SARS-CoV-2 seroprevalence and living conditions in Bamako (Mali): a cross-sectional multistage household survey after the first epidemic wave, 2020.

OBJECTIVES: In low-income settings with limited access to diagnosis, COVID-19 information is scarce. In September 2020, after the first COVID-19 wave, Mali reported 3086 confirmed cases and 130 deaths. Most reports originated from Bamako, with 1532 cases and 81 deaths (2.42 million inhabitants). This observed prevalence of 0.06% appeared very low. Our objective was to estimate SARS-CoV-2 infection among inhabitants of Bamako, after the first epidemic wave. We assessed demographic, social and living conditions, health behaviours and knowledges associated with SARS-CoV-2 seropositivity. SETTINGS: We conducted a cross-sectional multistage household survey during September 2020, in three neighbourhoods of the commune VI (Bamako), where 30% of the cases were reported. PARTICIPANTS: We recruited 1526 inhabitants in 3 areas, that is, 306 households, and 1327 serological results (≥1 years), 220 household questionnaires and collected answers for 962 participants (≥12 years). PRIMARY AND SECONDARY OUTCOME MEASURES: We measured serological status, detecting SARS-CoV-2 spike protein antibodies in blood sampled. We documented housing conditions and individual health behaviours through questionnaires among participants. We estimated the number of SARS-CoV-2 infections and deaths in the population of Bamako using the age and sex distributions. RESULTS: The prevalence of SARS-CoV-2 seropositivity was 16.4% (95% CI 15.1% to 19.1%) after adjusting on the population structure. This suggested that ~400 000 cases and ~2000 deaths could have occurred of which only 0.4% of cases and 5% of deaths were officially reported. Questionnaires analyses suggested strong agreement with washing hands but lower acceptability of movement restrictions (lockdown/curfew), and mask wearing. CONCLUSIONS: The first wave of SARS-CoV-2 spread broadly in Bamako. Expected fatalities remained limited largely due to the population age structure and the low prevalence of comorbidities. Improving diagnostic capacities to encourage testing and preventive behaviours, and avoiding the spread of false information remain key pillars, regardless of the developed or developing setting. ETHICS: This study was registered in the registry of the ethics committee of the Faculty of Medicine and Odonto-Stomatology and the Faculty of Pharmacy, Bamako, Mali, under the number: 2020/162/CA/FMOS/FAPH.

Sub-national tailoring of seasonal malaria chemoprevention in Mali based on malaria surveillance and rainfall data.

BACKGROUND: In malaria endemic countries, seasonal malaria chemoprevention (SMC) interventions are performed during the high malaria transmission in accordance with epidemiological surveillance data. In this study we propose a predictive approach for tailoring the timing and number of cycles of SMC in all health districts of Mali based on sub-national epidemiological surveillance and rainfall data. Our primary objective was to select the best of two approaches for predicting the onset of the high transmission season at the operational scale. Our secondary objective was to evaluate the number of malaria cases, hospitalisations and deaths in children under 5 years of age that would be prevented annually and the additional cost that would be incurred using the best approach. METHODS: For each of the 75 health districts of Mali over the study period (2014-2019), we determined (1) the onset of the rainy season period based on weekly rainfall data; (ii) the onset and duration of the high transmission season using change point analysis of weekly incidence data; and (iii) the lag between the onset of the rainy season and the onset of the high transmission. Two approaches for predicting the onset of the high transmission season in 2019 were evaluated. RESULTS: In the study period (2014-2019), the onset of the rainy season ranged from week (W) 17 (W17; April) to W34 (August). The onset of the high transmission season ranged from W25 (June) to W40 (September). The lag between these two events ranged from 5 to 12 weeks. The duration of the high transmission season ranged from 3 to 6 months. The best of the two approaches predicted the onset of the high transmission season in 2019 to be in June in two districts, in July in 46 districts, in August in 21 districts and in September in six districts. Using our proposed approach would prevent 43,819 cases, 1943 hospitalisations and 70 deaths in children under 5 years of age annually for a minimal additional cost. Our analysis shows that the number of cycles of SMC should be changed in 36 health districts. CONCLUSION: Adapting the timing of SMC interventions using our proposed approach could improve the prevention of malaria cases and decrease hospitalisations and deaths. Future studies should be conducted to validate this approach.

[Study on the clinical and epidemiological characteristics of the first 1030 cases of covid-19 in Mali]. / Etude sur les caracteristiques cliniques et epidemiologiques des 1030 premiers cas de covid-19 au Mali.

