Influenza A(H1N1)pdm09 outbreak detected in inter-seasonal months during the surveillance of influenza-like illness in Pune, India, 2012-2015.

An outbreak of influenza A(H1N1)pdm09 was detected during the ongoing community-based surveillance of influenza-like illness (ILI). Among reported 119 influenza A(H1N1)pdm09 cases (59 cases in the year 2012 and 60 cases in 2015) in summer months, common clinical features were fever (100%), cough (90·7%), sore throat (85·7%), nasal discharge (48·7%), headache (55·5%), fatigue (18·5%), breathlessness (3·4%), and ear discharge (1·7%). Rise in ILI cases were negatively correlated with the seasonal factors such as relative humidity (Karl Pearson's correlation coefficient, i.e. r = -0·71 in the year 2012 and r = -0·44 in the year 2015), while rise in ILI cases were positively correlated with the temperature difference (r = 0·44 in the year 2012 and r = 0·77 in the year 2015). The effective reproduction number R, was estimated to be 1·30 in 2012 and 1·64 in 2015. The study highlights the rise in unusual influenza activity in summer month with high attack rate of ILI among children aged ⩽9 years. Children in this age group may need special attention for influenza vaccination. Influenza A(H1N1)pdm09 outbreak was confirmed in inter-seasonal months during the surveillance of ILI in Pune, India, 2012-2015.

A meta-analysis of the diagnostic accuracy of dengue virus-specific IgA antibody-based tests for detection of dengue infection.

Immunoglobulin A (IgA)-based tests have been evaluated in different studies for their utility in diagnosing dengue infections. In most of the studies, the results were inconclusive because of a small sample size. Hence, a meta-analysis involving nine studies with 2096 samples was performed to assess the diagnostic accuracy of IgA-based tests in diagnosing dengue infections. The analysis was conducted using Meta-Disc software. The results revealed that IgA-based tests had an overall sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratios of 73·9%, 95·2%, 66·7, 22·0 and 0·25, respectively. Significant heterogeneity was observed between the studies. The type of test, infection status and day of sample collection influenced the diagnostic accuracy. The IgA-based diagnostic tests showed a greater accuracy when the samples were collected 4 days after onset of symptoms and for secondary infections. The results suggested that IgA-based tests had a moderate level of accuracy and are diagnostic of the disease. However, negative results cannot be used alone for dengue diagnosis. More prospective studies comparing the diagnostic accuracy of combinations of antigen-based tests with either IgA or IgM are needed and might be useful for suggesting the best strategy for dengue diagnosis.