Epidemiology and Laboratory Features of Imported Malaria in a Tertiary Care Hospital, Hangzhou, China: A 12-Year Retrospective Cohort (2013–2024)

Epidemiology and Laboratory Features of Imported Malaria in a Tertiary Care Hospital, Hangzhou, China: A 12-Year Retrospective Cohort (2013–2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiology and Laboratory Features of Imported Malaria in a Tertiary Care Hospital, Hangzhou, China: A 12-Year Retrospective Cohort (2013–2024) Kenv Pan, Wenyan Yu, Fengqin Wu, Yijun Yan, Yujiao Jin, Man Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8787994/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Malaria remains a significant public health threat globally, especially because of the increasing number of imported cases in non-endemic areas such as China, despite the successful local elimination of the disease. Methods This study aimed to investigate the epidemiological trends, clinical characteristics, and laboratory parameters of severe and uncomplicated imported malaria cases at Hangzhou Xixi Hospital, between January 2013 and December 2024. Results A retrospective analysis of 120 confirmed cases revealed that Plasmodium falciparum was the predominant species (77.5%), with 48.33% originating from West Africa, and severe cases constituting 89.2% of the total. Notably, the overlap with the COVID-19 pandemic, and severe cases were characterized by older age of patients, longer diagnostic delays, and significantly higher parasitemia levels (67.86% versus 15.22%). Laboratory findings indicated that severe cases exhibited lower hemoglobin and platelet counts, along with elevated inflammatory markers and impaired liver and kidney function. Conclusion This research underscores the complexities of managing imported malaria and emphasizes the urgent need for the development of comprehensive early warning systems incorporating epidemiological data, clinical manifestations, and laboratory biomarkers to facilitate early identification and treatment of severe cases. The findings contribute essential insights towards enhancing malaria prevention and control initiatives in low-risk, non-endemic areas, ultimately aiming for improved patient outcomes and public health safety. Imported malaria Severe malaria Uncomplicated malaria Epidemiological trends Clinical characteristics Laboratory parameters Diagnostic delay Figures Figure 1 Figure 2 Introduction In recent years, malaria has remained a significant public health concern worldwide, particularly posing a significant threat to human health in Africa and Southeast Asia. Although global malaria cases have decreased, transmission patterns and epidemiological characteristics continue to evolve as a result of increased globalization and climate change [ 1 , 2 ]. According to the World Health Organization’s World Malaria Report 2024, approximately 263 million cases occurred in 83 endemic countries worldwide in 2023. This represents an increase of 11 million cases compared to the previous year, with around 597,000 deaths globally. Notably, the African region accounted for 95% of these cases, which bear the heaviest burden [ 3 ]. In some non-endemic countries, the number of imported malaria cases each year has been rising, especially in countries with strong travel and business connections to high-transmission regions [ 4 ]. Therefore, vigilance should be maintained as efforts to track imported malaria are increased. Since 2017, China has not reported any local malaria cases [ 5 ]. Although local malaria has been successfully eradicated, the number of imported malaria cases has steadily increased because of increased international travel and rapid economic growth. Hangzhou, as an international city, has seen a rising number of imported malaria cases in recent years [ 6 ]. Relatively few epidemiological and clinical studies have been conducted on these patients, highlighting an important research gap. A systematic analysis of the epidemiological characteristics and laboratory data of imported malaria patients can provide valuable evidence to inform public health policy and enhance malaria control and prevention efforts. This study retrospectively examined imported malaria patients diagnosed at Xixi Hospital in Hangzhou from 2013 to 2024. The aim was to analyze the epidemiological features of both severe and uncomplicated malaria, as well as laboratory findings. We collected patients’ demographic data, travel history, clinical symptoms, and laboratory data. Through statistical analysis of the collected data, we identified potential risk factors associated with severe malaria. These findings provide evidence to support the development of targeted prevention and control strategies. Additionally, this study offers valuable insights for malaria prevention and control efforts worldwide. By conducting an in-depth analysis of the epidemiological and laboratory characteristics of imported malaria patients in Hangzhou, we aimed to strengthen the evidence base. Our goal was to support future prevention and control measures targeting imported malaria. Methods Study setting Hangzhou Xixi Hospital is a municipal Grade A, Class III public hospital in Zhejiang Province, China. Specializing in the diagnosis and treatment of infectious diseases, the hospital has received several key designations, including the Provincial Key Medical Discipline and the Provincial-Level Regional Specialized Disease Center. Medical records of all confirmed cases of Plasmodium species, including P. falciparum, P. vivax, P. ovale, and P. malariae, as well as cases of mixed infection identified by methods such as microscopy or polymerase chain reaction, were retrospectively collected and analyzed. These records covered both inpatient and outpatient settings at the hospital for infectious diseases from 1st January 2013 to 31st December 2024. Data collection The research team collected and analyzed data on the demographic characteristics of imported malaria cases at Hangzhou Xixi Hospital, including infection regions, travel purposes, the onset and duration of fever, types of complications, and laboratory test results. Infection regions were classified according to the United Nations geographical classification [ 7 ]. The attending physician determines medical interventions and the criteria for admission to and discharge from the intensive care unit (ICU). The severity of malaria is assessed according to the World Health Organization (WHO) Malaria Guidelines 2021[ 8 ]. Malaria complications are defined as conditions arising during the disease, excluding pre-existing health issues of the patient. These complications include cerebral complications, acute respiratory distress syndrome, shock, hemolysis, severe anemia, acidosis, severe kidney injury, liver damage, pulmonary oedema, and gastrointestinal problems [ 9 ]. Statistical analyses All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Chicago, IL, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Graphs were created with GraphPad Prism v9.0 (San Diego, CA, USA) and the "ggplot2" package in R. A two-sided p-value < 0.05 was considered statistically significant unless otherwise specified. Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation (SD) and compared using Student's t-test. Non-normally distributed data are presented as median (interquartile range, IQR) and compared using the Mann-Whitney U test. Categorical variables are expressed as frequencies (percentages) and compared using the chi-squared test or Fisher's exact test, as appropriate. The heatmap was generated using the "heatmap" package in R (version 1.0.12). Laboratory parameters were standardized using z-scores calculated as (value - mean)/SD for each parameter across all samples. Row-wise (parameter-wise) z-score standardization was applied to visualize relative deviations from the population mean. Hierarchical clustering was performed using Euclidean distance and complete linkage. The color gradient ranges from blue (z-score 2, markedly above normal). Ethics approval This retrospective study was approved by the Ethics Committee of Hangzhou Xixi Hospital (Approval No:2025-038), which waived the requirement for informed consent. Results General trend of imported malaria cases from 2013 to 2024 A total of 120 imported malaria cases admitted to Xixi Hospital in Hangzhou, Zhejiang Province, between 2013 and 2024 were included in this retrospective analysis. The annual proportions of cases varied, with rates of 9.9% (12/120) in 2016, 14.9% (18/120) in 2019, 16.5% (20/120) in 2021, and 14.9% (18/120) in 2024. In terms of species distribution, Plasmodium falciparum accounted for 77.5% (93/120) of cases, followed by cases of P. ovale (10.8%, 13/120), P. vivax (7.5%, 9/120), and P. malariae (4.17%, 5/120). Notably, the peak incidence occurred in 2021 (16.5%, 20/120), coinciding with the period of the COVID-19 pandemic, which mainly corresponded to increased cases of both P. falciparum and P. ovale infections (Fig. 1 ). Characteristics of demographic and epidemiological data distribution The demographic and clinical features of the 120 imported malaria cases are summarized in Table 1 . The mean age of patients was 40 years (range 30–50). Patients with severe malaria were significantly older than those with uncomplicated malaria, with a mean age of 46 years (range 37–58) compared to 38 years (range 29–48) for severe and uncomplicated malaria, respectively ( p = 0.03). In most cases, 95% (114/120) were men. By occupation, manual laborers and business travelers accounted for 54% (65/120) and 34% (41/120) of cases, respectively. Most patients were of Chinese nationality (87.5%,105/120), while the remaining 15 were foreign nationals, mainly international students. Geographically, nearly half of the cases (48.33%,58/120) were imported from West Africa. Regarding parasite load, parasitemia levels exceeded 10% of red blood cells in 27.5% (33/120) of cases, with a significantly higher proportion seen in severe malaria cases (67.86%, 19/28) compared to uncomplicated cases (15.22%, 14/92). Among the 28 severe malaria cases, Plasmodium falciparum was the most common species (89.2%, 25/28). The other cases involved P. vivax (7.14%, 2/28) and P. malariae (3.57%, 1/28) Table 1 Epidemiological characteristics of imported malaria cases during the period 2013 to 2024 Characteristics b Total (n = 120) severe malaria a (n = 28) uncomplicated malaria (n = 92) p value Male gender, n (%) 114(95.0%) 28(100%) 88(93.62%) 0.572 Age, median (IQR) 40(30–50) 46(37–58) 38(29–48) 0.03 Purpose of travel 0.463 Labor, n (%) 65/120(54.17%) 18/28(64.29%) 47/92(51.09%) Official