Stratification at the health district level for targeting malaria control interventions in Mali

Seroprevalence of Arboviruses in a Malaria Hyperendemic Area in Southern Mali

Diagnostics for febrile illnesses other than malaria are not readily available in rural sub-Saharan Africa. This study assessed exposure to three mosquito-borne arboviruses-dengue virus (DENV), Zika virus (ZIKV), and chikungunya virus (CHIKV)-in southern Mali. Seroprevalence for DENV, CHIKV, and ZIKV was analyzed by detection of IgG antibodies and determined to be 77.2%, 31.2%, and 25.8%, respectively. Among study participants, 11.3% were IgG-positive for all three arboviruses. DENV had the highest seroprevalence rate at all sites; the highest seroprevalence of CHIKV and ZIKV was observed in Bamba. The seroprevalence for all three arboviruses increased with age, and the highest seroprevalence was observed among adults older than 50 years. The prevalence of Plasmodium spp. in the cohort was analyzed by microscopy and determined to be 44.5% (N = 600) with Plasmodium falciparum representing 95.1% of all infections. This study demonstrates the co-circulation of arboviruses in a region hyperendemic for malaria and highlights the needs for arbovirus diagnostics in rural sub-Saharan Africa.

Malaria risk stratification in Lao PDR guides program planning in an elimination setting

Malaria in Lao People's Democratic Republic (Lao PDR) has declined rapidly over the last two decades, from 279,903 to 3926 (99%) cases between 2001 and 2021. Elimination of human malaria is an achievable goal and limited resources need to be targeted at remaining hotspots of transmission. In 2022, the Center of Malariology, Parasitology and Entomology (CMPE) conducted an epidemiological stratification exercise to assign districts and health facility catchment areas (HFCAs) in Lao PDR based on malaria risk. The stratification used reported malaria case numbers from 2019 to 2021, risk maps derived from predictive modelling, and feedback from malaria staff nationwide. Of 148 districts, 14 were deemed as burden reduction (high risk) districts and the remaining 134 as elimination (low risk) districts. Out of 1235 HFCAs, 88 (7%) were classified as highest risk, an improvement from 187 (15%) in the last stratification in 2019. Using the HFCA-level stratification, the updated stratification resulted in the at-risk population (total population in Strata 2, 3 and 4 HFCAs) declining from 3,210,191 to 2,366,068, a 26% decrease. CMPE are using the stratification results to strengthen targeting of resources. Updating national stratifications is a necessary exercise to assess progress in malaria control, reassign interventions to the highest risk populations in the country and ensure greatest impact of limited resources.

Malaria in Burkina Faso: A comprehensive analysis of spatiotemporal distribution of incidence and environmental drivers, and implications for control strategies

BACKGROUND

The number of malaria cases worldwide has increased, with over 241 million cases and 69,000 more deaths in 2020 compared to 2019. Burkina Faso recorded over 11 million malaria cases in 2020, resulting in nearly 4,000 deaths. The overall incidence of malaria in Burkina Faso has been steadily increasing since 2016. This study investigates the spatiotemporal pattern and environmental and meteorological determinants of malaria incidence in Burkina Faso.

METHODS

We described the temporal dynamics of malaria cases by detecting the transmission periods and the evolution trend from 2013 to 2018. We detected hotspots using spatial scan statistics. We assessed different environmental zones through a hierarchical clustering and analyzed the environmental and climatic data to identify their association with malaria incidence at the national and at the district's levels through generalized additive models. We also assessed the time lag between malaria peaks onset and the rainfall at the district level. The environmental and climatic data were synthetized into indicators.

RESULTS

The study found that malaria incidence had a seasonal pattern, with high transmission occurring during the rainy seasons. We also found an increasing trend in the incidence. The highest-risk districts for malaria incidence were identified, with a significant expansion of high-risk areas from less than half of the districts in 2013-2014 to nearly 90% of the districts in 2017-2018. We identified three classes of health districts based on environmental and climatic data, with the northern, south-western, and western districts forming separate clusters. Additionally, we found that the time lag between malaria peaks onset and the rainfall at the district level varied from 7 weeks to 17 weeks with a median at 10 weeks. Environmental and climatic factors have been found to be associated with the number of cases both at global and districts levels.

CONCLUSION

The study provides important insights into the environmental and spatiotemporal patterns of malaria in Burkina Faso by assessing the spatio temporal dynamics of Malaria cases but also linking those dynamics to the environmental and climatic factors. The findings highlight the importance of targeted control strategies to reduce the burden of malaria in high-risk areas as we found that Malaria epidemiology is complex and linked to many factors that make some regions more at risk than others.

