Environmental and meteorological factors linked to malaria transmission around large dams at three ecological settings in Ethiopia

Drivers of autochthonous malaria cases over time: could the Central European present the African future?

BACKGROUND

Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases.

METHODS

Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann-Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations.

RESULTS

In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact.

CONCLUSIONS

The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas.

Malaria risk mapping among children under five in Togo

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

Cost effectiveness of malaria vector control activities in Sudan

BACKGROUND

Malaria vector control activities in Sudan rely largely on Long-Lasting Insecticidal Nets (LLINs), Indoor Residual Spray (IRS) and Larval Source Management (LSM). The present study attempted to determine cost effectiveness of inputs and operations of vector control interventions applied in different environmental settings in central and eastern Sudan, as well as their impact.

METHODS

The inputs utilized and cost of each vector control activity, operational achievements and impact of the applied malaria vector control activities; IRS, LLINs and LSM were determined for eight sites in Al Gazira state (central Sudan) and Al Gadarif state (eastern Sudan). Operational costs were obtained from data of the National Malaria Control Program in 2017. Impact was measured using entomological indicators for Anopheles mosquitoes.

RESULTS

The total cost per person per year was $1.6, $0.85, and $0.32 for IRS, LLINs and LSM, respectively. Coverage of vector control operations was 97%, 95.2% and 25-50% in IRS, LLINs and LSM, respectively. Vectorial capacity of malaria vectors showed statistically significant variations (P < 0.034) and ranged 0.294-0.65 in areas implemented LSM in comparison to 0.097-0.248 in areas applied IRS and LLINs, respectively. Both indoor and outdoor biting Anopheles mosquitoes showed noticeable increase that reached 3-12 folds in areas implemented LSM in comparison to areas implemented IRS and LLINs. Annual malaria prevalence was 13.1-21.1% in areas implemented LSM in comparison to 3.20%, 4.77% in areas implemented IRS and LLINs, respectively.

CONCLUSION

IRS and LLINs are cost effective control measures due to adequate inputs and organized process. However, the unit cost of LSM intervention per outcome and subsequently the impact is hugely affected by the low coverage. The very weak support for implementation of LSM which includes inputs resulted in weakness of its process and consequently its impact. Implementation of LSM by local government in urban settings is challenged by many factors the most important are maintenance of adequate stable level of funding, un-adequate number of well trained health workers, unstable political and administrative conditions and weak infrastructure. These challenges are critical for proper implementation of LSM and control of malaria in urban settings in Sudan.

Clinical and Biological Characteristics of Severe Malaria in Children under 5 Years Old in Benin

Background

Malaria is a global public health concern, mainly occurring in sub-Saharan Africa. Children infected with malaria are more likely to develop severe disease, which can be fatal. During COVID-19 in 2020, diagnosing and treating malaria became difficult. We analyzed the clinical characteristics and laboratory indicators of children with severe malaria in Benin to provide important information for designing effective prevention and treatment strategies to manage pediatric cases.

Methods

Clinical characteristics of pediatric patients with severe malaria admitted to two hospitals in Benin (Central Hospital of Lokossa and Regional Hospital of Natitingou, located ∼650 kilometers apart) were collected from January to December 2020. Patients were grouped according to age (group A: 4-12 months old, group B: 13-36 months old, and group C: 37-60 months old), and clinical and laboratory indicators were compared. The incidences of severe pediatric malaria in both hospitals in 2020 were calculated. Inclusion, exclusion, and blood transfusion criteria were identified.

Results

We analyzed 236 pediatric cases. The main clinical symptoms among all patients were severe anemia, vomiting, prostration, poor appetite, dysphoria, and dyspnea. Over 50% of patients in group A experienced vomiting and severe anemia. Most patients in group B had severe anemia and prostration. Delirium affected significantly more patients in group C than in groups A and B. In group C, the hemoglobin and hematocrit levels were significantly higher (p < 0.05), and the leukocyte count was significantly lower (p < 0.01) than in groups A and B. Parasitemia was significantly higher in group C than in group A (p < 0.01). Twelve deaths occurred.

Conclusions

Severe pediatric malaria is seasonal in Benin. The situation in children under 5 years old is poor. The main problems are severe disease conditions and high fatality rates. Effective approaches such as prevention and early and appropriate treatment are necessary to reduce the malaria burden in pediatric patients.

Burden and seasonal distribution of malaria in Ziquala district, Northeast Ethiopia: a 5-year multi-centre retrospective study

OBJECTIVE

This study was aimed to determine the 5-year trend of malaria positivity rate in Ziquala district, Northeast Ethiopia.

