Symptomatic malaria diagnosis overestimate malaria prevalence, but underestimate anaemia burdens in children: results of a follow up study in Kenya

Choge, Joseph K.; Magak, Ng’wena Gideon; Akhwale, Willis; Koech, Julius; Ngeiywa, Moses; Oyoo–Okoth, Elijah; Esamai, Fabian; Osano, Odipo; Khayeka–Wandabwa, Christopher; Kweka, Eliningaya J.

doi:10.1186/1471-2458-14-332

Cited by 21 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the estimated incidence rates may differ from those occurring in the population. This situation has been previously detected and acknowledged in a range of settings (Choge et al, 2014;Kyabayinze et al, 2012;Nankabirwa et al, 2009;WHO, 2013;Yeka et al, 2012). However, according to WHO (2013), epidemiological surveillance systems do not need to detect all cases to allow the assessment of malaria incidence.…”

Section: Discussionmentioning

confidence: 96%

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

Colón‐González¹,

Tompkins²,

Biondi³

et al. 2016

Geospat Health

View full text Add to dashboard Cite

We investigate the short-term effects of air temperature, rainfall, and socioeconomic indicators on malaria incidence across Rwanda and Uganda from 2002 to 2011. Delayed and nonlinear effects of temperature and rainfall data are estimated using generalised additive mixed models with a distributed lag nonlinear specification. A time series cross-validation algorithm is implemented to select the best subset of socioeconomic predictors and to define the degree of smoothing of the weather variables. Our findings show that trends in malaria incidence agree well with variations in both temperature and rainfall in both countries, although factors other than climate seem to play an important role too. The estimated short-term effects of air temperature and precipitation are nonlinear, in agreement with previous research and the ecology of the disease. These effects are robust to the effects of temporal correlation. The effects of socioeconomic data are difficult to ascertain and require further evaluation with longer time series. Climate-informed models had lower error estimates compared to models with no climatic information in 77 and 60% of the districts in Rwanda and Uganda, respectively. Our results highlight the importance of using climatic information in the analysis of malaria surveillance data, and show potential for the development of climateinformed malaria early warning systems. IntroductionDespite the global contraction in range over the past century , malaria still imposes a significant health and socioeconomic burden to many countries (WHO, 2013). The World Health Organization estimates that about 3.4 billion people are at risk of malaria (WHO, 2013). Approximately 207 million cases and 627,000 deaths occurred in 2012 worldwide (WHO, 2013). About 90% of the total mortality occurs in sub-Saharan Africa, and 77% of that percentage happens in children under 5 years of age (WHO, 2013). Two countries significantly affected by malaria are Rwanda and Uganda. Malaria has long been considered the main cause of morbidity and mortality in both countries (NISR, MOH and ICF International, 2012; UBOS and ICF International, 2012). Over the period 2002 to 2011, more than five million malaria cases were reported in Rwanda to government health facilities. The number was significantly greater in Uganda with about 100 million reports of suspected malaria cases between 2002 and 2010.Trends in malaria incidence could be attributed to the complex interplay of a range of determinants including climatic, environmental, and socioeconomic factors (Kazembe et al., 2006;Lowe et al., 2013;Rulisa et al., 2013). Statistical models are useful tools that allow us: i) to understand how disease outcomes change as a function of variations in their key driver; and ii) to predict disease outcomes based on the dynamics of such drivers (James et al., 2013). This paper aims to investigate the ways in which malaria incidence varies as a function of short-term changes in air temperature and rainfall over the period [2002][2003][2004][2005][...

show abstract

Section: Discussionmentioning

confidence: 96%

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

Colón‐González¹,

Tompkins²,

Biondi³

et al. 2016

Geospat Health

View full text Add to dashboard Cite

show abstract

“…Despite the declining of malaria infections in Ethiopia, the disease still remains one of the leading causes of morbidity and mortality a ecting all age groups [1,3,4,17]. It can cause severe anemia, hypoglycemia, renal failure [15], loss of productivity, school absenteeism, and other complications [1]. So, accurate malaria prevalence data are vital for the proper diagnosis, treatment, prevention/control, and policy formulation [70].…”

Section: Discussionmentioning

confidence: 99%

“…Areas located less than 2,000 meters above the sea level are considered malarious [7]. Regarding clinical manifestations, fever, high temperature, sweating, shivering, vomiting, and severe headache are the peculiar malarial precursors [15]. Currently, several types of malaria diagnostic tests are available including; microscopy, rapid diagnostic tests (RDTs) and polymerase chain reaction (PCR) assays.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Plasmodium falciparumandPlasmodium vivaxPrevalence in Ethiopia: A Systematic Review and Meta-Analysis