duties or business, n (%) 41/120(34.17%) 8/28(28.57%) 33/92(35.87%) Students, n (%) 8/120(6.67%) 1/28(3.57%) 7/92(7.61%) Others, n (%) 6/120(5.00%) 1/28(3.57%) 5/92(5.43%) Nationality 0.156 China, n (%) 105/120(87.50%) 27/28(96.40%) 78/92(84.78%) Non-China, n (%) 15/120(12.50%) 1/28(3.57%) 14/92(15.22%) Country/region of acquisition 0.289 West Africa, n (%) 58/120(48.33%) 13/28(46.43%) 45/92(48.91%) Central Africa, n (%) 29/120(24.17%) 4/28(14.29%) 25/92(27.17%) East Africa, n (%) 17/120(14.17%) 6/28(21.43%) 11/92(11.96%) Southern Africa, n (%) 14/120(11.67%) 4/28(14.29%) 10/92(10.87%) Others, n (%) 2/120(1.67%) 1/28(3.57%) 1/92(1.09%) Malaria species 0.11 P. falciparum, n (%) 93/120(77.50%) 25/28(89.29%) 68/92(73.91%) P. vivax, n (%) 9/120(7.50%) 2/28(7.14%) 7/92(7.61%) P. ovale, n (%) 13/120(10.83%) 0/28(0.00%) 13/92(14.13%) P. malariae, n (%) 5/120(4.17%) 1/28(3.57%) 4/92(4.35%) Hyper parasitemia (> 10%), n (%) 33/120(27.50%) 19/28(67.86%) 14/92(15.22%) p < 0.001 a Severe malaria is defined by WHO severity criteria. b Continuous data are presented as the IQR, and categorical data are presented as the number and percentage of participants. IQR = interquartile range. Clinical Features of Imported Malaria Tables 2 and Table 3 show the diagnostic intervals, clinical signs, and hospitalization outcomes of the study group. The median time from symptom onset to lab confirmation was 3 days (interquartile range, IQR: 2–5 days). There was a significant difference in median times between severe and uncomplicated cases. Uncomplicated cases had a median of 3 days (IQR: 1–4 days), while severe cases had a median of 5 days (IQR: 4–7 days) ( p < 0.05). Similarly, the median time from entry into Hangzhou until diagnosis was 9 days overall (IQR: 4–14 days), with uncomplicated cases diagnosed at a median of 7 days (IQR: 4–12 days) compared to 12 days (IQR: 8–16 days) for severe cases ( p < 0.05). The main symptoms included fever, chills, headache, abdominal pain, nausea, vomiting, myalgia, and arthralgia. Fever and chills were the most common symptoms, present in 73.3% of all cases and 100% of severe malaria cases. Four asymptomatic cases (3.3%) were found incidentally during routine blood smear tests. Co-infections were present in 10.8% (13/120) of patients, including hepatitis B virus, SARS-CoV-2, Epstein-Barr virus, syphilis, HIV, and dengue virus. Notably, all five cases of Plasmodium/SARS-CoV-2 co-infection occurred during the 2019–2022 pandemic period and were classified as uncomplicated malaria. Most patients (96.7%, 116/120) required hospitalization. The overall median hospital stay was 6 days (IQR: 4–9 days). Patients with severe malaria stayed significantly longer (median 10 days, IQR: 7–9 days) than those with uncomplicated malaria (median 5 days, IQR: 4–7 days; p < 0.01). Table 2 Duration of symptoms and time since arrival in Hangzhou for imported malaria cases during the period 2013 to 2024 Total severe malaria a uncomplicated malaria p value (n = 120) (n = 28) (n = 92) Interval from symptom onset to laboratory diagnosis (days) Median (IQR) 3(2–5) 5(4–7) 3(1–4) < 0.001 Interval from arrival in Hangzhou to diagnosis (days) Median (IQR) 9(4–14) 12.5(8–16) 7(4–12) < 0.001 a Severe malaria is defined by WHO severity criteria. IQR = interquartile range. Table 3 Presenting Symptoms and Clinical Outcomes of Patients with Imported Malaria from 2013 to 2024 Total severe malaria a uncomplicated malaria p value (n = 120) (n = 28) (n = 92) Clinical symptoms Fever/Chills 88/120(73.33%) 28/28(100.00%) 60/92(65.22%) p < 0.001 Headache 30/120(25.00%) 9/28(32.14%) 21/92(22.83%) 0.463 Abdominal pain 6/120(5.00%) 2/28(7.14%) 4/92(4.35%) 0.621 Vomiting and/or diarrhea 23/120(19.17%) 9/28(32.14%) 14/92(15.22%) 0.102 Myalgias/arthralgias 24/120(20.00%) 6/28(21.43%) 18/92(19.57%) 0.830 Asymptomatic 4/120(3.33%) 0/28(0.00%) 4/92(4.35%) 0.572 Comorbidities Hepatitis B virus 3/13(23.08%) 1/4(25.00%) 2/10(20.00%) 1.000 COVID-19 5/13(38.46%) 0/4(0.00%) 5/10(50.00%) 0.250 Syphilis 1/13(7.69%) 0/4(0.00%) 1/10(10.00%) 1.000 EB virus 1/13(7/69%) 1/4(25.00%) 1/10(10.00%) 0.470 HIV virus 1/13(7.69%) 1/4(25.00%) 0/10(0.00%) 0.290 Dengue virus 2/13(15.38%) 1/4(25.00%) 1/10(10.00%) 0.470 Disposition Hospitalized 116/120(96.67%) 28/28(100.00%) 84/92(91.30%) 0.572 Intensive care unit 4/120(3.33%) 4/28(14.28%) 0/0(0.00%) p < 0.001 Length of stay in hospital 6 (4–9) 10 (7–19) 5 (4–7) p < 0.001 a Severe malaria is defined by WHO severity criteria; COVID-19: Coronavirus Disease 2019; EB virus: Epstein-Barr virus; HIV virus: Human Immunodeficiency Virus. Continuous data are presented as the IQR, and categorical data are presented as the number and percentage of participants. IQR = interquartile range. Analysis of Laboratory Parameters in Imported Malaria Cases A comprehensive analysis of laboratory parameters was conducted on 120 cases of imported malaria. The distinct patterns of physiological changes are visually summarized in the heatmap (Fig. 2 ). The color gradient (from light to dark) reflects the degree of deviation from reference ranges: blue indicates values below, red above, and white within normal limits. Hematological and inflammatory profiles consistently showed abnormalities across all malaria cases. Hemoglobin levels and platelet count significantly decreased below reference ranges (depicted in blue). Conversely, markers of systemic inflammation including C-reactive protein (CRP), serum amyloid A (SAA), procalcitonin (PCT), D-dimer, and lactate were uniformly elevated above normal limits (shown in red). Biochemical parameters displayed severity-dependent patterns. Markers of liver and kidney dysfunction: total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), urea, and creatinine were elevated only in severe malaria cases (red zones). Lipid metabolism was significantly affected across all patients, with total cholesterol and high-density lipoprotein (HDL) levels notably below normal (blue zones). Notably, all biochemical parameters remained within normal ranges (white zones) in uncomplicated cases. A comparison between severe and uncomplicated cases (Table 4 ) revealed significant differences across multiple laboratory parameters. Patients with severe malaria (n = 28) had markedly higher levels of several parameters, including leukocytes, neutrophils, and markers of liver and kidney function (total bilirubin, ALT, AST, LDH, urea, and creatinine). Inflammatory markers such as CRP, SAA, PCT, lactate, and D-dimer were also significantly elevated (all p < 0.001). Conversely, hemoglobin, platelet count, total cholesterol, HDL, and low-density lipoprotein (LDL) were significantly lower in severe cases (all p < 0.001). Additionally, no significant differences were observed in γ-glutamyl transpeptidase(γ-GT), uric acid, or glucose levels between the groups ( p = 0.646, 0.915, and 0.218, respectively). These findings demonstrate that imported malaria impacts multiple systems, causing hemolytic anemia, increased inflammation, liver and kidney dysfunction, and dyslipidemia. Severe cases show more significant clinical and biochemical changes, indicating a higher pathophysiological burden. Table 4 Distribution of laboratory parameters of imported malaria cases from 2013 to 2024 severe malaria a uncomplicated malaria p value (n = 28) (n = 92) Hematologic parameters White blood cell (×10⁹/L) 7.52 (6.09, 11.31) 5.60 (4.50, 6.87) p < 0.001 Neutrophile (×10⁹/L) 6.03 (4.41, 8.43) 3.79 (2.81, 5.57) p < 0.001 Hemoglobin (g/L) 104 (92, 127) 133 (123, 147) p < 0.001 Platelet count (×10⁹/L) 25 (21, 45) 90 (65, 139) p < 0.001 Biochemical parameters Total Bilirubin (µmol/L) 57.4 (26.9, 127.6) 18.4 (11.4, 28.9) p < 0.001 Alanine aminotransferase (U/L) 44 (25, 67) 29 (20, 43) 0.001 Aspartate aminotransferase (U/L) 74 (43, 145) 27 (21, 45) p < 0.001 γ-glutamyl transpeptidase (U/L) 52 (26, 72) 43 (26, 73) 0.289 Lactate dehydrogenase (U/L) 985 (525, 1324) 287 (212, 376) p < 0.001 Urea (mmol/L) 10.95 (6.32, 15.27) 5.00 (3.70, 6.12) p < 0.001 Creatinine (µmol/L) 98 (84, 144) 82 (72, 93) p < 0.001 Uric acid (µmol/L) 281 (210, 384) 278 (228, 332) 0.156 Total cholesterol (µmol/L) 2.25 (1.59, 2.58) 2.94 (2.45, 3.30) p < 0.001 Triglyceride (mmol/L) 2.43 (1.89, 3.30) 1.42 (1.02, 1.84) p < 0.001 High density lipoprotein (mmol/L) 0.22 (0.09, 0.31) 0.62 (0.43, 0.81) p < 0.001 Low density lipoprotein (mmol/L) 0.70 (0.45, 1.18) 1.57 (1.20, 1.87) p < 0.001 Glucose (mmol/L) 6.12 (5.65, 6.88) 5.87 (4.80, 6.91) 0.102 Inflammatory parameters C-reaction protein (mg/L) 155.5 (104.0, 180.00) 60.40 (46.21, 124.00) p < 0.001 Serum Amyloid A (mg/L) 149.00 (116.00, 240.00) 131.00 (98.00, 150.00) 0.004 Procalcitonin (ng/mL) 21.44 (9.87, 41.96) 0.694 (0.398, 2.440) p < 0.001 Lactic acid(mmol/L) 3.43 (2.27, 5.82) 1.71 (1.40, 2.47) p < 0.001 D-dimer (ng/mL) 7.93 (4.76, 14.21) 1.99 (0.86, 3.23) p < 0.001 a Severe malaria is defined by WHO severity criteria. Continuous data are presented as the IQR; IQR = interquartile range. Discussion Malaria remains a major global health issue, primarily caused by Plasmodium parasites transmitted through the bites of infected Anopheles mosquitoes [ 10 ]. Despite significant progress in malaria control, globalization has increased the movement of people, resulting in an increased number of imported cases in non-endemic areas. These cases present a substantial challenge for healthcare systems, underscoring the need for a comprehensive understanding and effective disease prevention. Malaria, primarily caused by Plasmodium falciparum, is responsible for the highest infection and mortality rates among parasitic diseases worldwide, especially among travelers returning from endemic regions [ 11 , 12 , 13 ]. The complexity of malaria’s epidemiology, clinical presentation, and related complications necessitates ongoing research essential for improving diagnostic accuracy and treatment strategies, particularly in regions where the disease is not usually endemic [ 14 ]. This study aims to provide a comprehensive analysis of the epidemiology and clinical features of imported malaria. We examined demographic, clinical, and laboratory data collected from patients in regions outside endemic areas. Our results reveal notable differences between severe and uncomplicated cases, particularly in terms of age, diagnostic delay, pathogen type, and length of hospital stay. Many cases involve middle-aged men, predominantly from West Africa. Additionally, severe malaria is strongly associated with older age, delayed diagnosis, and high parasite loads. These results align with global patterns of imported malaria and underscore the importance of enhanced