The use of spatial and genetic tools to assess Plasmodium falciparum transmission in Lusaka, Zambia between 2011 and 2015

Bridges, Daniel J.; Chishimba, Sandra; Mwenda, Mulenga; Winters, Anna M.; Slawsky, Erik; Mambwe, Brenda; Mulube, Conceptor; Searle, Kelly M.; Hakalima, A.T.; Mwenechanya, Roy; Larsen, David A.

doi:10.1186/s12936-020-3101-7

Cited by 6 publications

(6 citation statements)

References 55 publications

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“…In this study precipitation, slope, and LULC were related to malaria risk. Similar results have been reported in Chimoio [11], in Zimbabwe [22], Burundi [23], Zambia [27], Ghana [28], Colombia [28,29], and China [30].…”

Section: Discussionsupporting

confidence: 89%

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Ferrão¹,

Earland²,

Novela³

et al. 2021

Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

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Background: Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. (2) Methods: One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. (3) Results:The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. (4) Conclusion:In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions.

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Section: Discussionsupporting

confidence: 89%

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Ferrão¹,

Earland²,

Novela³

et al. 2021

Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

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“…Many African cities show a clear trend of increasing malaria transmission from urban to peri-urban to rural settings [ 74 ]. The spatial extent of RACD in urban Lusaka, where population density is higher and more people are tested, was associated with a higher probability of identifying additional cases within 250 m of uninhabited land [ 25 ]. Urban malaria transmission may vary depending on an assortment of factors, such as vector breeding sites, local vector species, location, human movement patterns, waste management, socioeconomic factors, land use, climate, and local malaria intervention programs [ 75 ].…”

Section: Discussionmentioning

confidence: 99%

“… Risk of malaria infection amongst persons living with index cases [ 14 , 15 , 17 , 18 , 19 , 23 , 24 , 25 , 29 , 30 , 31 , 32 , 34 , 35 , 36 , 37 , 41 , 46 , 47 , 50 , 51 , 52 , 54 , 55 , 57 , 62 , 63 , 64 , 66 ]. SE: Standard Error, Log(odds ratio) values with “-“ are negative values.…”

Section: Figurementioning

confidence: 99%

Reactive Case Detection Strategy for Malaria Control and Elimination: A 12 Year Systematic Review and Meta-Analysis from 25 Malaria-Endemic Countries

et al. 2023

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Reactive case detection (RACD) is the screening of household members and neighbors of index cases reported in passive surveillance. This strategy seeks asymptomatic infections and provides treatment to break transmission without testing or treating the entire population. This review discusses and highlights RACD as a recommended strategy for the detection and elimination of asymptomatic malaria as it pertains in different countries. Relevant studies published between January 2010 and September 2022 were identified mainly through PubMed and Google Scholar. Search terms included “malaria and reactive case detection”, “contact tracing”, “focal screening”, “case investigation”, “focal screen and treat”. MedCalc Software was used for data analysis, and the findings from the pooled studies were analyzed using a fixed-effect model. Summary outcomes were then presented using forest plots and tables. Fifty-four (54) studies were systematically reviewed. Of these studies, 7 met the eligibility criteria based on risk of malaria infection in individuals living with an index case < 5 years old, 13 met the eligibility criteria based on risk of malaria infection in an index case household member compared with a neighbor of an index case, and 29 met the eligibility criteria based on risk of malaria infection in individuals living with index cases, and were included in the meta-analysis. Individuals living in index case households with an average risk of 2.576 (2.540–2.612) were more at risk of malaria infection and showed pooled results of high variation heterogeneity chi-square = 235.600, (p < 0.0001) I2 = 98.88 [97.87–99.89]. The pooled results showed that neighbors of index cases were 0.352 [0.301–0.412] times more likely to have a malaria infection relative to index case household members, and this result was statistically significant (p < 0.001). The identification and treatment of infectious reservoirs is critical to successful malaria elimination. Evidence to support the clustering of infections in neighborhoods, which necessitates the inclusion of neighboring households as part of the RACD strategy, was presented in this review.

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“…and Beira in Mozambique [25,26,27], in Comoros, Zambia, and Madagascar in Africa [28,29,30] in Latin America [31] and in China [32]. Heterogeneity in spatial distribution in small settlement area is probably caused by land use, landscape due human activities in the settlement and the proximity of individuals to places with elevated vector presence [15].…”

Section: Discussionmentioning

confidence: 99%

Sociodemographic and Economic Traits That Affect Malaria Prevalence in Sussundenga Village, Mozambique.

Ferrão¹,

Earland²,

Novela³

et al. 2020

Preprint

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Malaria is still one of the leading causes of mortality and morbidity in Mozambique with the 5 th highest prevalence in the world, with little progress in malaria control over the past 20 years. Sussundenga village is one of most affected areas, and lies along the Zimbabwe border, making evaluation of malaria transmission and control policies integral for regional efforts. The objective of this study was to map and quantify malaria parasite prevalence and model its relationship with sociodemographic and economic traits in Sussundenga Village. Houses in the study area were digitalized and enumerated using GoogleEarth Pro TM . A sample of 125 houses was drawn to conduct a community survey of P. falciparum parasite prevalence using rapid diagnostic test (RDT). During the survey, a questionnaire was conducted to assess the socio-demographic and economic traits of the participants. Descriptive statistics were analyzed and logistic regression was performed to establish the relationship between positive cases and the traits. Using GIS a map the prevalence of malaria was produced. The analysis was carried out using SPSS version 20 package and ArcGis 10.7.1. 358 participants were enrolled, completed the survey, and were tested for malaria. The overall P. falciparum prevalence was 31.6 % and spatiality identified. Half of the malaria positive cases occurred in age group 5 to 14, 40 % more than expected and age group over 24 accounted for 17.6 % the cases, around 50 % less than expected. The model explained 15 % of the variance in malaria positive cases and sensitivity of the final model was 91.8 %. The increase in malaria treated cases, having paid employment, education level and age category, will decrease the probability of malaria positive cases in Sussundenga Village. Conclusion In this area the highest burden of P. falciparum infection was among those 5-14 years old. Malaria infection was related to socio-demographic and economic traits. Targeting malaria control at community level can contributed better than waiting for cases at health centers. These finding can be used to guide more effective interventions in this region.

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The use of spatial and genetic tools to assess Plasmodium falciparum transmission in Lusaka, Zambia between 2011 and 2015

Cited by 6 publications

References 55 publications

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Reactive Case Detection Strategy for Malaria Control and Elimination: A 12 Year Systematic Review and Meta-Analysis from 25 Malaria-Endemic Countries

Sociodemographic and Economic Traits That Affect Malaria Prevalence in Sussundenga Village, Mozambique.

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