ObjectiveGuidelines for thyroid function evaluation recommend testing TSH first, then assessing fT4 only if TSH is out of the reference range (two-step), but many clinicians initially request both TSH and fT4 (one-step). Given limitations of previous studies, we aimed to compare the two-step with the one-step approach in an unselected community-dwelling study population, and develop a prediction score based on clinical parameters that could identify at-risk patients for thyroid dysfunction.DesignCross-sectional analysis of the population-based Busselton Health Study.MethodsWe compared the two-step with the one-step approach, focusing on cases that would be missed by the two-step approach, i.e. those with normal TSH, but out-of-range fT4. We used likelihood ratio tests to identify demographic and clinical parameters associated with thyroid dysfunction and developed a clinical prediction score by using a beta-coefficient based scoring method.ResultsFollowing the two-step approach, 93.0% of all 4471 participants had normal TSH and would not need further testing. The two-step approach would have missed 3.8% of all participants (169 of 4471) with a normal TSH, but a fT4 outside the reference range. In 85% (144 of 169) of these cases, fT4 fell within 2 pmol/l of fT4 reference range limits, consistent with healthy outliers. The clinical prediction score that performed best excluded only 22.5% of participants from TSH testing.ConclusionThe two-step approach may avoid measuring fT4 in as many as 93% of individuals with a very small risk of missing thyroid dysfunction. Our findings do not support the simultaneous initial measurement of both TSH and fT4.

Census employment and income data for persons working in creative industries and creative occupations.

This dataset consists of 14 individual datasets that underpin the interactive dashboards on the project's Data Tables webpage.

Project background:

Australian cultural and creative activity: A population and hotspot analysis is an Australian Research Council Linkage project (LP160101724) being undertaken by QUT and the University of Newcastle, in partnership with Arts Queensland, Create NSW, Creative Victoria, Arts South Australia and the Western Australian Department of Local Government, Sport and Cultural Industries.

This comprehensive project aims to grasp the contemporary dynamics of cultural and creative activity in Australia. It brings together population-level and comparative quantitative and qualitative analyses of local cultural and creative activity. The project will paint a complete national picture, while also exploring the factors that are producing local and regional creative hotspots.

Creative hotspots for study were selected in consultation with state research partners:

Queensland – Cairns, Sunshine Coast + Noosa, Gold Coast, Central West Queensland
New South Wales – Coffs Harbour, Marrickville, Wollongong, Albury
Victoria – Geelong + Surf Coast, Ballarat, Bendigo, Wodonga
Western Australia – Geraldton, Fremantle, Busselton, Albany + Denmark
South Australia – to be confirmed shortly

Statistical summaries drawn from a diverse range of data sources including the Australian Census, the Australian Business Register, IP Australia registration data, infrastructure availability lists and creative grants and rights payments as well as our fieldwork, inform hotspot reports.

BackgroundHistorical data on smoking can enhance our comprehension of the effectiveness of past tobacco control policies and play a key role in developing targeted public health interventions. This study was undertaken to assess trends in smoking initiation and cessation in Australia for the period 1910–2005.MethodsRates of smoking initiation and cessation were calculated for participants in two population-based cohorts, the Busselton Health Study and the Tasmanian Longitudinal Health Study. The effects of time trends, gender and age group were evaluated.ResultsOf the 29,971 participants, 56.8% ever smoked. In males, over the period 1910–1999, the rate of smoking initiation in young adolescents remained high with a peak in the 1970s; in older adolescents it peaked in the 1940s and then declined; in young adults it showed a steady decline. In females, the rate of smoking initiation in young adolescents rose sharply in the 1960s and peaked in the 1970s, in older adolescents it increased throughout the period, and in young adults it declined after 1970. In the period 1930–2005, 27.3% of 9,605 people aged 36–50 years who smoked ceased smoking. Rates of cessation in this age group increased throughout but decreased in males after 1990 and plateaued around 2000 in females.ConclusionOur findings show substantial variation in the efficacy of tobacco control policies across age groups, with a notable lack of success among the younger population.

1AusDiab: Australian Diabetes Obesity and Lifestyle Study; 2BHS: Busselton Health Study; 3HDL-C: high density cholesterol; 4FBG: fasting blood glucose; 5SBP: systolic blood pressure; 6DBP: diastolic blood pressure; 72h-PLG: 2-h post load glucose; 8HbA1C: glycated hemoglobin; 9HOMA-IR: homeostasis model assessment of insulin resistance. aValues expressed as mean (±SD); bValues expressed as frequency, n (%) for dichotomous variables; cData in Median, (IQR) as Triglyceride distribution was right skewed. (XLSX)

Genome-Wide Association Study was performed on 82 Lipid Ratios using imputed genotype (13.8 million SNPs) data from the Busselton Health Study (n = 4,492). The top 10 SNPs to associate with each lipid ratio were included in the table. 1Lipid nomenclature is read as follows: letters denote the head group; () brackets denote the sum composition of the lipid measured including total number of carbons and double bonds; [] denote the position of the acyl chain; n3 and n6 denote omega-3 and omega-6 poly-unsaturated fatty acid species; 2SNP: single-nucleotide polymorphism; 3EAF: effect allele frequency; 4GWAS was performed on lipid ratios and individual lipid species to identify the significance of each SNP; 5SE: standard error; 6p-gain was calculated by dividing the lowest p-value among the lipid species used in the ratio by the p-value of the lipid ratio. The p-gain was deemed significant if it exceeded 10 × the number of significant SNPs identified for each ratio. Significant p-gains are depicted in red. (XLSX)

Linear regression analysis, adjusting for age and sex, was performed between 82 lipid ratios and various markers of obesity in the BHS cohort (n = 4,492). 1Lipid nomenclature is read as follows: letters denote the head group; () brackets denote the sum composition of the lipid measured including total number of carbons and double bonds; [] denote the position of the acyl chain; n3 and n6 denote omega-3 and omega-6 poly-unsaturated fatty acid species; 2SD-change denotes the standard deviation change per unit of waist circumference (WC), body-mass-index (BMI) or waist–hip ratio (WHR); 3p-gain was calculated by dividing the lowest p-value among the lipid species used in the ratio by the p-value of the lipid ratio. The p-gain was deemed significant if it exceeded 10 × the number of ratios tested (p-gain >820). (XLSX)

Linear regression analysis was performed between 82 lipid ratios and various markers of obesity, including sex as the interaction term and adjusting for age, in the BHS cohort (n = 4,492). 1Lipid nomenclature is read as follows: letters denote the head group; () brackets denote the sum composition of the lipid measured including total number of carbons and double bonds; [] denote the position of the acyl chain; n3 and n6 denote omega-3 and omega-6 poly-unsaturated fatty acid species; 2SD-change denotes the standard deviation change per unit of waist circumference (WC), body-mass-index (BMI), waist–hip ratio (WHR). 3Interaction p-value denotes whether the associations between each lipid ratio and obesity-marker are statistically different between males and females. 4p-gain was calculated by dividing the lowest p-value among the lipid species used in the ratio by the p-value of the lipid ratio. The p-gain was deemed significant if it exceeded 10 × the number of ratios tested (p-gain >820). (XLSX)