screening and timely treatment for high-risk groups, such as returning migrant workers and business travelers from endemic countries [ 12 ] [ 15 ]. Demographically, the average patient age is 40 years, with patients with critical illness being notably older (average 46 versus 38 years). This difference likely stems from age-related declines in immune response and/or an increase in comorbidities [ 16 ]. Men constitute the majority (95%), consistent with previous studies, possibly due to their higher involvement in occupations such as manual labor and travel [ 17 ]. Geographically, nearly half of the cases originate from West Africa, reflecting the region’s high malaria endemicity and highlighting the need for enhanced monitoring of travelers from this area. Occupationally, manual laborers and business travelers are the primary groups affected, underscoring the role of occupational exposure in malaria transmission [ 18 , 19 , 20 ]. Most patients are Chinese nationals, but foreign citizens, especially international students, also comprise a significant portion. This shows that imported malaria affects a diverse population, emphasizing the need for public health strategies that include multilingual and cross-cultural health education. Delayed diagnosis remains a significant issue in clinical practice. The median time from symptom onset to diagnosis is 3 days overall; however, severe cases experience a longer delay of 5 days, compared to 3 days in non-severe cases. Similarly, the median time from admission to diagnosis is 9 days overall, with severe cases taking 12 days compared to 7 days for non-severe cases. This delay occurs because symptoms such as fever, chills, and headache are nonspecific, which may lead patients to delay seeking healthcare and result in inadequate identification by healthcare providers. For instance, failure by clinicians to inquire about epidemiological history may increase the risk of severe illness [ 21 , 22 ]. Therefore, timely diagnosis and appropriate treatment are crucial for preventing morbidity and fatal outcomes. Fever and chills are present in most clinical patients, occurring in 73.3% of all instances and 100% of severe cases. Additionally, four asymptomatic cases were identified through routine blood smears, emphasizing the importance of active screening among returnees in endemic regions. Pathogen analysis reveals that Plasmodium falciparum (89.2%) dominates in severe cases, reflecting high pathogenicity. Cases caused by Plasmodium vivax and other species are relatively rare. This rarity may be related to the higher proportion of Plasmodium falciparum in African endemic regions associated with imported malaria. Comparing severe and non-severe cases reveals several risk factors: patients with severe disease tend to be older, experience longer diagnostic delays, have parasite densities above 10% more often (67.86% vs. 15.22%), and stay longer in the hospital (median of 10 days vs. 5 days). This suggests that a high parasite load and delayed treatment may contribute to disease severity, which is consistent with research identifying parasite density as a prognostic indicator [ 23 , 24 ]. The co-infection rate is 10.8%, involving pathogens including the hepatitis B virus and SARS-CoV-2. Notably, all Plasmodium/SARS-CoV-2 co-infections occurred during the 2019–2022 pandemic and were classified as non-severe cases [ 25 ]. This may be due to the small sample size or unobserved viral interactions. Nevertheless, this highlights the need to remain vigilant for multiple infections that may complicate diagnosis during the pandemic. The study’s laboratory analysis reveals complex multi-systemic pathophysiological changes in imported malaria cases. Heat maps highlight significant differences in the pathophysiological changes between severe and uncomplicated cases of malaria. All cases showed hemolytic anemia, systemic inflammatory response, and lipid metabolism disorders. However, impairment of liver and kidney function was only observed in critically ill cases, indicating a direct link between disease severity and multi-organ involvement [ 26 ]. These findings are consistent with the known pathological mechanisms of malaria, where parasitic infection leads to red blood cell destruction, immune activation, and metabolic imbalance [ 27 , 28 ]. However, this study also investigates these changes in the imported population through detailed parameter analysis. All cases showed significantly lower hematology and platelet counts among hematological and inflammatory markers. In contrast, inflammatory markers such as C-reactive protein, serum amyloid A, procalcitonin, D-dimer, and lactate were elevated. This clearly indicates the typical hemolytic anemia and heightened inflammatory state associated with malaria. The decrease in hemoglobin is likely due to direct destruction of red blood cells by the malaria parasite and increased splenic clearance [ 29 ]. The exact pathogenesis of thrombocytopenia remains unclear. However, previous studies suggest it may involve immune-mediated consumption, myelosuppression, and oxidative stress [ 30 ]. A widespread increase in inflammatory markers suggests a systemic cytokine storm and activation of coagulation. This phenomenon is particularly pronounced in severe malaria and aligns with previously reported malaria-related inflammatory syndromes [ 31 , 32 ]. Notably, high levels of D-dimer and lactate further indicate the risk of microvascular thrombosis and tissue hypoxia, which serve as early warning signs of disease progression [ 33 ]. The analysis of liver and kidney function, along with lipid metabolism parameters, reveals elevated levels of total bilirubin, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, urea, and creatinine in critically ill cases. These changes indicate hepatocellular injury and renal insufficiency, possibly due to hemodynamic dysfunction, inflammation, and immunological dysregulation caused by parasitemia, consistent with previous studies [ 34 , 35 , 36 ]. In contrast, total cholesterol and high-density lipoprotein levels are significantly reduced in all cases, reflecting dyslipidemias associated with malaria. This decrease may result from inflammation, which lowers lipoprotein production, or from increased metabolic demands. Biochemical parameters in uncomplicated patients remain within normal ranges, highlighting their potential usefulness in disease assessment. For instance, abnormalities in liver and kidney function markers may help in identifying severe cases. Lipid disorders might also serve as common indicators of the metabolic effects of malaria [ 37 , 38 , 39 ]. While hypocholesterolemia is traditionally viewed as a marker of disease severity, recent evidence suggests Plasmodium parasites actively scavenge host cholesterol for membrane biogenesis [ 38 ]. Whether dyslipidemia is purely a disease consequence or partly reflects host-parasite metabolic competition warrants further investigation. Comparing severe illness to uncomplicated cases further confirms the predictive value of laboratory parameters: patients with severe illness show increased leukocytosis, higher neutrophil counts, and more impaired liver and kidney function indicators, along with more severe anemia, thrombocytopenia, and dyslipidemia. These differences underscore the increased pathophysiological burden of severe malaria, which may be linked to higher parasitic load, immune response dysregulation, or secondary organ damage. Parameters that showed no significant differences, such as γ-GT, uric acid, and glucose, highlight the limitations of these markers in imported malaria. This underscores the need to validate their specificity in larger sample sizes. Limitations and future directions This investigation is subject to several limitations. First, the facility involved is a specialized infectious disease center. Nonetheless, this retrospective study has some restrictions. The study is limited by its single-center design and a relatively small sample size. These factors may limit the generalizability of the results to a broader population. Future research should employ a multicenter approach with a larger sample to validate and expand these findings. Additionally, future research should investigate the long-term effects in patients with imported malaria. Second, variations in laboratory parameters might be influenced by comorbid conditions or treatments. Researchers should conduct larger, longitudinal studies to evaluate how these parameters evolve over time. Additionally, research should investigate molecular mechanisms, such as inflammatory pathways and metabolic shifts, that could inform targeted therapies. In summary, these laboratory findings emphasize the multisystem effects of imported malaria. Careful monitoring of hematological, inflammatory, and biochemical markers in clinical practice may facilitate early identification of severe cases and improve treatment strategies. Conclusions In conclusion, this study provides valuable insights into the demographic characteristics and clinical features of imported malaria cases, highlighting the disease’s complexity and the need for prompt diagnosis and treatment. The identification of key risk factors, clinical signs, and laboratory abnormalities associated with severe cases emphasizes the necessity for targeted public health strategies and improved treatment protocols. These findings are significant for healthcare providers and policymakers. This is especially important in non-endemic areas where awareness and preparedness for imported malaria are crucial to reducing its public health impact. Future research should focus on improving diagnostic methods and exploring innovative treatments to enhance patient outcomes in the management of imported malaria cases. Abbreviations ICU Intensive Care Unit WHO World Health Organization SD Standard Deviation IQR Interquartile Range COVID-19 Coronavirus Disease 2019 EB Epstein-Barr Virus HIV Human Immunodeficiency Virus CRP C-reactive Protein SAA Serum Amyloid A PCT Procalcitonin ALT Alanine Aminotransferase AST Aspartate Aminotransferase γ-GT γ-Glutamyl Transpeptidase LDH Lactate Dehydrogenase HDL High-density Lipoprotein LDL Low-density Lipoprotein Declarations Funding details This work was supported by the Construction Fund of Key Medical Disciplines of Hangzhou (2025HZZD13). Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of Hangzhou Xixi Hospital (Approval No:2025-038), which waived the requirement for informed consent. Consent for publication Not applicable Conflict of interest The authors declare they have no conflicts of interest. Author Contribution All authors contributed to the conception and design of the study. Kenv Pan and Wenyan Yu were responsible for the investigation, statistical analysis, and manuscript writing. Yijun Yan and Man Wang handled data collection. Fengqin Wu and Yazheng Zhang were responsible for project administration. Shourong Liu was responsible for funding acquisition and resources. Aifang Xu was responsible for supervision and manuscript review. Kenv Pan wrote the first draft of the manuscript, and all authors provided feedback on earlier drafts. All authors have read and approved the final manuscript. Acknowledgements Not applicable Data Availability The datasets used are available from the corresponding author on reasonable request. References Rossati A, Bargiacchi O, Kroumova V, Zaramella M, Caputo A, Garavelli PL. Climate, environment and transmission of malaria. 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Chen LH, Leder K, Barbre KA, Schlagenhauf P, Libman M, Keystone J, et al. Business travel-associated illness: a GeoSentinel analysis. J Travel Med. 2018;25(1):tax097. https://doi.org/10.1093/jtm/tax097 . Smith JL, Ntuku H, Rerolle F, Burke AM, Mwema T, Turcios K, et al. Targeting malaria in high-risk populations in low endemic regions in northern Namibia: a quasi-experimental controlled trial to reduce malaria in seasonal agricultural workers and cattle herders. BMJ Glob Health. 2025;10(2):e015565. https://doi.org/10.1136/bmjgh-2024-015565 . Hase R. Diagnostic delay for imported malaria: a case of Plasmodium falciparum malaria misdiagnosed as common cold. J Gen Fam Med. 2018;19(1):27–9. https://doi.org/10.1002/jgf2.149 . Bartoloni A, Zammarchi L. Clinical aspects of uncomplicated and severe malaria. Mediterr J Hematol Infect Dis. 2012;4(1):e2012026. https://doi.org/10.4084/mjhid.2012026 . Troselj-Vukić B, Vuksanović-Mikulicić S, Sladoje-Martinović B, Milotić I, Slavuljica I. Unrecognized malaria and its consequences—a case report of severe malaria with acute renal failure. Coll Antropol. 2013;37(2):611–3. PMID: 23941011. Kotepui M, Piwkham D, PhunPhuech B, Phiwklam N, Chupeerach C, Duangmano S. Effects of malaria parasite density on blood cell parameters. PLoS ONE. 2015;10(3):e0121057. https://doi.org/10.1371/journal.pone.0121057 . Huang Q, Xu WJ, Wang XX, Zhang X, Pan KN, Zhang JQ, et al. SARS-CoV-2 and Plasmodium falciparum co-infection in a returning traveler. Front Public Health. 2022;10:871374. https://doi.org/10.3389/fpubh.2022.871374 . Gupta H, Rubio M, Sitoe A, Varo R, Cisteró P, Madrid L, et al. Plasma microRNA profiling of Plasmodium falciparum biomass and association with severity of malaria disease. Emerg Infect Dis. 2021;27(2):430–42. https://doi.org/10.3201/eid2702.191795 . Vijay R, Guthmiller JJ, Sturtz AJ, Crooks S, Johnson JT, Li L, et al. Hemolysis-associated phosphatidylserine exposure promotes polyclonal plasmablast differentiation. J Exp Med. 2021;218(6):e20202359. https://doi.org/10.1084/jem.20202359 . Erice C, Kain KC. New insights into microvascular injury to inform enhanced diagnostics and therapeutics for severe malaria. Virulence. 2019;10(1):1034–46. https://doi.org/10.1080/21505594.2019.1696621 . Kho S, Siregar NC, Qotrunnada L, Fricot A, Sissoko A, Shanti PAI, et al. Retention of uninfected red blood cells causing congestive splenomegaly is the major mechanism of anemia in malaria. Am J Hematol. 2024;99(2):223–35. https://doi.org/10.1002/ajh.27152 . Achame MS, Gedefie A, Debash H, Tesfaye A, Tiruneh KT, Kassaw AB. Prevalence of thrombocytopenia among patients with malaria in Ethiopia: a systematic review and meta-analysis. Malar J. 2025;24(1):61. https://doi.org/10.1186/s12936-025-05296-8 . Popa GL, Popa MI. Recent advances in understanding the inflammatory response in malaria: a review of the dual role of cytokines. J Immunol Res. 2021;2021:7785180. https://doi.org/10.1155/2021/7785180 . Wilairatana P, Mahannop P, Tussato T, Hayeedoloh IM, Boonhok R, Klangbud WK, et al. C-reactive protein as an early biomarker for malaria infection and monitoring of malaria severity: a meta-analysis. Sci Rep. 2021;11(1):22033. https://doi.org/10.1038/s41598-021-01556-0 . Nguyen HB, Loomba M, Yang JJ, Jacobsen G, Shah K, Otero RM, et al. Early lactate clearance is associated with biomarkers of inflammation, coagulation, apoptosis, organ dysfunction and mortality in severe sepsis and septic shock. J Inflamm (Lond). 2010;7:6. https://doi.org/10.1186/1476-9255-7-6 . Prenen F, Van den Steen PE. Malaria-associated liver dysfunction: a forgotten challenge. Trends Parasitol. 2025;41(7):547–59. https://doi.org/10.1016/j.pt.2025.05.010 . Bereda G. The most lethal human protozoan parasite is Plasmodium falciparum: severe malaria-associated acute renal failure - a case report. Ann Med Surg (Lond). 2024;86(12):7314–7. https://doi.org/10.1097/MS9.0000000000000988 . Katsoulis O, Georgiadou A, Cunnington AJ. Immunopathology of acute kidney injury in severe malaria. Front Immunol. 2021;12:651739. https://doi.org/10.3389/fimmu.2021.651739 . Mesquita TC, Martin TG, Alves ER Jr, Mello MB, Nery AF, Gomes LT, Fontes CJ. Changes in serum lipid profile in the acute and convalescent Plasmodium vivax malaria: a cohort study. Acta Trop. 2016;163:1–6. https://doi.org/10.1016/j.actatropica.2016.07.010 . Fraser M, Curtis B, Phillips P, Yates PA, Lam KS, Netzel O, et al. Harnessing cholesterol uptake of malaria parasites for therapeutic applications. EMBO Mol Med. 2024;16(7):1515–32. https://doi.org/10.1038/s44321-024-00087-1 . Hamilton F, Pedersen KM, Ghazal P, Nordestgaard BG, Smith GD. Low levels of small HDL particles predict but do not influence risk of sepsis. Crit Care. 2023;27(1):389. https://doi.org/10.1186/s13054-023-04589-1 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 15 Feb, 2026 Editor invited by journal 13 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8787994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612156825,"identity":"6402763f-1960-4b6a-943a-799c48b0ec5b","order_by":0,"name":"Kenv Pan","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Kenv","middleName":"","lastName":"Pan","suffix":""},{"id":612156826,"identity":"f49d706b-ce24-4336-831b-2f8dcdd21949","order_by":1,"name":"Wenyan Yu","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Wenyan","middleName":"","lastName":"Yu","suffix":""},{"id":612156827,"identity":"4b9e8aff-eb31-418a-a7cf-61e1348a4c5c","order_by":2,"name":"Fengqin Wu","email":"","orcid":"","institution":"Chun’an County Center for Disease Control and Prevention (Chun’an County Health Supervision Institution)","correspondingAuthor":false,"prefix":"","firstName":"Fengqin","middleName":"","lastName":"Wu","suffix":""},{"id":612156828,"identity":"b0f38900-9d5e-49f1-badd-72f9ee19e91c","order_by":3,"name":"Yijun Yan","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Yan","suffix":""},{"id":612156829,"identity":"8c280bd4-5fcc-4418-be30-386238295df1","order_by":4,"name":"Yujiao Jin","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Jin","suffix":""},{"id":612156830,"identity":"f3651926-edab-4889-99f3-9d5360bc394e","order_by":5,"name":"Man Wang","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Wang","suffix":""},{"id":612156831,"identity":"b510b637-d9f5-48f6-acc9-53e781fd89b9","order_by":6,"name":"Yazhen Zhang","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Yazhen","middleName":"","lastName":"Zhang","suffix":""},{"id":612156832,"identity":"8ddeb9a4-7f3a-44e3-b3b6-819257f82884","order_by":7,"name":"Shourong Liu","email":"","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Shourong","middleName":"","lastName":"Liu","suffix":""},{"id":612156833,"identity":"8d86db4b-8b76-46e4-b67b-525cf3826adc","order_by":8,"name":"Aifang Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCAxVgiofxQUJFDZFazkC0MBs8OHOMSGugWtgkH7YwE1ZtcCP54YEDNXfsNtzIPVaR2MDGwN/enYBXi+SMNIMDB449S57Zcy7tRuIOGQaJM2c34NXCL5FgcPgD2+FkfvYesxuJZ9gYDCRy8Wthk0j/cODAv8PJbMw8ZgWJbcyEtfBL5BgcONh22A5kCwNRWiR73hQcONh3OEGy54yxRMKZYzwE/WJwPH3zhwPfDtsb3Mgx/PijokaOv70XvxYYSGyAMniIUg4C9kSrHAWjYBSMgpEHAAlnUEkP6ZUtAAAAAElFTkSuQmCC","orcid":"","institution":"Xixi Hospital of Hangzhou","correspondingAuthor":true,"prefix":"","firstName":"Aifang","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-02-04 14:56:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105538973,"identity":"ffac8058-1278-48c7-857a-28bc8e7341ed","added_by":"auto","created_at":"2026-03-27 07:48:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84488,"visible":true,"origin":"","legend":"\u003cp\u003eImported malaria, by Plasmodium species, in a tertiary hospital, Hangzhou, China (2013–2024).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8787994/v1/b630bb4f0f231c12a14d078a.png"},{"id":105538974,"identity":"68328304-b8c9-404e-b7ec-fe3d0be8931c","added_by":"auto","created_at":"2026-03-27 07:48:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73784,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of laboratory parameter deviations in imported malaria cases.\u003c/p\u003e\n\u003cp\u003eThe heatmap summarizes patterns of physiological changes across 120 cases of imported malaria. Parameters are grouped by pathophysiological categories (e.g., hematological, inflammatory, biochemical markers). The color indicates whether values are below, within, or above the normal reference range, with blue representing below normal, white representing within normal, and red representing above normal. Row-wise Z-score normalization was applied.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8787994/v1/c302bd4817e298917d7a4599.png"},{"id":105567305,"identity":"2377617d-0466-4fe1-b546-3a4053c0b0de","added_by":"auto","created_at":"2026-03-27 12:58:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1264628,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787994/v1/9bfd27eb-7f3e-4fee-b5a5-5d1c695819d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology and Laboratory Features of Imported Malaria in a Tertiary Care Hospital, Hangzhou, China: A 12-Year Retrospective Cohort (2013–2024)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, malaria has remained a significant public health concern worldwide, particularly posing a significant threat to human health in Africa and Southeast Asia. Although global malaria cases have decreased, transmission patterns and epidemiological characteristics continue to evolve as a result of increased globalization and climate change [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization\u0026rsquo;s World Malaria Report 2024, approximately 263\u0026nbsp;million cases occurred in 83 endemic countries worldwide in 2023. This represents an increase of 11\u0026nbsp;million cases compared to the previous year, with around 597,000 deaths globally. Notably, the African region accounted for 95% of these cases, which bear the heaviest burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In some non-endemic countries, the number of imported malaria cases each year has been rising, especially in countries with strong travel and business connections to high-transmission regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, vigilance should be maintained as efforts to track imported malaria are increased.