Diagnosis Of Malaria Research Articles

Health workers’ adherence to malaria case management protocols in Northern Sudan: a qualitative study

BackgroundNonadherence to national standards for malaria diagnosis and treatment has been reported in Sudan. In this study, qualitative research examined the clinical domains of nonadherence, factors influencing nonadherent practices and health workers’ views on how to improve adherence.MethodsIn September 2023, five Focus Group Discussions (FGDs) were undertaken with 104 health workers from 42 health facilities in Sudan’s Northern State. The participants included medical assistants, doctors, nurses, laboratory personnel, pharmacists and public health officers. The FGDs followed a semi-structured guide reflecting the national malaria case management protocol. Qualitative thematic analysis was performed.ResultsNonadherent practices included disregarding parasitological test results, suboptimal paediatric artemether–lumefantrine (AL) dosing, lack of counselling, use of prohibited artemether injections for uncomplicated and severe malaria, artesunate dose approximations and suboptimal preparations, lack of AL follow on treatment for severe malaria; and rare use of primaquine for radical Plasmodium vivax treatment and dihydroartemisinin-piperaquine as the second-line treatment for uncomplicated malaria. Factors influencing nonadherence included stock-outs of anti-malarials and RDTs; staff shortages; lack of training, job aids and supervision; malpractice by specialists; distrust of malaria microscopy and RDTs; and patient pressure for diagnosis and treatment. Health workers recommended strengthening the supply chain; hiring personnel; providing in-service protocol training including specialists; establishing external quality assurance for malaria diagnosis; and providing onsite supportive supervision and public health campaigns.ConclusionsThis study revealed a broad spectrum of behavioural and systemic challenges in malaria management among frontline health workers in Northern Sudan, including nonadherence to protocols due to resource shortages, training gaps, a lack of supportive supervision and patient pressure. These insights, including health workers’ views about improvements, will inform evidence-based interventions by Sudan’s National Malaria Control Programme to improve health systems readiness and the quality of malaria case management.

Classification Of Malaria Types Using Naïve Bayes Classification

This study was conducted to determine the level of accuracy of the naïve bayes classification method in the process of determining the group type of malaria. This method is used to predict the category of malaria based on the symptoms displayed. In this study, the dataset used was divided into 60% for training and 40% for testing. The results showed that the naïve bayes algorithm had an accuracy rate of 99.8% in predicting malaria categories. Evaluation of model performance using confusion matrix and ROC curve also showed good results, with classification accuracy of 0.998, error 0.002, and AUC 0.999. The results of the classification report show that the Quartana, Tertiana, and Tropica categories are more dominant than the Ovale category, based on precision, recall, and f1-score. These results show that the naïve bayes classification method is effective in classifying types of malaria and can be used as an aid in the diagnosis of malaria.

#2624 Point of care creatinine for early detection of community acquired acute kidney injury in a primary health care centre in Nigeria

Abstract Background and Aims Community acquired Acute kidney injury (AKI) and leads to poor outcomes in low and middle-income countries due to delayed diagnosis however its epidemiology is poorly studied due to lack of biochemical diagnosis. Supported by the International Society of Nephrology, we evaluated the accuracy of point of care creatinine (POC Cr) technology using capillary samples [1] and its use implementing a clinical algorithm to select patients at risk of AKI for POC Cr testing in the Hospital Emergency Department in Port Harcourt in Nigeria. In this phase of the project POC Cr was used in a large primary care health centre for early detection and management of community acquired AKI. Method The study is conducted in Ozuoba Model Comprehensive Primary Health Care Centre. The centre has 4148 patient attendances per month (20.3% medical, 18.8% obstetrics and 60.8% paediatrics) with 11.1% considered acute requiring short admission at the centre usually for intravenous fluids. Historically, decision making is based largely on clinical judgement and renal function tests when requested from external laboratories are reported in > 48 hours. During this project POC Cr was offered on high-risk patients based on the clinical algorithm from the previous stage (Fig. 1) and patients with detected AKI were offered short admission for intravenous fluids and antibiotics. Results To date 54 patients have been screened with POC Cr. Median age is 25.5 years, minimum 2 months, maximum 85 years, interquartile range (48-10), 62% are females, 57% presented with malaria diagnosis and 9% with gastroenteritis. Mean (standard deviation) of POC Cr was 105 (54) µmol/L. There were 5 cases (9%) of AKI (Cr >150 µmol) detected in patients aged 5 months, 13 months, 7 years, 19 years and 31 years. All were treated with short admission and intravenous fluids and were discharged after clinical improvement. Knowledge of the presence of AKI altered management in all patients prompting short admission. Conclusion Community acquired AKI is common in young patients with malaria and early biochemical diagnosis can improve outcomes. The study continues to recruit and aims to investigate the characteristics of community acquired AKI in Africa and to contribute to the design of optimal and sustainable pathway for early detection and management of AKI.

