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Manasi Gaikwad,1,2 Simon Vanlint,1 G Lorimer Moseley,2,3 Murthy N Mittinty,4 Nigel Stocks1 1Discipline of General Practice, School of Medicine, University of Adelaide, Adelaide, SA, 2Sansom Institute for Health Research, University of South Australia, Adelaide, SA, 3Neuroscience Research Australia, Sydney, NSW, 4School of Public Health, University of Adelaide, Adelaide, SA, Australia Background: It is widely recognized that both doctors and patients report discontent regarding pain management provided and received. The impact of chronic pain on an individual’s life resonates beyond physical and mental suffering; equal or at times even greater impact is observed on an individual’s personal relationships, ability to work, and social interactions. The degree of these effects in each individual varies, mainly because of differences in biological factors, social environment, past experiences, support, and belief systems. Therefore, it is equally possible that these individual patient characteristics could influence their treatment outcome. Research shows that meeting patient expectations is a major challenge for health care systems attempting to provide optimal treatment strategies. However, patient perspectives and expectations in chronic pain management have not been studied extensively. The aim of this study is to investigate the views, perceptions, beliefs, and expectations of individuals who experience chronic pain on a daily basis, and the strategies used by them in managing chronic pain. This paper describes the study protocol to be used in a cross sectional survey of chronic pain patients.Methods and analysis: The study population will comprise of individuals aged ≥18 years, who have experienced pain for ≥3 months with no restrictions of sex, ethnicity, or region of residence. Ethics approval for our study was obtained from Humans research ethics committees, University of Adelaide and University of South Australia. Multinomial logistic regression will be used to estimate the effect of duration and character of pain, on patient’s perception of time to recovery and supplement intake. Logistic regression will also be used for estimating the effect of patient-provider relationship and pain education on patient-reported recovery and pain intensity.Discussion: Knowledge about the perceptions and beliefs of patients with chronic pain could inform future policies, research, health care professional education, and development of individualized treatment strategies. Keywords: chronic pain, pain management, patient perspectives, survey, time to recovery

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.