An epidemiological analysis of severe imported malaria infections in Sri Lanka, after malaria elimination.

BACKGROUND: Imported malaria continues to be reported in Sri Lanka after it was eliminated in 2012, and a few progress to life-threatening severe malaria. METHODS: Data on imported malaria cases reported in Sri Lanka from 2013 to 2023 were extracted from the national malaria database maintained by the Anti Malaria Campaign (AMC) of Sri Lanka. Case data of severe malaria as defined by the World Health Organization were analysed with regard to patients' general characteristics and their health-seeking behaviour, and the latter compared with that of uncomplicated malaria patients. Details of the last three cases of severe malaria in 2023 are presented. RESULTS: 532 imported malaria cases were diagnosed over 11 years (2013-2023); 46 (8.6%) were severe malaria, of which 45 were Plasmodium falciparum and one Plasmodium vivax. Most severe malaria infections were acquired in Africa. All but one were males, and a majority (87%) were 26-60 years of age. They were mainly Sri Lankan nationals (82.6%). Just over half (56.5%) were treated at government hospitals. The average time between arrival of the person in Sri Lanka and onset of illness was 4 days. 29 cases of severe malaria were compared with 165 uncomplicated malaria cases reported from 2015 to 2023. On average both severe and uncomplicated malaria patients consulted a physician equally early (mean = 1 day) with 93.3% of severe malaria doing so within 3 days. However, the time from the point of consulting a physician to diagnosis of malaria was significantly longer (median 4 days) in severe malaria patients compared to uncomplicated patients (median 1 day) (p = 0.012) as was the time from onset of illness to diagnosis (p = 0.042). All severe patients recovered without sequelae except for one who died. CONCLUSIONS: The risk of severe malaria among imported cases increases significantly beyond 5 days from the onset of symptoms. Although patients consult a physician early, malaria diagnosis tends to be delayed by physicians because it is now a rare disease. Good access to expert clinical care has maintained case fatality rates of severe malaria at par with those reported elsewhere.

[Collaboration between clinical and public health services: an important guarantee for consolidating malaria elimination achievements].

There are still multiple challenges in China during the malaria post-elimination phase, including a large number of imported malaria cases with widespread distribution, low awareness of timely healthcare seeking, insufficient malaria diagnosis and treatment capacity of medical institutions and insufficient malaria surveillance and response capability of disease control and prevention institutions. As the core technical institutions for preventing the re-establishment of malaria transmission, both medical institutions and disease control and prevention institutions are required to enhance the collaboration between clinical and public health services, improve the malaria diagnosis and quality management system, intensify case identification and epidemiological investigations, and improve the management mechanism of antimalarial drug reserves. In addition, doctors are encouraged to become the main force in the health education and promotion of malaria prevention to improve the public health literacy. These approaches are recommended to improve the overall capability of timely identification, standardized treatment and effective response of imported malaria cases, so as to continuously consolidate the malaria elimination achievements in China.

Imported Malaria at the Ibn Sina University Hospital in Rabat: A Retrospective Study of 81 Cases.

Background In parallel with the eradication of indigenous malaria since 2005 and the certification of Morocco as a malaria-free country by the World Health Organization in 2010, imported malaria cases are still being notified in Morocco. This study aims to describe the epidemiological profile and characterize the demographic, clinical, and biological profile of imported malaria cases diagnosed at the Central Laboratory of Parasitology-Mycology of the Ibn Sina University Hospital in Rabat, Morocco. Methodology This retrospective study analyzed 81 cases of imported malaria at Ibn Sina University Hospital's Central Laboratory of Parasitology-Mycology in Rabat, Morocco from January 2015 to December 2023. Patients meeting the inclusion criteria had contracted malaria in endemic regions, confirmed through parasitological evidence on blood smears. Results Among the 81 positive cases, 55 (63%) were male, resulting in a male-to-female ratio of approximately 3:1. The imported cases came from 15 countries in sub-Saharan Africa, mainly from Ivory Coast (31 patients, 31%) and Guinea (16 patients, 16%). The main clinical sign was fever (79 patients, 97.53%). The majority of patients (70 patients, 86%) suffered from anemia, while thrombocytopenia was present in 76% of patients (62 patients). Plasmodium falciparum was the most common species found in 77 (95%) cases and Plasmodium ovale in two (2.5%) cases. However, Plasmodium vivax was isolated in only one (1.23%) case. Only one case of co-infection by P. falciparum and Plasmodium malariae (1.23%) was found. Parasitemia values due to P. falciparum were between 0.1% and 30%. On the other hand, those of other species did not exceed 2%. Conclusions In summary, among 81 imported malaria cases, 55 (63%) were men, imported mainly from 15 sub-Saharan African countries. P. falciparum was the predominant species. Fever was the most common clinical sign, accompanied by high rates of anemia and thrombocytopenia.

