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

Cissoko, Mady; Sagara, Issaka; Sankare, Moussa Hama; Dieng, Sokhna; Guindo, Abdoulaye; Doumbia, Zoumana; Allasseini, Balam; Traoré, Diakaridia; Fomba, Seydou; Bendiane, Marc Karim; Landier, Jordi; Dessay, Nadine; Gaudart, Jean

doi:10.3390/ijerph17113982

Cited by 22 publications

(47 citation statements)

References 39 publications

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“… 37 Additionally, six (6%) studies used the total-sets analysis based on Bayesian information criterion (BIC) statistic to identify the optimal variable combination. Principal component analysis was employed by eight (8%) studies to reduce dimensions and avoid collinearities in environmental factors, 38 39 meteorological factors 38 40 and household demographics. 32 33 41–43 The Bayesian stochastic search was used by three (3%) studies to identify covariates with the highest inclusion probability.…”

Section: Resultsmentioning

confidence: 99%

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

et al. 2020

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BackgroundApproaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).MethodsA systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.ResultsOne hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.ConclusionsOur review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

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Section: Resultsmentioning

confidence: 99%

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

et al. 2020

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“…Most studies in the literature have shown that rainfall and humidity increase the risk of malaria by developing suitable breeding sites and increasing mosquito density [ 30 , 33 , 34 ]. Studies show the persistence of malaria transmission despite the intensification of control strategies.…”

Section: Discussionmentioning

confidence: 99%

Evolution of Malaria Incidence in Five Health Districts, in the Context of the Scaling up of Seasonal Malaria Chemoprevention, 2016 to 2018, in Mali

Sacko

Sagara

Berthé

et al. 2021

IJERPH

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Context: In Mali, malaria transmission is seasonal, exposing children to high morbidity and mortality. A preventative strategy called Seasonal Malaria Chemoprevention (SMC) is being implemented, consisting of the distribution of drugs at monthly intervals for up to 4 months to children between 3 and 59 months of age during the period of the year when malaria is most prevalent. This study aimed to analyze the evolution of the incidence of malaria in the general population of the health districts of Kati, Kadiolo, Sikasso, Yorosso, and Tominian in the context of SMC implementation. Methods: This is a transversal study analyzing the routine malaria data and meteorological data of Nasa Giovanni from 2016 to 2018. General Additive Model (GAM) analysis was performed to investigate the relationship between malaria incidence and meteorological factors. Results: From 2016 to 2018, the evolution of the overall incidence in all the study districts was positively associated with the relative humidity, rainfall, and minimum temperature components. The average monthly incidence and the relative humidity varied according to the health district, and the average temperature and rainfall were similar. A decrease in incidence was observed in children under five years old in 2017 and 2018 compared to 2016. Conclusion: A decrease in the incidence of malaria was observed after the SMC rounds. SMC should be applied at optimal periods.

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“…Lagged SMI selection Several studies have observed a lag between malaria time series and meteorological data time series [24,25,26]. In view of this, we decided to investigate the time lag (in weeks) between the time series of weekly malaria cases and the time series of SMIs.…”

Section: Temporal Analysesmentioning

confidence: 99%

“…SMC or LLIN distribution). Indeed, as numerous studies have shown, malaria incidence at the local level is heterogeneous and associated with spatio-temporal clusters [12,13,14] that are likely to maintain transmission during low-risk periods and, consequently, to increase transmission during high-risk periods [15,16,17]. Identifying these clusters can therefore help to improve the fight against malaria and to anticipate future outbreaks.…”

Section: Introductionmentioning

confidence: 99%

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

Bationo

Gaudart

Sokhna

et al. 2021

Preprint

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Background: 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 centers (HCs). Methods: Case data for 27 villages were collected in 13 HCs using continuous passive case detection. 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. Results: Overall, the incidence rate in the area was 429.13 cases per 1,000 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 1,000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1,750.75 cases per 1,000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The epidemic year was characterized by a major peak during the second part of the rainy season and a secondary peak during the dry-hot season. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables and associated with the first peak of cases) and 16 weeks for SMI2 (positively correlated with temperature variables and associated with the secondary peak of cases). Euclidian distance to HC was not found to be a predictor of malaria cases recorded in HC. 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. Conclusions: Our spatio-temporal analysis of malaria cases in Diebougou 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. Keywords Geo-epidemiology, Spatial Clusters, temporal dynamics, nonlinear relationship, prediction.

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

Cited by 22 publications

References 39 publications

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Evolution of Malaria Incidence in Five Health Districts, in the Context of the Scaling up of Seasonal Malaria Chemoprevention, 2016 to 2018, in Mali

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

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