The new guideline for gastrointestinal infections

Gastrointestinal infections are still responsible for around 60% of the infectious diseases that must be reported in Germany and are probably among the most common gastroenterological diseases. The main therapy for gastrointestinal infections remains oral fluid replacement. The recommendations for Clostridioides difficile infections (CDI) have been adapted according to the current data and based on international guidelines; vancomycin or, especially if there is an increased risk of recurrence, fidaxomicin should now be used primarily in CDI. In the case of febrile diarrhea and/or bloody diarrhea, malaria diagnosis should be carried out immediately.

Biological threats to global malaria elimination II Deletion in the malaria rapid diagnostic test target Plasmodium falciparum histidine-rich protein 2/3 genes

The global malaria epidemic is still severe. Because of simple procedures, rapid detection and accuracy results, rapid diagnostic test (RDT) has become the most important and the most widely used diagnostic tool for malaria prevention and control. However, deletions in the RDT target Plasmodium falciparum histidine-rich protein 2/3 (Pfhrp2/3) genes may cause false-negative results of RDT, which has been included as one of the four biological threats to global malaria elimination. This article reviews the applications of RDT in the global malaria diagnosis, analyzes the threats and challenges caused by Pfhrp2/3 gene deletion, proposes methods for monitoring Pfhrp2/3 gene deletion, and summarizes the causes and countermeasures of negative RDT detections, so as to provide insights into consolidation of malaria elimination achievements in China and contributions to global malaria elimination.

Cost analyses for malaria molecular diagnosis for research planners in India and beyond

ABSTRACT Background Malaria elimination mandates early and accurate diagnosis of infection. Although malaria diagnosis is programmatically dependent on microscopy/RDTs, molecular diagnosis has much better diagnostic accuracy. Higher cost of molecular diagnoses is a recognized challenge for use at the point of care. Because funding is always a recognized constraint, we performed financial cost-analyses of available molecular platforms for better utilization of available budget. Methods Two strategies were applied to deduce the cost per sample. Strategy 1 included recurring components (RC) in minimum pack size, and biologist’s time whereas strategy 2 included only RC and non-recurring components and costs are calculated for sample sizes (1–1,000,000) to infer the sample size effect. Results Spin column-based manual DNA extraction (US$ 3.93 per sample) is the lowest-cost method, followed by magnetic bead-based automated, semi-automated, and PCI-based manual method. Further, DNA extraction cost per sample via spin column-based manual method and semi-automated method decreases with an increase in sample size up to 10,000. Real-time PCRs are ~ 2-fold more economical than conventional PCR, regardless of sample size. Conclusions This study is the first for malaria to estimate systematic molecular diagnosis financial costs. Kit-based and automated methods may replace conventional DNA extraction and amplification methods for a frugal high-throughput diagnosis.

A digital microscope for the diagnosis of Plasmodium falciparum and Plasmodium vivax, including P. falciparum with hrp2/hrp3 deletion.