METHODS

Review of blood film reports from health institutions' laboratory record books using predesigned checklists was done as part of an institution-based retrospective study to assess the 5-year (2016/2017-2020/2021) trend of malaria. To display data and analyse patterns in the trend of malaria over the course of years, months and seasons, descriptive statistics were used. The results of the data analysis were displayed in tables and figures using SPSS V.26.0. P values under 0.05 were considered as statistically significant for all comparisons.

RESULTS

A total of 46 365 blood films from malaria suspected individuals were diagnosed using microscopy over the last 5 years. Of the diagnosed individuals, 14 429 (31.1%) were confirmed positive for Plasmodium infection. Plasmodium falciparum (59.7%) and Plasmodium vivax (37.0%) were the dominant species. The positivity rate of mixed infection (P. falciparum and P. vivax) was 3.3%. The maximum (3598; 29.6%) and minimum (2085; 29.1%) number of cases were reported in 2019/2020 and 2020/2021, respectively. Of the total cases, 9206 (63.8%) were in males. Moreover, the highest malaria positivity rate was observed in the age group of 15-45 (4040; 28.0%). Among the six health facilities, Ziquala district hospital had the highest malaria positivity rate (35.8%), followed by Tsitsika health centre (27.3%) and Mishra health centre (14.2%).

CONCLUSION

With P. falciparum being the most common species, malaria remains a severe public health threat in the district. Therefore, the district health office and other concerned bodies should strengthen and implement evidence-based malaria prevention and control measures.

Trend analysis of malaria in urban settings in Ethiopia from 2014 to 2019

BACKGROUND

Urbanization generally improves health outcomes of residents and is one of the potential factors that might contribute to reducing malaria transmission. However, the expansion of Anopheles stephensi, an urban malaria vector, poses a threat for malaria control and elimination efforts in Africa. In this paper, malaria trends in urban settings in Ethiopia from 2014 to 2019 are reported with a focus on towns and cities where An. stephensi surveys were conducted.

METHODS

A retrospective study was conducted to determine malaria trends in urban districts using passive surveillance data collected at health facilities from 2014 to 2019. Data from 25 towns surveyed for An. stephensi were used in malaria trend analysis. Robust linear models were used to identify outliers and impute missing and anomalous data. The seasonal Mann-Kendal test was used to test for monotonic increasing or decreasing trends.

RESULTS

A total of 9,468,970 malaria cases were reported between 2014 and 2019 through the Public Health Emergency Management (PHEM) system. Of these, 1.45 million (15.3%) cases were reported from urban settings. The incidence of malaria declined by 62% between 2014 and 2018. In 2019, the incidence increased to 15 per 1000 population from 11 to 1000 in 2018. Both confirmed (microscopy or RDT) Plasmodium falciparum (67%) and Plasmodium vivax (28%) were reported with a higher proportion of P. vivax infections in urban areas. In 2019, An. stephensi was detected in 17 towns where more than 19,804 malaria cases were reported, with most of the cases (56%) being P. falciparum. Trend analysis revealed that malaria cases increased in five towns in Afar and Somali administrative regions, decreased in nine towns, and had no obvious trend in the remaining three towns.

CONCLUSION

The contribution of malaria in urban settings is not negligible in Ethiopia. With the rapid expansion of An. stephensi in the country, the receptivity is likely to be higher for malaria. Although the evidence presented in this study does not demonstrate a direct linkage between An. stephensi detection and an increase in urban malaria throughout the country, An. stephensi might contribute to an increase in malaria unless control measures are implemented as soon as possible. Targeted surveillance and effective response are needed to assess the contribution of this vector to malaria transmission and curb potential outbreaks.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

A Retrospective Study on the Burden of Malaria in Northeastern Ethiopia from 2015 to 2020: Implications for Pandemic Preparedness

Background

Regardless of various prevention and control strategies, malaria continues to be a significant public health problem in Ethiopia. As there are few studies on malaria trend analysis in Northeastern Ethiopia, it hinders the evaluation of ongoing and prioritization of new malaria intervention strategies, particularly during the period of pandemics. Therefore, the present study investigated the trend of malaria prevalence in Northeastern Ethiopia from 2015 to 2020.

