 26.3% [ 19 ], Sudan 22% [ 20 ], Uganda 19.5% [ 21 ], and Mozambique 33% [ 22 ]. Those studies were conducted in low land areas, and the difference could be due to a study population difference in the case of a study conducted in Mozambique in which the study population was people with comorbidity.

In Ethiopia, there is spatial and temporal variability in the occurrence of malaria. The current findings also demonstrated similar spatial variations in the proportion of Plasmodium species, with the predominant occurrence of P . falciparum infections at 65% over P . vivax at 35%. This estimate is approximately similar to the study conducted by the Carter Center in Amhara, Oromia, and Southern Ethiopia, where P . falciparum accounted for 56.5% and P . vivax for 43.5% [ 17 ], and a 7-year trend of malaria study done at primary health facilities in Northwest Ethiopia P . falciparum accounted for 15.6% of the participants, which was threefold higher than P . vivax in the seven-year trend [ 23 ]. However, other studies reported a different proportion, such as those conducted in East Shewa ( P . falciparum = 41.2%, P . vivax = 57.1 and Mixed = 1.8%) [ 19 ]; Hadiya ( P . falciparum = 25.5%, P . vivax = 71.7% and Mixed = 2.8%) [ 24 ] and Dilla town ( P . falciparum = 26.8%, P . vivax = 62.5%, and Mixed = 10.7%) [ 15 ]. The variability could be related to the wide climatic diversity between the areas.

Sleeping under LLIN for the last two weeks was found to be protective against malaria. This evidence is supported by other similar studies conducted in East Shewa [ 18 ], Amhara, Oromia, and SNNRP [ 17 ], Dilla [ 15 ], Ethiopia [ 25 ], Ghana [ 26 ], and Uganda [ 21 ]. It was evident that using ITN properly decreased mosquito bites, and thereby decreased malaria infection.

In this study, malaria was highly prevalent among children living in households with stagnant water in the compound compared to their counterparts. This is consistent with a facility-based cross-sectional study conducted in a low transmission area of the Hadiya zone, south Ethiopia [ 24 ]. This is because water collection is one of the favorable conditions for mosquito breeding, which in turn increases malaria transmission. Staying outside during the night showed a statistically significant association with malaria. Staying outside during the night increases the probability of mosquito bites due to the nocturnal nature of the mosquito.

Limitations of the study

As a limitation of this study, since it is a cross-sectional study, it only captures the point prevalence and can not account for seasonal trends in transmission. All surveys are self-report with no confirmation of bed net ownership or use. RDTs with PCR confirmed were not conducted, nor are there details on the life stages of detected parasites observed–gametocytemia, parasitemia.

The prevalence of malaria in under-five children attending health care facilities in Wogera district was high. Regularly sleeping under a bed net, staying outside during the night, and stagnant water around the household were the main correlates of malaria. Focusing on LLIN distribution, environmental management, and changing attitudes towards malaria prevention and control through health education would help minimize the burden of malaria.

Acknowledgments

We would like to thank the Wogera health bureau, the study participants, data collectors, and supervisors who participated in this study for their commitment and cooperation.

• View Article
• PubMed/NCBI
• Google Scholar
• 2. Communicable disease control (CDC). Malaria Impact of Malaria. 2019. https://www.cdc.gov/malaria/malaria_worldwide/impact.html .
• 3. World health organization (WHO). Fact sheet malaria. 30 November 2020. https://www.who.int/news-room/fact-sheets/detail/malaria .
• 4. President’s Malaria Initiative, Ethiopia. Malaria Operational Plan FY. 2019. https://www.pmi.gov/docs/default-source/default-documentlibrary/malariaoperational-plans/fy19/fy-2019-ethiopia-malaria-operational-plan.pdf?sfvrsn=3 .
• 5. Ethiopia National Malaria Indicator Survey. 2015. https://www.ephi.gov.et/images/pictures/download2009/MIS-2015-FinalReport-December-_2016.pdf .
• 6. World health organization (WHO). Malaria in children under five. 2019. https://www.who.int/malaria/areas/high_risk_groups/children/en/ .
• 9. World health organization (WHO). World malaria report. 2016. https://www.who.int/malaria/publications/world-malaria-report-2016/report/en/ .
• 11. Wogera Woreda health bureau Annual Report. 2016.
• 13. Federal Democratic Republic of Ethiopia Ministry of Health. National Malaria Guidelines fourth edition. November 2017 Addis Ababa. https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/documents/files/eth_national_malaria_guidline_4th_edition.pdf .
• 14. Centres for disease control. Malaria Diagnostic Tests.2017. https://www.cdc.gov/malaria/diagnosis_treatment/diagnostic_tools .
• 17. The Carter Center. Prevalence and Risk Factors for Malaria And Trachoma In Ethiopia.Report of Malaria and Trachoma Survey in Ethiopia.2007.
• 19. Mushashu u. Prevalence of malaria infection among under-fives and the associated factors in muleba district-kagera region tanzania. 2012.

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Project Brief: Determining the malaria burden and long-term complications following SARS-CoV-2 infection - Establishing the relationship between malaria and COVID-19

• Malaria Consortium

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Malaria and COVID-19: A pioneering study on co-infection and long-term implications in Ethiopia and Uganda

In one of the first studies globally to explore the potential clinical interactions between COVID-19, long COVID and malaria infections, Malaria Consortium has completed participant enrolment and data collection for a pioneering study in Ethiopia and Uganda.

Malaria Consortium’s research project, Determining the prevalence of malaria burden and long-term complications following SARS-CoV-2 infection , seeks to determine how common malaria is among previous SARS-CoV-2 patients and describe the incidence, clinical characteristics, and outcomes of long COVID in this group. The study focuses on the interactions between SARS-CoV-2 and malaria, two major public health challenges with significant geographical and symptom overlap, particularly in sub-Saharan Africa.

Malaria remains a leading cause of morbidity and mortality in the region, with an estimated 249 million cases and 608,000 deaths reported globally in 2022. The COVID-19 pandemic strained health systems and disrupted malaria control efforts, raising concerns about potential increases in malaria cases and deaths.

“This research will shed light on an underexplored area with significant public health implications, while improving our understanding of how future outbreaks, pandemics or regional outbreaks might interact with the infectious agents that are endemic” , commented Dr Jane Achan, Malaria Consortium’s Principal Advisor and Principal Investigator for the study.

Understanding the interactions between SARS-CoV-2 and Plasmodium falciparum co-infections is crucial for preparedness efforts to help with forecasting disease burden, healthcare resource needs, and implementing appropriate public health measures when multiple pathogens are circulating.

The COVID-19 pandemic led to a substantial number of patients suffering from long COVID (also referred to as post-acute sequelae of SARS-CoV-2 infection, or PASC). However, there has been relatively little exploration of the impact of long COVID in African populations, and populations in which malaria is highly endemic.

In Ethiopia, where limited research has been conducted on the long-term impacts of COVID-19 and its interaction with malaria, the study aims to provide crucial data to inform policymakers and guide actionable steps:

“Public awareness and healthcare systems have yet to embrace the implications of long COVID within a malaria-endemic context. This study will yield crucial data, shedding light on gaps and guiding actionable steps for addressing them” , added Dr Yonas Teshome, Malaria Consortium’s Project Manager in Ethiopia.

Malaria Consortium's approach to this project emphasises generating robust evidence to guide health interventions and policies related to COVID-19 and malaria co-infections and long COVID. A prospective cohort study was conducted across multiple sites in Uganda to characterise the prevalence, clinical interactions and outcomes of SARS-CoV-2 and Plasmodium falciparum co-infections in a high malaria transmission setting.

The research is expected to have significant implications for disease morbidity, mortality, and management strategies. As the analysis is finalised, the researchers stress the importance of integrating their findings into national policy and decision-making. The goal is to work with partners to effectively mitigate the long-term health effects of COVID-19, even amidst existing challenges like malaria.

Read the project brief to find out more

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Jul 20, 2024

Community health workers and supervisors play key role in Liberia’s new national malaria vaccine

Vaccinating a child during the launch of Liberia’s malaria vaccine rollout.

In Liberia, malaria is the leading cause of death among children under five years old. This devastating problem is especially acute in rural and remote communities, where malaria’s prevalence reaches as high as 19%. Community health workers in the country’s National Community Health Program have driven progress against the treatable disease, educating families and providing diagnoses, treatment, and referrals for cases of malaria. Prevention education includes interventions such as sleeping under insecticide-treated nets and closing windows at dusk. These tactics have driven improvements in disease prevalence, but for infants and young children, serious risks remain. Now, the addition of a new national malaria vaccine campaign is changing the reality for families in Liberia’s most remote communities, and community health workers will play a critical role.

Community health worker references and community awareness materials developed for the launch of the malaria vaccine in Liberia.

