Determinants of quality in home-based management of malaria by community health volunteers in rural Kenya

Marita, Enock; Gichuki, Richard; Watulo, Elda; Thiam, Sylla; Karanja, Sarah

doi:10.3855/jidc.13565

Cited by 3 publications

(2 citation statements)

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“…Moreover, 70% of the population of Kenya who live in rural areas lives below poverty level [20]. Malignant tertian malaria caused by plasmodium falciparum species accounts for over 99% of malaria cases in Kenya [9].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence System for Malaria Diagnosis

Barracloug,

Were,

Mwangakala

et al. 2024

IJACSA

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Malaria threats have remained one of the major global health issues over the past decades specifically in lowmiddle income countries. 70% of the Kenya population lives in malaria endemic zones and the majority have barriers to access health services due to factors including lack of income, distance, and social culture. Despite various research efforts using blood smears under a microscope to combat malaria with advantages, this method is time consuming and needs skillful personnel. To effectively solve this issue, this study introduces a new method integrating InfoGainAttributeEval feature selection techniques and parameter tuning method based on Artificial Intelligence and Machine Learning (AIML) classifiers with features to diagnose types of malaria more accurately. The proposed method uses 100 features extracted from 4000 samples. Sets of experiments were conducted using Artificial Neural Network (ANNs), Naïve Bayes (NB), Random Forest (RF) classifiers and Ensemble methods (Meta Bagging, Random Committee Meta, and Voting). Naïve Bayes has the best result. It achieved 100% accuracy and built the model in 0.01 second. The results demonstrate that the proposed method can classify malaria types accurately and has the best result compared to the reported results in the field.

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Section: Introductionmentioning

confidence: 99%

“…A new annual World Malaria report from the World Health Organization has shown a dramatic rise in malaria deaths [19]. Besides, there is a lack of malaria diagnosis Apps stated by Marita et al [9].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence System for Malaria Diagnosis

Barracloug,

Were,

Mwangakala

et al. 2024

IJACSA

View full text Add to dashboard Cite

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Community case management of malaria in Western Kenya: performance of community health volunteers in active malaria case surveillance

Otambo¹,

Ochwedo²,

Omondi³

et al. 2023

Malar J

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Background In western Kenya, not all malaria cases are reported as stipulated in the community case management of malaria (CCMm) strategy. This underreporting affects the equity distribution of malaria commodities and the evaluation of interventions. The current study aimed to evaluate the effectiveness of community health volunteers’ active case detection and management of malaria in western Kenya. Methods Cross-sectional active case detection (ACD) of malaria survey was carried out between May and August 2021 in three eco-epidemiologically distinct zones in Kisumu, western Kenya: Kano Plains, Lowland lakeshore and Highland Plateau. The CHVs conducted biweekly ACD of malaria household visits to interview and examine residents for febrile illness. The Community Health Volunteers (CHVs) performance during the ACD of malaria was observed and interviews done using structured questionnaires. Results Of the total 28,800 surveyed, 2597 (9%) had fever and associated malaria symptoms. Eco-epidemiological zones, gender, age group, axillary body temperature, bed net use, travel history, and survey month all had a significant association with malaria febrile illness (p < 0.05). The qualification of the CHV had a significant influence on the quality of their service. The number of health trainings received by the CHVs was significantly related to the correctness of using job aid (χ2 = 6.261, df = 1, p = 0.012) and safety procedures during the ACD activity (χ2 = 4.114, df = 1, p = 0.043). Male CHVs were more likely than female CHVs to correctly refer RDT-negative febrile residents to a health facility for further treatment (OR = 3.94, 95% CI = 1.85–5.44, p < 0.0001). Most of RDT-negative febrile residents who were correctly referred to the health facility came from the clusters with a CHV having 10 years of experience or more (OR = 1.29, 95% CI = 1.05–1.57, p = 0.016). Febrile residents in clusters managed by CHVs with more than 10 years of experience (OR = 1.82, 95% CI = 1.43–2.31, p < 0.0001), who had a secondary education (OR = 1.53, 95% CI = 1.27–1.85, p < 0.0001), and were over the age of 50 (OR = 1.44, 95% CI = 1.18–1.76, p < 0.0001), were more likely to seek malaria treatment in public hospitals. All RDT positive febrile residents were given anti-malarial by the CHVs, and RDT negatives were referred to the nearest health facility for further treatment. Conclusions The CHV’s years of experience, education level, and age had a significant influence on their service quality. Understanding the qualifications of CHVs can assist healthcare systems and policymakers in designing effective interventions that assist CHVs in providing high-quality services to their communities.

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Proactive home-based malaria management in rural communities of Bassar Health District in northern Togo from 2014 to 2017: PECADOM + , a pilot experiment

Bakai,

Gense,

Vanhems

et al. 2024

Malar J

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Background Togo's National Malaria Control Programme has initiated an active home-based malaria management model for all age groups in rural areas of Bassar Health District. This report describes the model, reports its main results, and determines the factors associated with positive rapid diagnostic test results. Methods From 2014 to 2017, in three peripheral care units of Bassar Health District (Binaparba, Nangbani, and Baghan), community health workers visited residents' homes weekly to identify patients with malaria symptoms, perform rapid diagnostic tests in symptomatic patients, and give medication to positive cases. Univariate and multivariate logistic regression models were used to determine the factors associated with positive tests. Results The study covered 11,337 people (817 in 2014, 1804 in 2015, 2638 in 2016, and 6078 in 2017). The overall mean age was 18 years (95% CI 5–29; min–max: 0–112 years). The median age was 10 years (SD: 16.9). The proportions of people tested positive were 75.3% in Binaparba, 77.4% in Nangbani, and 56.6% in Baghan. The 5–10 age group was the most affected category (24.2% positive tests). Positive tests were more frequent during the rainy than during the dry season (62 vs. 38%) and the probability of positive test was 1.76 times higher during the rainy than during the dry season (adjusted OR = 1.74; 95% CI 1.60–1.90). A fever (37.5 °C or higher) increased significantly the probability of positive test (adjusted OR = 2.19; 95% CI 1.89–2.54). The risk of positive test was 1.89 times higher in passive than in active malaria detection (adjusted OR = 1.89; 95% CI 1.73–2.0). Conclusions This novel experimental community and home-based malaria management in Togo suggested that active detection of malaria cases is feasible within 24 h, which allows rapid treatments before progression to often-fatal complications. This PECADOM + program will help Togo's National Malaria Control Programme reduce malaria morbidity and mortality in remote and hard-to-reach communities.

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Determinants of quality in home-based management of malaria by community health volunteers in rural Kenya

Cited by 3 publications

References 10 publications

Artificial Intelligence System for Malaria Diagnosis

Artificial Intelligence System for Malaria Diagnosis

Community case management of malaria in Western Kenya: performance of community health volunteers in active malaria case surveillance

Proactive home-based malaria management in rural communities of Bassar Health District in northern Togo from 2014 to 2017: PECADOM + , a pilot experiment

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