Methods

An institution-based retrospective study was employed to assess the trend of malaria prevalence over a 6-year period (2015-2020) in three districts (Jile tumuga, Aruma fursi, and Dawachefa) of Northeastern Ethiopia. Data were extracted from clinical records of malaria cases by trained medical laboratory technologists. The associations between the prevalence of malaria and independent variables (age group, malaria transmission season, and districts) were assessed using chi-square test. P-values with a cut-off point of 0.05 were used to determine statistically significant associations.

Results

In our study area, a total of 212,952 malaria suspected patients were diagnosed over the 6 years. Of these, 33,005 (15.5%) were confirmed malaria cases. The identified Plasmodium species were Plasmodium falciparum and P. vivax, accounting for 66.4% and 33.6%, respectively. These with the age of >15 years old were the most affected (41.9%). The highest numbers of malaria cases (34.6%) were recorded during spring season (September to November). The prevalence of Plasmodium species showed a significant association with age (X²=9.7; p=0.002), districts (X²=13.5; p<0.001), and malaria transmission season (X²=16.5; p<0.001).

Conclusion

In our study area, P. falciparum is the dominant species. We noted that malaria remains a public health concern and fluctuates throughout the years. Therefore, national, regional, zonal, and district health bureaus should strengthen the ongoing and devise appropriate prevention and control strategies even during the period of pandemics.

Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo

BACKGROUND

Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

METHODS

Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis.

RESULTS

The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001).

CONCLUSIONS

The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion − 0.25 (BCI: − 0.46, − 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.

Prevalence and risk factors of malaria among first antenatal care attendees in rural Burkina Faso

BACKGROUND

The WHO recommends continuous surveillance of malaria in endemic countries to identify areas and populations most in need for targeted interventions. The aim of this study was to assess the prevalence of malaria and its associated factors among first antenatal care (ANC) attendees in rural Burkina Faso.

METHODS

A cross-sectional survey was conducted between August 2019 and September 2020 at the Yako health district and included 1067 first ANC attendees. Sociodemographic, gyneco-obstetric, and medical characteristics were collected. Malaria was diagnosed by standard microscopy and hemoglobin level was measured by spectrophotometry. A multivariate logistic regression analysis was used to identify factors associated with malaria infection.

RESULTS

Overall malaria infection prevalence was 16.1% (167/1039). Among malaria-positive women, the geometric mean parasite density was 1204 [95% confidence interval (CI) 934-1552] parasites/µL and the proportion of very low (1-199 parasites/µL), low (200-999 parasites/µL), medium (1000-9999 parasites/µL) and high (≥ 10,000 parasites/µL) parasite densities were 15.0%, 35.3%, 38.3% and 11.4%, respectively. Age < 20 years (adjusted odds ratio (aOR): 2.2; 95% CI 1.4-3.5), anemia (hemoglobin < 11 g/deciliter) (aOR: 3.4; 95% CI 2.2-5.5), the non-use of bed net (aOR: 1.8; 95% CI 1.1-2.8), and the absence of intermittent preventive treatment with sulfadoxine-pyrimethamine (aOR: 5.8; 95% CI 2.1-24.5) were positively associated with malaria infection.

CONCLUSIONS

The study showed that one out of six pregnant women had a microscopy-detected P. falciparum malaria infection at their first ANC visit. Strengthening malaria prevention strategies during the first ANC visit is needed to prevent unfavorable birth outcomes.

Malaria Threatens to Bounce Back in Abergele District, Northeast Ethiopia: Five-Year Retrospective Trend Analysis from 2016-2020 in Nirak Health Center

Background

In Sub-Saharan African countries, malaria is a leading cause of morbidity and mortality. In Ethiopia, malaria is found in three-fourths of its land mass with more than 63 million people living in malaria endemic areas. Nowadays, Ethiopia is implementing a malaria elimination program with the goal of eliminating the disease by 2030. To assist this goal, the trends of malaria cases should be evaluated with a function of time in different areas of the country to develop area-specific evidence-based interventions. Therefore, this study was aimed at analysing a five year trend of malaria in Nirak Health Center, Abergele District, Northeast Ethiopia, from 2016 to 2020.

Methods

A retrospective study was conducted at Nirak Health Center, Abergele District, Northeast Ethiopia from February to April 2021. Five-year (2016 to 2020) retrospective data were reviewed from the malaria registration laboratory logbook. The sociodemographic and malaria data were collected using a predesigned data collection sheet. Data were entered, cleaned, and analysed using SPSS version 26.