Sensitive and accurate malaria diagnosis is required for case management to accelerate control efforts. Diagnosis is particularly challenging where multiple Plasmodium species are endemic, and where P. falciparum hrp2/3 deletions are frequent. The Noul miLab is a fully automated portable digital microscope that prepares a blood film from a droplet of blood, followed by staining and detection of parasites by an algorithm. Infected red blood cells are displayed on the screen of the instrument. Time-to-result is approximately 20 minutes, with less than two minutes hands-on time. We evaluated the miLab among 659 suspected malaria patients in Gondar, Ethiopia, where P. falciparum and P. vivax are endemic, and the frequency of hrp2/3 deletions is high, and 991 patients in Ghana, where P. falciparum transmission is intense. Across both countries combined, the sensitivity of the miLab for P. falciparum was 94.3% at densities >200 parasites/μL by qPCR, and 83% at densities >20 parasites/μL. The miLab was more sensitive than local microscopy, and comparable to RDT. In Ethiopia, the miLab diagnosed 51/52 (98.1%) of P. falciparum infections with hrp2 deletion at densities >20 parasites/μL. Specificity of the miLab was 94.0%. For P. vivax diagnosis in Ethiopia, the sensitivity of the miLab was 97.0% at densities >200 parasites/μL (RDT: 76.8%, microscopy: 67.0%), 93.9% at densities >20 parasites/μL, and specificity was 97.6%. In Ethiopia, where P. falciparum and P. vivax were frequent, the miLab assigned the wrong species to 15/195 mono-infections at densities >20 parasites/μL by qPCR, and identified only 5/18 mixed-species infections correctly. In conclusion, the miLab was more sensitive than microscopy and thus is a valuable addition to the toolkit for malaria diagnosis, particularly for areas with high frequencies of hrp2/3 deletions.

Clinical and Laboratory Diagnosis of Malaria in the Context of Imported Parasitosis Incidence in the Republic of Belarus

Clinical and Laboratory Diagnosis of Malaria in the Context of Imported Parasitosis Incidence in the Republic of Belarus

Sensitivity Assessment of a Multiplex and Real-Time PCR Protocols for the Detection of Malaria in External Quality Control Samples in the Malaria Reference Center in Greece

Background: Accurate malaria diagnosis constitutes a challenging task, necessitating the need for the implementation of targeted and effective diagnostic tools. The purpose of the current study was to evaluate the effectiveness of two different molecular methodologies in terms of sensitivity for the detection of External Quality Assessment (EQA) Plasmodium samples. Methods: A total of 104 lyophilized blood samples from 14 different UK-NEQAS (National External Quality Assessment Site) (2016–2021) and eight WHO-NEQAS distributions (2017–2020) were analyzed. An in-house multiplex PCR protocol, followed by single target real-time PCR protocols for all five Plasmodium species, was implemented. Results: The multiplex PCR had a success rate of 10/16 and 20/28 for P. vivax and P. falciparum species, respectively. On the other hand, the respective results for real-time PCR had a success rate of 13/16 (P. vivax), 28/28 (P. falciparum), 5/8 (P. malariae), 8/10 (P. ovale), and 10/14 (P. knowlesi). Plasmodium falciparum samples displayed the highest sensitivity of detection, 0.02 parasites/μL. Plasmodium vivax samples displayed a 0.1 parasites/μL cutoff value, greater than the respective value for whole blood samples, while P. ovale species displayed a respective cutoff value of 0.05 parasites/μL. Due to the limited number of tested samples, data obtained for P. malariae and P. knowlesi species samples were inconclusive. Conclusions: Real-time PCR comprises a credible molecular methodology in terms of sensitivity assessment and detection of low parasitemia levels of Plasmodium sp. in EQA lyophilized blood samples.