Pregnancy outcomes in women with imported malaria in mainland France: A retrospective study from 2004 to 2014.

OBJECTIVES: There is little data on pregnant women with imported malaria in high-income countries, especially regarding offspring outcomes. We wanted to determine pregnancy outcomes of imported malaria in pregnant women in mainland France. PATIENTS AND METHODS: We conducted a retrospective, descriptive study of outcomes in pregnant women hospitalized with malaria from 2004 to 2014 in two regions of mainland France. An adverse outcome was defined as a miscarriage, stillbirth, preterm birth (<35 weeks of gestation), low birth weight (LBW) defined as less than 2500 g, or congenital malaria. RESULTS: Of 60 pregnancies, 5 were excluded because of elective abortions; 55 were investigated, of which 11 were primigravidae and 44 multigravidae. Pregnancies were singleton (n = 51) or twin (n = 4). Mean age was 30.4 years (range:19-45 y). Among the 55 cases, 9 ended in a miscarriage (8 singletons and 1 twin pregnancy) and 1 had a stillbirth at 21 weeks of gestation, all immediately after the malarial episode. 45 gave birth (29 vaginal deliveries and 16 caesarean sections) to 48 (42 singletons and 6 twins) newborns. Amongst these, 30 were healthy full-term newborns, 10 had LBW, and 8 were preterm. Overall, 26 of 55 (47.3%) pregnancies, and 29 of 59 (49.2%) offsprings had adverse outcomes. Compared to singleton pregnancies, twin pregnancies were associated with adverse outcomes (p = 0.0438). CONCLUSIONS: Imported malaria has a severe impact on pregnancy outcomes. Prevention and management of imported malaria in pregnancy should be optimized.

Might procalcitonin help predict imported falciparum malaria in children?

BACKGROUND: Procalcitonin (PCT) has been proposed as a marker for malaria severity in adults, with a threshold of 10 ng/ml for severe falciparum disease. Whether PCT is useful in children is debated. PATIENTS & METHODS: A retrospective case-control study was conducted to compare initial PCT levels in children with uncomplicated malaria and a control group, and between children with uncomplicated and severe malaria. RESULTS: Results showed significantly higher PCT levels in malaria cases compared to the control group and in malaria severe cases compared to uncomplicated cases. A Receiving Operator Characteristic curve established a PCT threshold of 0.65 ng/ml with a negative predictive value of 98.8 % based on a prevalence of 10 %. Analyzing the pooled results of five studies suggested a threshold of 6.17 ng/ml for differentiating uncomplicated and severe malaria. CONCLUSION: PCT might be a useful tool to help rule out malaria and predict potential disease severity in returning travelers.

Neglected Time Intervals Before "1-3-7" Approach About Imported Malaria Cases - China, 2014-2021.