On April 25, 2024, alongside partners including Last Mile Health, Liberia’s Ministry of Health marked World Malaria Day with the launch of the new RTS,S malaria vaccine, introducing it into the country’s routine childhood immunization program. Vaccination began in Rivercess County, where Last Mile Health directly manages the National Community Health Program in partnership with the Ministry of Health. Community health workers and supervisors both play key roles in ensuring the vaccine reaches rural and remote communities, with vaccine administration taking place not only at health facilities but within the community. Community health workers raise community awareness and create demand for the vaccine as well as tracking records to identify eligible children. Working from patient lists they compile, community health supervisors—typically nurses or midwives—administer the vaccines alongside other routine childhood immunizations during regular visits to communities more than five kilometers from healthcare facilities. They will track children aged five months to 15 months, ensuring that they have received all four doses before their second birthdays, and will also identify older children for “catch-up” immunization.

Community health workers receive training in preparation to assist with the rollout of the malaria vaccine in their communities.

Rivercess County is among six high-prevalence counties identified by the Ministry of Health for the initial introduction and rollout of the malaria vaccine. Community health workers will be key to closing the distance to care for every child in rural and remote communities. Already, community health workers diagnose 50% of confirmed malaria cases in Liberia, and provide 51% of malaria treatments amongst children under age five in rural areas. As of June 2024, 1,027 (99.4%) community health workers from communities beyond 5km of healthcare facilities and 145 (100%) community health supervisors in these counties have received training to implement the new vaccine—a strong foundation toward ensuring families can access it. “Community health workers are such a powerful resource—they start talking, and everybody comes out to listen,” explains Marion Subah, Last Mile Health’s Country Director in Liberia. “There is so much trust in the community.” This trust, Marion explains, is crucial in ensuring families will seek out the new vaccine to protect their children.

Delivering the malaria vaccine.

The national rollout is ambitious: by 2027, the Ministry of Health aims to achieve an 80% rate of full vaccination for children 15 months or older living in communities more than 5km from healthcare facilities. More than 500 community health supervisors will administer the malaria vaccine, supported by more than 4,500 community health workers educating communities and generating demand.

“For far too long, malaria has stolen the laughter and dreams of our children,” said Dr. Louise Kpoto, Liberia’s Minister of Health, speaking at the launch event in Rivercess County. “But today, with this vaccine and the unwavering commitment of our communities, healthcare workers and our partners, we break the chain. We have a powerful tool that will protect them from this devastating illness and related deaths, ensuring their right to health and a brighter future. Let’s end malaria in Liberia and pave the way for a healthier, more just society.”

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• Open access
• Published: 12 May 2024

Prevalence of malaria and associated risk factors among household members in South Ethiopia: a multi-site cross-sectional study

• Girma Yutura 1 ,
• Fekadu Massebo 2 ,
• Nigatu Eligo 2 ,
• Abena Kochora 3 &
• Teklu Wegayehu 2  

Malaria Journal volume  23 , Article number:  143 ( 2024 ) Cite this article

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Despite continuous prevention and control strategies in place, malaria remains a major public health problem in sub-Saharan Africa including Ethiopia. Moreover, prevalence of malaria differs in different geographical settings and epidemiological data were inadequate to assure disease status in the study area. This study was aimed to determine the prevalence of malaria and associated risk factors in selected rural kebeles in South Ethiopia.

A community-based cross-sectional study was conducted between February to June 2019 in eight malaria-endemic kebeles situated in four zones in South Ethiopia. Mult-stage sampling techniques were employed to select the study zones, districts, kebeles and households . Blood sample were collected from 1674 participants in 345 households by finger prick and smears were examined by microscopy. Sociodemographic data as well as risk factors for Plasmodium infection were collected using questionnaires. Bivariate and multivariate logistic regressions were used to analyse the data.

The overall prevalence of malaria in the study localities was 4.5% (76/1674). The prevalence was varied among the study localities with high prevalence in Bashilo (14.6%; 33/226) followed by Mehal Korga (12.1%; 26/214). Plasmodium falciparum was the dominant parasite accounted for 65.8% (50/76), while Plasmodium vivax accounted 18.4% (14/76). Co-infection of P. falciparum and P. vivax was 15.8% (12/76). Among the three age groups prevalence was 7.8% (27/346) in age less than 5 years and 7.5% (40/531) in 5–14 years. The age groups > 14years were less likely infected with Plasmodium parasite (AOR = 0.14, 95% CI 0.02–0.82) than under five children. Non-febrile individuals 1638 (97.8%) were more likely to had Plasmodium infection (AOR = 28.4, 95% CI 011.4–70.6) than febrile 36 (2.2%). Individuals living proximity to mosquito breeding sites have higher Plasmodium infection (AOR = 6.17, 95% CI 2.66–14.3) than those at distant of breeding sites.

Conclusions

Malaria remains a public health problem in the study localities. Thus, malaria prevention and control strategies targeting children, non-febrile cases and individuals living proximity to breeding sites are crucial to reduce malaria related morbidity and mortality.

Malaria continues to remain a global burden and a public health threat despite increasing efforts aimed at improving vector control, therapeutics and diagnostics approaches worldwide [ 1 ]. According to World Health Organization (WHO), there were 249 million estimated malaria cases in 85 malaria endemic countries in 2022, an increase of 5 million cases compared with 2021 [ 1 ]. Most of the increase in case numbers and deaths over the past 5 years occurred in countries in the WHO African Region. Ethiopia is one of the main countries contributing to the increase in cases and death between 2021 and 2022 [ 1 ].

In Ethiopia, malaria transmission is seasonal with two peak transmissions seasons following the bimodal rainfall pattern. Like in most parts of Ethiopia, the peak season for the transmission of malaria in the current study area is from September to December, following the major rainy season [ 2 ]. It affects two-thirds of landmass with 60% of the population living in low to high malaria risk areas, making malaria a leading public health problem in the country [ 3 ]. Plasmodium falciparum and Plasmodium vivax accounting to 60% and 40% of the disease in the country [ 2 , 4 ]. Plasmodium falciparum is highly virulent species which causes severe malaria and death in the country [ 5 , 6 ]. In the country, there were 2.78 million cases and 8041 deaths were reported in 2021 [ 7 ].

Ethiopia is currently working on a malaria elimination programme that aims to eradicate the disease by 2030 [ 8 , 9 ]. In the fight against the disease, the distribution of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) are critical. Additionally, increased healthcare utilization, early diagnosis, prompt treatment, prevention, and rapid management of malaria epidemics, were among the interventions used. However, malaria control programmes need to target active case detection for capturing asymptomatic infections as it challenges the ongoing malaria control and elimination efforts worldwide [ 10 , 11 ]. Most P. falciparum and P. vivax infections are likely to be asymptomatic [ 12 ]. Such infections are missed by passive surveillance, but remain infectious to mosquitoes. Treatment of asymptomatic carriers could help reduce disease transmission by depleting the reservoir of parasites available for infection of mosquitoes [ 13 ]. Without identification and targeting of asymptomatic infectious pool, transmission interruption might not be possible [ 12 ].

Several studies have been conducted to describe parasitological and entomological data of malaria in various malaria-endemic areas in Ethiopia. A recent study conducted in South Ethiopia has indicated Anopheles arabiensis to be the primary vector of P. falciparum after decades of malaria control [ 14 ]. On the other hand, studies consider malaria prevalence and risk in remote Ethiopian communities like the current study setting are limited. Therefore, a community-based study on malaria will provide data that is critical for making evidence-based decisions. The aim of the present study was to assess the prevalence of malaria and the associated risk factors among communities in various geographical settings in selected sites of South Ethiopia.

Study areas description

This study was conducted in four zones namely South Omo, Gamo, Wolaita, and Hadiya Zones of the former South Nations Nationalities Peoples Regional State (SNNPRs) (Fig.  1 ). The SNNPR was one of the regional states in Ethiopia, which include 17 administrative zones and 7 special woredas . The region has an elevation of 376 to 4207 m above sea level. Average elevation of the study kebeles ranged from 553 m a.s.l at Duma to 1720 m a.s.l. at Mehal Korga. The mean annual rainfall ranges from 500 – 2200 mm and temperature ranges between 15 °C and 30 °C. Malaria continues to be a significant health problem in the region, but the transmission intensity varies across different local settings [ 15 ].

Map of study areas (Arc GIS version 10.1)

Study design and period

Community based cross-sectional study was conducted between February to June, 2019 to determine prevalence of Plasmodium infection and associated risk factors among household members in South Ethiopia.

Study participants

People residing in all the study kebeles could be taken as source population and individuals in selected households were included as study participants based on the following inclusion and exclusion criteria.

Inclusion and exclusion criteria

All household members who lived in the kebele for at least 6 months were included in the study regardless of the age and sex. Individuals, who receiving malaria treatment during survey and non-consenting respondents were excluded.

