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The effects of religion, population sub-division and geography on the prevalence of deaf-mutism were investigated using information collected in the 1921 Census of Punjab. The total sample size was 9·36 million, and comprised data on thirteen Hindu castes, seventeen Muslim biraderis and two Sikh castes. A two-way analysis of variance comparing males in Hindu castes in which consanguineous marriage was prohibited, with males in Muslim biraderis which favoured first cousin marriage, indicated major differences with respect to the patterns of deaf-mutism within each religion. In the Muslim population 9·1% of the relative variation in the prevalence of deaf-mutism was inter-biraderi, 36·8% between geographical regions, and 48·8% an interaction between biraderi and region, whereas among Hindus 46·8% of the observed variation was inter-caste, 12·8% inter-region and 33·6% due to caste–region interaction. From a wider disease perspective the results obtained with the Hindu community indicate the significant genetic differentiation associated with caste endogamy. As the overwhelming majority of Hindu marriages continue to be within-caste, it can be predicted that similar levels of inter-caste differences in disease frequency currently exist. By comparison, the lower level of inter-biraderi variation among Muslims is probably indicative of the dissolution of pre-existing caste boundaries and the resultant gene pool mixing that followed the large-scale conversion of Hindus to Islam during Muslim rule in North India from the 13th to the 19th centuries.

Data were pooled from three Australian sentinel general practice influenza surveillance networks to estimate Australia-wide influenza vaccine coverage and effectiveness against community presentations for laboratory-confirmed influenza for the 2012, 2013 and 2014 seasons. Patients presenting with influenza-like illness at participating GP practices were swabbed and tested for influenza. The vaccination odds of patients testing positive were compared with patients testing negative to estimate influenza vaccine effectiveness (VE) by logistic regression, adjusting for age group, week of presentation and network. Pooling of data across Australia increased the sample size for estimation from a minimum of 684 to 3,683 in 2012, from 314 to 2,042 in 2013 and from 497 to 3,074 in 2014. Overall VE was 38% [95% confidence interval (CI) 24-49] in 2012, 60% (95% CI 45-70) in 2013 and 44% (95% CI 31-55) in 2014. For A(H1N1)pdm09 VE was 54% (95% CI-28 to 83) in 2012, 59% (95% CI 33-74) in 2013 and 55% (95% CI 39-67) in 2014. For A(H3N2), VE was 30% (95% CI 14-44) in 2012, 67% (95% CI 39-82) in 2013 and 26% (95% CI 1-45) in 2014. For influenza B, VE was stable across years at 56% (95% CI 37-70) in 2012, 57% (95% CI 30-73) in 2013 and 54% (95% CI 21-73) in 2014. Overall VE against influenza was low in 2012 and 2014 when A(H3N2) was the dominant strain and the vaccine was poorly matched. In contrast, overall VE was higher in 2013 when A(H1N1)pdm09 dominated and the vaccine was a better match. Pooling data can increase the sample available and enable more precise subtype- and age group-specific estimates, but limitations remain. ; The Australian Sentinel Practices Research Network and the Melbourne WHO Collaborating Centre for Reference and Research on Influenza are supported by the Australian Government Department of Health. The Sentinel Practitioners Network of Western Australia is funded by the Western Australian Department of Health. The Victorian Sentinel Practice Influenza Network is funded by the Victorian Government Department of Health and Human Services.