Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia

Bria, Yulianti Paula; Yeh, Chung‐Hsing; Bedingfield, Susan

doi:10.1016/j.ijid.2020.11.177

Cited by 23 publications

(28 citation statements)

References 41 publications

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“…This indicates that kin observation of both non-symptoms, such as where the patients live and their sex, are significant in observing the patient malaria diagnosis and raising awareness in the community. 37 With all that has been observed developing a tool that can give patients the probability of being malaria positive when observing any malaria-related symptoms might be a possible solution to reduce the rate of self-medication. 37 Prediction models are among those tools that can improve the diagnosis and awareness of the patient's state before buying over-the-counter medication.…”

Section: Discussionmentioning

confidence: 99%

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

Mariki

Mduma

Mkoba

2022

EAHRJ

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Background: Malaria remains a significant cause of morbidity and mortality, especially in the sub-Saharan African region. Malaria is considered preventable and treatable,but in recent years, it has increased outpatient visits, hospitalisation, and deaths worldwide, reaching a 9% prevalence in Tanzania. With the massive number of patient records in the health facilities, this study aims to understand the key characteristics and trends of malaria diagnostic symptoms, testing and treatment data in Tanzania’s high and low endemic regions. Methods: This study had retrospective and cross-sectional designs. The data were collected from four facilities in two regions in Tanzania,i.e., Morogoro Region (high endemicity) and Kilimanjaro Region (low endemicity). Firstly, malaria patient records were extracted from malaria patients’ files from 2015 to 2018. Data collected include (i) the patient’s demographic information, (ii) the symptoms presented by the patient when consulting a doctor, (iii) the tests taken and results, (iv) diagnosis based on the laboratory results and (v) the treatment provided. Apart from that, we surveyed patients who visited the health facility with malaria-related symptoms to collect extra information such as travel history and the use of malaria control initiatives such as insecticide-treated nets. A descriptive analysis was generated to identify the frequency of responses. Correlation analysis random effects logistic regression was performed to determine the association between malaria-related symptoms and positivity. Significant differences of p < 0.05 (i.e., a Confidence Interval of 95%) were accepted. Results: Of the 2556 records collected, 1527(60%) were from the high endemic area, while 1029(40%) were from the low endemic area. The most observed symptoms were the following: for facilities in high endemic regions was fever followed by headache, vomiting and body pain; for facilities in the low endemic region was high fever, sweating, fatigue and headache. The results showed that males with malaria symptoms had a higher chance of being diagnosed with malaria than females. Most patients with fever had a high probability of being diagnosed with malaria. From the interview, 68% of patients with malaria-related symptoms treated themselves without proper diagnosis. Conclusions: Our data indicate that proper malaria diagnosis is a significant concern. The majority still self-medicate with anti-malaria drugs once they experience any malaria-related symptoms. Therefore, future studies should explore this challenge and investigate the potentiality of using malaria diagnosis records to diagnose the disease.

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Section: Discussionmentioning

confidence: 99%

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

Mariki

Mduma

Mkoba

2022

EAHRJ

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“…Malaria itself, regardless of the causative species, is clinically divided into acute, fulminant or chronic, chronologically [16]. In general, infected individuals present with certain duration of intermittent fever, chills, headache, asthenia, anorexia, myalgia-arthralgia, nausea and vomiting [17]. According to Bria et al [17], the previous symptoms combined with patients' history of malaria as a non symptom-related factor contribute most to correct malaria diagnosis.…”

Section: Malaria Pathogenesismentioning

confidence: 99%

“…In general, infected individuals present with certain duration of intermittent fever, chills, headache, asthenia, anorexia, myalgia-arthralgia, nausea and vomiting [17]. According to Bria et al [17], the previous symptoms combined with patients' history of malaria as a non symptom-related factor contribute most to correct malaria diagnosis. Diagnosis made microscopically by discovering intracellular parasite inside erythrocytes in stained thin blood films using Giemsa's while the thick blood smear is only to show the result is positive or negative [18,19].…”

Section: Malaria Pathogenesismentioning

confidence: 99%

Complications of Kidney in Severe Malaria

Siagian

2022

AJRID

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Malaria is an infectious protozoan parasitic disease transmitted by Anopheles spp mosquito. As a disease entity, malaria is still a major global health problem due to its morbidity and mortality that endangered millions of individuals inhabitants in tropical region. Kidney derangement is moderately frequent in severe malaria caused by the species P. falciparum and also P. malariae, but seldom this condition has also been reported in malaria due to P. vivax. Severe or complicated malaria can occur due to delay or failure of treatment. Making correct and early diagnosis is a prerequisite for successful treatment. Actually, a series of events that begins with the entry of blood protozoa into the host which then causes hemodynamic disturbances and loss of normal capillary capacity. Hemodynamic dysfunction, immune response and with addition from the consequences of severe malaria complications in other organs (e.g., liver) leading to worsening of kidney function in severe malaria. This paper cover the recent findings of kidney involvement in severe malaria and other important facts related to this issue.

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“…The 6 classifiers chosen were: K Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) classifiers. These were chosen due to their popularity in disease diagnosis [20], [30], [31].…”

Section: Evaluation Of Selected Featuresmentioning

confidence: 99%

Feature Selection Approach to Improve Malaria Diagnosis Model for High and Low Endemic Areas of Tanzania

Mariki¹,

Mduma²,

Mkoba³

2021

Preprint

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Malaria remains an important cause of death, especially in sub-Saharan Africa with about 228 million malaria cases worldwide and an estimated 405,000 deaths in 2019. Currently, malaria is diagnosed in the health facility using a microscope (BS) or rapid malaria diagnostic test (MRDT) and with area where these tools are inadequate the presumptive treatment is performed. Apart from that self-diagnosis and treatment is also practiced in some of the households. With the high-rate self-medication on malaria drugs, this study aimed at computing the most significant features using feature selection methods for best prediction of malaria in Tanzania that can be used in developing a machine learning model for malaria diagnosis. A malaria symptoms and clinical diagnosis dataset were extracted from patients’ files from four (4) identified health facilities in the regions of Kilimanjaro and Morogoro. These regions were selected to represent the high endemic areas (Morogoro) and low endemic areas (Kilimanjaro) in the country. The dataset contained 2556 instances and 36 variables. The random forest classifier a tree based was used to select the most important features for malaria prediction. Regional based features were obtained to facilitate accurate prediction. The feature ranking as indicated that fever is universally the most influential feature for predicting malaria followed by general body malaise, vomiting and headache. However, these features are ranked differently across the regional datasets. Subsequently, six predictive models, using important features selected by feature selection method, were used to evaluate the features performance. The features identified complies with malaria diagnosis and treatment guideline provided with WHO and Tanzania Mainland. The compliance is observed so as to produce a prediction model that will fit in the current health care provision system in Tanzania.

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Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia

Cited by 23 publications

References 41 publications

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

Complications of Kidney in Severe Malaria

Feature Selection Approach to Improve Malaria Diagnosis Model for High and Low Endemic Areas of Tanzania

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