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

Pokhrel, Anil; Rayamajhee, Binod; Khadka, Saroj; Thapa, Sushma; Kapali, Samjhana; Pun, Sher Bahadur; Banjara, Megha Raj; Joshi, Prakash; Lekhak, Binod; Rijal, Komal Raj

doi:10.3390/tropicalmed6020078

Cited by 10 publications

(10 citation statements)

References 52 publications

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“…Out of 988 acute febrile cases, 98 (10%) patients were seropositive for ST, which was comparable to the study reported by Pokharel A et al, [ 6 ]12.2% but was higher than the study done in Patan hospital [ 14 ]. The reason for this disparity may be due to difference in the ecological niche in different geographical locations, non-uniform clinical presentations, sample size and diagnostic procedure used.…”

Section: Discussionsupporting

confidence: 78%

“…The prevalence of scrub typhus is unknown because of the imprecision of diagnosis and the inadequacy of reporting and surveillance systems. An estimated one billion people are at risk of ST and one million cases are reported annually [ 6 ]. The disease is endemic in eastern and southern Asia, northern Australia, islands of the western Pacific region, and the Indian ocean [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Clinical profile and biochemical abnormalities in Scrub Typhus: A cross-sectional study

Shrestha¹,

Karn²,

Regmi³

et al. 2022

Annals of Medicine &Amp; Surgery

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Clinical profile and biochemical abnormalities in Scrub Typhus: A cross-sectional study

Shrestha¹,

Karn²,

Regmi³

et al. 2022

Annals of Medicine &Amp; Surgery

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“…The prevalence of scrub typhus is higher (16.9%) than other two studies done in Kathmandu Valley, 12.2% in 2021 and 3.2% in 2001. 20,21 The rise in sero-prevalence could be due to increased awareness leading to easy availability of diagnostic tools and improved testing facilities. However, some other studies have reported higher prevalence in Nepal: 22% in 2004 and 40.3% in 2015.…”

Section: Discussionmentioning

confidence: 99%

Seroprevalence of Scrub Typhus in Patients Attending Dhulikhel Hospital, Kavre

Madhup¹,

Shrestha²,

Katuwal³

et al. 2021

Kathmandu Univ. Med. J.

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Background Scrub typhus is a largely ignored tropical disease and a leading cause of undifferentiated febrile illness. It is caused by Orientia tsutsugamushi. Scrub Typhus is frequently observed in South Asian countries. However, clear epidemiological information of this disease is lacking in case of Nepal. Nepal has shown steady increase in cases of Scrub Typhus since 2015. The epidemiological data related to this disease would support the decision making and surveillance design for early outbreak detection and immediate responses including prevention and treatment of scrub typhus in Nepal. Objective To understand prevalence of Scrub Typhus in subjects who had visited outpatient department at Dhulikhel Hospital. Method In this study, we have studied antibody test data (n=784) for Scrub Typhus from 2019 to 2021. The tests were performed on serum samples of patients who had visited OPD at Dhulikhel Hospital with fever lasting more than 5 days. The kit used in analysis was Scrub Typhus Detect™ IgM ELISA Kit from InBios International. Result Out of the total subjects (n=784), 133 were positive (16.9%) for IgM antibody of Scrub Typhus. The positivity in female (18.6%) was higher than the male subjects (15.3%). The positivity rate was variable among the different age groups, with highest positivity for age group 0-14 years (25%). The seasonal variation was also observed among the seropositive cases. Conclusion Scrub Typhus being a neglected tropical disease has high prevalence. It can be postulated that female subjects and subjects of age group 0-14 years are vulnerable to the infection with Scrub Typhus. There is need to increase the surveillance of Scrub Typhus to add the knowledge for diagnosis and treatment.

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“…Biological factors like residential status (rural vs. urban), living conditions, climatic conditions, nutritional status, environmental factors, poverty, and population density are some of the contributory risk factors [ 6 , 7 ]. Tropical infections are a serious threat to public health, especially in industrialized countries.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in differentiating tropical infections: A step ahead

et al. 2022

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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.

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

Cited by 10 publications

References 52 publications

Clinical profile and biochemical abnormalities in Scrub Typhus: A cross-sectional study

Clinical profile and biochemical abnormalities in Scrub Typhus: A cross-sectional study

Seroprevalence of Scrub Typhus in Patients Attending Dhulikhel Hospital, Kavre

Artificial intelligence in differentiating tropical infections: A step ahead

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