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Economic burden of typhoid fever by antimicrobial resistance in India: a modelling study 2023

2026-04-13T08:40:18Z

Title: Economic burden of typhoid fever by antimicrobial resistance in India: a modelling study 2023 Authors: V. Mogasale, Vijayalaxmi; John, Jacob; Ray, Arindam; Farooqui, Habib Hasan; Mogasale, Vittal; Hutubessy, Raymond; Dhoubhadel, Bhim Gopal; Edmunds, W. John; Clark, Andrew; Abbas, Kaja Abstract: Background Typhoid fever and rising antimicrobial resistance contribute towards substantial morbidity in India. Introduction of typhoid conjugate vaccine in national immunisation schedule is under consideration to address this growing disease burden. In this study we estimated the economic burden of typhoid fever for 2023, disaggregated by age, provincial states of India, and fluoroquinolone resistance, from societal and government perspectives, to support national vaccination policy. Methods We developed a decision-tree model using Indian empirical data on typhoid epidemiology, care-seeking, clinical outcomes, and estimated direct and indirect costs for hospitalised and non-hospitalised typhoid fever patients. To reflect age-specific uncertainty in hospitalisation patterns and resulting economic burden, we used two primary scenarios. We estimated productivity losses due to premature mortality using the human capital approach, with the friction-cost approach as an alternative. We assessed uncertainty through probabilistic sensitivity analysis. Findings The economic burden of typhoid fever in India in 2023 was estimated at INR 123.0 billion (95% UI 76.7-215.5; US$ 1.5 billion, 0.9-2.6), including a cost of INR 13.0 billion (6.6-27.0; US$ 157 million, 80-326) to the public health system (government perspective). Fluoroquinolone-resistant infections accounted for 87% of total costs. Children under ten years of age incurred the highest economic burden, contributing over half of the total costs. Households bore 91% of expenses, and 70,000 families faced catastrophic health expenditure. Maharashtra, Uttar Pradesh, Andhra Pradesh (including Telangana), Tamil Nadu, and West Bengal were the states estimated to account for 51% of the national costs. Productivity loss was INR 42.6 billion (15.7-111.1; US$ 515 million) based on the human capital approach and declined by 99.8% under the friction-cost approach. Interpretation Typhoid fever imposes a significant economic burden in India, shaped by fluoroquinolone resistance, children less than ten years of age, and high-burden provincial states of the country, resulting in considerable household financial strain. Our findings provide key evidence to support the introduction of the typhoid conjugate vaccine, enhance antimicrobial resistance control, and guide national health financing policies.

Burden of antimicrobial resistance in culture-confirmed Salmonella Typhi isolates in India from 1977 to 2024: A systematic review and meta-analysis

2026-04-30T01:53:12Z

Title: Burden of antimicrobial resistance in culture-confirmed Salmonella Typhi isolates in India from 1977 to 2024: A systematic review and meta-analysis Authors: Mogasale, Vijayalaxmi V.; Zhang, Peixuan; John, Jacob; Farooqui, Habib Hasan; Ray, Arindam; Mogasale, Vittal; Parry, Christopher M.; Dhoubhadel, Bhim Gopal; Edmunds, W. John; Clark, Andrew; Abbas, Kaja Abstract: Background Antimicrobial-resistant (AMR) Salmonella Typhi (S. Typhi) is a persistent public health threat in India. We conducted a systematic review and meta-analysis to estimate the prevalence and temporal trends of AMR S. Typhi across Indian states to inform prevention and treatment strategies. Methods We systematically reviewed antimicrobial resistance in S. Typhi isolates from India, including studies published up to March 31, 2025, with no start-date restriction. We screened eligible articles from PubMed-MEDLINE, EMBASE, Scopus, Web of Science, and AMR surveillance networks and excluded studies on chronic carriers and travellers. We assessed risk of bias using the Risk Of Bias In Non-randomised Studies - of Exposures (ROBINS-E) tool. We conducted meta-analysis using a random-effects model to estimate the prevalence of multidrug resistance (MDR; resistance to chloramphenicol, ampicillin, and co-trimoxazole) and resistance to fluoroquinolones, third-generation cephalosporins, and azithromycin across five-year periods, Indian states, and age groups (<18 versus all ages). Results We analysed data from 188 of 4,430 identified studies. MDR declined from 65% (95% CI 63-67) in 1990-94 to nearly 0% (95% CI 0-1) >= 2020. Fluoroquinolone resistance rose from 2% (95% CI 1-2) in 1990-94-85% (95% CI 84-86) in 2015-19, then declined slightly, with similar trends in children but marked variation across Indian states. Resistance to third-generation cephalosporins, azithromycin, and carbapenems remained below 5%, 8%, and 2%, respectively. Our meta-analysis of studies with samples collected from 2015 onwards showed a pooled summary resistance estimate of 82% (95% CI 74-87) for fluoroquinolones, 3% (95% CI 2-4) for third-generation cephalosporins, and 3% (95% CI 1-6) for azithromycin. Conclusion Fluoroquinolone resistance in S. Typhi in India is high but is declining, while resistance to third-generation cephalosporins and azithromycin is low. As many isolates originated from major cities, caution is warranted when generalising nationally. Continued AMR surveillance is crucial to guide vaccination strategies and treatment decisions.

