Seroprevalence and Clinical Features of Scrub Typhus among Febrile Patients Attending a Referral Hospital in Kathmandu, Nepal

Scrub typhus in Northeast India: epidemiology, clinical presentations, and diagnostic approaches

Scrub typhus is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the 'tsutsugamushi triangle', diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters the human body through the bite of trombiculid mites (also known as chiggers) of the genus Leptotrombidium deliense. Diagnosis of the disease is challenging, as its early symptoms mimic other febrile illnesses like dengue, influenza and corona viruses. Lack of rapid, reliable and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent populations, habitat characteristics and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns and behavioural interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

Epidemiology, clinical characteristics, diagnosis, and complications of scrub typhus infection in Nepal: a systematic review

Introduction

Scrub typhus (ST) is a neglected tropical disease of serious concern in Nepal. This systematic review aims to describe the burden of disease, clinical presentation, and complications of ST infection in Nepal.

Methods

A systematic search of PubMed, EMBASE, Google Scholar, and national databases was conducted for any literature published in English between January 2000 and January 2023. Any type of study design (observational studies, case series, and interventional studies) that reported laboratory-confirmed ST and was conducted in Nepal among patients of all age groups was included. The seroprevalence of ST among acute undifferentiated febrile illness (AUFI) cases, geographical distribution, monthly distribution, clinical presentations, complications, and treatment were assessed by the study.

Result

A total of 15 studies with 10, 977 participants were included in the review. The seroprevalence of ST among the AUFI cases in Nepal was 19.31%. Young people at or below 20 years of age were mostly affected. The maximum number of cases were reported from Bagmati province (59.46%) and in the month of August (26.33%). Fever, headache, cough, shortness of breath, nausea, and abdominal pain were the clinical characteristics in decreasing order of occurrence. The most common complication was acute kidney injury, followed by respiratory problems, cardiac issues, and neurological manifestations. The case fatality rate of ST in Nepal was 2.56%.

Conclusion

The authors findings showed a significant burden of ST among AUFI cases in Nepal. Improved surveillance, general public awareness, and early detection post-calamities could help reduce the disease burden and improve patient outcomes.

Multiple Orientia clusters and Th1-skewed chemokine profile: a cross-sectional study in patients with scrub typhus from Nepal

OBJECTIVES

Scrub typhus is an emerging infectious disease in Asia caused by Orientia tsutsugamushi (Ot). From Nepal, only scant data on the genetic epidemiology of this agent is available, and determinants of immunoregulation are poorly understood.

METHODS

Patients (n = 238) referred to the National Public Health Laboratory (Kathmandu, Nepal) from all over Nepal for suspected scrub typhus were enrolled upon positive immunoglobulin (Ig)M testing between July and October 2015. From Ot 16S and 47 kD polymerase chain reaction (PCR)-positive samples, the variable domain I of the 56 kD gene was sequenced and phylogenetically analyzed. T helper (Th) cell-associated cytokines (n = 13) and chemokines (n = 12) were quantified by multiplex bead arrays.

RESULTS

In 93/238 (39.1%) IgM-positive samples, Ot DNA was detected by quantitative PCR. Phylogenetic analysis of 56 kD sequences revealed seven distinct clusters, six of them with high homologies to strains detected in other countries. The Th1-related cytokines interferon-γ and C-X-C motif chemokine ligand 10 were strongly upregulated and correlated with bacteremia, while levels of Th2-associated chemokines were reduced. Bacteremia also correlated with concentrations of interleukin (IL)-6 and IL-10 but not tumor necrosis factor-α.

CONCLUSION

We identified a considerable genetic heterogeneity of human-pathogenic Ot strains circulating in Nepal. Acute Nepalese scrub typhus patients showed strong Th1 but impaired Th2 responses, especially on the chemokine level.

Co-infection of dengue, scrub typhus, and typhoid during dengue outbreak in Nepal, 2022: A case report

In midst of the recent dengue outbreak in Nepal, in 2022, the risk of co-infection increases and may lead to fatal outcomes if the diagnosis of multiple infections is delayed. Thus, all available diagnostic approaches must be taken to decrease the burden of illness and lessen mortality.

Artificial intelligence in differentiating tropical infections: A step ahead

Background and objective

Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.

Methodology

A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.

Results

A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55–60%, whereas a binary classification machine learning algorithms showed an average of 79–84% for one vs other and 69–88% for one vs one disease category.

Conclusion

This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.

Distinguishing tropical infections is difficult due to its homogeneous nature from clinical and laboratory presentations among them. This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings as per the need analysis. Better predictors in terms of laboratory parameters and clinical presentations were identified from retrospective analysis and used for the regression and machine learning models. The parameters such as accuracy, true positive rate/sensitivity/recall, false positive rate, precision/positive predictive value, F-measure and ROC area for both the training and validation sets (10-fold cross validation) for all modelling approaches and diseases (One vs One and One vs others) were calculated. All the models observed to have an acceptable range of model performance in differentiating tropical infections. Albumin can be considered as the main parameter in differentiating these tropical infections. These models should be implemented in daily clinical routine practice via mobile or desktop assisted applications or tools.