Mixed-method evaluation study of a targeted mass drug administration of long-acting anti-malarials among children aged 3 months to 15 years in the Bossangoa sub-prefecture, Ouham, Central African Republic, during the COVID-19 pandemic

BackgroundIn 2020, during the COVID-19 pandemic, Médecins Sans Frontières (MSF) initiated three cycles of dihydroartemisin-piperaquine (DHA-PQ) mass drug administration (MDA) for children aged three months to 15 years within Bossangoa sub-prefecture, Central African Republic. Coverage, clinical impact, and community members perspectives were evaluated to inform the use of MDAs in humanitarian emergencies.MethodsA household survey was undertaken after the MDA focusing on participation, recent illness among eligible children, and household satisfaction. Using routine surveillance data, the reduction during the MDA period compared to the same period of preceding two years in consultations, malaria diagnoses, malaria rapid diagnostic test (RDT) positivity in three MSF community healthcare facilities (HFs), and the reduction in severe malaria admissions at the regional hospital were estimated. Twenty-seven focus groups discussions (FGDs) with community members were conducted.ResultsOverall coverage based on the MDA card or verbal report was 94.3% (95% confidence interval (CI): 86.3–97.8%). Among participants of the household survey, 2.6% (95% CI 1.6–40.3%) of round 3 MDA participants experienced illness in the preceding four weeks compared to 30.6% (95% CI 22.1–40.8%) of MDA non-participants. One community HF experienced a 54.5% (95% CI 50.8–57.9) reduction in consultations, a 73.7% (95% CI 70.5–76.5) reduction in malaria diagnoses, and 42.9% (95% CI 36.0–49.0) reduction in the proportion of positive RDTs among children under five. A second community HF experienced an increase in consultations (+ 15.1% (− 23.3 to 7.5)) and stable malaria diagnoses (4.2% (3.9–11.6)). A third community HF experienced an increase in consultations (+ 41.1% (95% CI 51.2–31.8) and malaria diagnoses (+ 37.3% (95% CI 47.4–27.9)). There were a 25.2% (95% CI 2.0–42.8) reduction in hospital admissions with severe malaria among children under five from the MDA area. FGDs revealed community members perceived less illness among children because of the MDA, as well as fewer hospitalizations. Other indirect benefits such as reduced household expenditure on healthcare were also described.ConclusionThe MDA achieved high coverage and community acceptance. While some positive health impact was observed, it was resource intensive, particularly in this rural context. The priority for malaria control in humanitarian contexts should remain diagnosis and treatment. MDA may be additional tool where the context supports its implementation.

Epidemiological characteristics and diagnosis of imported Plasmodium malariae and Plasmodium ovale malaria cases in five provinces of China from 2014 to 2021

To investigate the epidemiological characteristics and diagnosis of imported Plasmodium malariae and P. ovale malaria cases in Anhui Province, Hubei Province, Zhejiang Province, Guangxi Zhuang Autonomous Region and Henan Province from 2014 to 2021, so as to provide insights into malaria control in these five provinces. All data pertaining to malaria cases reported in five provinces of China were captured from Chinese Disease Control and Prevention Information System from 2014 to 2021, and the epidemiological characteristics of imported P. malariae and P. ovale malaria cases were analysed using a descriptive epidemiological method. The duration from onset of malaria to initial diagnosis, duration from initial diagnosis to definitive diagnosis, institutions of initial and definitive diagnoses, and proportion of correct malaria diagnosis at initial diagnosis were statistically analyzed. A total of 1 223 imported P. malariae and P. ovale malaria cases were reported in Anhui Province, Hubei Province, Zhejiang Province, Henan Province and Guangxi Zhuang Autonomous Region from 2014 to 2021, there were 158 P. malariae malaria cases (12.92%) and 1 065 P. ovale malaria cases (87.08%). Totally 98.53% (1 205/1 223) of the imported malaria cases were from Africa, with Angola (18.99%, 30/158), Nigeria (11.39%,18/158), Cameroon (10.76%, 17/158), Ghana (10.13%, 16/158) and the Democratic Republic of the Congo (10.13%,16/158) as predominant countries where P. malariae malaria cases were from, and Ghana (23.19%, 247/1 065), Cameroon (14.74%, 157/1 065), Nigeria (9.39%, 100/1 065) and Angola (6.95%, 74/1 065) as predominant countries where P. ovale malaria cases were from. There were significant differences in the duration from onset of malaria to initial diagnosis (χ2 = 27.673, P = 0.000) and duration from initial diagnosis to definitive diagnosis of P. malariae and P. ovale malaria cases (χ2 = 29.808, P = 0.000), and the proportions of correct initial diagnosis of P. malariae and P. ovale malaria cases were 38.61% (61/158) and 56.53% (602/1 065). There were 74.69% (118/158) of P. malariae malaria cases with definitive diagnosis in county-, city-, and province-level medical institutions, and 79.25% (844/1 065) of P. ovale malaria cases with definitive diagnosis in county- and city-level medical institutions and county-level centers for disease control and prevention. The imported P. malariae and P. ovale malaria cases in Anhui Province, Hubei Province, Zhejiang Province, Henan Province and Guangxi Zhuang Autonomous Region from 2014 to 2021 were mainly returned from Africa and the proportion of correct diagnosis of P. malariae and P. ovale malaria was low at initial diagnosis. Persistent improvements in the diagnostic capability of malaria are required in medical institutions.