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types

2026-03-25T07:27:25Z

Title: Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types Authors: Park, Phillip; Choi, Yeonho; Han, Nayoung; Park, Soobin; Park, Ye-Lin; Hwang, Juyeon; Choi, Kui Son; Yoo, Chong Woo; Kim, Hyun-Jin Abstract: Background: Pathological reports provide comprehensive insights into the clinical and pathological features of different cancer types. However, extraction of this semi-structured data for research is challenging. To better utilize pathology reports in cancer studies, we developed an efficient natural language processing (NLP) system to automate the extraction of items from pathology reports, facilitating streamlined storage, retrieval, and analysis of clinical data in a centralized database. Methods: To determine the optimal model for our study, we conducted a comparative analysis of various deep learning architectures, including long short-term memory, convolutional neural network, and transformer-based models such as bidirectional encoder representations from transformers (BERT), BioBERT, and ClinicalBERT. The proficiency of the ClinicalBERT model in medical terminology and context significantly enhanced the accuracy and efficiency of data extraction from these reports. Results: Among the aforementioned models, ClinicalBERT exhibited the best performance and was selected as the base model. The ClinicalBERT model demonstrated an exceptional performance in accurately classifying variables across multiple cancer types. Regarding stomach cancer, F1 scores (F1 = 1.0) were achieved for variables such as angiolymphatic invasion, and operation name (F1 = 1.0); however, a lower performance was observed for distant metastasis (F1 = 0.3889). Regarding liver cancer, high performance was consistently observed for most variables, with F1 scores above 0.99. Regarding colorectal cancer, F1 scores were achieved for variables such as Dworak's grade, lymph node, operation name, and total mesorectal excision (F1 = 1.0), while slightly lower but acceptable performance was noted for surgical margin (F1 = 0.9259). Regarding breast cancer, F1 scores were achieved for several variables including nipple margin, organ, and superficial margin (F1 = 1.0), while strong performances were noted for lateral and medial margins (F1 > 0.94). Conclusion: This study underscores the efficacy of NLP systems, specifically the ClinicalBERT model, in automating the extraction of important clinical data from pathology reports across various cancer types. This approach can not only simplify the process but also enhance the accuracy of the extracted information.

Adiponectin as a Predictor of Metabolic Dysfunction-Associated Steatotic Liver Disease and Non-Alcoholic Fatty Liver Disease: A 17-Year Korean Cohort Study (Diabetes Metab J 2026;50:331-42)

2026-03-26T02:47:48Z

Title: Adiponectin as a Predictor of Metabolic Dysfunction-Associated Steatotic Liver Disease and Non-Alcoholic Fatty Liver Disease: A 17-Year Korean Cohort Study (Diabetes Metab J 2026;50:331-42) Authors: Yang, Yeun Soo; Zhang, Hyun Soo; Kimm, Heejin; Jung, Keum Ji; Kim, Soyoung; Baek, Ji Woo; Lee, Sunmi; Ha Jee, Sun