\u003c/p\u003e \u003cp\u003eSince 2017, China has not reported any local malaria cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although local malaria has been successfully eradicated, the number of imported malaria cases has steadily increased because of increased international travel and rapid economic growth. Hangzhou, as an international city, has seen a rising number of imported malaria cases in recent years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Relatively few epidemiological and clinical studies have been conducted on these patients, highlighting an important research gap. A systematic analysis of the epidemiological characteristics and laboratory data of imported malaria patients can provide valuable evidence to inform public health policy and enhance malaria control and prevention efforts.\u003c/p\u003e \u003cp\u003eThis study retrospectively examined imported malaria patients diagnosed at Xixi Hospital in Hangzhou from 2013 to 2024. The aim was to analyze the epidemiological features of both severe and uncomplicated malaria, as well as laboratory findings. We collected patients\u0026rsquo; demographic data, travel history, clinical symptoms, and laboratory data. Through statistical analysis of the collected data, we identified potential risk factors associated with severe malaria. These findings provide evidence to support the development of targeted prevention and control strategies. Additionally, this study offers valuable insights for malaria prevention and control efforts worldwide. By conducting an in-depth analysis of the epidemiological and laboratory characteristics of imported malaria patients in Hangzhou, we aimed to strengthen the evidence base. Our goal was to support future prevention and control measures targeting imported malaria.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eHangzhou Xixi Hospital is a municipal Grade A, Class III public hospital in Zhejiang Province, China. Specializing in the diagnosis and treatment of infectious diseases, the hospital has received several key designations, including the Provincial Key Medical Discipline and the Provincial-Level Regional Specialized Disease Center. Medical records of all confirmed cases of Plasmodium species, including P. falciparum, P. vivax, P. ovale, and P. malariae, as well as cases of mixed infection identified by methods such as microscopy or polymerase chain reaction, were retrospectively collected and analyzed. These records covered both inpatient and outpatient settings at the hospital for infectious diseases from 1st January 2013 to 31st December 2024.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe research team collected and analyzed data on the demographic characteristics of imported malaria cases at Hangzhou Xixi Hospital, including infection regions, travel purposes, the onset and duration of fever, types of complications, and laboratory test results. Infection regions were classified according to the United Nations geographical classification [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The attending physician determines medical interventions and the criteria for admission to and discharge from the intensive care unit (ICU). The severity of malaria is assessed according to the World Health Organization (WHO) Malaria Guidelines 2021[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Malaria complications are defined as conditions arising during the disease, excluding pre-existing health issues of the patient. These complications include cerebral complications, acute respiratory distress syndrome, shock, hemolysis, severe anemia, acidosis, severe kidney injury, liver damage, pulmonary oedema, and gastrointestinal problems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 26.0 (IBM Corp., Chicago, IL, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Graphs were created with GraphPad Prism v9.0 (San Diego, CA, USA) and the \"ggplot2\" package in R. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant unless otherwise specified. Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using Student's t-test. Non-normally distributed data are presented as median (interquartile range, IQR) and compared using the Mann-Whitney U test. Categorical variables are expressed as frequencies (percentages) and compared using the chi-squared test or Fisher's exact test, as appropriate.\u003c/p\u003e \u003cp\u003eThe heatmap was generated using the \"heatmap\" package in R (version 1.0.12). Laboratory parameters were standardized using z-scores calculated as (value - mean)/SD for each parameter across all samples. Row-wise (parameter-wise) z-score standardization was applied to visualize relative deviations from the population mean. Hierarchical clustering was performed using Euclidean distance and complete linkage.\u003c/p\u003e \u003cp\u003eThe color gradient ranges from blue (z-score \u0026lt;-2, markedly below normal) through white (z-score\u0026thinsp;\u0026asymp;\u0026thinsp;0, within normal range) to red (z-score\u0026thinsp;\u0026gt;\u0026thinsp;2, markedly above normal).\u003c/p\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of Hangzhou Xixi Hospital (Approval No:2025-038), which waived the requirement for informed consent.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral trend of imported malaria cases from 2013 to 2024\u003c/h2\u003e \u003cp\u003eA total of 120 imported malaria cases admitted to Xixi Hospital in Hangzhou, Zhejiang Province, between 2013 and 2024 were included in this retrospective analysis. The annual proportions of cases varied, with rates of 9.9% (12/120) in 2016, 14.9% (18/120) in 2019, 16.5% (20/120) in 2021, and 14.9% (18/120) in 2024. In terms of species distribution, Plasmodium falciparum accounted for 77.5% (93/120) of cases, followed by cases of P. ovale (10.8%, 13/120), P. vivax (7.5%, 9/120), and P. malariae (4.17%, 5/120). Notably, the peak incidence occurred in 2021 (16.5%, 20/120), coinciding with the period of the COVID-19 pandemic, which mainly corresponded to increased cases of both P. falciparum and P. ovale infections (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacteristics of demographic and epidemiological data distribution\u003c/h3\u003e\n\u003cp\u003eThe demographic and clinical features of the 120 imported malaria cases are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was 40 years (range 30\u0026ndash;50). Patients with severe malaria were significantly older than those with uncomplicated malaria, with a mean age of 46 years (range 37\u0026ndash;58) compared to 38 years (range 29\u0026ndash;48) for severe and uncomplicated malaria, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). In most cases, 95% (114/120) were men. By occupation, manual laborers and business travelers accounted for 54% (65/120) and 34% (41/120) of cases, respectively. Most patients were of Chinese nationality (87.5%,105/120), while the remaining 15 were foreign nationals, mainly international students. Geographically, nearly half of the cases (48.33%,58/120) were imported from West Africa. Regarding parasite load, parasitemia levels exceeded 10% of red blood cells in 27.5% (33/120) of cases, with a significantly higher proportion seen in severe malaria cases (67.86%, 19/28) compared to uncomplicated cases (15.22%, 14/92). Among the 28 severe malaria cases, Plasmodium falciparum was the most common species (89.2%, 25/28). The other cases involved P. vivax (7.14%, 2/28) and P. malariae (3.57%, 1/28)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEpidemiological characteristics of imported malaria cases during the period 2013 to 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esevere malaria\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003euncomplicated malaria\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(95.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(93.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(30\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(37\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(29\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurpose of travel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65/120(54.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/28(64.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47/92(51.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOfficial duties or business, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41/120(34.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/28(28.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33/92(35.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudents, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/120(6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/28(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/92(7.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/120(5.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/28(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/92(5.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105/120(87.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27/28(96.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78/92(84.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-China, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/120(12.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/28(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14/92(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry/region of acquisition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Africa, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58/120(48.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13/28(46.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45/92(48.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Africa, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/120(24.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/28(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25/92(27.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Africa, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/120(14.