Results

In the five-year period, a total of 19,433 malaria suspected patients were diagnosed by microscopic examination. Of these, 6,473 (33.3%) were positive for malaria parasites. Of the total confirmed cases, 5,900 (91.2%) were P. falciparum and 474 (7.2%) were P. vivax. Majority of the cases were males (62.2%) and in the age group of 15-45 years old (52.8%). The findings of this study showed an increasing trend in malaria cases in the past five years (2016-2020). The maximum number of confirmed malaria cases reported was in the year 2020, while the minimum number of confirmed malaria cases registered was in 2016. Regarding the seasonal distribution of malaria, the highest number of malaria cases (55.2%) was observed in Dry season (September to January) and also the least (15.9%) was observed in Autumn (March to May) replaced by the least (21.6%) was observed in Rainy season (June to August), that is, the major malaria transmission season in Ethiopia and the least (15.9%) was observed in autumn (March to May).

Conclusion

The trends of malaria in Nirak Health Center showed steadily increasing from the year 2016–2020, and the predominant species isolated was P. falciparum. This showed that the malaria control and elimination strategy in the area were not properly implemented or failed to achieve its designed goal. Therefore, this finding alarms the local governments and other stack holders urgently to revise their intervention strategies and take action in the locality.

Achievements, Gaps, and Emerging Challenges in Controlling Malaria in Ethiopia

Controlling malaria is one of the top health sector priorities in Ethiopia. The concrete prevention, control, and treatment interventions undertaken in the past two decades have substantially reduced the morbidity and mortality attributable to malaria. Emboldened by these past achievements, Ethiopia envisages to eliminate malaria by 2030. Realizing this ambition, however, needs to further strengthen the financial, technical, and institutional capacities to address the current as well as emerging challenges. It particularly needs to step up measures pertaining to diagnosis, domestic resource mobilization, vector surveillance, and seasonal weather forecasting.

Molecular identification and anti-malarial drug resistance profile of Plasmodium falciparum from patients attending Kisoro Hospital, southwestern Uganda

Background

The evolution of malaria infection has necessitated the development of highly sensitive diagnostic assays, as well as the use of dried blood spots (DBS) as a potential source of deoxyribonucleic acid (DNA) yield for polymerase chain reaction (PCR) assays. This study identified the different Plasmodium species in malaria-positive patients, and the anti-malarial drug resistance profile for Plasmodium falciparum using DBS samples collected from patients attending Kisoro Hospital in Kisoro district, Southwestern Uganda.

Methods

The blood samples were prospectively collected from patients diagnosed with malaria to make DBS, which were then used to extract DNA for real-time PCR and high-resolution melting (HRM) analysis. Plasmodium species were identified by comparing the control and test samples using HRM-PCR derivative curves. Plasmodium falciparum chloroquine (CQ) resistance transporter (pfcrt) and kelch13 to screen the samples for anti-malarial resistance markers. The HRM-PCR derivative curve was used to present a summary distribution of the different Plasmodium species as well as the anti-malarial drug profile.

Results

Of the 152 participants sampled, 98 (64.5%) were females. The average age of the participants was 34.9 years (range: 2 months–81 years). There were 134 samples that showed PCR amplification, confirming the species as Plasmodium. Plasmodium falciparum (N = 122), Plasmodium malariae (N = 6), Plasmodium ovale (N = 4), and Plasmodium vivax (N = 2) were the various Plasmodium species and their proportions. The results showed that 87 (71.3%) of the samples were sensitive strains/wild type (CVMNK), 4 (3.3%) were resistant haplotypes (SVMNT), and 31 (25.4%) were resistant haplotypes (CVIET). Kelch13 C580Y mutation was not detected.

Conclusion

The community served by Kisoro hospital has a high Plasmodium species burden, according to this study. Plasmodium falciparum was the dominant species, and it has shown that resistance to chloroquine is decreasing in the region. Based on this, molecular identification of Plasmodium species is critical for better clinical management. Besides, DBS is an appropriate medium for DNA preservation and storage for future epidemiological studies.