Sample size determination and sampling techniques

The sample size was determined using single population proportion formula of Fink and Kosecoff [ 16 ] assuming, 16% expected prevalence [ 17 ], 2.5% margin error, design effect 2, α = 5% (95% confidence level), and 15% non-response rate. Accordingly, the sample size was calculated as follows:

where n = the sample size, Z 1 -α/2 = the Z-value at a given confidence level, P = estimated prevalence of malaria in the study population, d = margin of error or sample error. Therefore, sample size was calculated as

Multistage sampling was used to select districts, kebeles , and households. According to the zonal health department report, one high-malaria-prevalent district in each zone were included, except the Gamo zone, where two districts were included. The Gamo zone included two districts as it had wider geographical coverage during conception of the study as Gamo-Gofa Zone. However, the Gamo-Gofa Zone became two independent zones during the study period and two of the districts located in Gamo Zone. Finally, two malarious kebeles were purposefully selected in each district based on the malaria incidence (Fig.  2 ).

Sampling framework of the study sites and households

According to Ethiopian population and housing census of 2007, average family size for the region was 4.9 [ 18 ]; and hence the calculated household was 345 (Table  1 ). The total sample size (1674) was allocated to HHs proportionally to individual kebeles based on entire population of study sites as indicated in Table  1 . Systematic sampling was carried out using the lists of households in each kebele health post to select the households. The first household was selected randomly by lottery method and every k th household was included in the study. Where K is calculated by the formula of K =  \(\frac{N}{n}, {\text{K}}=\frac{4729}{345}\) , Where, K = the gap between every household, N = total number of households in the study kebeles and n = sample size of households was calculated from individual sample size. Therefore, K = 13, thus every thirteenth household was included. Few houses were replaced by nearby houses when the selected household heads were absent or did not volunteer to participate in the study.

Sample collection and processing

Blood sample collection and processing.

Capillary blood sample was collected using sterile blood lancets from participants after obtaining written consent during house-to-house visits. Blood sample collection was done by senior medical laboratory technicians, following standard guidelines [ 19 ]. Thick and thin blood smears were prepared at field and dried by air. The air dried blood thin and thick smears were transported to nearby health centres’ laboratories using slide boxes. The smears were fixed using 99.8% methanol, dried, and stained with a 10% Giemsa solution for 10 min. Then, microscopy was employed by experienced laboratory technicians to detect and identify Plasmodium parasite species according to laboratory guidelines. Slides were declared negative for Plasmodium parasites after thorough examination of 100 fields and no Plasmodium parasite is detected by microscopy.

Sociodemographic data collection

Sociodemographic data were collected from 345 households based on structured questionnaire. The questioner prepared in local language was sought information on sociodemographic characteristics, and malaria prevention and control practices. After having the written consents, both individual and household-level factors associated with malaria transmission was obtained from the participant. During the time of sample collection, fever of study participants was checked and signs and symptoms of malaria such as headache, chills, sweating were asked. Fever of individuals was measured using thermometers (Hanimax) and auxiliary body temperature (> 37.5 ℃) were considered as febrile.

Data quality assurance

Data quality was maintained using various approaches. First, training was given for field assistants (data collectors) to have a common understanding to collect the appropriate demographic information. Second, blood sample collection and microscopy were done by senior laboratory technologists and discussion was held to apply standard operational diagnostic procedures during laboratory work. Each questioner and the collected sample were cross-checked for completeness, accuracy, and consistency by the group members and corrective measures taken. Moreover, all houses were coordinated using geographical position system and study individuals were coded during blood sample collection. All positive slides and 10% of negative slides were re-examined by another senior laboratory technologist blinded to previous slide results.

Study variables

The outcome variable for examination of blood films was Plasmodium infection status.

Independent variables included house structure (the roof material, floor material, presence of visible holes on wall), IRS spraying in the last 12 months, LLINs ownership (presence of bed nets, total number of nets, access to LLINs and use of mosquito nets), presence of mosquito breeding site. The variables like sex, age, and fever (auxiliary temperature) were considered as individual level for analysis of data.

Data analysis

Data was entered into Microsoft Excel spreadsheets and analysed using SPSS version 20.0. Descriptive statistics were used to determine the frequencies of variables. Bivariate logistic regression analysis was conducted to examine the association between Plasmodium infections with associated risk factors. Multivariate logistic regression analysis was conducted to test potential predicators’ variable that was the main risk factor for Plasmodium infection. The goodness of model fit was checked by Hosmer-Leme show-test and the logistic regression was fit for the test. Data normality was checked by non-parametric test of one-sample Kolmogorove-Smirnov test (1-sample K-S). Logistic regression statistical method of multivariate logistic regression was used with a 95% confidence interval and odds ratio was used to control confounders with the level of statistical significance was taken as P -value < 0.05 for analysis of independent and outcome variables. During binary logistic regression if the p  ≤ 0.025 was considered as a candidate for multivariate logistic regression.

Sociodemographic characteristics

The sociodemographic characteristic of the study participants was summarized in Table  2 . From the total of 1674 participants, 748 (44.7%) were males and 926 (55.3%) were female. With regard to the age, 346 (20.7%), 531 (31.7%) and 797 (47.6%) were in the age groups < 5, 5–14 and > 14 age groups, respectively. Of the total, 1638 (97.8%) were non-febrile and the rest 36 (2.2%) were febrile cases.

Overall, and site-specific prevalence of malaria

The overall prevalence of malaria was 4.5% (76/1674) confirmed by microscopy (Table  3 ). The Plasmodium infection was more prevalent in Bashilo kebele 14.6% (33/226) followed by Mehal Korga 12.1% (26/214). Plasmodium infection was detected in seven study kebeles and no malaria cases were detected in Gocho Hambisa kebele .

Among the confirmed malaria cases, P. falciparum was dominant species accounting 65.8% (50/76), while P. vivax was 18.4% (14/76). Mixed infections with P. falciparum and P. vivax were accounted 15.8% (12/76). Higher prevalence of P. falciparum 10.18% (23/226) was observed in Bashilo kebele . Among study kebeles , Mehal Korga had the high prevalence of P. vivax 3.74% (8/214) (Table  3 ).

Sex and age-related prevalence of malaria

Of the study participants, 5.2% (39/748) males and 4% (37/926) females were found positive for Plasmodium parasite (Table  4 ). The prevalence of Plasmodium parasites among age groups were 7.8% (27/346) in under five children, 7.5% (40/531) in 5–14 years and 1.1 (9/797) in > 14 years. The greatest malaria prevalence was observed among under five children followed by school age groups.

Malaria-associated factors analysis

A total of eight independent variables were considered for bivariate logistic regression analysis of individuals and household associated risk factors for malaria parasite infections (Table  5 ). The variables associated with individual and household-level risk factors of malaria parasite infection was age, fever during survey time, LLINs utilization, IRS spray status, house structure (main roof material), main wall material, presence of visible hole on the wall, and living proximity to breeding sites. Among those variables, the age of individuals, fever, LLINs utilization and living proximity to the breeding site were a candidate for multivariate analysis.

In the multivariate logistic regression analysis, the predictors of Plasmodium infections after controlling confounders of the variables were the age of individuals (AOR = 0.14, 95% CI 0.02–0.82) and fever during survey time (AOR = 0.37, 95% CI0.19–0.72). Household-level predictor variables of Plasmodium infections were LLINs utilization (AOR = 0.37, 95% CI 0.19–0.72) and proximity mosquito breeding sites (AOR = 6.17, 95% CI 2.66–14.3) were a significant association with Plasmodium infection.

The individuals who’s aged < 5 was 86% more likely to have a malaria as compared with individuals whose age > 14 with the p-value = 0.029 (IC = 0.02-0.82). Individuals who do not have a fever during study time were 28.4 times more likely have Plasmodium parasite as compared to individuals with fever with the p-value = 0.001 (CI 11.4–70.06).

LLINs utilization was significantly associated with Plasmodium species. The individuals that have not to use LLINs during a sleeping time were 63% more likely have a chance to Plasmodium parasite infection as compared with their counterparts with the p-value = 0.003 (CI 0.19–0.72) (Table  5 ). Those individuals who live proximity to the breeding site were 6.17 times more likely have a chance to develop malaria as compared to individuals do not live around breeding site with the p-value = 0.001 (CI 2.66–14.3).

Malaria affects the lives of almost all people living in sub-Saharan African countries. In Ethiopia, malaria remains a major public health problem despite continuous control and preventive strategies in place. The overall prevalence of malaria in this study was 4.5% with varying prevalence in different study sites in South Ethiopia. Both P. falciparum and P. vivax has been identified with P. falciparum dominant species accounted for 65.8% (50/76). It was also observed that lower age group, non-febrile case, and individuals who live proximity to mosquito breeding site had higher Plasmodium infection.

The overall prevalence of malaria in this study (4.5%) was in line with reports from various parts of Ethiopia including 4.4% in Butajira, 6.1% in Benatsemay district (South Omo), 6.7% in Dembia districts, 6.8% in Sanja town, and 4% in Jimma zone [ 20 , 21 , 22 , 23 ]. This finding is higher than the prevalence reported in another study in Butajira and national malaria indicator survey 2015 result, with prevalence of 0.9% and 0.5%, respectively [ 24 , 25 ]. On the other hand, the present finding is much lower than the prevalence reported in Kisumu country in the Kenya with 28% [ 26 ], Armachiho districts, North West Ethiopia with 18.4% [ 27 ], and Dilla town and surrounding areas with 16.0% [ 17 ]. The difference in findings might be associated with sociodemographic, socioeconomic and environmental factors that could affect the epidemiology of malaria.