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/28(21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/92(11.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Africa, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/120(11.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/28(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/92(10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/120(1.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/28(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/92(1.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalaria species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP. falciparum, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93/120(77.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25/28(89.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68/92(73.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP. vivax, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/120(7.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/28(7.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/92(7.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP. ovale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13/120(10.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/28(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13/92(14.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP. malariae, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/120(4.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/28(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/92(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyper parasitemia (\u0026gt;\u0026thinsp;10%), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33/120(27.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/28(67.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14/92(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Severe malaria is defined by WHO severity criteria. \u003csup\u003eb\u003c/sup\u003e Continuous data are presented as the IQR, and categorical data are presented as the number and percentage of participants. IQR\u0026thinsp;=\u0026thinsp;interquartile range.\u003c/p\u003e\n\u003ch3\u003eClinical Features of Imported Malaria\u003c/h3\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the diagnostic intervals, clinical signs, and hospitalization outcomes of the study group. The median time from symptom onset to lab confirmation was 3 days (interquartile range, IQR: 2\u0026ndash;5 days). There was a significant difference in median times between severe and uncomplicated cases. Uncomplicated cases had a median of 3 days (IQR: 1\u0026ndash;4 days), while severe cases had a median of 5 days (IQR: 4\u0026ndash;7 days) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, the median time from entry into Hangzhou until diagnosis was 9 days overall (IQR: 4\u0026ndash;14 days), with uncomplicated cases diagnosed at a median of 7 days (IQR: 4\u0026ndash;12 days) compared to 12 days (IQR: 8\u0026ndash;16 days) for severe cases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The main symptoms included fever, chills, headache, abdominal pain, nausea, vomiting, myalgia, and arthralgia. Fever and chills were the most common symptoms, present in 73.3% of all cases and 100% of severe malaria cases. Four asymptomatic cases (3.3%) were found incidentally during routine blood smear tests. Co-infections were present in 10.8% (13/120) of patients, including hepatitis B virus, SARS-CoV-2, Epstein-Barr virus, syphilis, HIV, and dengue virus. Notably, all five cases of Plasmodium/SARS-CoV-2 co-infection occurred during the 2019\u0026ndash;2022 pandemic period and were classified as uncomplicated malaria. Most patients (96.7%, 116/120) required hospitalization. The overall median hospital stay was 6 days (IQR: 4\u0026ndash;9 days). Patients with severe malaria stayed significantly longer (median 10 days, IQR: 7\u0026ndash;9 days) than those with uncomplicated malaria (median 5 days, IQR: 4\u0026ndash;7 days; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDuration of symptoms and time since arrival in Hangzhou for imported malaria cases during the period 2013 to 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esevere malaria\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003euncomplicated malaria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval from symptom onset to laboratory diagnosis (days) Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval from arrival in Hangzhou to diagnosis (days) Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(4\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5(8\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(4\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Severe malaria is defined by WHO severity criteria. IQR\u0026thinsp;=\u0026thinsp;interquartile range.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePresenting Symptoms and Clinical Outcomes of Patients with Imported Malaria from 2013 to 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esevere malaria\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003euncomplicated malaria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever/Chills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88/120(73.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/28(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60/92(65.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30/120(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/28(32.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21/92(22.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/120(5.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/28(7.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/92(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVomiting and/or diarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23/120(19.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/28(32.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14/92(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyalgias/arthralgias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24/120(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/28(21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18/92(19.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymptomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/120(3.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/28(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/92(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis B virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/13(23.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/10(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/13(38.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/4(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/10(50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/13(7.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/4(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/10(10.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEB virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/13(7/69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/10(10.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/13(7.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/10(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDengue virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/13(15.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/4(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/10(10.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116/120(96.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/28(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84/92(91.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/120(3.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/28(14.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay in hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Severe malaria is defined by WHO severity criteria; COVID-19: Coronavirus Disease 2019; EB virus: Epstein-Barr virus; HIV virus: Human Immunodeficiency Virus. Continuous data are presented as the IQR, and categorical data are presented as the number and percentage of participants. IQR\u0026thinsp;=\u0026thinsp;interquartile range.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Laboratory Parameters in Imported Malaria Cases\u003c/h2\u003e \u003cp\u003eA comprehensive analysis of laboratory parameters was conducted on 120 cases of imported malaria. The distinct patterns of physiological changes are visually summarized in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The color gradient (from light to dark) reflects the degree of deviation from reference ranges: blue indicates values below, red above, and white within normal limits. Hematological and inflammatory profiles consistently showed abnormalities across all malaria cases. Hemoglobin levels and platelet count significantly decreased below reference ranges (depicted in blue). Conversely, markers of systemic inflammation including C-reactive protein (CRP), serum amyloid A (SAA), procalcitonin (PCT), D-dimer, and lactate were uniformly elevated above normal limits (shown in red). Biochemical parameters displayed severity-dependent patterns. Markers of liver and kidney dysfunction: total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), urea, and creatinine were elevated only in severe malaria cases (red zones). Lipid metabolism was significantly affected across all patients, with total cholesterol and high-density lipoprotein (HDL) levels notably below normal (blue zones). Notably, all biochemical parameters remained within normal ranges (white zones) in uncomplicated cases. A comparison between severe and uncomplicated cases (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed significant differences across multiple laboratory parameters. Patients with severe malaria (n\u0026thinsp;=\u0026thinsp;28) had markedly higher levels of several parameters, including leukocytes, neutrophils, and markers of liver and kidney function (total bilirubin, ALT, AST, LDH, urea, and creatinine). Inflammatory markers such as CRP, SAA, PCT, lactate, and D-dimer were also significantly elevated (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, hemoglobin, platelet count, total cholesterol, HDL, and low-density lipoprotein (LDL) were significantly lower in severe cases (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, no significant differences were observed in γ-glutamyl transpeptidase(γ-GT), uric acid, or glucose levels between the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.646, 0.915, and 0.218, respectively).