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This study presents enhanced surveillance data from 2004 – 2018 for all community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) specimens collected in Western Australia (WA), and describes the changing epidemiology over this period. A total of 57,557 cases were reviewed. Annual incidence rates increased from 86.2 cases per 100,000 population to 245.6 per 100,000 population (IRR = 2.9, CI95 2.7 – 3.0). The proportion of isolates carrying Panton-Valentine leukocidin (PVL)-associated genes increased from 3.4% to 59.8% (χ2 test for trend 7021.9, p<0.001). The emergence of PVL-positive, “Queensland CA-MRSA” (ST93- IV) and “WA 121” (ST5-IV) accounted for the majority of increases in CA-MRSA across the study period. It is unclear why some clones are more prolific in certain regions. In WA, CA-MRSA rates increase as indices of temperature and humidity increase after controlling for socioeconomic disadvantage. We suggest climatic conditions may contribute to transmission, along with other socio-behavioural factors. A better understanding of the ability for certain clones to form ecological niches and cause outbreaks is required.

Clostridium difficile, the most common cause of hospital-associated diarrhoea in developed countries, presents major public health challenges. The high clinical and economic burden from C. difficile infection (CDI) relates to the high frequency of recurrent infections caused by either the same or different strains of C. difficile. An interval of 8 weeks after index infection is commonly used to classify recurrent CDI episodes. We assessed strains of C. difficile in a sample of patients with recurrent CDI in Western Australia from October 2011 to July 2017. The performance of different intervals between initial and subsequent episodes of CDI was investigated. Of 4612 patients with CDI, 1471 (32%) were identified with recurrence. PCR ribotyping data were available for initial and recurrent episodes for 551 patients. Relapse (recurrence with same ribotype (RT) as index episode) was found in 350 (64%) patients and reinfection (recurrence with new RT) in 201 (36%) patients. Our analysis indicates that 8- and 20-week intervals failed to adequately distinguish reinfection from relapse. In addition, living in a non-metropolitan area modified the effect of age on the risk of relapse. Where molecular epidemiological data are not available, we suggest that applying an 8-week interval to define recurrent CDI requires more consideration.

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.

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.

Alfayyadh, M., Collins, D. A., Tempone, S., McCann, R., Armstrong, P. K., Riley, T. V., & Cook, A. (2019). Recurrence of Clostridium difficile infection in the Western Australian population. Epidemiology and Infection, 147, Article e153. Available here