Deress

Girma

2019

Malaria Research and Treatment

View full text Add to dashboard Cite

Background. Malaria is a protozoan disease caused by the Plasmodium species. Among the five Plasmodium species known to infect human beings, Plasmodium falciparum and Plasmodium vivax malaria are by far the most predominant and widely distributed in Ethiopia. Malaria is one of the leading causes of morbidity and mortality globally, particularly in the sub-Saharan countries including Ethiopia. It is also a major obstacle to socio-economic development in the country. Methods. Articles were searched from PubMed, Google Scholar, and Science Direct databases. The pooled prevalence estimates were analyzed using the DerSimonian-Laird random-effects model and the possible sources of heterogeneity were evaluated through subgroup analysis, metaregression, and sensitivity analysis. Publication bias was analyzed using funnel plots and Egger’s test statistics. The data management and analysis were done using STATA 15.1 version software. Results. Among 922 studies initially identified, thirty-five full-text articles fulfilled the inclusion criteria and included in the study. The combined, Plasmodium falciparum, Plasmodium vivax, and mixed infections pooled prevalence estimates were 25.8% (95% CI: 21.3, 30.4), 14.7% (95% CI: 11.4, 18.1), 8.7% (95% CI: 7.0, 10.4), and 1.2% (95% CI: 20.7, 29.9), respectively. Based on agro-ecological subgroup analysis, the highest malaria prevalence (37.6%) was obtained from studies conducted on mixed regions of low lands and midlands while the least (20.7%) was from low lands. In Ethiopia, malaria transmission is seasonal, variable, and coincides with the peak agricultural activities that greatly affected the country’s socio-economic development. Conclusions. This systematic review and meta-analysis showed a high malaria prevalence in Ethiopia. Therefore, previous prevention and control measures should be revised and/or strengthened as appropriate and new strategies should be implemented. In addition, technical, financial and material support, and coordination of the regional capacity building and logistics should be adequately implemented.

show abstract

“…Headache (S2) is another significant feature for malaria diagnosis. Some malaria studies and Indonesian guidelines about malaria treatment include headache as a malaria symptom [10,[42][43][44].…”

Section: Significant Symptoms and Non-symptom-related Factorsmentioning

confidence: 99%

Significant Symptoms and Non-Symptom-Related Factors for Malaria Diagnosis in Endemic Regions of Indonesia

Bria

Yeh

Bedingfield

2020

Preprint

View full text Add to dashboard Cite

Background: In rural areas of Indonesia, where parasitological tests are not always available, a knowledge of significant symptoms and non-symptom-related factors is crucial for clinical diagnosis of malaria in order to take appropriate actions for treatment. Malaria diagnosis at early stages can avoid a progression in severity of the conditions and possible transmission of the disease. With insufficient knowledge of significant malaria symptoms and non-symptom-related factors for clinical diagnosis of malaria, medical doctors may presumptively treat febrile patients, which leads to an increase in resistance to antimalarial drugs.Methods: The study involves five stages. First, medical records are collected from patients suffering from malaria and other febrile diseases such as dengue fever, typhoid fever, respiratory tract infection, dyspepsia, pneumonia, common cold and gastritis. The records are obtained from two public hospitals in endemic regions in Indonesia’s East Nusa Tenggara Province, East Sumba and Lembata. Second, interviews with eight Indonesian medical doctors are conducted to understand the symptoms and non-symptom-related factors they use for clinical diagnosis of malaria. Third, symptoms and non-symptom-related factors from 472 collected medical records are selected and ranked using the correlation matrix method. Fourth, the 17 feature sets extracted from the ranked features are used to develop and compare various malaria classifiers using three machine-learning techniques, including logistic regression, support vector machines and k-nearest neighbors. Fifth, the most significant symptoms and non-symptom-related factors are identified for malaria diagnosis using the malaria classifiers developed. In addition, further experiments and comparisons are conducted to examine the performance of malaria classifiers using various feature sets.Results: (a) Out of 17 features identified from medical records, six significant symptoms (duration of fever, headache, nausea and vomiting, heartburn, pain and severe symptoms), and two non-symptom-related factors (history of malaria and age) contribute most to malaria diagnosis. These significant features can be used for malaria diagnosis with an average 85% accuracy. (b) As a symptom, duration of fever is more significant than temperature or fever for distinguishing malaria from other febrile diseases. (c) Trios malaria symptoms (fever, shivering and sweating) are shown to be less significant than other symptoms in endemic regions.Conclusions: The findings of this study can be used to raise the public awareness of significant malaria symptoms and non-symptom-related factors within societies vulnerable to malaria. These findings can also be used for helping unexperienced medical doctors with clinical diagnosis of malaria especially in rural areas, where the availability of parasitological tests is scarce.

show abstract

Symptomatic malaria diagnosis overestimate malaria prevalence, but underestimate anaemia burdens in children: results of a follow up study in Kenya

Cited by 21 publications

References 33 publications

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

Plasmodium falciparumandPlasmodium vivaxPrevalence in Ethiopia: A Systematic Review and Meta-Analysis

Significant Symptoms and Non-Symptom-Related Factors for Malaria Diagnosis in Endemic Regions of Indonesia

Contact Info

Product

Resources

About