Literature DB >> 25648664

Identification of factors for physicians to facilitate early differential diagnosis of scrub typhus, murine typhus, and Q fever from dengue fever in Taiwan.

Ko Chang1, Nan-Yao Lee2, Wen-Chien Ko2, Jih-Jin Tsai3, Wei-Ru Lin4, Tun-Chieh Chen5, Po-Liang Lu6, Yen-Hsu Chen7.   

Abstract

BACKGROUND: Dengue fever, rickettsial diseases, and Q fever are acute febrile illnesses with similar manifestations in tropical areas. Early differential diagnosis of scrub typhus, murine typhus, and Q fever from dengue fever may be made by understanding the distinguishing clinical characteristics and the significance of demographic and weather factors.
METHODS: We conducted a retrospective study to identify clinical, demographic, and meteorological characteristics of 454 dengue fever, 178 scrub typhus, 143 Q fever, and 81 murine typhus cases in three Taiwan hospitals.
RESULTS: Case numbers of murine typhus and Q fever correlated significantly with temperature and rainfall; the scrub typhus case number was only significantly related with temperature. Neither temperature nor rainfall correlated with the case number of dengue fever. The rarity of dengue fever cases from January to June in Taiwan may be a helpful clue for diagnosis in the area. A male predominance was observed, as the male-to-female rate was 2.1 for murine typhus and 7.4 for Q fever. Multivariate analysis revealed the following six important factors for differentiating the rickettsial diseases and Q fever group from the dengue fever group: fever ≥8 days, alanine aminotransferase > aspartate aminotransferase, platelets >63,000/mL, C-reactive protein >31.9 mg/L, absence of bone pain, and absence of a bleeding syndrome.
CONCLUSION: Understanding the rarity of dengue in the first half of a year in Taiwan and the six differentiating factors may help facilitate the early differential diagnosis of rickettsial diseases and Q fever from dengue fever, permitting early antibiotic treatment.
Copyright © 2015. Published by Elsevier B.V.

Entities:  

Keywords:  Q fever; dengue fever; murine typhus; rainfall; scrub typhus; temperature

Mesh:

Substances:

Year:  2014        PMID: 25648664     DOI: 10.1016/j.jmii.2014.12.001

Source DB:  PubMed          Journal:  J Microbiol Immunol Infect        ISSN: 1684-1182            Impact factor:   4.399


  8 in total

1.  Meteorological factors affecting scrub typhus occurrence: a retrospective study of Yamagata Prefecture, Japan, 1984-2014.

Authors:  J Seto; Y Suzuki; R Nakao; K Otani; K Yahagi; K Mizuta
Journal:  Epidemiol Infect       Date:  2016-10-28       Impact factor: 4.434

2.  Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

Authors:  Jaewon Kwak; Soojun Kim; Gilho Kim; Vijay P Singh; Seungjin Hong; Hung Soo Kim
Journal:  Int J Environ Res Public Health       Date:  2015-06-29       Impact factor: 3.390

Review 3.  Scrub typhus: risks, diagnostic issues, and management challenges.

Authors:  John Antony Jude Prakash
Journal:  Res Rep Trop Med       Date:  2017-08-07

4.  Selection of Diagnostic Cutoffs for Murine Typhus IgM and IgG Immunofluorescence Assay: A Systematic Review.

Authors:  Sandhya Dhawan; Matthew T Robinson; John Stenos; Stephen R Graves; Tri Wangrangsimakul; Paul N Newton; Nicholas P J Day; Stuart D Blacksell
Journal:  Am J Trop Med Hyg       Date:  2020-04-02       Impact factor: 2.345

5.  Serum C-reactive protein and procalcitonin values in acute Q fever, scrub typhus, and murine typhus.

Authors:  I-Fan Lin; Jiun-Nong Lin; Chia-Ta Tsai; Yu-Ying Wu; Yen-Hsu Chen; Chung-Hsu Lai
Journal:  BMC Infect Dis       Date:  2020-05-12       Impact factor: 3.090

Review 6.  Comparative effectiveness of azithromycin for treating scrub typhus: A PRISMA-compliant systematic review and meta-analysis.

Authors:  Szu-Chia Lee; Yu-Jyun Cheng; Chao-Hsu Lin; Wei-Te Lei; Hung-Yang Chang; Ming-Dar Lee; Jui-Ming Liu; Ren-Jun Hsu; Nan-Chang Chiu; Hsin Chi; Chun-Chih Peng; Te-Lung Tsai; Chien-Yu Lin
Journal:  Medicine (Baltimore)       Date:  2017-09       Impact factor: 1.889

7.  C-reactive protein as a potential biomarker for disease progression in dengue: a multi-country observational study.

Authors:  Nguyen Lam Vuong; Huynh Thi Le Duyen; Phung Khanh Lam; Dong Thi Hoai Tam; Nguyen Van Vinh Chau; Nguyen Van Kinh; Ngoun Chanpheaktra; Lucy Chai See Lum; Ernesto Pleités; Nick Keith Jones; Cameron Paul Simmons; Kerstin Rosenberger; Thomas Jaenisch; Christine Halleux; Piero Luigi Olliaro; Bridget Wills; Sophie Yacoub
Journal:  BMC Med       Date:  2020-02-17       Impact factor: 8.775

8.  Comparing machine learning with case-control models to identify confirmed dengue cases.

Authors:  Tzong-Shiann Ho; Ting-Chia Weng; Jung-Der Wang; Hsieh-Cheng Han; Hao-Chien Cheng; Chun-Chieh Yang; Chih-Hen Yu; Yen-Jung Liu; Chien Hsiang Hu; Chun-Yu Huang; Ming-Hong Chen; Chwan-Chuen King; Yen-Jen Oyang; Ching-Chuan Liu
Journal:  PLoS Negl Trop Dis       Date:  2020-11-10
  8 in total

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