Prevalence of Plasmodium infection was relatively high in Bashilo (14.6%) and Mehal Korga kebeles (12.6%) as compared to Enchete, Duma, Dana, Gocho Hambisa, Abaya Gurucho and Abaya Bilate. The same holds true in other studies conducted in different parts of Ethiopia [ 20 , 28 , 29 ]. The heterogeneity of Plasmodium infection in the present study settings might be because of ecologic and environmental factors, host and vector characteristics, social, biological and socio demographic factors.

Plasmodium falciparum and P. vivax were identified as co-endemic species in study areas while P. falciparum was dominant species of parasite. The dominance of P. falciparum was consistent with the study conducted in Benatsemay districts in South Omo, Ethiopia [ 23 ]. In addition, the national community-based malaria indicator surveys conducted during peak malaria transmission season in the 2007 and 2011 reported the dominance of P. falciparum as 83% and 77%, respectively [ 30 , 31 ]. The dominance of P. falciparum species might be more widely distributed in many parts of Ethiopia. This might be associated to the capacity of P. falciparum parasite to develop resistance against anti-malarial drugs represents a central challenge in the global control and elimination of malaria [ 32 ]. In contrast to this finding, other studies conducted in different geographical settings in Ethiopia [ 28 , 29 ] monitoring changing of the epidemiology of malaria beyond Gark projects [ 33 ] and the facility-based cross-sectional study in Hadiya Zone [ 34 ] the P. vivax dominates over P. falciparum . One possible reason for predominance of P. vivax might be improper management of primaquine that lead to the relapse of hyponozoites.

Regarding the age groups, the likelihood of having higher malaria cases was found among under five children and school age children than other age groups. This finding was in line with malaria prevalence in Ethiopian on malaria indicator survey [ 25 ], in Arba Minch Zuria district [ 35 ] children this age groups are more vulnerable and had have Plasmodium parasite infections. The reason why high malaria cases in this age groups might be due to immunity status, more exposed to mosquito bites before bedding, and less awareness of self-care for utilization of malaria preventive measures.

Non-febrile Plasmodium infection was common in endemic areas. In malaria-endemic areas, people may develop partial immunity, allowing the non-febrile infection to occur. The odds of Plasmodium infection were higher in individuals that do not have fevers than those who have fever. The result consistent with the study conducted in Senegal that indicated P. falciparum was dominant species in asymptomatic cases [ 36 ]. In other way, in low transmission settings, asymptomatic cases are common and most of the asymptomatic infections are sub-microscopic [ 28 , 37 ]. Study showed that asymptomatic cases could serve as reservoirs of infections to the mosquito vectors [ 38 ]. Thus, they could serve as a major source of gametocytes and contributed to residual transmissions of malaria as asymptomatic carriers do not visit health facility for treatment. In many countries P. falciparum is asymptomatic or sub-clinical. In very low transmission settings, sub-microscopic carriers may contribute up to 50% of humans to mosquito transmission [ 39 ].

Appropriate use of the utilization of LLINs is one of the key interventions for the prevention of malaria [ 40 ]. In this study, ownership of LLINs was 76.9%. This finding was higher than the previous findings in Hadiya zones with LLINs ownership of 41.6% [ 34 ]. On the other hand, national malaria indicator survey conducted in 2011 and 2015 showed 55% and 64% of households have at least one LLINs of any type [ 25 , 30 ] and a community-based cohort study in South Central Ethiopia [ 41 ]. However, the accesses to LLINs were not significantly associated with Plasmodium infection in study sites.

The utilization of LLINs has an association with malaria cases among study participants. The current study showed that participants who use LLINs had lower malaria cases than those do not use. This findings is in line with the study conducted Dilla and surroundings areas, Dembia districts, and Hadiya zones where participants do not use bed nets were 0.2, 0.2 and 4.6 times more likely developed Plasmodium parasite infections, respectively [ 17 , 22 , 34 ]. The finding speculates the proper usage of LLINs protects from malaria through protecting mosquito bites depending on biting activity. It is noticeable that the proper utilization of LLINs will prevent mosquito that in turn prevent Plasmodium parasite infection. These findings might the implication of possession and efficacy of LLINs utilization in the community and less attention to frequent utilization in different local settings.

Another important factor that determines the odds of Plasmodium infection is living proximity to the breeding site. In this study, a participant who live proximity to mosquito breeding sites was at high risk of Plasmodium infections. The study participants those lives proximity to the stagnant water of mosquito the breeding sites 6.17 times more likely have a chance to develop Plasmodium infection as compared to individuals do not live around the breeding site. This finding in agreement with the study conducted in Dilla and surrounding areas and Dembia districts [ 17 , 22 ] by increasing the probability of having Plasmodium infection. This is because proximity mosquito breeding sites give more chances to exposure mosquito bites in the community.

This study has some limitations. One of the limitations of this study is the laboratory diagnosis which is limited to microscopy only, a low sensitive tool. The second limitation is seasonality of transmission was not determined. The community-based nature of the study can be viewed as one of the strengths of this study as it enables us to screen the non-febrile cases who could serve as potential reservoir of malaria parasite. High response rate of study participants can also be viewed as another strength of this study.

Malaria is still important public health problems, although the prevalence of disease was varying in the study sites . Lower age children, non-febrile cases and those who reside proximity to mosquito breeding sites were at higher risk of Plasmodium infection. Thus, malaria prevention and control strategies addressing communities at high risk of infection should be in place to reduce malaria associated morbidity and mortality in the study localities.

Availability of data and materials

The data supporting the conclusions conferred in this article is presented in the main paper.

WHO. World Malaria Report 2023. Geneva: World Health Organization; 2023.

Google Scholar  

FDRoE. National Strategic Plan for Malaria Prevention, Control and Elimination in Ethiopia 2011–2015. Addis Ababa, Ethiopia. Federal Democratic Republic of Ethiopia Ministry of Health, 2010.

President’s Malaria Initiative Ethiopia. Malaria operational plan. 2019. https://reliefweb.int/sites/reliefweb.int/files/resources/fy-2019-ethiopia-malaria-operational-plan.pdf .

Carter Center. Annual malaria control program review enhancing impact through integrated strategies malaria programs Ethiopia and Nigeria. Atlanta, Georgia. 2012.

Yewhalaw D, Legesse W, Bortel WV, GebreSelassie S, Kloos H, Duchateau L, et al. Malaria and water resource development: the case of Gilgel-Gibehydroelectric dam in Ethiopia. Malar J. 2009;8:21.

Article   PubMed   PubMed Central   Google Scholar  

Ketema TG, Bacha K. Therapeutic efficacy of chloroquine for treatment of Plasmodium vivax malaria cases in Halaba district, South Ethiopia. Parasit Vectors. 2011;4:46.

WHO. World malaria report 2022. Geneva: World Health Organization; 2022.

Fedral Minstry of Health (FMoH). National Malaria Elimination Strategic Plan: 2021–2025, Addis Ababa, Ethiopia. 2020.

Bugssa G, Tedla K. Feasibility of malaria elimination in Ethiopia. Ethiop J Health Sci. 2020;30:607–14.

PubMed   PubMed Central   Google Scholar  

WHO. World Malaria Report 2018. Geneva: World Health Organization; 2018.

Gueye CS, Sanders KC, Galappaththy GN, Rundi C, Tobgay T, Sovannaroth S, et al. Active case detection for malaria elimination: a survey among Asia Pacific countries. Malar J. 2013;12:358.

Article   Google Scholar  

Cotter C, Sturrock HW, Hsiang MS, Llu J, Phillips AA, Hwang J, et al. The changing epidemiology of malaria elimination: new strategies for new challenges. Lancet. 2013;382:900–11.

Kokwaro G. Ongoing challenges in the management of malaria. Malar J. 2009;8:S2.

Eligo N, Wegayehu T, Pareyn M, Tamiru G, Lindtjørn B, Massebo F. Anopheles arabiensis continues to be the primary vector of Plasmodium falciparum after decades of malaria control in southwestern Ethiopia. Malar J. 2023;23:14.

Loha E, Lindtjørn B. Model variation in predicting incidence of Plasmodium falciparium malaria using 1998–2007 morbidity and metereological data from South Ethiopia. Malar J. 2010;9:166.

Fink AKJ. How to conduct surveys: a step by step guide. Thousand Oaks: Sage Publications; 1995.

Molla E, Ayele B. Prevalence of malaria and associated factors in Dilla Town and the surrounding rural areas, Gedeo Zone, Southwest Ethiopia. J Bacteriol Parasitol. 2015;6:5.

Central Statistical Agency (CSA). 2007 Population and housing census of Ethiopia. Addis Ababa, Ethiopia: 2007.