Exploring the Impact of Batch Size on Deep Learning Artificial Intelligence Models for Malaria Detection.

Introduction Malaria is a major public health concern, especially in developing countries. Malaria often presents with recurrent fever, malaise, and other nonspecific symptoms mistaken for influenza. Light microscopy of peripheral blood smears is considered the gold standard diagnostic test for malaria. Delays in malaria diagnosis can increase morbidity and mortality. Microscopy can be time-consuming and limited by skilled labor, infrastructure, and interobserver variability. Artificial intelligence (AI)-based tools for diagnostic screening can automate blood smear analysis without relying on a trained technician. Convolutional neural networks (CNN), deep learning neural networks that can identify visual patterns, are being explored for use in abnormality detection in medical images. A parameter that can be optimized in CNN models is the batch size or the number of images used during model training at once in one forward and backward pass. The choice of batch size in developing CNN-based malaria screening tools can affect model accuracy, training speed, and, ultimately, clinical usability. This study explores the impact of batch size on CNN model accuracy for malaria detection from thin blood smear images. Methods We used the publicly available "NIH-NLM-ThinBloodSmearsPf" dataset from the United States National Library of Medicine, consisting of blood smear images forPlasmodium falciparum. The collection consists of 13,779 "parasitized" and 13,779 "uninfected" single-cell images. We created four datasets containing all images, each with unique randomized subsets of images for model testing. Using Python, four identical 10-layer CNN models were developed and trained with varying batch sizes for 10 epochs against all datasets, resulting in 16 sets of outputs. Model prediction accuracy, training time, and F1-score,an accuracy metric used to quantify model performance, were collected. Results All models produced F1-scores of 94%-96%, with 10 of 16 instances producing F1-scores of 95%. After averaging all four dataset outputs by batch size, we observed that, as batch size increased from 16 to 128, the average combined false positives plus false negatives increased by 15.4% (130-150), and the average model F1-score accuracy decreased by 1% (95.3%-94.3%). The average training time also decreased by 28.11% (1,556-1,119 seconds). Conclusion In each dataset, we observe an approximately 1% decrease in F1-score as the batch size was increased. Clinically, a 1% deviation at the population level can create a relatively significant impact on outcomes. Results from this study suggest that smaller batch sizes could improve accuracy in models with similar layer complexity and datasets, potentially resulting in better clinical outcomes. Reduced memory requirement for training also means that model training can be achieved with more economical hardware. Our findings suggest that smaller batch sizes could be evaluated for improvements in accuracy to help develop an AI model that could screen thin blood smears for malaria.

Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review

Despite the persistency of World Health Organization to eliminate malaria since 1987, malaria disease continues to pose a significant threat to global health. As the severity of malaria persists over the years, there is a critical need for an automated diagnosis system for more efficient diagnosis and effective treatment administration. To mitigate the increase in mosquito-borne diseases, there has been a heightened interest in the application of Artificial intelligence (AI), specifically deep learning. With the assistance of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework, an extensive review of current state of automated malaria diagnosis systems utilizing machine learning and deep learning approaches was performed across eight scientific databases, with 50 articles shortlisted from the years 2015–2023. Besides, identifying the research gaps, we synthesise the existing literature, analyse the outcomes, and explore the critical parameters that influence model performance. From the review, the prevailing models primarily focus on binary classification while disregarding cross-dataset validations and multi-stage classification. This gap challenges the delivering effective treatments, especially considering potential drug resistance. Established protocols and classification models are needed to anticipate the specific malaria species. The keywords in automated malaria diagnosis that we identified include machine learning, deep learning, transfer learning, and convolutional neural networks. Through examinations of the constraints in current methodologies, we provide valuable suggestions that could propel the field of automated malaria diagnosis. This systematic review provides a comprehensive overview, critical insights, and a roadmap for future research endeavours in this vital domain of healthcare.

Trends in imported infections among migrants and travellers to Spain: a decade of analysis through the +Redivi network (2012-2022).

High-speed global travel, increased trade, world population growth, migration, urbanization and climate change have favoured the emergence and spread of pathogens. We aimed to analyse the evolution of imported infections in Spain during 2012-2022 and the potential impact of some of the abovementioned factors on differential morbidity patterns. In this retrospective study (January 2012 to December 2022), we analysed data collected by the +Redivi network across 25 health centres. The network's standardised database records new cases of imported infections, including patient demographics, travel history, pre-travel advice and diagnostic information. To assess outcome rates over time and potential interactions, we constructed penalized weighted models to reduce the bias related to a low event rate and used weighted logistic regression for morbidity outcomes. We recorded 25 632 episodes, comprising 13 913 migrants, 4047 visiting friends and relatives (VFR) immigrants, 392 VFR travellers and 7280 travellers. Most immigrants came from South America (48.3%), Sub-Saharan Africa (28.5%), North Africa (6.6%), South Central Asia (5.4%) and Central America/Caribbean (5.3%). The most common regions visited by travellers were Sub-Saharan Africa (33.5%), South America (24.5%), Central America/Caribbean (13.5%), Southeast Asia (12%) and South Central Asia (10%). The proportion of diagnoses of malaria, strongyloidiasis and unspecified self-limiting febrile syndrome < 3weeks remained unchanged during the study period. An increased frequency of diagnosis was reported for schistosomiasis, blastocystosis, giardiasis, dengue, diarrhoea, new cases of HIV, latent and pulmonary tuberculosis, whereas a decrease was reported for syphilis, chikungunya fever, Chagas disease and eosinophilia. We detected interactions between time and sex or type of participant across the different diagnoses. Our study underscores the importance of epidemiological data in understanding infectious diseases dynamics among travellers and migrants, emphasizing how demographic shifts, migration trends and healthcare policies affect disease profiles. Comprehensive data play an essential role in enhancing public health policies and travel advice.

Malaria Slide Bank to Strengthen and Improve the Quality of Malaria Diagnosis: A National Slide Repository in India.

Malaria elimination is one of the top health care priorities in India, necessitating accessible and accurate diagnosis for effective treatment. A malaria slide bank in India is a collection of quality-controlled malaria-positive and -negative slides and is considered a vital asset for quality diagnosis. The collection of blood samples, preparation of blood smears, staining, quality control, molecular characterizations, and slide validation were carried out according to standard operating procedures in accordance with the WHO reference laboratory. The true count and parasite density per microliter were computed in accordance with WHO guidelines. Over 27 months, 48 batches (8,196 slides) were prepared. Overall, the majority of slide batches were Plasmodium vivax (45.9%; 22/48), followed by Plasmodium falciparum (25%; 12/48), malaria-negative infections (25%; 12/48), and mixed infections (4.1%; 2/48). All 48 batches passed internal validation by WHO-certified level-1 microscopists. For a batch, the true count was the median of the validators' counts (range, 111-280,795 parasites/µL). Except for mixed infections, the PCR results agreed with the verified microscopy results. Malaria slide bank slides would be a valuable tool for quality control, assurance, and microscopist training.