What is already known about this topic?: China's "1-3-7" approach outlines specific targets to guide and monitor the processes of case reporting, investigation, and response. However, few studies have examined the time intervals preceding the initial step, and the timeline from the arrival of imported malaria cases in China to their diagnosis has been largely overlooked. What is added by this report?: The study demonstrated that the median duration from arrival in China to the onset of symptoms for P. ovale was 78 days, with 71.59% of imported cases manifesting symptoms after more than one month. For P. vivax, the median interval was 42 days, with 55.91% exceeding one month. Additionally, the median time from symptom onset to malaria treatment in China between 2014 and 2021 was 2 days, with an interquartile range (IQR) of 1-4 days. What are the implications for public health practice?: This study represents the initial effort to delineate the chronology of imported malaria cases, from their arrival in China to their subsequent treatment. The results underscore the importance of providing malaria health education to populations arriving from overseas. Furthermore, enhancing physician training is crucial for improving the diagnosis of malaria.

A case of possible transfusion-acquired malaria in Zaragoza, Spain.

We present the case of a 75-year-old patient diagnosed with malaria, a native of Zaragoza, Spain, despite having no travel history to malaria-endemic regions. Following an extensive investigation, transfusion emerged as the most probable mode of transmission.

Establishing and applying an adaptive framework for imported malaria: a field practice in Anhui Province, China from 2012 to 2022.

BACKGROUND: Anhui Province is currently facing an increase in imported malaria cases as a result of globalization and international travel. In response, Anhui Province has implemented a comprehensive adaptive framework to effectively address this threat. METHODS: This study collected surveillance data from 2012 to 2022 in Anhui Province. Descriptive statistics were used to analyze the epidemiological characteristics of imported malaria cases. Additionally, multivariate logistic regression was employed to identify factors associated with severe malaria. Documents were reviewed to document the evolution of the adaptive framework designed to combat imported malaria. The effectiveness of the adaptive framework was evaluated based on the rates of timely medical visits, timely diagnosis, and species identification. RESULTS: During the study period, a total of 1008 imported malaria cases were reported across 77 out of 105 counties in Anhui Province, representing a coverage of 73.33%. It was found that 10.52% of imported cases went undiagnosed for more than seven days after onset. The multivariate analysis revealed several potential risk factors for severe malaria, including increasing age (OR = 1.049, 95%CI:1.015-1.083), occupation (waitperson vs. worker, OR = 2.698, 95%CI:1.054-6.906), a longer time interval between onset and the initial medical visit (OR = 1.061, 95%CI:1.011-1.114), and misdiagnosis during the first medical visit (OR = 5.167, 95%CI:2.535-10.533). Following the implementation of the adaptive framework, the rates of timely medical visits, timely diagnosis, and species identification reached 100.00%, 78.57%, and 100.00%, respectively. CONCLUSIONS: Anhui Province has successfully developed and implemented an adaptive framework for addressing imported malaria, focusing on robust surveillance, prompt diagnosis, and standardized treatment. The experiences gained from this initiative can serve as a valuable reference for other non-endemic areas.

Factors affecting delays in seeking treatment among malaria patients during the pre-certification phase in China.

BACKGROUND: Delays in malaria treatment can not only lead to severe and even life-threatening complications, but also foster transmission, putting more people at risk of infection. This study aimed to investigate the factors influencing treatment delays among malaria patients and their health-seeking behaviour. METHODS: The medical records of 494 patients diagnosed with malaria from 6 different malaria-endemic provinces in China were analysed. A bivariate and multivariable regression model was used to investigate the association between delays in seeking treatment and various factors. A Sankey diagram was used to visualize the trajectories of malaria patients seeking medical care. Total treatment delays were categorized as patient delays and doctor delays. RESULTS: The incidence of total delays in seeking malaria treatment was 81.6%, of which 28.4% were delayed by patients alone and 34.8% by doctors alone. The median time from the onset of symptoms to the initial healthcare consultation was 1 day. The median time from the initial healthcare consultation to the conclusive diagnosis was 2 day. After being subjected to multiple logistic regression analysis, living in central China was less likely to experience patient delays (OR = 0.43, 95% CI 0.24-0.78). The factors significantly associated with the lower likelihood of doctor delays included: age between 30 to 49 (OR = 0.43, 95% CI 0.23-0.81), being single/divorce/separated (OR = 0.48, 95% CI 0.24-0.95), first visiting a county-level health institution (OR = 0.25, 95% CI 0.14-0.45), first visiting a prefectural health institution (OR = 0.06, 95% CI 0.03-0.12) and first visiting a provincial health institution (OR = 0.05, 95%CI 0.02-0.12). Conversely, individuals with mixed infections (OR = 2.04, 95% CI 1.02-4.08) and those experiencing periodic symptoms (OR = 1.71, 95% CI 1.00-2.92) might face increased doctor delays. Furthermore, higher financial burden and complications were found to be associated with patient delays. Doctor delays, in addition to incurring these two consequences, were associated with longer hospital stays. CONCLUSION: There was a substantial delay in access to health care for malaria patients before China was certified malaria free. Region, marital status, periodic symptoms and the level of health institutions were factors contributing to delays in treatment-seeking among malaria patients.

Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

Nine years of imported malaria in a teaching hospital in Belgium: Demographics, clinical characteristics, and outcomes.

BACKGROUND: Imported malaria is often misdiagnosed due to the aspecific symptoms and lack of familiarity among clinicians. This study aims to evaluate a decade-long trend of imported malaria cases in a Belgian teaching hospital by analyzing demographics, clinical characteristics, and outcomes. METHODS: Medical records of 223 patients with confirmed malaria diagnoses between 2010 and 2019 were analyzed. RESULTS: Most patients were male (63.2%), aged 18-65 years (77.1%), and visiting friends or relatives (40.8%). Central Africa was the most common travel destination (54.3%), and 63.7% did not take prophylaxis. Symptoms were flu-like, with fever (91.9%) being most prevalent. P. falciparum was identified in 88.3% of cases. A high proportion of severe cases (41.7%) and a low mortality rate (0.9%) were recorded. A severe form of the disease is associated with a more extended hospital stay than uncomplicated form (median of 5 vs. 4 days, p < 0.001). Thirty-five-point five percent [33/93] of patients with severe malaria have had a previous malaria infection compared to 50.8% [66/130] of uncomplicated patients (p= 0.013) wich was statistically significant. CONCLUSION: Malaria disproportionately affects VFRs traveling to Central Africa, and flu-like symptoms should raise suspicion. Prophylaxis is essential to prevent the disease, and early diagnosis is critical for effective management. A severe form of the disease is associated with a more extended hospital stay than uncomplicated form and people with a previous history of malaria have a less severe disease.

A machine learning approach for early identification of patients with severe imported malaria.

BACKGROUND: The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting. METHODS: This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis. RESULTS: A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria. CONCLUSION: In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment.

Imported malaria: A 20-year retrospective study from a tertiary public hospital in Brussels, Belgium.

BACKGROUND: Malaria continues to cause a significant number of infections in non-endemic regions. In this paper, we describe the epidemiological trend and morbidity of imported malaria diagnosed in a tertiary hospital in Brussels. METHODS: We conducted a retrospective study describing a cohort of malaria episodes (in- and outpatients) at Centre Hospitalier Universitaire Saint-Pierre from 1998 to 2017. Epidemiological and clinical data were collected by reviewing medical files. RESULTS: A total of 1011 malaria episodes were analyzed. Median age at diagnosis was 35 years, and 66 % of patients were men (672/1011). Malaria cases significantly increased over the two decades (from 17 in 1998 to 79 in 2017). Plasmodium falciparum malaria was most often diagnosed (846/935, 89 %), primarily from Central (530/935, 57 %) and West Africa (324/935, 35 %). Many cases (383/764, 50 %) were diagnosed in patients "visiting friends and relatives". HIV-infected and other immunocompromised patients were significantly more likely to present with severe malaria (at least one severity criteria as defined by the WHO) compared to other patients (24/57, 42 % vs 138/732, 19 %, p < 0.01 and 15/21, 71 % vs 147/767, 19 %, p < 0.001). Severe malaria was diagnosed in 16.9 % and the mortality rate was low (5/1011, 0.5 %). CONCLUSION: Imported malaria increased over the years with a large, albeit stable number of cases diagnosed in patients visiting friends and relatives. These findings, along with the high rate of severe malaria in HIV and immunocompromised patients, underscore an urgent need for strengthened malaria surveillance and targeted preventive interventions.