AIMS: Since the confirmation of the first cases of COVID-19 in Mali in March 2020 and the outbreakspreading to the whole country, clinical and epidemiological data fromaffected patients are used to characterize the disease. This study was to describe the clinica lsigns and epidemiologicalparameters of COVID-19 in the Malian context. MATERIALS AND METHODS: This is a cross-sectional study. All confirmed cases of COVID-19 in Mali between March 25, 2020 to May 24, 2020 have been included. Clinical and epidemiological data from patients with COVID-19 were extracted from the official line list of cases and the national reference laboratory register. RESULTS: The mean age of the 1,030 patients was 45.6 ± 18.4 years; 67.2% of patients were men. Asymptomatic patients accounted for 31.1%. The most common symptoms on admission were cough (60.8%) followed by fever (47.6%). The largest number of cases was recorded in Bamako. CONCLUSION: SARS-CoV-2 infection of the first 1,030 cases in Mali was marked by the predominance of cough and fever.

BUTS: Depuis la confirmation des premiers cas de COVID-19 au Mali en Mars 2020 et sa propagation à tout le pays, des données cliniques et épidémiologiques des patients atteints sont utilisées pour caractériser la maladie. Cette étude avait pour objectif d'étudier les signes cliniques et épidémiologiques de la COVID-19 dans le contexte malien. MATÉRIELS ET MÉTHODES: Il s'agit d'une étude transversale. Tous les cas confirmés de COVID-19 du Mali entre le 25 Mars 2020 au 24 Mai 2020 ont été inclus. Les données cliniques et épidémiologiques des patients atteints de COVID-19ont été extraites. RÉSULTATS: L'âge moyen descas était de 45,6±18,4 ans ; 67,2% des patients étaient des hommes. Les patients asymptomatiques représentaient 31,1%. Les symptômes les plus courants à l'admission étaient la toux (60,8%) suivi de la fièvre (47,6%). Le plus grand nombre de cas a été enregistré à Bamako. CONCLUSION: L'infection par le SARS-CoV-2 des 1 030 premiers cas au Mali a été marquée par la prédominance de la toux et de la fièvre.

[Clinical and epidemiological characteristic of COVID-19 deaths in Mali]. / Caracteristiques clinique et epidemiologique des deces COVID-19 au Mali.

INTRODUCTION: Mali recorded its first COVID-19's death related case on March 26, 2020. The aim of this study was to evaluate the comorbidity of COVID-19's death related cases in the Malian context. METHOD: A cross-sectional study was conducted between March 25 and October 11, 2020. Community death information was analyzed from the patient descriptive list, and from the hospitalization registry of the treatment sites. RESULT: Of the 3,286 COVID-19 confirmed cases, 132 died making a lethality rate of 4.00% (132/3286). Men were the most represented with 75.76% (100/132). The mean age was 63.77 ± 15.25 years. The mean time of hospital stay was 4.50 days ± 6.35. Diabetes and cardiovascular disease remain the most frequent comorbidities with death patients with 20.45% and 17.42%, respectively. CONCLUSION: The results of this study allow to draw map of patients who died from COVID-19 as well as provide information on the comorbidities for better management of hospitalized patients.

INTRODUCTION: Le Mali a enregistré son premier cas de décès lié à la COVID-19, le 26 mars 2020.Le but de cette étude est d'étudier la comorbidité des cas de décès de COVID-19 dans le contexte malien. MÉTHODE: Il s'agissait d'une étude transversale allant de la période du 25 mars au 11 octobre 2020. Nous avons réalisé une analyse des informations de la liste descriptives des cas pour les décès communautaire et des registres d'hospitalisation des sites de prise en charge. RÉSULTAT: Sur les 3286 cas confirmés par la COVID-19, 132 malades en sont décédés soit une létalité de4,00%. Les hommes étaient les plus représentés avec 75,76 % (100/132). La moyenne d'âge était de 63,77 ans ± 15,25. La durée moyenne d'hospitalisation était de4,50 jours ± 6,35. Le diabète et l'HTA étaient les facteurs de comorbidité les plus fréquents rencontrés dans les cas de décès avec respectivement 20,45% et 17,42%. CONCLUSION: Cette étude a montré que les cas de décès liés au COVID-19 au Mali étaient observés chez les personnes âgées, diabétiques et hypertendues. Ces informations aideront à optimiser la prise en charge des malades hospitalisés.

Impact of Three-Year Intermittent Preventive Treatment Using Artemisinin-Based Combination Therapies on Malaria Morbidity in Malian Schoolchildren.