\u003c/p\u003e \u003cp\u003eThese findings demonstrate that imported malaria impacts multiple systems, causing hemolytic anemia, increased inflammation, liver and kidney dysfunction, and dyslipidemia. Severe cases show more significant clinical and biochemical changes, indicating a higher pathophysiological burden.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of laboratory parameters of imported malaria cases from 2013 to 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esevere malaria\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003euncomplicated malaria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.52 (6.09, 11.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60 (4.50, 6.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.03 (4.41, 8.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.79 (2.81, 5.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (92, 127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (123, 147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (21, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (65, 139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.4 (26.9, 127.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4 (11.4, 28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (25, 67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (20, 43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (43, 145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (21, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-glutamyl transpeptidase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (26, 72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (26, 73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.289\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e985 (525, 1324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287 (212, 376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.95 (6.32, 15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00 (3.70, 6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (84, 144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (72, 93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (210, 384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (228, 332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.156\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25 (1.59, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94 (2.45, 3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.43 (1.89, 3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.02, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.09, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.43, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.45, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (1.20, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.12 (5.65, 6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87 (4.80, 6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.102\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reaction protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.5 (104.0, 180.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.40 (46.21, 124.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Amyloid A (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.00 (116.00, 240.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.00 (98.00, 150.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.44 (9.87, 41.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.694 (0.398, 2.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactic acid(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43 (2.27, 5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71 (1.40, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.93 (4.76, 14.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99 (0.86, 3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Severe malaria is defined by WHO severity criteria. Continuous data are presented as the IQR; IQR\u0026thinsp;=\u0026thinsp;interquartile range.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMalaria remains a major global health issue, primarily caused by Plasmodium parasites transmitted through the bites of infected Anopheles mosquitoes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite significant progress in malaria control, globalization has increased the movement of people, resulting in an increased number of imported cases in non-endemic areas. These cases present a substantial challenge for healthcare systems, underscoring the need for a comprehensive understanding and effective disease prevention. Malaria, primarily caused by Plasmodium falciparum, is responsible for the highest infection and mortality rates among parasitic diseases worldwide, especially among travelers returning from endemic regions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The complexity of malaria\u0026rsquo;s epidemiology, clinical presentation, and related complications necessitates ongoing research essential for improving diagnostic accuracy and treatment strategies, particularly in regions where the disease is not usually endemic [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to provide a comprehensive analysis of the epidemiology and clinical features of imported malaria. We examined demographic, clinical, and laboratory data collected from patients in regions outside endemic areas. Our results reveal notable differences between severe and uncomplicated cases, particularly in terms of age, diagnostic delay, pathogen type, and length of hospital stay. Many cases involve middle-aged men, predominantly from West Africa. Additionally, severe malaria is strongly associated with older age, delayed diagnosis, and high parasite loads. These results align with global patterns of imported malaria and underscore the importance of enhanced screening and timely treatment for high-risk groups, such as returning migrant workers and business travelers from endemic countries [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Demographically, the average patient age is 40 years, with patients with critical illness being notably older (average 46 versus 38 years). This difference likely stems from age-related declines in immune response and/or an increase in comorbidities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Men constitute the majority (95%), consistent with previous studies, possibly due to their higher involvement in occupations such as manual labor and travel [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Geographically, nearly half of the cases originate from West Africa, reflecting the region\u0026rsquo;s high malaria endemicity and highlighting the need for enhanced monitoring of travelers from this area. Occupationally, manual laborers and business travelers are the primary groups affected, underscoring the role of occupational exposure in malaria transmission [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Most patients are Chinese nationals, but foreign citizens, especially international students, also comprise a significant portion. This shows that imported malaria affects a diverse population, emphasizing the need for public health strategies that include multilingual and cross-cultural health education.\u003c/p\u003e \u003cp\u003eDelayed diagnosis remains a significant issue in clinical practice. The median time from symptom onset to diagnosis is 3 days overall; however, severe cases experience a longer delay of 5 days, compared to 3 days in non-severe cases. Similarly, the median time from admission to diagnosis is 9 days overall, with severe cases taking 12 days compared to 7 days for non-severe cases. This delay occurs because symptoms such as fever, chills, and headache are nonspecific, which may lead patients to delay seeking healthcare and result in inadequate identification by healthcare providers. For instance, failure by clinicians to inquire about epidemiological history may increase the risk of severe illness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, timely diagnosis and appropriate treatment are crucial for preventing morbidity and fatal outcomes. Fever and chills are present in most clinical patients, occurring in 73.3% of all instances and 100% of severe cases. Additionally, four asymptomatic cases were identified through routine blood smears, emphasizing the importance of active screening among returnees in endemic regions. Pathogen analysis reveals that Plasmodium falciparum (89.2%) dominates in severe cases, reflecting high pathogenicity. Cases caused by Plasmodium vivax and other species are relatively rare. This rarity may be related to the higher proportion of Plasmodium falciparum in African endemic regions associated with imported malaria. Comparing severe and non-severe cases reveals several risk factors: patients with severe disease tend to be older, experience longer diagnostic delays, have parasite densities above 10% more often (67.86% vs. 15.22%), and stay longer in the hospital (median of 10 days vs. 5 days). This suggests that a high parasite load and delayed treatment may contribute to disease severity, which is consistent with research identifying parasite density as a prognostic indicator [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The co-infection rate is 10.8%, involving pathogens including the hepatitis B virus and SARS-CoV-2. Notably, all Plasmodium/SARS-CoV-2 co-infections occurred during the 2019\u0026ndash;2022 pandemic and were classified as non-severe cases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This may be due to the small sample size or unobserved viral interactions. Nevertheless, this highlights the need to remain vigilant for multiple infections that may complicate diagnosis during the pandemic.