Delayed care-seeking in international migrant workers with imported malaria in China.

BACKGROUND: Imported malaria cases continue to pose major challenges in China as well as in other countries that have achieved elimination. Early diagnosis and treatment of each imported malaria case is the key to successfully maintaining malaria elimination success. This study aimed to build an easy-to-use predictive nomogram to predict and intervene against delayed care-seeking among international migrant workers with imported malaria. METHODS: A prediction model was built based on cases with imported malaria from 2012 to 2019, in Jiangsu Province, China. Routine surveillance information (e.g. sex, age, symptoms, origin country and length of stay abroad), data on the place of initial care-seeking and the gross domestic product (GDP) of the destination city were extracted. Multivariate logistic regression was performed to identify independent predictors and a nomogram was established to predict the risk of delayed care-seeking. The discrimination and calibration of the nomogram was performed using area under the curve and calibration plots. In addition, four machine learning models were used to make a comparison. RESULTS: Of 2255 patients with imported malaria, 636 (28.2%) sought care within 24 h after symptom onset, and 577 (25.6%) sought care 3 days after symptom onset. Development of symptoms before entry into China, initial care-seeking from superior healthcare facilities and a higher GDP level of the destination city were significantly associated with delayed care-seeking among migrant workers with imported malaria. Based on these independent risk factors, an easy-to-use and intuitive nomogram was established. The calibration curves of the nomogram showed good consistency. CONCLUSIONS: The tool provides public health practitioners with a method for the early detection of delayed care-seeking risk among international migrant workers with imported malaria, which may be of significance in improving post-travel healthcare for labour migrants, reducing the risk of severe malaria, preventing malaria reintroduction and sustaining achievements in malaria elimination.

The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas.

Even if malaria is rare in Europe, it is a medical emergency and programs for its control should ensure both an early diagnosis and a prompt treatment within 24-48 h from the onset of the symptoms. The increasing number of imported malaria cases as well as the risk of the reintroduction of autochthonous cases encouraged laboratories in non-endemic countries to adopt diagnostic methods/algorithms. Microscopy remains the gold standard, but with limitations. Rapid diagnostic tests have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. As reported in the World Health Organization Global Technical Strategy for Malaria 2016-2030, the development of point-of-care testing is important for the improvement of diagnosis with beneficial consequences for prompt/accurate treatment and for preventing the spread of the disease. Despite their limitations, diagnostic methods contribute to the decline of malaria mortality. Recently, evidence suggested that artificial intelligence could be utilized for assisting pathologists in malaria diagnosis.

Length of stay in at-risk areas and time to malaria attack on return.

BACKGROUND: Experimental infection with Plasmodium falciparum results in malaria attack within a few days of exposure. However, we have regularly observed malaria attack within a short time after return, regardless of the time spent in an endemic area. We therefore aimed to assess whether the time before return and malaria attack varies according to length of stay. METHODS: We used anonymized data from the French National Reference Centre for Malaria between 2006 and 2016. We analyzed 11,823 cases aged at least 1 year and diagnosed with P. falciparum malaria 1 day to 1 year after returning to France, after a stay of 1 day to 1 year in an at-risk area. RESULTS: Trips had a median duration of 31 days [IQR: 19-56]. Median time between return from the endemic area and onset of malaria symptoms was 5 days [IQR: 0-10], and the median between return and malaria diagnosis was 9 days [IQR: 5-14]. Times to symptom onset or diagnosis were longer for stays of fewer than 15 days vs 15 days or more (for symptoms: 7 vs 4 days for longer stays, for diagnosis: 11 vs 9 days). For stays longer than 15 days, no variation was observed according to length of stay. CONCLUSIONS: Aside from at-risk stays of fewer than 15 days, the time between return and malaria attack is constant and rather short, even after long stays. The 2 weeks following return should be considered as a risk period whatever the length of stay in an at-risk area.