Ecological Predictors of Human Malaria Risk During Different Phases of the Elimination: An Analysis of Historical Data

To understand the evolutionary ecology of disease dynamics, it is crucial to identify the environmental factors that mediate the spread and abundance of parasites and their vectors. However, human-mediated changes in the biotic and abiotic environment and intervention programs are intensifying in the past 30-40 years at a rate that masks the causal effect of the original ecological predictors. In this study, we used archived epidemiological data spanning over 100 years on malaria risk in Hungary to demonstrate that different associations exist between infection risk and environmental predictors during different phases of the elimination program. In the early 20th century, when malaria was quite common in the country and no defense program was operating, as predicted, there was a positive relationship between the area of flooded habitats and the intensity of malaria infection. In contrast, this relationship was absent during middle of the century, when an effective elimination program was already in effect. Furthermore, malaria morbidity in a given year was predicted by the degree of stagnant water cover of the previous year when considering the period before the launch of a drastic mosquito control program by dichloro-diphenyl-trichloroethane (DDT), whereas such relationship could not be revealed for a latter period. Our results highlight that human-induced alterations of the socioecological environment considerably reorganizes the ecological landscape of pathogens and their vectors.

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

Residual malaria in Jazan region, southwestern Saudi Arabia: the situation, challenges and climatic drivers of autochthonous malaria

Background

Saudi Arabia and Yemen are the only two countries in the Arabian Peninsula that are yet to achieve malaria elimination. Over the past two decades, the malaria control programme in Saudi Arabia has successfully reduced the annual number of malaria cases, with the lowest incidence rate across the country reported in 2014. This study aims to investigate the distribution of residual malaria in Jazan region and to identify potential climatic drivers of autochthonous malaria cases in the region.

Methods

A cross-sectional study was carried out from 1 April 2018 to 31 January 2019 in Jazan region, southwestern Saudi Arabia, which targeted febrile individuals attending hospitals and primary healthcare centres. Participants’ demographic data were collected, including age, gender, nationality, and residence. Moreover, association of climatic variables with the monthly autochthonous malaria cases reported during the period of 2010–2017 was retrospectively analysed.

Results

A total of 1124 febrile subjects were found to be positive for malaria during the study period. Among them, 94.3 and 5.7% were infected with Plasmodium falciparum and Plasmodium vivax, respectively. In general, subjects aged 18–30 years and those aged over 50 years had the highest (42.7%) and lowest (5.9%) percentages of malaria cases. Similarly, the percentage of malaria-positive cases was higher among males than females (86.2 vs 13.8%), among non-Saudi compared to Saudi subjects (70.6 vs 29.4%), and among patients residing in rural rather than in urban areas (89.8 vs 10.2%). A total of 407 autochthonous malaria cases were reported in Jazan region between 2010 and 2017. Results of zero-inflated negative binomial regression analysis showed that monthly average temperature and relative humidity were the significant climatic determinants of autochthonous malaria in the region.

Conclusion

Malaria remains a public health problem in most governorates of Jazan region. The identification and monitoring of malaria transmission hotspots and predictors would enable control efforts to be intensified and focused on specific areas and therefore expedite the elimination of residual malaria from the whole region.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03846-4.

Malaria prevalence, knowledge, attitude, and practice among febrile patients attending Chagni health center, Northwest Ethiopia: a cross-sectional study

Background

Ethiopia has achieved considerable progresses in the prevention and control of malaria in the past decades; hitherto it is a formidable health concern and socio-economic impediment. This study aimed at assessing the magnitude, knowledge, attitudes and practices towards malaria among febrile patients attending Chagni health center, northwest Ethiopia.

Methods

Health facility-based cross-sectional study was conducted to estimate the prevalence of malaria and KAP towards malaria among febrile patients at Chagni health center in Chagni Town during September 2017 to February 2018. In order to determine the magnitude of malaria, finger prick blood samples were collected and thick and thin smears were prepared and microscopically examined for the presence of malaria parasites. A pre-tested structured questionnaire was also applied to assess KAP of suspected malaria patients, attending the health center. Data were analyzed using SPSS version 20.0.

Results

Prevalence of malaria among febrile patients, who visited the sampled health facility, was 7.3%. Of these, Plasmodium falciparum, P. vivax, and mixed infections accounted for 55, 44.3 and 0.7% of the cases, respectively. This study also revealed that 97% of the respondents had ever heard about malaria and recognized it as a serious health problem. Mosquito bite was identified as the main malaria transmission. Taking drug (86.3%), use of mosquito nets (73.3%), drain stagnated water (68%), and house spay with insecticides (66%) were mentioned as the main malaria prevention methods. Mosquito net coverage and utilizations in the prior night were 98 and 75%, respectively. Indoor residual spraying (IRS) coverage was 99%, of which 77.5% of study participants’ houses have been sprayed in the last 6 months.