Geo-epidemiology of malaria incidence in the Vhembe District to guide targeted elimination strategies, South-Africa, 2015-2018: a local resurgence

In South Africa, the population at risk of malaria is 10% (around six million inhabitants) and concern only three provinces of which Limpopo Province is the most affected, particularly in Vhembe District. As the elimination approaches, a finer scale analysis is needed to accelerate the results. Therefore, in the process of refining local malaria control and elimination strategies, the aim of this study was to identify and describe malaria incidence patterns at the locality scale in the Vhembe District, Limpopo Province, South Africa. The study area comprised 474 localities in Vhembe District for which smoothed malaria incidence curve were fitted with functional data method based on their weekly observed malaria incidence from July 2015 to June 2018. Then, hierarchical clustering algorithm was carried out considering different distances to classify the 474 smoothed malaria incidence curves. Thereafter, validity indices were used to determine the number of malaria incidence patterns. The cumulative malaria incidence of the study area was 4.1 cases/1000 person-years. Four distinct patterns of malaria incidence were identified: high, intermediate, low and very low with varying characteristics. Malaria incidence increased across transmission seasons and patterns. The localities in the two highest incidence patterns were mainly located around farms, and along the rivers. Some unusual malaria phenomena in Vhembe District were also highlighted as resurgence. Four distinct malaria incidence patterns were found in Vhembe District with varying characteristics. Findings show also unusual malaria phenomena in Vhembe District that hinder malaria elimination in South Africa. Assessing the factors associated with these unusual malaria phenome would be helpful on building innovative strategies that lead South Africa on malaria elimination.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Malaria prevalence in Mauritania: a systematic review and meta-analysis

BACKGROUND

Understanding malaria epidemiology is a critical step toward efficient malaria control and elimination. The objective of this meta-analysis was to derive robust estimates of malaria prevalence and Plasmodium species from studies conducted in Mauritania and published since 2000.

METHODS

The present review followed the PRISMA guidelines. Searches were conducted in various electronic databases such as PubMed, Web of Science, and Scopus. To obtain pooled prevalence of malaria, meta-analysis was performed using the DerSimonian-Laird random-effects model. Methodological quality of eligible prevalence studies was assessed using Joanna Briggs Institute tool. Inconsistency and heterogeneity between studies were quantified by the I² index and Cochran's Q test. Publication bias was assessed with funnel plots and Egger's regression tests.

RESULTS

A total of 16 studies with a good individual methodological quality were included and analysed in this study. The overall random effects pooled prevalence of malaria infection (symptomatic and asymptomatic) across all included studies was 14.9% (95% confidence interval [95% CI]: 6.64, 25.80, I² = 99.8%, P < 0.0001) by microscopy, 25.6% (95% CI: 8.74, 47.62, I² = 99.6%, P < 0.0001) by PCR and 24.3% (95% CI: 12.05 to 39.14, I² = 99.7%, P < 0.0001) by rapid diagnostic test. Using microscopy, the prevalence of asymptomatic malaria was 1.0% (95% CI: 0.00, 3.48) against 21.46% (95% CI: 11.03, 34.21) in symptomatic malaria. The overall prevalence of Plasmodium falciparum and Plasmodium vivax was 51.14% and 37.55%, respectively. Subgroup analysis showed significant variation (P = 0.039) in the prevalence of malaria between asymptomatic and symptomatic cases.

CONCLUSION

Plasmodium falciparum and P. vivax are widespread in Mauritania. Results of this meta-analysis implies that distinct intervention measures including accurate parasite-based diagnosis and appropriate treatment of confirmed malaria cases are critical for a successful malaria control and elimination programme in Mauritania.

Spatiotemporal analysis of malaria incidence in Côte d'Ivoire from 2015 to 2019

Background

The collection of malaria cases over time allows the identification of areas with the highest incidence. Our objective was to characterize the spatial distribution of malaria in Côte d’Ivoire from 2015 to 2019 at the health district level.

Methods

Data on the number of reported malaria cases confirmed by rapid diagnostic test (RDT) in the general population, the number of patients attending medical consultations and the total population by health district and year were collected from the National Malaria Control Program in Côte d’Ivoire. Crude and adjusted incidence rates were estimated for each health district and year. Adjusted incidence rates were used to perform global (Moran's index) and local indicators of spatial autocorrelation (LISA) analyses.

