Indian Journal of Public Health Research & Development
  • Year: 2017
  • Volume: 8
  • Issue: 1

Clinical Predictors of Associated with Influenza A (H1N1) during Epidemic 2015 at Tertiary Care Hospital, Ahmedabad

1Resident, Department of Microbiology, B.J. Medical Collage, Ahmedabad, Gujarat, India

2Professor and Head of the Department, Department of Microbiology, B.J. Medical Collage, Ahmedabad, Gujarat, India

3Associate Professor, Department of Microbiology, B.J. Medical Collage, Ahmedabad, Gujarat, India

*Corresponding author: Gamit Mital, 3rd Year Resident, Microbiology Department B.J. Medical Collage, Ahmedabad, India, Email id: gamit.mital@gmail.com

Online published on 4 January, 2017.

Abstract

Influenza is a viral infection that can lead to serious complications and death(s) in vulnerable groups if not diagnosed and managed in a timely manner. This study was conducted to improve the accuracy of predicting influenza through various clinical and statistical models.

A retrospective cross sectional analysis was done on demographic and epidemiological data collected from January 2015 to March 2015. Patients were classified as ILI or SARI using WHO case definitions. Respiratory specimens were tested by rRT-PCR. Clinical symptoms were analyzed using by CDC Epi info 7.

In the first approach, analysis compared children (12) and adults (12). Of 1782 cases, 938 (52.64%) tested positive for novel influenza A(H1N1) and the proportion were 43.93 and 55.39% respectively. Symptoms predicted in children: fever (OR 1.909, 95% CI 0.99–3.794), cough (OR 1.762, 95% CI 1.114–2.816), and shortness of breath (OR 1.453, 95% CI 0.98–2.158). In adults, the strongest clinical predictor was fever (OR 1.448, 95% CI 1.046–2.007) and coughs (OR 1.594, 95% CI1.219–2.087). In the second instance, patients were separated into two groups: SARI 1398 and ILI 384 cases. Chi-square test showed that fever, cough, sore throat and shortness of breath were significant predictors.

Studies in a primary care setting should be encouraged focused on patients with influenza-like illness to develop sensitive clinical case definition that will help to improve accuracy of detecting influenza infections. Formulation of a standard ‘‘one size fits all’ case definition that best correlates with influenza infections can help guide decisions for additional diagnostic testing and also discourage unjustified antibiotic prescription and usage in clinical practice.

Keywords

Influenza A H1N1, Epidemic, rRT-PCR, ILI (influenza like illness), SARI (severe acute respiratory illness)