Self-Medication Practice against Malaria and Associated Factors in the City of Parakou in Northern Benin: Results of a Population Survey in 2017

Attinsounon, Cossi Angelo; Sissinto, Yollande; Avokpaho, Euripide Frinel G Arthur; Alassani, Adébayo; Sanni, Majid; Zannou, Marcel

doi:10.4236/aid.2019.93020

Cited by 8 publications

(12 citation statements)

References 14 publications

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“…Similar findings were also observed in the studies done in Kenya, Benin and Ghana, where self-medication is still practised in these counties and Tanzania is no different. 32,33 Furthermore, we found that patients from high endemic facilities, who are male, and those who come with high fever, headache, abdominal pain, joint pain, general body malaise, and vomiting symptoms have a high chance of being diagnosed with malaria. This finding aligns with the Tanzania malaria diagnosis guideline, where the guideline also identifies the symptoms observed in this study.…”

Section: Discussionmentioning

confidence: 91%

“…13 This is further compounded by the practice of self-medication which has been described as a significant hindrance to proper disease management in many developing countries. [14][15][16][17] Recently, in Tanzania, the "not every fever is Malaria" campaign aims to educate people that not every fever episode experienced requires an antimalarial. 18 Other diseases such as typhoid, dengue, chikungunya, and urinary tract infections present the same symptoms as malaria.…”

Section: Introductionmentioning

confidence: 99%

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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: 91%

Section: Introductionmentioning

confidence: 99%

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

Mariki

Mduma

Mkoba

2022

EAHRJ

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“…However, most of the time, out-of-pocket expenses were required, despite the availability of some of the drugs for free in public health facilities. This is because of the requested payments for medical consultation and lab exams before obtaining the drug for free; the poorest of the population prefer to spend the little money they have to buy medicines instead, and thus resort to self-medication [ 20 ]. In addition, in contrast with the recommendations, the most frequently used drug remains quinine, which is not provided for free.…”

Section: Discussionmentioning

confidence: 99%

Determinants of Patients’ Adherence to Malaria Treatment in the Democratic Republic of the Congo

et al. 2022

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(1) Background: Malaria heavily affects the Democratic Republic of the Congo (DRC) despite the use of effective drugs. Poor adherence to malaria treatment may contribute to this problem. (2) Methods: In one rural and one urban health area in each of the 11 former provinces of the DRC, all households with a case of malaria in the 15 days preceding the survey were selected and the patients or caregivers were interviewed. Adherence to malaria treatment was assessed by self-declaration about its completion. Logistic regression was used to assess predictors. (3) Results: 1732 households participated. Quinine was the most used drug; adherence to artesunate–amodiaquine was the lowest and the main reason for treatment discontinuation was adverse reactions. Predictors of adherence were residence in an urban area, university education, catholic religion, and adoption of recommended behaviour towards a malaria case. Adherence was significantly lower for responders who obtained information on antimalarials from Community Health Workers (CHW). (4) Conclusions: Usage of recommended drugs and adherence to malaria treatment need to be promoted, especially in rural areas, and CHW involvement needs to be improved. Awareness messages need to be made accessible and comprehensible to poorly educated populations and churches need to be involved.

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“…In most developing countries, proper diagnosis of malaria has been a challenge due to the lack of testing equipment, few personnel to run diagnostic tests, and patients' self-medicating [32]- [34]. Machine learning can relieve this burden by providing a high-accuracy disease prediction tool that doesn't require expensive equipment or trained personnel to run.…”

Section: Discussionmentioning

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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Self-Medication Practice against Malaria and Associated Factors in the City of Parakou in Northern Benin: Results of a Population Survey in 2017

Cited by 8 publications

References 14 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

Determinants of Patients’ Adherence to Malaria Treatment in the Democratic Republic of the Congo

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

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