Results

Between 2015 and 2019, mean crude incidence rates increased from 155.5‰ to 229.8‰. We observed significant heterogeneity in malaria incidence rates across the study period and within a given year. The overall Moran index showed spatial autocorrelation for every year analysed except 2017. The LISA analysis showed that the health districts with high incidence rates were concentrated in the western zone of Côte d'Ivoire.

Conclusions

The use of spatial analyses to identify the areas with the highest malaria incidence rates is a relevant approach to optimize control measures in targeted areas.

Shifts in the clinical epidemiology of severe malaria after scaling up control strategies in Mali

A decrease in malaria incidence following implementation of control strategies such as use of artemisinin-based combination therapies, insecticide-impregnated nets, intermittent preventive treatment during pregnancy and seasonal malaria chemoprevention (SMC) has been observed in many parts of Africa. We hypothesized that changes in malaria incidence is accompanied by a change in the predominant clinical phenotypes of severe malaria. To test our hypothesis, we used data from a severe malaria case-control study that lasted from 2014–2019 to describe clinical phenotypes of severe forms experienced by participants enrolled in Bandiagara, Bamako, and Sikasso, in Mali. We also analyzed data from hospital records of inpatient children at a national referral hospital in Bamako. Among 97 cases of severe malaria in the case-control study, there was a predominance of severe malarial anemia (49.1%). The frequency of cerebral malaria was 35.4, and 16.5% of cases had a mixed clinical phenotype (concurrent cerebral malaria and severe anemia). National referral hospital record data in 2013–15 showed 24.3% of cases had severe malarial anemia compared to 51.7% with cerebral malaria. In the years after SMC scale-up, severe malarial anemia cases increased to 30.1%, (P = 0.019), whereas cerebral malaria cases decreased to 45.5% (P = 0.025). In addition, the predominant age group for each severe malaria phenotype was the 0–1-year-olds. The decrease in malaria incidence noted with the implementation of control strategies may be associated with a change in the clinical expression patterns of severe malaria, including a potential shift in severe malaria burden to age groups not receiving seasonal malaria chemoprevention.

Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015-2019

INTRODUCTION

Despite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali.

METHODS

Daily malaria cases were collected from the consultation records of the 35 health areas of Kati's health district, for the period 2015-2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use-land cover were extracted from open-access remote sensing sources, while data on the Niger River's height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis.

RESULTS

The peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person-weeks in 2015, to 27 cases per 1000 person-weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July-corresponding to a persisting transmission during the dry season-and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period.

DISCUSSION

Our study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.

Genome-wide SNP analysis of Plasmodium falciparum shows differentiation at drug-resistance-associated loci among malaria transmission settings in southern Mali

Plasmodium falciparum malaria cases in Africa represent over 90% of the global burden with Mali being amongst the 11 highest burden countries that account for 70% of this annual incidence. The persistence of P. falciparum despite massive global interventions is because of its genetic diversity that drives its ability to adapt to environmental changes, develop resistance to drugs, and evade the host immune system. Knowledge on P. falciparum genetic diversity across populations and intervention landscape is thus critical for the implementation of new strategies to eliminate malaria. This study assessed genetic variation with 12,177 high-quality SNPs from 830 Malian P. falciparum isolates collected between 2007 and 2017 from seven locations. The complexity of infections remained high, varied between sites, and showed a trend toward overall decreasing complexity over the decade. Though there was no significant substructure, allele frequencies varied geographically, partly driven by temporal variance in sampling, particularly for drug resistance and antigen loci. Thirty-two mutations in known drug resistance markers (pfcrt, pfdhps, pfdhfr, pfmdr1, pfmdr2, and pfk13) attained a frequency of at least 2% in the populations. SNPs within and around the major markers of resistance to quinolines (pfmdr1 and pfcrt) and antifolates (pfdhfr and pfdhps) varied temporally and geographically, with strong linkage disequilibrium and signatures of directional selection in the genome. These geo-temporal populations also differentiated at alleles in immune-related loci, including, protein E140, pfsurfin8, pfclag8, and pfceltos, as well as pftrap, which showed signatures of haplotype differentiation between populations. Several regions across the genomes, including five known drug resistance loci, showed signatures of differential positive selection. These results suggest that drugs and immune pressure are dominant selective forces against P. falciparum in Mali, but their effect on the parasite genome varies temporally and spatially. Interventions interacting with these genomic variants need to be routinely evaluated as malaria elimination strategies are implemented.