Conclusions

The current study revealed that prevalence of malaria among febrile illnesses in the study area was relatively low (7.3%) with a high proportion of P. falciparum. Besides, participants had adequate knowledge, encouraging attitudes, and good practices about prevention and control of malaria. However, some misconceptions on malaria disease, its transmission, and prevention have been noted that actually require due attention by the concerned stakeholders. The findings of this study could be used as important inputs for the implementation of effective malaria prevention and control methods, including community health education programs, and scaling up coverage of evidence-based interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40794-021-00146-2.

Associating the scale-up of insecticide-treated nets and use with the decline in all-cause child mortality in the Democratic Republic of Congo from 2005 to 2014

BACKGROUND

To reduce the malaria burden and improve the socioeconomic status of its citizens, the Democratic Republic of Congo scaled up key malaria control interventions, especially insecticide-treated nets (ITNs), between 2005 and 2014. Since then, the effects of these interventions on malaria mortality and morbidity have not been assessed. This study aimed to measure the impact of the National Malaria Control Programme's efforts and to inform future control strategies.

METHODS

The authors used data from the Demographic and Health Surveys 2007 and 2013-2014 to assess trends in all-cause childhood mortality (ACCM) against trends in coverage of malaria interventions at national and subnational levels. The authors used the plausibility argument to assess the impact of the malaria control interventions and used Kaplan-Meier survival probability and Cox proportional hazard models to examine the effect of ITN ownership on child survival. Contextual factor trends affecting child survival were also considered.

RESULTS

Countrywide, household ownership of at least one ITN increased, from 9% in 2007 to 70% in 2013-2014. All provinces experienced similar increases, with some greater than the national level. ITN use increased between 2007 and 2013-2014 among children under five (6% to 55%). Severe anaemia (haemoglobin concentration < 8 g/dl) prevalence among children aged 6-59 months significantly decreased, from 11% (95% confidence interval [CI] 9-13%) in 2007 to 6% (95% CI 5-7%) in 2013-2014. During the same period, ACCM declined, from 148 (95% CI 132-163) to 104 (95% CI 97-112) deaths per 1000 live births. The decline in ACCM was greater among children aged 6-23 months (relative reduction of 36%), compared to children aged 24-59 months (relative reduction of 12%). Cox regression indicated that household ownership of at least one ITN reduced the risk of mortality by 24% among children under five (risk ratio = 0.76, 95% CI 0.64-0.90). Contextual factor analysis revealed marginal improvements in socioeconomic indicators and other health interventions.

CONCLUSIONS

Given the patterns of the coverage of malaria control interventions, patterns in ACCM by province, and marginal improvements in contextual factors, the authors conclude that the malaria control interventions have plausibly contributed to the decrease in ACCM in the Democratic Republic of Congo from 2005 to 2014.

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Predictors of malaria rapid diagnostic test positivity in a high burden area of Paletwa Township, Chin State in Western Myanmar

Background

Despite major reductions in malaria burden across Myanmar, clusters of the disease continue to persist in specific subregions. This study aimed to assess the predictors of test positivity among people living in Paletwa Township of Chin State, an area of persistently high malaria burden.

Methods

Four villages with the highest malaria incidence from Paletwa Township were purposively selected. The characteristics of 1045 subjects seeking malaria diagnosis from the four assigned village health volunteers from January to December, 2018 were retrospectively analyzed. Their household conditions and surroundings were also recorded using a checklist. Descriptive statistics and logistic regression models were applied to investigate potential associations between individual and household characteristics and malaria diagnosis.

Results

In 2017, the Paletwa township presented 20.9% positivity and an annual parasite index of 46.9 cases per 1000 people. Plasmodium falciparum was the predominant species and accounted for more than 80.0% of all infections. Among 1045 people presenting at a clinic with malaria symptoms, 31.1% were diagnosed with malaria. Predictors for test positivity included living in a hut [adjusted odds ratios (a OR): 2.3, 95% confidence intervals (CI): 1.2–4.6], owning farm animals (aOR: 1.7, 95% CI: 1.1–3.6), using non-septic type of toilets (aOR: 1.9, 95% CI: 1.1–8.4), presenting with fever (aOR: 1.9, 95% CI: 1.1–3.0), having a malaria episode within the last year (aOR: 2.9, 95% CI: 1.4–5.8), traveling outside the village in the previous 14 days (aOR: 4.5, 95% CI: 1.5–13.4), and not using bed nets (a OR: 3.4, 95% CI: 2.3–5.1). There were no statistically significant differences by age or gender in this present analysis.

