The economic burden of malaria: a systematic review

Andrade, Mônica Viégas; Noronha, Kenya; Diniz, Bernardo P. C.; Guedes, Gilvan Ramalho; Carvalho, Lucas Roberto de; Silva, Valéria A.; Calazans, Júlia Almeida; Santos, André Soares; Silva, Daniel; Castro, Márcia C.

doi:10.1186/s12936-022-04303-6

Cited by 57 publications

(35 citation statements)

References 52 publications

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“…[42] This indicates that the direction of malaria risk depends on temperature, rainfall, and humidity. [42,43] Other studies [44,45] have also revealed that a reduction in malaria infections is associated with economic growth.…”

Section: Discussionmentioning

confidence: 99%

Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Iddrisu,

Otoo,

Hinneh

et al. 2024

Infect Dis Immun

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Background Malaria remains a significant public health concern in Ghana, with varying risk levels across different geographical areas. Malaria affects millions of people each year and imposes a substantial burden on the health care system and population. Accurate risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease hotspots. This study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented, and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria control. Methods The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical Service. We studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal models. The malaria risk for each region was mapped to visually identify regions with malaria hotspots. Clustering and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models, respectively. Parameter estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R software. Results The spatial model indicated an increased risk of malaria in the North East, Bono East, Ahafo, Central, Upper West, Brong Ahafo, Ashanti, and Eastern regions. The spatiotemporal model results highlighted an elevated malaria risk in the North East, Upper West, Upper East, Savannah, Bono East, Central, Bono, and Ahafo regions. Both spatial and spatiotemporal models identified the North East, Upper West, Bono East, Central, and Ahafo Regions as hotspots for malaria risk. Substantial variations in risk were evident across regions (H = 104.9, P < 0.001). Although climatic and economic factors influenced malaria infection, statistical significance was not established. Conclusions Malaria risk was clustered and varied among regions in Ghana. There are many regions in Ghana that are hotspots for malaria risk, and climate and economic factors have no significant influence on malaria risk. This study could provide information on malaria transmission patterns in Ghana, and contribute to enhance the effectiveness of malaria control strategies.

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Section: Discussionmentioning

confidence: 99%

Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Iddrisu,

Otoo,

Hinneh

et al. 2024

Infect Dis Immun

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“…In contrast, many countries in Africa have a GDPPC well below the regional threshold ($2,250) for high malaria prevalence (Gallup and Sachs, 2000). Multiple studies have shown a strong correlation between high malaria burden and low GDPPC (Andrade et al, 2022), or low per capita income (Gallup and Sachs, 2000;Sarma et al, 2019). For example, the Sarma et al (2019) study found a 10% decrease in malaria incidence with an increase of 0.3% in average of income per capita.…”

Section: The Roles Of Socioeconomic and Demographic Factorsmentioning

confidence: 99%

How environmental, geographic, socio-demographic, and epidemiological indicators influence malaria prevalence

Villena,

Arab,

Lippi

et al. 2023

Preprint

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Background: The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of prevalence data (1990-2017) were used to examine the effects of environmental (temperature, precipitation, normalized difference vegetation index, isothermality), geographic (elevation), socio-economic (population density, gross domestic product per capita, human development index), and epidemiological (basic reproductive number) factors on Plasmodium falciparum and Plasmodium vivax malaria in Africa and Asia. Methods: Monthly long-term, open-source data for each factor were compiled and analyzed using two approaches: generalized linear models (GLM) and classification and regression trees (CART). Both approaches were used to identify associations between environmental, geographic, socioeconomic, and epidemiological factors with malaria prevalence (the proportion of people infected with malaria in a given time period). Plasmodium. falciparum and P. vivax malaria prevalence were examined in Asia, and only P. falciparum in Africa. Model fit was assessed, estimating the accuracy, precision, recall, and the f1-score of the models. Results: Both CART and GLMs provided robust predictions of malaria prevalence, but the CART model exhibited slightly better accuracy and precision. An optimum temperature for malaria prevalence around 25 ◦C for P. falciparum prevalence was observed in Africa but not for P. falciparum and P. vivax in Asia which was below 25 ◦C. The decline of malaria prevalence from 2000 to 2012 was well capture by the models as well as the resurgence of P. falciparum prevalence in Africa and Asia. Precipitation showed a unimodal response to malaria prevalence with a positive effect up to a point of 200 - 400 mm of rainfall; then a negative effect was observed as precipitation increased. Higher predicted malaria prevalence in regions with lower GDPPC and lower HDI and a positive correlation with the basic reproductive number. Conclusion: This study showed that there was a combination of environmental, geographic, socioeconomic, and epidemiological indicators driving malaria prevalence. Identifying these, and describing their associations provides key information to inform planning strategies and interventions to reduce malaria burden in Africa and Asia.

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“…Malaria imposes a significant economic and societal burden, which includes (i) direct healthcare costs associated with diagnosis and treatment, (ii) productivity fall translated to economic losses for individuals and communities, (iii) preventive measures carried out by governments, organizations, and individuals, (iv) research and development for new malaria treatments and improved diagnostic tools, and (v) tourism decrease in endemic areas. However, estimating the global malaria budget is challenging due to the lack of a standardized breakdown of expenses and a comprehensive, universally comparable quantification of the disease's economic impact on society and governments (Andrade et al, 2022). Nevertheless, it is worth mentioning that the total fund allocated to combat malaria through different initiatives has remained relatively stagnant since 2010, reaching only US$ 3.5 billion in 2021 (RBM Partnership to End Malaria, 2023; World Health Organization, 2022).…”

Section: Introductionmentioning

confidence: 99%

Nanotherapeutics against malaria: A decade of advancements in experimental models

Avalos‐Padilla,

Fernàndez‐Busquets

2024

WIREs Nanomed Nanobiotechnol

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Malaria, caused by different species of protists of the genus Plasmodium, remains among the most common causes of death due to parasitic diseases worldwide, mainly for children aged under 5. One of the main obstacles to malaria eradication is the speed with which the pathogen evolves resistance to the drug schemes developed against it. For this reason, it remains urgent to find innovative therapeutic strategies offering sufficient specificity against the parasite to minimize resistance evolution and drug side effects. In this context, nanotechnology‐based approaches are now being explored for their use as antimalarial drug delivery platforms due to the wide range of advantages and tuneable properties that they offer. However, major challenges remain to be addressed to provide a cost‐efficient and targeted therapeutic strategy contributing to malaria eradication. The present work contains a systematic review of nanotechnology‐based antimalarial drug delivery systems generated during the last 10 years.This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease

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The economic burden of malaria: a systematic review

Cited by 57 publications

References 52 publications

Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

How environmental, geographic, socio-demographic, and epidemiological indicators influence malaria prevalence

Nanotherapeutics against malaria: A decade of advancements in experimental models

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