Artemisinin resistance-associated gene mutations in Plasmodium falciparum: A case study of severe malaria from Mozambique.

BACKGROUND: The effectiveness of artemisinin-based combination therapies (ACT) in treating Plasmodium falciparum, is vital for global malaria control efforts, particularly in sub-Saharan Africa. The examination of imported cases from endemic areas holds implications for malaria chemotherapy on a global scale. METHOD: A 45-year-old male presented with high fever, dry cough, diarrhoea and generalized muscle pain, following a two-week trip to Mozambique. P. falciparum infection with hiperparasitemia was confirmed and the patient was treated initially with quinine and doxycycline, then intravenous artesunate. To assess drug susceptibility, ex vivo half-maximal inhibitory concentration assays were conducted, and the isolated P. falciparum genome was deep sequenced. RESULTS: The clinical isolate exhibited elevated ex vivo half-maximal inhibitory concentration values to dihydroartemisinin, lumefantrine, mefloquine and piperaquine. Genomic analysis identified a I416V mutation in the P. falciparum Kelch13 (PF3D7_1343700) gene, and several mutations at the Kelch13 interaction candidate genes, pfkics (PF3D7_0813000, PF3D7_1138700, PF3D7_1246300), including the ubiquitin carboxyl-terminal hydrolase 1, pfubp1 (PF3D7_0104300). Mutations at the drug transporters and genes linked to next-generation antimalarial drug resistance were also present. CONCLUSIONS: This case highlights the emergence of P. falciparum strains carrying mutations in artemisinin resistance-associated genes in Mozambique, couple with a reduction in ex vivo susceptibility to ACT drugs. Continuous surveillance of mutations linked to drug resistance and regular monitoring of drug susceptibility are imperative to anticipate the spread of potential resistant strains emerging in Mozambique and to maintain effective malaria control strategies.

A malaria death due to an imported Plasmodium falciparum infection in Sri Lanka during the prevention of re-establishment phase of malaria.

BACKGROUND: Sri Lanka has maintained a rigorous programme to prevent the re-establishment of malaria ever since the disease was eliminated in October 2012. It includes efforts to sustain case surveillance to ensure early diagnosis and management of malaria. Yet, in April of 2023 the death occurred of an individual with imported malaria. CASE PRESENTATION: The deceased was a 37-year-old Sri Lankan male who returned to Sri Lanka on the 10th of April after a business trip to several countries including Tanzania. He was febrile on arrival and consulted three Allopathic Medical Practitioners in succession in his home town in the Western Province of Sri Lanka, over a period of 5 days starting from the very day that he arrived in the country. Malaria was not tested for at any of these consultations and his clinical condition deteriorated. On the evening of 14th of April he was admitted to the medical intensive care unit of a major private hospital in the capital city of Colombo with multiple organ failure. There, on a request by the treating physician blood was tested for malaria and reported early the next morning as Plasmodium falciparum malaria with a high parasitaemia (> 10%). The patient died shortly after on the 15th of April before any anti-malarial medication was administered. The deceased had been a frequent business traveller to Africa, but with no past history of malaria. He had not taken chemoprophylaxis for malaria on this or previous travels to Africa. DISCUSSION: The patient's P. falciparum infection progressed rapidly over 5 days of arriving in Sri Lanka leading to severe malaria without being diagnosed, despite him seeking healthcare from three different Medical Practitioners. Finally, a diagnosis of malaria was made on admission to an intensive care unit; the patient died before anti-malarial medicines were administered. CONCLUSIONS: This first death due to severe P. falciparum malaria reported in Sri Lanka after elimination of the disease was due to the delay in diagnosing malaria.

[Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning].

OBJECTIVE: To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. METHODS: Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. RESULTS: A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). CONCLUSIONS: Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.