WHO. Basic malaria microscopy–Part I: Learner’s guide. 2nd ed. Geneva: World Health Organization; 2010.

Zemene E, Koepfli C, Tiruneh A, Yeshiwondim AK, Seyoum D, Lee MC, et al. Detection of foci of residual malaria transmission through reactive case detection in Ethiopia. Malar J. 2018;17:390.

Tesfaye S, Belyhun Y, Teklu T, Mengesha T, Petros B. Malaria prevalence pattern observed in the highland fringe of Butajira, Southwest Ethiopia: a longitudinal study from parasitological and entomological survey. Malar J. 2011;10:153.

Fekadu M, Yenit MK, Lakew AA. The prevalence of asymptomatic malaria parasitemia and associated factors among adults in Dembia district, northwest Ethiopia, 2017. Arch Public Health. 2018;76:74.

Debo GW, Kassa DH. Prevalence of malaria and associated factors in Benna Tsemay district of pastoralist community, Southwest Ethiopia. Travel Med Vacc. 2016;2:16.

Woyessa A, Deressa W, Ali A, Lindtjørn AB. Prevalence of malaria infection in Butajira area, south-central Ethiopia. Malar J. 2012;11:84.

Ethiopia National Malaria Indicator Survey (EMIS). Ethiopia National Malaria Indicator Survey 2015. Addis Ababa: Ethiopian Public Health Institute; 2016.

Jenkins R, Omollo R, Ongecha M, Sifuna P, Othieno C, Ongeri L, et al. Prevalence of malaria parasites in adults and its determinants in malaria endemic area of Kisumu County, Kenya. Malar J. 2015;14:26.

Aschale Y, Mengist A, Bitew A, Kassie B, Talie A. Prevalence of malaria and associated risk factors among asymptomatic migrant laborers in Armachiho District, Northwest Ethiopia. Res Rep Trop Med. 2018;9:95–101.

Tadesse FG, Slater HC, Chali W, Teelen K, Lanke K, Belachew M, et al. The relative contribution of symptomatic and asymptomatic Plasmodium vivax and Plasmodium falciparum infections to the infectious reservoir in a low-endemic setting in Ethiopia. Clin Infect Dis. 2018;66:1883–91.

Article   CAS   PubMed   Google Scholar  

Haji Y, Fogarty AW, Deressa W. Prevalence and associated factors of malaria among febrile children in Ethiopia: a cross-sectional health facility-based study. Acta Trop. 2015;155:63–70.

Article   PubMed   Google Scholar  

Ethiopia National Malaria Indicator Survey (EMIS). Ethiopia National Malaria Indicator Survey 2011: Technical Summary. Addis Ababa: The Ethiopian Health and Nutrition Research Institute & Partners; 2012.

Ethiopia National Malaria Indicator Survey (EMIS). Ethiopian National Malaria Indicator Survey (2007). Addis Ababa: Technical Summary Ethiopian; 2008.

Gil JP, Fançony C. Plasmodium falciparum multidrug resistance proteins (pfMRPs). Front Pharmacol. 2021;12: 759422.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Abeku TA, Helinski ME, Kirby JM, Kefyalew T, Awano T, Batisso E, et al. Monitoring changes in malaria epidemiology and effectiveness of interventions in Ethiopia and Uganda: beyond Garki Project baseline survey. Malar J. 2015;14:337.

Delil R, Dileba TK, Habtu YA, Gone T, Leta TJ. Magnitude of malaria and factors among febrile cases in low transmission areas of Hadiya Zone, Ethiopia: a facility based crosssectional study. PLoS ONE. 2016;5:11.

Abose T, Ye Y, Olana D, Alamirew D, Beyene Y, Regassa L, et al. Re-orientation and definition of the role of malaria vector control in Ethiopia: the epidemiology and control of malaria with special emphasis on the distribution, behaviour and susceptibility of insecticides of anopheline vectors and chloroquine resistance in Zwai, Central Ethiopia and other areas Geneva: World Health Organization. 1998

Niang M, Thiam LG, Sane R, Diagne N, Talla C, Doucoure S, et al. Substantial asymptomatic submicroscopic Plasmodium carriage during dry season in low transmission areas in Senegal: implications for malaria control and elimination. PLoS ONE. 2017;12: e0182189.

Federal Ministry of Health (FMOH). Annual Performance Report 2015 (HSDP IV) for EFY 2007 (2014/15) version 1. Addis abeba, Ethiopia. 2015.

Kiattibutr K, Roobsoong W, Sriwichai P, Saeseu T, Rachaphaew N, Suansomjit C, et al. Infectivity of symptomatic and asymptomatic Plasmodium vivax infections to a Southeast Asian vector. Anopheles dirus Int J Parasitol. 2017;47:163–70.

Chen I, Clarke SE, Gosling R, Hamainza B, Killeen G, Magill A, et al. “Asymptomatic” malaria: a chronic and debilitating infection that should be treated. PLoS Med. 2016;13: e1001942.

Fedal Ministry of Health (FMoH). National Malaria Guidelines. 3 rd edn. Accessed at www.medbox.org . Addis Abeba, Ethiopia. 2012.

Solomon T, Loha E, Deressa W, Gari T, Overgaard HJ, Lindtjørn B. Low use of long-lasting insecticidal nets for malaria prevention in south-central Ethiopia: a community-based cohort study. PLoS ONE. 2019;14: e0210578.

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Acknowledgements

We would like to thank Arba Minch University for its financial support and study participants for taking parts in the study. We would also like to thank all the health centers and laboratory technicians of study sites for their cooperation during sample collection and processing. We are also grateful to South Omo, Gamo, Wolaita and Hadiya zonal and districts health departments and Kebele administrators for their technical support.

Financial support was obtained from Arba Minch University.

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Fekadu Massebo, Nigatu Eligo & Teklu Wegayehu

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G.Y., F.M. and T.W. Conception and design of the study. G.Y. and N.E. Data accusation and management. G.Y., F.M. and T.W. Analyzed and interpreted data. G.Y., F.M., AK and T.W. Drafted the work and substantively revised it.

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The study was reviewed and approved by the Ethical Review Committee of Arba Minch University (Ref.No.CMHS/12033592/111). Prior to the study, permission letter was obtained from selected Zonal Health Departments. Written consent (assent for children) was obtained from head of the household before undertaking the data collection and official letter was sought from the respective district’s health office. For children and younger participants’ consents were obtained from their parents/guardians. The purpose of the study and procedure of blood sample collection were explained to the participants. The Study participants those positive for P. falciparum and P. vivax was treated free of charge at nearby health facilities.

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Yutura, G., Massebo, F., Eligo, N. et al. Prevalence of malaria and associated risk factors among household members in South Ethiopia: a multi-site cross-sectional study. Malar J 23 , 143 (2024). https://doi.org/10.1186/s12936-024-04965-4

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How is new malaria vaccine drive working in West Africa?

How does the new R21/Matrix-M vaccine work and where is it being administered?

The Ivory Coast has received the first doses of malaria vaccine from the world’s largest vaccine maker, the Serum Institute of India, in collaboration with the University of Oxford, and began rolling out a new vaccination drive across the country earlier this week.

Malaria remains a significant health issue in the Ivory Coast, causing up to four deaths per day in the country with a population of 28 million. According to a 2022 report from the World Health Organization (WHO), malaria causes more than 600,000 deaths worldwide per year with 95 percent of cases occurring in Africa and 80 percent of those cases in children under the age of five.

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A total of 656,600 doses of the new R21/Matrix-M malaria vaccine have been delivered to the Ivory Coast, where clinicians will initially vaccinate 250,000 children aged from newborn to 23 months across 16 regions, the government said.

Professor Adrian Hill, Lakshmi Mittal professor of vaccinology and director of the Jenner Institute at Oxford University in the United Kingdom, told the media on Monday that the drive had been made possible through a joint initiative with the Serum Institute of India because of its “scale” and ability to mass-produce millions of vaccines at low cost.

After vaccines are rolled out in the Ivory Coast, the drive will move to other African countries, starting with Burkina Faso, Professor Hill said.

According to Gavi, an international health organisation which works alongside WHO and UNICEF, 15 African countries are expected to roll out malaria vaccines in 2024. Countries plan to reach about 6.6 million children with the malaria vaccine in 2024 and 2025.

Here is what we know about the malaria vaccine drive so far:

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Health workers are administering doses of the R21/Matrix-M malaria vaccine, the second malaria vaccine to have been approved by the WHO in December last year, in Ivory Coast vaccination centres.

Research suggests R21/Matrix-M can reduce symptomatic malaria cases by 75 percent in a community in the 12 months following a three-dose series, with efficacy sustained by a fourth dose administered a year later.

“Over 600,000 deaths mainly amongst children are caused by malaria each year. The disease presents a uniquely difficult scientific challenge: the complex composition of malaria parasites with shape-shifting pathogen that has learned how to evade our immune system, has made the development of an effective vaccine a formidable task,” Professor Hill said in a statement last December.