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s laboratory analysis reveals complex multi-systemic pathophysiological changes in imported malaria cases. Heat maps highlight significant differences in the pathophysiological changes between severe and uncomplicated cases of malaria. All cases showed hemolytic anemia, systemic inflammatory response, and lipid metabolism disorders. However, impairment of liver and kidney function was only observed in critically ill cases, indicating a direct link between disease severity and multi-organ involvement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings are consistent with the known pathological mechanisms of malaria, where parasitic infection leads to red blood cell destruction, immune activation, and metabolic imbalance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, this study also investigates these changes in the imported population through detailed parameter analysis. All cases showed significantly lower hematology and platelet counts among hematological and inflammatory markers. In contrast, inflammatory markers such as C-reactive protein, serum amyloid A, procalcitonin, D-dimer, and lactate were elevated. This clearly indicates the typical hemolytic anemia and heightened inflammatory state associated with malaria. The decrease in hemoglobin is likely due to direct destruction of red blood cells by the malaria parasite and increased splenic clearance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The exact pathogenesis of thrombocytopenia remains unclear. However, previous studies suggest it may involve immune-mediated consumption, myelosuppression, and oxidative stress [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A widespread increase in inflammatory markers suggests a systemic cytokine storm and activation of coagulation. This phenomenon is particularly pronounced in severe malaria and aligns with previously reported malaria-related inflammatory syndromes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Notably, high levels of D-dimer and lactate further indicate the risk of microvascular thrombosis and tissue hypoxia, which serve as early warning signs of disease progression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The analysis of liver and kidney function, along with lipid metabolism parameters, reveals elevated levels of total bilirubin, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, urea, and creatinine in critically ill cases. These changes indicate hepatocellular injury and renal insufficiency, possibly due to hemodynamic dysfunction, inflammation, and immunological dysregulation caused by parasitemia, consistent with previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, total cholesterol and high-density lipoprotein levels are significantly reduced in all cases, reflecting dyslipidemias associated with malaria. This decrease may result from inflammation, which lowers lipoprotein production, or from increased metabolic demands. Biochemical parameters in uncomplicated patients remain within normal ranges, highlighting their potential usefulness in disease assessment. For instance, abnormalities in liver and kidney function markers may help in identifying severe cases. Lipid disorders might also serve as common indicators of the metabolic effects of malaria [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While hypocholesterolemia is traditionally viewed as a marker of disease severity, recent evidence suggests Plasmodium parasites actively scavenge host cholesterol for membrane biogenesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Whether dyslipidemia is purely a disease consequence or partly reflects host-parasite metabolic competition warrants further investigation. Comparing severe illness to uncomplicated cases further confirms the predictive value of laboratory parameters: patients with severe illness show increased leukocytosis, higher neutrophil counts, and more impaired liver and kidney function indicators, along with more severe anemia, thrombocytopenia, and dyslipidemia. These differences underscore the increased pathophysiological burden of severe malaria, which may be linked to higher parasitic load, immune response dysregulation, or secondary organ damage. Parameters that showed no significant differences, such as γ-GT, uric acid, and glucose, highlight the limitations of these markers in imported malaria. This underscores the need to validate their specificity in larger sample sizes.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThis investigation is subject to several limitations. First, the facility involved is a specialized infectious disease center. Nonetheless, this retrospective study has some restrictions. The study is limited by its single-center design and a relatively small sample size. These factors may limit the generalizability of the results to a broader population. Future research should employ a multicenter approach with a larger sample to validate and expand these findings. Additionally, future research should investigate the long-term effects in patients with imported malaria. Second, variations in laboratory parameters might be influenced by comorbid conditions or treatments. Researchers should conduct larger, longitudinal studies to evaluate how these parameters evolve over time. Additionally, research should investigate molecular mechanisms, such as inflammatory pathways and metabolic shifts, that could inform targeted therapies. In summary, these laboratory findings emphasize the multisystem effects of imported malaria. Careful monitoring of hematological, inflammatory, and biochemical markers in clinical practice may facilitate early identification of severe cases and improve treatment strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study provides valuable insights into the demographic characteristics and clinical features of imported malaria cases, highlighting the disease\u0026rsquo;s complexity and the need for prompt diagnosis and treatment. The identification of key risk factors, clinical signs, and laboratory abnormalities associated with severe cases emphasizes the necessity for targeted public health strategies and improved treatment protocols. These findings are significant for healthcare providers and policymakers. This is especially important in non-endemic areas where awareness and preparedness for imported malaria are crucial to reducing its public health impact. Future research should focus on improving diagnostic methods and exploring innovative treatments to enhance patient outcomes in the management of imported malaria cases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpstein-Barr Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSerum Amyloid A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eγ-GT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eγ-Glutamyl Transpeptidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate Dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding details\u003c/h2\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003eThis work was supported by the Construction Fund of Key Medical Disciplines of Hangzhou (2025HZZD13).\u003c/p\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis retrospective study was approved by the Ethics Committee of Hangzhou Xixi Hospital (Approval No:2025-038), which waived the requirement for informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare they have no conflicts of interest.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception and design of the study. Kenv Pan and Wenyan Yu were responsible for the investigation, statistical analysis, and manuscript writing. Yijun Yan and Man Wang handled data collection. Fengqin Wu and Yazheng Zhang were responsible for project administration. Shourong Liu was responsible for funding acquisition and resources. Aifang Xu was responsible for supervision and manuscript review. Kenv Pan wrote the first draft of the manuscript, and all authors provided feedback on earlier drafts. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRossati A, Bargiacchi O, Kroumova V, Zaramella M, Caputo A, Garavelli PL. Climate, environment and transmission of malaria. 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Crit Care. 2023;27(1):389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-023-04589-1\u003c/span\u003e\u003cspan address=\"10.1186/s13054-023-04589-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Imported malaria, Severe malaria, Uncomplicated malaria, Epidemiological trends, Clinical characteristics, Laboratory parameters, Diagnostic delay","lastPublishedDoi":"10.21203/rs.3.rs-8787994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria remains a significant public health threat globally, especially because of the increasing number of imported cases in non-endemic areas such as China, despite the successful local elimination of the disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the epidemiological trends, clinical characteristics, and laboratory parameters of severe and uncomplicated imported malaria cases at Hangzhou Xixi Hospital, between January 2013 and December 2024.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA retrospective analysis of 120 confirmed cases revealed that Plasmodium falciparum was the predominant species (77.5%), with 48.33% originating from West Africa, and severe cases constituting 89.2% of the total. Notably, the overlap with the COVID-19 pandemic, and severe cases were characterized by older age of patients, longer diagnostic delays, and significantly higher parasitemia levels (67.86% versus 15.22%). Laboratory findings indicated that severe cases exhibited lower hemoglobin and platelet counts, along with elevated inflammatory markers and impaired liver and kidney function.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis research underscores the complexities of managing imported malaria and emphasizes the urgent need for the development of comprehensive early warning systems incorporating epidemiological data, clinical manifestations, and laboratory biomarkers to facilitate early identification and treatment of severe cases. The findings contribute essential insights towards enhancing malaria prevention and control initiatives in low-risk, non-endemic areas, ultimately aiming for improved patient outcomes and public health safety.\u003c/p\u003e","manuscriptTitle":"Epidemiology and Laboratory Features of Imported Malaria in a Tertiary Care Hospital, Hangzhou, China: A 12-Year Retrospective Cohort (2013–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 07:48:33","doi":"10.21203/rs.3.rs-8787994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-12T18:57:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153955257991338465458952155273117674911","date":"2026-04-02T07:56:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T20:07:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-15T18:19:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-13T17:34:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T09:22:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-13T09:12:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6590c305-b68a-47b4-a6c1-23a5c49be285","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T07:48:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 07:48:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8787994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8787994","identity":"rs-8787994","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}