Access to quality-assured artemisinin-based combination therapy and associated factors among clients of selected private drug outlets in Uganda

BackgroundMalaria treatment in sub-Saharan Africa is faced with challenges including unreliable supply of efficacious agents, substandard medicines coupled with high price of artemisinin-based combinations. This affects access to effective treatment increasing risk of malaria parasite resistance development and adverse drug events. This study investigated access to quality-assured artemisinin-based combination therapy (QAACT) medicines among clients of selected private drug-outlets in Uganda.MethodsThis was a cross sectional study where exit interviews were conducted among clients of private drug outlets in low and high malaria transmission settings in Uganda. This study adapted the World Health Organization/Health Action International (WHO/HAI) standardized criteria. Data was collected using a validated questionnaire. Data entry screen with checks was created in Epi-data ver 4.2 software and data entered in duplicate. Data was transferred to STATA ver 14.0 and cleaned prior to analysis. The analysis was done at 95% level of significance.ResultsA total of 1114 exit interviews were conducted among systematically sampled drug outlet clients. Over half, 54.9% (611/1114) of the participants were males. Majority, 97.2% (1083/1114) purchased an artemisinin-based combination anti-malarial. Most, 55.5% (618/1114) of the participants had a laboratory diagnosis of malaria. Majority, 77.9% (868/1114) of the participants obtained anti-malarial agents without a prescription. Less than a third, 27.7% (309/1114) of the participants obtained a QAACT. Of the participants who obtained QAACT, more than half 56.9% (173/309) reported finding the medicine expensive. The predictors of accessing a QAACT anti-malarial among drug outlet clients include type of drug outlet visited (aPR = 0.74; 95%CI 0.6, 0.91), not obtaining full dose (3-day treatment) of ACT (aPR = 0.49; 95%CI 0.33, 0.73), not finding the ACT expensive (aPR = 1.24; 95%CI 1.03, 1.49), post-primary education (aPR = 1.29; 95%CI 1.07,1.56), business occupation (aPR = 1.24; 95%CI 1.02,1.50) and not having a prescription (aPR = 0.76; 95%CI 0.63, 0.92).ConclusionLess than a third of the private drug outlet clients obtained a QAACT for management of malaria symptoms. Individuals who did not find artemisinin-based combinations to be expensive were more likely to obtain a QAACT anti-malarial. The Ministry of Health needs to conduct regular surveillance to monitor accessibility of QAACT anti-malarial agents under the current private sector copayment mechanism.

Fidelity of implementation of national guidelines on malaria diagnosis for children under-five years in Rivers State, Nigeria