“R21/Matrix-M represents the culmination of 30 years of collaborative research and development by the University of Oxford Jenner Institute and, since 2017, in partnership with the Serum Institute of India.”

The WHO and local health authorities are also delivering shots of the Mosquirix vaccine , also known as the RTS,S malaria vaccine, to more than 2.3 million children across Africa this year.

This vaccine has been primarily introduced in Cameroon, Ghana, Kenya and Malawi, focusing on children aged five months and older in regions with a significant prevalence of Plasmodium falciparum malaria.

How does the R21 vaccine work?

The R21 vaccine uses adjuvant technology, which enhances the immune response to the vaccine, allowing protection from future infections of a disease.

The vaccine is designed to specifically target the sporozoite stage of the malaria parasite. This is the initial form of the parasite that enters the human body when bitten by a mosquito. By focusing on this stage it helps to boost the immune system’s response, leading to higher efficacy in preventing malaria. In addition, the vaccine can prevent the parasite from infecting the liver and causing illness.

How widely available is the vaccine?

The Serum Institute of India, which was responsible for delivering more than 2 billion doses of COVID-19 vaccines around the world, is capable of producing 100 to 200 million doses annually, making it more cost-effective and accessible. Professor Hill told the UK’s BBC Radio Four Today programme on Monday that the Serum Institute’s ability to mass-produce vaccines had reduced the cost of each R21/Matrix-M shot from about $8 or $9 to about $4.

It will also be made available in several other African countries. “The new vaccine has been authorised by Ghana, Nigeria, Burkina Faso and the Central African Republic, and many others are preparing to receive shipments,” the University of Oxford said in a statement to CNN.

How can malaria be eliminated?

The goal set by the WHO Global Malaria Programme (GMP) is to reduce cases of malaria by 90 percent by 2030.

Measures in place to achieve this are:

• Early detection and diagnosis of malaria cases to prevent transmission and deaths.
• The effective mass distribution of affordable vaccines in countries with high infection rates.
• The use of insecticide-treated nets (ITNs) and indoor residual spraying (IRS) to reduce mosquito populations and transmission through mosquito bites.
• Building awareness about the disease and how it spreads and how to treat it in malaria-prone communities while ensuring proper access to the necessary health facilities.

According to the WHO report, however, some challenges remain. “Countries, subnational areas and communities are situated at different points along the path towards malaria elimination, and their rate of progress will differ depending on the level of investment, biological determinants (related to the affected populations, the parasites and the vectors), environmental factors, strength of health systems, and social, demographic, political and economic realities,” the report states.

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Prevalence and Associated Factors of Malaria Infection among Outpatients Visiting Shewa Robit Health Center, Northcentral Ethiopia

Azene tesfaye.

1 Department of Medical Laboratory Science, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia

Tadegew Teshome

2 Department of Zoological Sciences, College of Natural Sciences, Addis Ababa University, Addis Ababa, Ethiopia

3 Shewa Robit Secondary and Preparatory School, Ministry of Education, Addis Ababa, Ethiopia

Associated Data

All data generated or analyzed during this study were included in this published article; thus, no additional data were available.

Introduction

Malaria infection is a serious health problem killing millions in tropical developing countries including Ethiopia. The present study focused on assessing malaria prevalence and identification of determinants in Shewa Robit, northcentral Ethiopia.

A cross-sectional study was conducted among 422 participants who visited Shewa Robit Health Center between 01/10/2017 and 30/04/2018, using a simple random sampling. Sociodemographic characteristics were recorded using a pre-tested semi-structured questionnaire and infection was confirmed by microscopic examination. Data were analyzed using the Statistical Program for Social Sciences (SPSS) version 20 and p < 0.05 was used to indicate the level of significance.

Eighty-one (19.0%) microscopically confirmed malaria cases were recorded, P.vivax was the most frequently detected species ( n  = 58; 71.6%). Interestingly, 73.2% ( n  = 309) of the participant did not utilize LLINs due to the fear of toxicity (37.4%, n  = 158), misconception (21.6%, n  = 91), and shortage (14.2%, n  = 60). The data showed age, gender, marital status, family size, usage of LLINs and application of IRS, proximity to mosquito breeding sites and less robust and porous walls were the determinants of the infection in the study area.

The prevalence of malaria in the study population was high and P. vivax being the most common causative agent. Environmental and behavioral factors related to LLIN are the potential determinants of malaria. Continued public health interventions, targeting proper utilization of bed nets, drainage of stagnant water, and improved public awareness about reducing the risk of insect bites have the potential to minimize the infection.

1. Introduction

Malaria is recognized around the world as a debilitating and terrible infectious disease that kills millions and causes serious complications such as severe anemia, cerebral involvement, acute renal failure, and hypoglycemia [ 1 , 2 ]. It is distributed worldwide including Africa, south and Central America, south and southeast Asia, particularly with very high transmission intensity in the Sub-Saharan Africa [ 3 ]. A recent WHO report on global malaria status revealed an estimated 241 million cases and 627,000 deaths in 2020 [ 2 ].

Malaria is widespread in almost 45 African nations, including Ethiopia, with nearly 3 million cases each year, and morbidity and mortality rates are growing substantially [ 4 ]. It is distributed almost everywhere in the country and affects about 70% of the population [ 5 ]. Previous research in Ethiopia has shown that a large part of the geographic and agroecological environment (68%) is conducive to the transmission of malaria, with altitude and rainfall appearing extremely significant. In particular, 75% of the topography is below 2000 meters from sea level, supporting its transmission [ 6 , 7 ]. Research report confirmed that around 68% of the total population live in malaria-prone areas with more than 50 million people are at risk of malaria, with an estimated 4-5 million cases and 70,000 deaths each year [ 8 , 9 ]. In addition, transmission follows the rainy season and occurs between September and December in almost every part of the country, while a minor transmission season occurs between April and May [ 10 ].

Previous research confirmed that age, sex, marital status of the respondent [ 5 ], proximity to mosquito breeding sites such as stagnant water [ 7 ], temperature, humidity, precipitation, education, occupation, and income are the main risk factors that favor the transmission of malaria [ 11 , 12 ].

As part of the global Roll Back Malaria project, Ethiopia has a long-term goal to eliminate malaria [ 13 ], and in this context, the government adopts a variety of interventional measures. Early detection and treatment, selective vector control measures such as indoor residual spray (IRS) and long-lasting insecticidal nets, (LLINs), and environmental management are important steps. In addition, quick diagnostic tests are performed along with the adaptation of artemisinin-based combination therapy [ 5 , 14 ].

Malaria remains one of the most serious public health concerns in Ethiopia, despite significant efforts to combat it [ 15 ]. Meanwhile, the disease is much more prevalent in rural areas due to favorable conditions for the establishment and proliferation of associated vector [ 16 ]. However, studies have already shown that malaria transmission has increased in metropolitan settings [ 17 , 18 ]. This could be linked to the growing urbanization, with a lack of proper sanitation, substandard housing, and poor surface water drainage, all of which enhance the exposure to mosquitoes and subsequent disease transmission [ 16 – 18 ]. Furthermore, poor health services, increased migration of people from malaria affected rural area to urban sites, limited extent of indoor residual insecticide spraying (IRS) and bed net use, an increase in the number of man-made mosquito breeding sites, unplanned irrigation schemes and water reservoirs may hasten the spread of the disease to urban habitats [ 17 ].

The terrain of Shewa Robit is favorable for the transmission of malaria, which is one of the top ten causes of morbidity (Health Office report, 2018). Despite the high rate of malaria infection, literature analyzes showed a paucity of recently updated data on the prevalence and risk factors in the study area [ 7 ]. In fact, effective public health programs to control and prevent malaria require current and consistent data on prevalence and existing risk factors [ 2 ]. However, the prevalence of malaria in the research area has been poorly studied. Therefore, the aim of this study was to determine the prevalence of malaria infection and its associated factors among suspected outpatients visiting Shewa Robit Health Center, northcenteral Ethiopia.

2. Materials and Methods

2.1. study area, design and study population.

A cross-sectional study was conducted among all malaria suspected outpatients who visited Shewa Robit Health Center located in Shewa Robit town, from 01/10/2017 to 30/04/2018. The town is located 225 km northeast of Addis Ababa, in the Amhara Regional State at an elevation of about 1,280 meters above sea level. The town lies at a longitude and latitude of 10°060 N39°590 E and 10.1°N39.983°E, respectively. The climate is tropical with an annual temperature ranging from 28 to 37°C with an annual rainfall of 1000 mm. It has nine administrative units (Kebele) with a total population of 50,528, of which 25,890 (51.2%) are women. Malaria is one of the top ten diseases in the town and is reported throughout the year [ 14 ], and the highest transmission rate usually occurs twice a year, from September to November and from June to August. The town is classified as a malarious area, with the disease spreading to the level of an epidemic once in every five years [ 1 ].

All suspected cases with a febrile illness (>37.5 C) who have been living in the administrative units of Shewa Robit for at least six months were included in the study. However, those who underwent chemotherapy with antimalarial drugs three months prior to the start of the study were excluded.