Conclusions

The results from this study, including a high proportion of P. falciparum infections, little difference in age, sex, or occupation, suggest that malaria is a major burden for these study villages. Targeted health education campaigns should be introduced to strengthen synchronous diagnosis-seeking behaviors, tighten treatment adherence, receiving a diagnosis after traveling to endemic regions, and using bed nets properly. We suggest increased surveillance, early diagnosis, and treatment efforts to control the disease and then to consider the local elimination.

A five year trend analysis of malaria prevalence in Guba district, Benishangul-Gumuz regional state, western Ethiopia: a retrospective study

Background

In Ethiopia, malaria is a serious public health concern and has great impact on socio-economy. The trend analysis of malaria data from health facilities is useful for understanding its transmission dynamics and implementing evidence-based malaria control strategies. The aim of this study was to determine the trends of malaria infection in Guba district, western Ethiopia.

Methods

A retrospective study was undertaken at Mankush Health Centre, western Ethiopia. All malaria cases reported from 2014 to 2018 were carefully reviewed from the laboratory record books to determine the trends of malaria morbidity. Data were analyzed using SPSS version 20.0.

Results

In total, 16,964 malaria suspects were diagnosed using microscopy over the last 5 years, of which 8658 (51.04%) were confirmed positive cases. Plasmodium falciparum, P. vivax, and mixed infection (both species) accounted for 75.2, 24.5 and 0.28% of the cases, respectively. Males patients were more affected (n = 5028, 58.1%) than female ones (n = 3630, 41.9%). Of the total confirmed cases, 60.4% were age group of subjects (≥ 15 years) followed by 22.6% of 5–14 years and 15.9% of under 5 years. High malaria prevalence was observed in spring (September to November) season, while the least was observed in autumn (March to May) with the prevalence of 45.6 and 11.5%, respectively.

Conclusions

The study demonstrated that malaria is a public health concern, in which P. falciparum is the predominant species followed by P. vivax. Therefore, the district health bureau and other concerned stakeholders should strength evidence-based malaria control and prevention interventions to interrupt disease transmission and eventual reduction malaria of malaria cases in Guba district.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

Improving the efficiency of reactive case detection for malaria elimination in southern Zambia: a cross-sectional study

Background

Reactive case detection (RCD) seeks to enhance malaria surveillance and control by identifying and treating parasitaemic individuals residing near index cases. In Zambia, this strategy starts with passive detection of symptomatic incident malaria cases at local health facilities or by community health workers, with subsequent home visits to screen-and-treat residents in the index case and neighbouring (secondary) households within a 140-m radius using rapid diagnostic tests (RDTs). However, a small circular radius may not be the most efficient strategy to identify parasitaemic individuals in low-endemic areas with hotspots of malaria transmission. To evaluate if RCD efficiency could be improved by increasing the probability of identifying parasitaemic residents, environmental risk factors and a larger screening radius (250 m) were assessed in a region of low malaria endemicity.

Methods

Between January 12, 2015 and July 26, 2017, 4170 individuals residing in 158 index and 531 secondary households were enrolled and completed a baseline questionnaire in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia. Plasmodium falciparum prevalence was measured using PfHRP2 RDTs and quantitative PCR (qPCR). A Quickbird™ high-resolution satellite image of the catchment area was used to create environmental risk factors in ArcGIS, and generalized estimating equations were used to evaluate associations between risk factors and secondary households with parasitaemic individuals.

Results

The parasite prevalence in secondary (non-index case) households was 0.7% by RDT and 1.8% by qPCR. Overall, 8.5% (n = 45) of secondary households had at least one resident with parasitaemia by qPCR or RDT. The risk of a secondary household having a parasitaemic resident was significantly increased in proximity to higher order streams and marginally with increasing distance from index households. The adjusted OR for proximity to third- and fifth-order streams were 2.97 (95% CI 1.04–8.42) and 2.30 (95% CI 1.04–5.09), respectively, and that for distance to index households for each 50 m was 1.24 (95% CI 0.98–1.58).

Conclusion

Applying proximity to streams as a screening tool, 16% (n = 3) more malaria-positive secondary households were identified compared to using a 140-m circular screening radius. This analysis highlights the potential use of environmental risk factors as a screening strategy to increase RCD efficiency.

Trend of malaria prevalence in Wolkite health center: an implication towards the elimination of malaria in Ethiopia by 2030

Background

Malaria is one of the main public health challenges in Ethiopia that hinder the productivity and development of the country. In 2018, Ethiopia is on track to minimize the incidence of malaria by 40% as per its 2020 malaria reduction strategy. Currently, Ethiopia is working to eliminate malaria in 2030 by extending the 2020 strategy. Hence, this study aimed to analyze the trend of malaria prevalence in Wolkite health center, Gurage zone, Southern Central Ethiopia from 2015 to 2018.

