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With more and more political candidates using social media for campaigning, researchers are looking at measuring the effectiveness of this medium. Most research, however, concentrates on the bare count of likes (or twitter mentions) in an attempt to correlate social media presence and winning. In this paper, we propose a novel method, Interaction Strength Plot (IntS) to measure the passive interactions between a candidate’s posts on Facebook and the users (liking the posts). Using this method on original Malaysian General Election (MGE13) and Australian Federal Elections (AFE13) Facebook Pages (FP) campaign data, we label an FP as performing well if both the posting frequency and the likes gathered are above average. Our method shows that over 60% of the MGE13 candidates and 85% of the AFE13 candidates studied in this paper had under-performing FP. Some of these FP owners would have been identified as popular based on bare count. Thus our performance chart is a vital step forward in measuring the effectiveness of online campaigning. [ABSTRACT FROM AUTHOR]

Background: Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment. Methods: We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates. Results: The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7). Conclusions: Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance. [ABSTRACT FROM AUTHOR]

Objective: Children with severe obesity have greater risk of adverse health outcomes. The purpose of this study was to assess trends in the prevalence of morbid and severe obesity in Australian children between 1985 and 2012. Methods: Secondary analysis of four national Australian cross-sectional surveys of measured height/weight in 7–15 year olds: Australian Health and Fitness Survey 1985 (n = 8,486), National Nutrition Survey 1995 (n = 1,541), the National Children’s Nutrition and Physical Activity Survey 2007 (n = 2,585) and the National Health Survey 2012 (n = 2,940). International Obesity Taskforce cut-point was used for morbid obesity (equivalent to a BMI ≥35kg/m2 at age 18 years). Severe obesity class 2 was defined as BMI ≥120% and <140% of the 95th percentile of the CDC 2000 growth charts or a BMI ≥35 and <40, and severe obesity class 3 as BMI ≥140% of the 95th percentile or a BMI ≥40. Results: Between 1985 and 2012 the prevalence of morbid obesity increased from 0.2% to 1.8%, class 2 severe obesity from 0.3% to 2.0%, and class 3 from 0.1% to 0.5%. Children with morbid obesity represented 11.3% of children with obesity in 1985 and increased to 22.5% in 2012 (P = 0.005). Children with severe obesity represented 19.3% of children with obesity in 1985 and increased to 32.0% in 2012 (P = 0.016). The greatest increase was observed between 1995 and 2007. The proportion of children who were classified as morbidly or severely obese was not significantly different between 2007 and 2012, nor was it significantly different between age and sex groups. Conclusion: Prevalence of morbid and severe obesity among children is low, but has significantly increased between 1985 and 2012. In contrast to overweight and obese children, children with morbid obesity require tertiary intervention. Failure to treat these children will have significant implications for the individual child and community. [ABSTRACT FROM AUTHOR]