2.2. Sample Size Determination and Sampling Methods

The sample size was calculated using a single population formula. A prevalence of 50% was chosen, as there were no specific reports in the study area (health center). In order to calculate sample size, the value of Z chosen was 1.96 at 95% CI and a 5% margin of error. Therefore, the final sample size was consolidated to 422, after adding 10% non-response rate.

2.3. Sampling Procedures and Techniques

To start with, all suspected outpatients i.e, these suspected cases with febrile illness attended to the health center were stratified according to sex and age, and a random sampling technique was used to select each participant from the kebeles chosen.

2.4. Specimen Collection and Processing

After a briefing on the purpose of the study, all participants submitted their informed consents and assents before the commencement of data and sample collection. Thin and thick blood smears have been used with finger pricks to detect the Plasmodium infection. Thin films were fixed with 100% methanol, and both thin and thick films were stained with 3% Giemsa according to the protocol [ 19 ]. Thick films were then examined under high magnification (100x) for the presence of Plasmodium parasites. In the case of the thin films being found to be positive, an investigation for species identification was done. A second expert laboratory technologist who was unaware of the diagnosis by the first reader reexamined all positive slides, as well as a random sample of 10% of negative slides. No disparity has been found between the opinions of the readers.

2.5. Data Collection Methods

Sociodemographic situation of the participants (sex, age, kebele, family size, marital status, occupation, income, and educational level), infection related factors (history of infection, availability and use of LLINs, application of IRS, proximity to mosquito breeding site, and holes b / n wall and roofs) were recorded. A face-to-face interview with well-trained health experts was conducted to obtain the information from each participant. Structured and pre-tested questions in English were prepared and translated into the regional language (Amharic) to ensure the quality and consistency of the data.

2.6. Quality Control

Standard operating procedures (in-house SOP manual) were followed during blood collection (aseptic method), preparation of blood smear, staining, and examination of blood films to maintain quality. An experienced laboratory technologist evaluated the quality of the laboratory reagents and instruments. The collection technique was ensured, as was the quality of the samples, and the serial numbers were checked.

2.7. Data Analysis

Before being entered into Epi Info 3.5.3 and exported to statistical package for Social Science (SPSS) 20, the data was cleaned, updated, and double-checked (IBM, USA). Frequency and percentage were used to describe the characteristics of the patients. The Pearson chi-square test was performed to examine the association among sociodemographic and topographic characteristics. Variables with a p value less than 0.25 were selected as candidates for the multivariable analysis and fitted into a logistic regression model in the bivariable analysis. A statistically significant association was confirmed at a p value of <0.05.

2.8. Ethical Considerations

The research was ethically approved by the Institutional Review Board (IRB) of Addis Ababa University, and an ethics clearance was provided to the Shewa Robit Health Center [No, S/R/H/C/44/017]. Participants were informed of the minor risks involved in this study, which was also conducted in accordance with the Declaration of Helsinki [ 20 ].

3.1. Sociodemographic Characteristics

Data showed that 422 individuals with a mean age of 12.53 ± 0.58 participated and majority (43.6%, n  = 184) of them were under 5 years. Furthermore, 57.1% ( n  = 241) were unmarried with a family size of more than five members. Majority (36.7%, n  = 155) of the respondents were an attendants of secondary education and above whereas, 33.9% ( n  = 143) of the participant were illiterate. Most of the study participants were farmers and merchants ie, 23.2% ( n  = 98) and 16.1% ( n  = 68) respectively by occupation with monthly family income of less than 18.30 USD ( Table 1 ).

Sociodemographic characteristics of the respondents in Shewa Robit, Ethiopia ( n  = 422).

$ United States dollar (USD).

3.2. Seasonal Pattern of Malaria Infection

Although the Plasmodium species and extent of malaria infection varied in the study area, it occurred practically in every month and season. The data showed that the highest rate of infection was recorded in October and November with an infection rate of 34.3 ( n  = 23) and 35.1% ( n  = 20), respectively. However, a lower incidence of infection was observed in January with an infection rate of only 4% ( n  = 2). In particular, the findings revealed that P. falciparum infection peaked in October and November with an infection rate of 43.5 ( n  = 10) and 20% ( n  = 4), respectively. The lowest infection with P. falciparum was recorded in January, February, and April with nil incidence. However, the infection rate caused by the malaria infection by P. vivax went up to the maximum in January, February and April (100%, n  = 16), while the lower infection caused by P. vivax was recorded in March with a transmission rate of 60% ( n  = 3). Similarly, mixed infection was recorded in October and November with a prevalence of 21.7% ( n  = 5) and 5% ( n  = 1) respectively ( Table 2 ).

Seasonal patterns and prevalence of plasmodium species in Shewa Robit, Ethiopia ( n  = 422).

3.3. Factors Related to Infection

It was found that 289 (75.3%) of the participants had a history of malaria infection in their households. Although (83.6%) of the participant have access to long-lasting insecticide nets (LLINs), they sleep under the net daily (26.8%) and during the high transmission season (43.1%). However, most of the study participants (73.1%) did not use LLINs for two reasons, fear of toxicity (37.45%) and misconception (21.6%) due to the belief that the net did not prevent infection ( Table 3 ).

Factors that contribute to the transmission of malaria infection in Shewa Robit, Ethiopia ( n  = 422).

a other during treatment; LLINs = long-lasting insecticidal nets; IRS = residual indoor residual spraying.

3.4. Prevalence of Infection

The findings show that 19% ( n  = 81) of the participants had malaria parasites in their blood that could be seen microscopically. The most prevalent Plasmodium species found in individuals with positive laboratory test results were, P. vivax 71.6% ( n  = 58), P. falciparum 21% ( n  = 17) and mixed infection (recurrence of both species) accounts 7.4% ( n  = 6), as shown in Figure 1 .

Distribution of plasmodium species in Shewa Robit, Ethiopia, 2018.

3.5. Factors Associated with Malaria

The bivariable and multivariable analyzes revealed that several factors in the research area contribute to malaria infection. Age, marital status, family size, LLIN use, IRS, proximity to mosquito breeding locations, and the presence of holes in wall and roof are all associated factors. Results suggest that malaria infection was significantly associated with marital status and the family size. Those who were married or having a family size of ≥5 were 4.97 (CI 95%: 2.67–9.28) and 2.20 (CI 95%: 1.2–4.06) more likely to be infected with malaria respectively. The result confirmed that usage of LLTN reduces malaria infection. Study participants who did not use LLTN (CI 95%: 0.69–2.83) were 1.4 more likely to be infected with malaria as compared to their counterparts. Furthermore, study participants who refused IRS (CI 95%: 1.21, 5.60) were 2.6 times more likely than their peers to develop malaria infection. The presence of a mosquito-nesting site close to the house and holes between the house wall and the roof had a strong relationship with the occurrence of malaria infection. According to the findings, study participants who had proximity to mosquito location were 3.91 times more likely to contract malaria than their peers CI 95%: 1.87, 5.18). However, the chance of malaria infection was 2.1 higher in the participant who lived in a house with holes between the wall and the roof (CI 95%: 1.13–3.67) as shown in Table 4 .

Bivariable and multivariable logistic regression analysis of malaria incidence and associated risk factors in Shewa Robit, Ethiopia ( n  = 422).

LLINs = long-lasting insecticidal nets, IRS = residual indoor residual spraying ∗ and ∗∗ indicate significance level at p < 0.05 and p < 0.001 respectively.

4. Discussion

This study evaluated the prevalence of malaria infection in Shewarobait, Ethiopia from October 2017 to April 2018. The result showed that malaria is still one of the most serious public burdens in the study area. In addition, it was evident that age of the participants, sex, marital status, family size, utilization of LLINs and IRS, proximity to mosquito breeding site, and presence of holes on the wall were determinants of malaria transmission. In the current study, the overall percentage of malaria cases detected was 81 (19%) ( n  = 422), with P. vivax being the most prevalent species, is lower than the previous findings from Wolaita Zone (33.27%) [ 21 ], and Hallaba (82.84%) [ 22 ], However, is higher than that reported Sudan (9.1%) [ 23 ] Kenya (18.0%) [ 24 ], Kenya (6.4%), Tanzania (12.1%), and Uganda (6.3%) [ 25 ]. Interestingly, some earlier studies conducted in Ethiopia showed much lower prevalence. For example, 11.45 and 5.4% corresponding to localities Aresi Negelle [ 22 ], and Wortea [ 26 ] respectively. These inconsistencies may be due to differences in geographic location and the seasonality of infection. The result showed a male preponderance with an infection rate of 25.9% and it was only 11.9% in case of female. These findings are in line with the outcomes of similar studies conducted in Oromia [ 5 ], Kombolcha [ 27 ], and Kenya [ 24 ]. However, is inconsistent with the results of research done in Woreta [ 28 ] and Wolaita Zone which confirmed that females was 1.3 times more likely to be infected [ 21 ].

