Seroprevalence trends of Scrub typhus among the febrile patients of Northern India: A prospective cross-sectional study

Human rickettsial infections in India - A review

Rickettsial infections are emerging and/or re-emerging disease that poses a serious global threat to humans and animals. Transmission to humans and animals is through the bite of the ectoparasites including ticks, fleas and chigger mites. Most of the rickettsial diseases are endemic in India, but underdiagnosed. This review is aimed at analyzing the prevalence of rickettsiosis in India and the advancement of rickettsial diagnosis. We have conducted a systematic review on the prevalence of rickettsial disease in India ranging from 1.3% to 46.6% for spotted fever, 2.4% to 77.8% for scrub typhus and 1% to 46.4% for Q fever, based on the literature published with the evidence of isolation, serological, and molecular diagnostics. Search engines Medline/PubMed, Science Direct, ProQuest, and EBSCO were used to retrieve the articles from electronic databases by using appropriate keywords to track the emergence of these rickettsial diseases in India for the period of 1865 to till date. We retrieved 153 published rickettsial articles on hospital-based studies from India that were purely made on the basis of prevalence and the laboratory parameters viz., Weil-Felix test (WF) and Rapid Immunochromatographic tests (RICT) with reference to the gold standard IFA and ELISA. More epidemiological studies are required for epidemic typhus to know the exact prevalence status of this louse-borne rickettsiosis in India. Currently, there is no confirmed specific inflammatory marker for rickettsial diseases. Moreover, serological cross-reactivity is an important aspect, and it should be investigated in endemic areas, there is also a need to include molecular diagnostic techniques for further confirmation in healthcare settings.

Scrub typhus seroprevalence from an eastern state of India: findings from the state-wide serosurvey

BACKGROUND

Scrub typhus is a mite-borne infectious disease caused primarily by the obligate intracellular bacteria Orientia tsutsugamushi, which is transmitted by chigger mites. The objective of this study was to determine the prevalence of scrub typhus among adults in Odisha, an eastern state of India.

METHODS

A descriptive cross-sectional study was conducted to analyse 1840 serum samples from five districts (Khordha, Cuttack, Ganjam, Malkangiri and Sundargarh) of Odisha collected during 2020-2021. Both immunoglobulin G (IgG) and IgM antibodies against scrub typhus were tested using commercial enzyme-linked immunosorbent assay kits. Point estimates of the 95% confidence interval and adjusted odds ratio were calculated.

RESULTS

Of 1840 participants, the prevalence of IgG positivity was 1034 (56.19%) and that of IgM was 523 (28.42%). The majority of participants were in the 18-45 y age group (53.7%). Cuttack had the highest IgG positivity (64.54%) and Malkangiri had the lowest (29.32%). The highest and lowest positivity for IgM were found in Malkangiri (40.84%) and Cuttack (25.30%), respectively.

CONCLUSIONS

With an increasing number of infections detected in the state, scrub typhus is emerging as a public health threat. Increasing awareness among the general public and healthcare professionals through health education campaigns regarding scrub typhus is essential. Early detection of the disease through the establishment of a laboratory surveillance system is required to control the transmission of scrub typhus.

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.