Previous studies have shown that a single season of intermittent preventive treatment in schoolchildren (IPTsc) targeting the transmission season has reduced the rates of clinical malaria, all-cause clinic visits, asymptomatic parasitemia, and anemia. Efficacy over the course of multiple years of IPTsc has been scantly investigated. METHODS: An open, randomized-controlled trial among schoolchildren aged 6-13 years was conducted from September 2007 to January 2010 in Kolle, Mali. Students were included in three arms: sulphadoxine-pyrimethamine+artesunate (SP+AS), amodiaquine+artesunate (AQ+AS), and control (C). All students received two full doses, given 2 months apart, and were compared with respect to the incidence of clinical malaria, all-cause clinic visits, asymptomatic parasitemia, and anemia. RESULTS: A total of 296 students were randomized. All-cause clinic visits were in the SP+AS versus control (29 (20.1%) vs. 68 (47.2%); 20 (21.7%) vs. 41 (44.6%); and 14 (21.2%) vs. 30 (44.6%); p < 0.02) in 2007, 2008, and 2009, respectively. The prevalence of asymptomatic parasitemia was lower in the SP+AS compared to control (38 (7.5%) vs. 143 (28.7%); and 47 (12.7%) vs. 75 (21.2%); p < 0.002) in 2007 and 2008, respectively. Hemoglobin concentration was significantly higher in children receiving SP+AS (11.96, 12.06, and 12.62 g/dL) than in control children (11.60, 11.64, and 12.15 g/dL; p < 0.001) in 2007, 2008, and 2009, respectively. No impact on clinical malaria was observed. CONCLUSION: IPTsc with SP+AS reduced the rates of all-cause clinic visits and anemia during a three-year implementation.

Geo-Epidemiology of Malaria at the Health Area Level, Dire Health District, Mali, 2013-2017.

Background: According to the World Health Organization, there were more than 228 million cases of malaria globally in 2018, with 93% of cases occurring in Africa; in Mali, a 13% increase in the number of cases was observed between 2015 and 2018; this study aimed to evaluate the impact of meteorological and environmental factors on the geo-epidemiology of malaria in the health district of Dire, Mali. Methods: Meteorological and environmental variables were synthesized using principal component analysis and multiple correspondence analysis, the relationship between malaria incidence and synthetic indicators was determined using a multivariate general additive model; hotspots were detected by SaTScan. Results: Malaria incidence showed high inter and intra-annual variability; the period of high transmission lasted from September to February; health areas characterized by proximity to the river, propensity for flooding and high agricultural yield were the most at risk, with an incidence rate ratio of 2.21 with confidence intervals (95% CI: 1.85-2.58); malaria incidence in Dire declined from 120 to 20 cases per 10,000 person-weeks between 2013 and 2017. Conclusion: The identification of areas and periods of high transmission can help improve malaria control strategies.

Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies.

We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward's method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012.

BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

Blastocystis Colonization Is Associated with Increased Diversity and Altered Gut Bacterial Communities in Healthy Malian Children.

Blastocystis is the most common protozoan colonizing the gut of vertebrates. It modulates the human digestive microbiota in the absence of inflammation and gastrointestinal disease. Although it has been associated with human diseases, including inflammatory bowel disease, its pathogenicity remains controversial. This study aimed to assess the influence of Blastocystis on the gut bacterial communities in healthy children. We conducted a cross-sectional study on 147 Blastocystis-colonized and 149 Blastocystis-noncolonized Malian children, with Blastocystis colonization assessed by real-time PCR and gut microbial communities characterized via 16S rRNA gene (Illumina MiSeq) sequencing and bioinformatics analysis. The gut microbiota diversity was higher in Blastocystis-colonized compared to Blastocystis-noncolonized children. The phyla Firmicutes, Elusimicrobia, Lentisphaerae, and Euryarchaeota were higher in Blastocystis-colonized children, whereas Actinobacteria, Proteobacteria, unassigned bacteria, and Deinococcus-Thermus were higher in Blastocystis-noncolonized children. Moreover, Faecalibacterium prausnitzii (family Ruminococcaceae) and Roseburia sp. (family Lachnospiraceae) abundance was higher in Blastocystis-colonized children. We conclude that Blastocystis colonization is significantly associated with a higher diversity of the gut bacterial communities in healthy children, while it is not associated with the presence of potentially pathogenic bacteria in the human gut.

"Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling".

BACKGROUND: In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity. METHODS: Using the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate. RESULTS: The level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates. CONCLUSION: Our results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method. TRIAL REGISTRATION: The dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry).

Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso.

BACKGROUND: With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. METHODS: Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff's elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. RESULTS: Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03-1.40). CONCLUSION: These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.