The predominant Plasmodium species detected among the participants in the current study participants was P. vivax. This is in agreement with previous report from Jimma Town [ 13 ], Aresi Negelle [ 29 ], Hallaba [ 22 ], there exists a the dominance of P. vivax over P. falciparum in recent years [ 30 ]. This could be due to the recurring nature and drug resistance of P. vivax against chloroquine [ 31 ].

According to the present study, 72.3% of participants had a history of malaria infection however, only 63 (20.7%) were infected with malaria. In particular, individuals who had a family history of malaria were 1.53 times more likely to be infected by Plasmodium species compared to their counterparts ( p < 0.001). These findings were supported by the Hamusite report, northwest Ethiopia [ 32 ]. This might be duto to family members with has a history of malaria infection may become reservoirs of Plasmodium parasites.

Different sociodemographic and other factors had been analyzed by taking into consideration of prevalence of malaria infection. Of these factors, the age was one of the significantly associated factors. Here, the odds of having malaria infection were 2.31 and 4.05 more likely among participants in the age group 5–14 years and above 15 compared to others. This aspect of the study is comparable to a previous work conducted in Woreta [ 28 ], Kombolcha [ 27 ], Dembia district [ 33 ] and Kola Diba [ 31 ] which reported that the prevalence of malaria high in the age group >15 years. This could be related to their frequent outdoor activities, such as agricultural practices related to irrigation during the peak period of malaria transmission [ 7 ].

The odds of being infected with malaria were 1.4 and 2.6 times higher among participants who did not use LLIN and apply IRS, respectively, and this is consistent with the results of other studies conducted in Jimma [ 17 ] and Shewa Robit [ 7 ] which proved that the use of LLIN and IRS and reduced the transmission. Our results showed that living near to mosquito breeding sites increased the probability of being infected. The study also highlighted that participants who lived near mosquito breeding sites was 1.4 more likely to be infected with malaria compared to their counterparts, who resided away. These findings were consistent with the results of an earlier research report from Arba Minch [ 34 ] and Jimma [ 17 ]. Less and porous walls and roof of household are significantly associated with malaria infection. The study indicates that participants living with such houses were 2.1 times more likely to be infected with malaria, and this is in line with the results of an earlier research done in Shewa Robit research report done in Shewa Robit [ 15 ].

5. Limitation of the Study

This is a cross-sectional study that addresses percentage prevalence and cannot account for seasonal transmission trends. In addition, the study is based on a single institution and has a shorter duration, including a smaller sample size. All surveys are self-reported without confirmation of bednet ownership and usage, and frequently application of IRS and RDTs. In addition, no PCR tests were performed to identify the infection and the Plasmodium species.

6. Conclusions

The study population who attended the Shewa Robit Health Center had a high incidence of malaria, with P. vivax being the most common causative agent. The main infection factors linked to the infection in the study area were age, sex, marital status, family size, use of LLIN and IRS, presence of mosquito breeding sites, and openings on their wall/roof. In addition, the main reason for rejecting LLIN is misconceptions about the toxicity of the treated net. The burden of malaria could be reduced by focusing on changing the attitudes towards malaria prevention and control through continued health education.

Acknowledgments

Our sincere thanks go to the North Shewa Zone Health Office, particularly the Shewa Robit Administration Health Department, for their assistance in conducting this study. The authors are very grateful to the data collectors and study participants who willingly took part, without their participation, this study would not have been possible. The authors did not receive a specific grant for this research from any funding agency, from the public, commercial or not-for-profit sectors.

Abbreviations

Data Availability

The authors did not receive a specific grant for this research from any funding agency, from the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors' Contributions

All authors are equally involved in the creation of all versions of the manuscript. TT conceived the study concept, study design and collected the data. AT helped to design the study, performed the statistical analysis, interpreted the result, and wrote the manuscript. All authors have read and approved the final document.

Supplementary Materials

Capillary blood collection and Malaria blood film preparation.

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Are climate change deaths increasing? Here's why experts expect humans to adapt to our heating world

While climate change-related deaths are a regular occurrence, humans are getting better at surviving extreme heat, by matthew rozsa.

Climate change poses a major existential threat to humanity, meaning billions of people could die as the planet becomes too hot and unstable to live. Case in point, the rash of record-breaking heat waves that have dominated this summer thanks to unprecedented temperatures  have caused mass casualty events . This includes more than 100 people in India dying of extreme heat in the last three-and-a-half months, to more than 60 people who died in a Mexican heat dome, to nine confirmed deaths in Las Vegas during its recent heat wave, to more than 550 people who died in Saudi Arabia while performing an important Islamic religious journey known as Hajj .

While summer heat waves have pretty much always been a thing, it's clear that human activity is making them hotter and deadlier. emit greenhouse gases like carbon dioxide, methane, nitrous oxide, fluorinated gases and water vapor into the atmosphere, they continue overheating the planet, pushing Earth's life forms to the limits of their thresholds for survival.

"If we don’t adapt, heat wave mortality will increase sharply."

"If we don’t adapt, heat wave mortality will increase sharply," Michael Wehner, a senior scientist in the Computational Research Division at the Lawrence Berkeley National Laboratory, told Salon. "Fortunately, humans are an adaptable species and some of that is already happening in efforts to increase awareness of heat wave dangers."

Yet this can only accomplish so much, as the high temperatures are inherently dangerous "and people will die from them as we won’t be able to adapt completely," Wehner said. "Those at risk — the very poor, the very old, the very young the very ill and those who work outdoors — must be very careful during these unprecedented heat waves."

Martin Siegert, a glaciology professor at the University of Exeter and former co-director of Imperial College London's Grantham Institute for Climate Change, elaborated on exactly why both heat waves and the other major extreme summer weather event linked to climate change — storms — are so dangerous.

"For heat, when temperatures get too hot for the human body, between 40º-50º C the body needs more energy to cool itself - and stops functioning properly," Siegert said. "As temperatures push above that, or consistently at it, then we expect many deaths. Perhaps in huge numbers in places where air conditioning and shelter is absent. For storms, the situation is different — here risk to life is in flying debris, floods and poor decisions when risks are high — such as driving through floods, or under flooded underpasses."

This is not the limit of how global heating will endanger people, Siegert said; in a seeming paradox, the warming phenomenon can actually lead to "some colder conditions as the atmosphere becomes more energetic — and this too is a killer."

Patrick Brown, a visiting research professor at San Jose State University's Wildlife Interdisciplinary Research Center, disagrees with those who say climate change is causing increased mortality, citing the IPCC Sixth Assessment Report  while doing so.

"Heat deaths are declining over time, despite warming, because societies are becoming less sensitive to temperature faster than temperatures are rising," Brown said. "Here is how the IPCC puts it: 'Heat-attributable mortality fractions have declined over time in most countries owing to general improvements in health care systems, increasing prevalence of residential air conditioning, and behavioural changes. These factors, which determine the susceptibility of the population to heat, have predominated over the influence of temperature change.'"

Some have argued that fossil fuel companies, being directly responsible for these deadly temperatures, should be charged with homicide. The consumer advocacy nonprofit  Public Citizen  released a model prosecution memo last month laying out a case to hold major fossil fuel companies criminally accountable for deaths from climate disasters as well as other climate-related harms in Maricopa County, Arizona.

Want more health and science stories in your inbox? Subscribe to Salon's weekly newsletter Lab Notes .

"Heat deaths are declining over time despite warming because societies are becoming less sensitive to temperature faster than temperatures are rising."

However, some experts believe climate-related deaths could peak as humans adapt. Indeed, because of humanity's technological advances, Brown expects death rates in general to go down in the foreseeable future.

"I don't expect death tolls to increase but instead continue to decrease because crop yields and calories available per person have increased," Brown said. "Death rates from malnutrition and famines have decreased; the share of the population with access to safe drinking water has increased; the rates of climate-influenced diseases like malaria and diarrheal disease have decreased; death rates from natural disasters have decreased; death rates from non-optimal temperatures (hot and cold) have decreased; and the fraction of people in extreme poverty has plummeted."

Siegert offered a contrasting perspective, anticipating that in addition to increased deaths caused by heat waves and super storms, people should also expect heightened mortality rates because of flooding, whether due to  sea level rise  or weather events.

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"The largest impacts from hurricanes are flood-related," Siegert said. "Many people drown in these events both from saltwater floods driven by storm surge and by freshwater flooding driven by copious amounts of rainfall. Many of these deaths are avoidable if people would heed evacuation notices."

Siegert added, "There is some concern about increases in the range of infectious tropical diseases but that is not as well understood as heat wave risk."

By contrast, Brown told Salon that finds it "interesting that many people are under the impression that we should expect to see large increases in deaths from climate change-related shifts in natural disasters when the evidence for this is so weak. I think this speaks to the information environment that we live in being potentially quite divorced from reality."

about climate change

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Matthew Rozsa is a staff writer at Salon. He received a Master's Degree in History from Rutgers-Newark in 2012 and was awarded a science journalism fellowship from the Metcalf Institute in 2022.

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