Methods

A retrospective study was conducted to determine the prevalence of malaria by reviewing the malaria registration laboratory logbook at Wolkite health center from 2015 to 2018. For all patients, blood films were done for the detection of malaria cases. All the socio-demographic data, year, month and malaria data were collected using a predesigned data collection sheet from January to March, 2019.

Results

From a 121,230 clinically malaria suspected patients, the overall prevalence of microscopically confirmed cases were 8.56% (n = 10,379/121,230). Plasmodium vivax was the most predominant species accounted for 69.7% (n = 7237/10,379) followed by Plasmodium falciparum 29.3% (n = 3044/10,379). Age group > 15 years old were more affected by malaria accounting 54% (n = 5609/10,379) and malaria cases regarding sex were proportional (51.1% of males and 48.3% of female). Among the catchment areas, a higher number of malaria prevalence was recorded in the Wolkite town 66.2% (n = 0.6538/10,379). Higher malaria cases were shown in the season of Spring 29.8% (n = 3096/10,379) while lower cases 20.4% (n = 2123/10,379) were seen in the Winter season.

Conclusion

The prevalence of malaria in Wolkite health center showed a consistent downward trend from the year of 2015–2018. Importantly, the higher prevalence of P. vivax seems overlooked in the study area. Therefore, malaria prevention and control strategy should be reinforced to reduce the prevalence of malaria in the study area.

Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria

Malaria still poses a significant threat in Nigeria despite the various efforts to abate its transmission. Certain environmental factors have been implicated to increase the risk of malaria in Nigeria and other affected countries. The study aimed to evaluate the spatial and temporal association between the incidence of malaria and some environmental risk factors in Nigeria. The study used malaria incidence and environmental risk factors data emanating from 2015 Nigeria Malaria Indicator Survey accessed from the Demographic and Health Survey database. A total of 333 and 326 clusters throughout the country were used for malaria incidence study and environmental variables respectively. The spatial autocorrelation of malaria incidence and hotspot analysis was determined by the Moran’s diagram and local Moran’s I index, respectively. The relationships between the malaria incidence and the ecological predictors of transmission were analysed in all the six geopolitical zones of Nigeria from 2000–2015 using ordinary least square (OLS), spatial lag model (SLM), and spatial error model (SEM). Annual rainfall, precipitation and proximity to water showed significant positive relationship with the incidence rate of malaria in the OLS model (P < 0.01), whereas aridity was negatively related to malaria incidence (P < 0.001) in the same model. The rate of incidence of malaria increased significantly with increase in temperature, aridity, rainfall and proximity to water in the SEM whereas only temperature and proximity to water have significant positive effect on malaria incidence in the SLM. The modelling of the ecological predictors of malaria transmission and spatial maps provided in this study could aid in developing framework to mitigate malaria and identify its hotspots for urgent intervention in the endemic regions.

Malaria around large dams in Africa: effect of environmental and transmission endemicity factors

Background

The impact of large dams on malaria has received widespread attention. However, understanding how dam topography and transmission endemicity influence malaria incidences is limited.

Methods

Data from the European Commission’s Joint Research Center and Shuttle Radar Topography Mission were used to determine reservoir perimeters and shoreline slope of African dams. Georeferenced data from the Malaria Atlas Project (MAP) were used to estimate malaria incidence rates in communities near reservoir shorelines. Population data from the WorldPop database were used to estimate the population at risk of malaria around dams in stable and unstable areas.

Results

The data showed that people living near (< 5 km) large dams in sub-Saharan Africa grew from 14.4 million in 2000 to 18.7 million in 2015. Overall, across sub-Saharan Africa between 0.7 and 1.6 million malaria cases per year are attributable to large dams. Whilst annual malaria incidence declined markedly in both stable and unstable areas between 2000 and 2015, the malaria impact of dams appeared to increase in unstable areas, but decreased in stable areas. Shoreline slope was found to be the most important malaria risk factor in dam-affected geographies, explaining 41–82% (P < 0.001) of the variation in malaria incidence around reservoirs.

Conclusion

Gentler, more gradual shoreline slopes were associated with much greater malaria risk. Dam-related environmental variables such as dam topography and shoreline slopes are an important factor that should be considered in efforts to predict and control malaria around dams.