BackgroundMalaria is still a disease of global public health importance and children under-five years of age are the most vulnerable to the disease. Nigeria adopted the “test and treat” strategy in the national malaria guidelines as one of the ways to control malaria transmission. The level of adherence to the guidelines is an important indicator for the success or failure of the country’s roadmap to malaria elimination by 2030. This study aimed to assess the fidelity of implementation of the national guidelines on malaria diagnosis for children under-five years and examine its associated moderating factors in health care facilities in Rivers State, Nigeria.MethodsThis was a descriptive, cross-sectional study conducted in Port Harcourt metropolis. Data were collected from 147 public, formal private and informal private health care facilities. The study used a questionnaire developed based on Carroll’s Conceptual Framework for Implementation Fidelity. Frequency, mean and median scores for implementation fidelity and its associated factors were calculated. Associations between fidelity and the measured predictors were examined using Mann Whitney U test, Kruskal Wallis test, and multiple linear regression modelling using robust estimation of errors. Regression results are presented in adjusted coefficient (β) and 95% confidence intervals.ResultsThe median (IQR) score fidelity score for all participants was 65% (43.3, 85). Informal private facilities (proprietary patent medicine vendors) had the lowest fidelity scores (47%) compared to formal private (69%) and public health facilities (79%). Intervention complexity had a statistically significant inverse relationship to implementation fidelity (β = − 1.89 [− 3.42, − 0.34]). Increase in participant responsiveness (β = 8.57 [4.83, 12.32]) and the type of malaria test offered at the facility (e.g., RDT vs. no test, β = 16.90 [6.78, 27.03]; microscopy vs. no test, β = 21.88 [13.60, 30.16]) were positively associated with fidelity score.ConclusionsThis study showed that core elements of the “test and treat” strategy, such as testing all suspected cases with approved diagnostic methods before treatment, are still not fully implemented by health facilities. There is a need for strategies to increase fidelity, especially in the informal private health sector, for malaria elimination programme outcomes to be achieved.

A qualitative study on determinants of the use of malaria rapid diagnostic test and anti-malarial drug prescription practices by primary healthcare workers in Ebonyi state, Nigeria

BackgroundThe increased availability and use of malaria rapid diagnostic test (RDT) by primary healthcare (PHC) workers has made universal diagnostic testing before malaria treatment more feasible. However, to meaningfully resolve the problem of over-treatment with artemisinin-based combination therapy and the heightened risk of selection pressure and drug resistance, there should be appropriate response (non-prescription of anti-malarial drugs) following a negative RDT result by PHC workers. This study explored the determinants of the use of RDT and anti-malarial drug prescription practices by PHC workers in Ebonyi state, Nigeria.MethodsBetween March 2 and 10, 2020, three focus group discussions were conducted in English with 23 purposively-selected consenting PHC workers involved in the diagnosis and treatment of malaria. Data was analysed thematically as informed by the method by Braun and Clarke.ResultsThe determinants of the use of RDT for malaria diagnosis were systemic (RDT availability and patient load), provider related (confidence in RDT and the desire to make correct diagnosis, PHC worker’s knowledge and training, and fear to prick a patient), client related (fear of needle prick and refusal to receive RDT, and self-diagnosis of malaria, based on symptoms, and insistence on not receiving RDT), and RDT-related (the ease of conducting and interpreting RDT). The determinants of anti-malarial drug prescription practices were systemic (drug availability and cost) and drug related (effectiveness and side-effects of the drugs). The determinants of the prescription of anti-malarial drugs following negative RDT were provider related (the desire to make more money and limited confidence in RDT) and clients’ demand while unnecessary co-prescription of antibiotics with anti-malarial drugs following positive RDT was determined by the desire to make more money.ConclusionsThis evidence highlights many systemic, provider, client, and RDT/drug related determinants of PHC workers’ use of RDT and anti-malarial drug prescription practices that should provide tailored guidance for relevant health policy actions in Ebonyi state, Nigeria, and similar settings.

Malaria Parasite Detection in Microscopic Blood Smear Images using Deep Learning Approach

Malaria remains a significant global health concern, posing formidable challenges to healthcare systems. Conventional diagnostic methods rely on manual examination of blood smears under a microscope, a process prone to inefficiencies and subjectivity. Despite prior attempts to leverage Deep Learning algorithms for malaria diagnosis, practical performance has often fallen short. This paper presents a novel machine learning model centred on Convolutional Neural Networks (CNNs) designed to automate the classification and prediction of infected cells in thin blood smears on standard microscope slides. Through rigorous ten-fold cross-validation with 27,558 single-cell images. This paper reviews various image processing techniques employed for the detection of malaria infection in humans, presenting a comparative analysis of these methods

Editorial: Challenges for diagnosis, treatment, and elimination of malaria

Editorial: Challenges for diagnosis, treatment, and elimination of malaria