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Towards developing an instrument to measure knee and hip osteoarthritis (KHOA) flare, the Outcome Measures in Rheumatology (OMERACT) Flares in OA Working Group first sought to identify and define relevant domains of flare in KHOA. Guided by OMERACT Filter 2.1, candidate domains were identified from data generated in interviews, in English or French, with persons with KHOA and health professionals (HPs) who treat OA. The first and second rounds of an online Delphi process with patients and HPs, including researchers, selected relevant domains. The third round provided agreement on the selected domains and their definitions. At the virtual OMERACT 2020 workshop, the proposed domains and their definitions were discussed in facilitated breakout groups with patients and HPs. Participants then voted, with consensus set at ≥70%. Qualitative interviews characterizing OA flare were completed with 29 persons with KHOA and 16 HPs. Content was analyzed and grouped into nine clusters. These candidate domains were included in two Delphi rounds, completed by 91 patients and 165 HPs then 50 patients and 116 HPs, per round, respectively. This resulted in selecting five relevant domains. A final Delphi round, completed by 38 patients and 89 HPs, provided agreement on these domains and their definitions. The OMERACT virtual vote included 27 patients and 106 HPs. The domains and their definitions were endorsed with ≥98% agreement. Domains include: Pain, Swelling, Stiffness, Psychological aspects, and Impact of symptoms, all defined "during flare". Using OMERACT methodology, we have developed five domains of KHOA flare that were highly endorsed by patients and HPs. [ABSTRACT FROM AUTHOR]

Findings from observational studies investigating the association between Dietary Inflammatory Index (DII®) scores and depression symptoms (DepS) are inconsistent. This study aims to assess the association between energy-adjusted DII (E-DII™) and DepS using the North West Adelaide Health Study (NWAHS) cohort as well as update a previous meta-analysis. A total of 1743 (mean ± SD age: 56.6 ± 13.6 years, 51% female) study participants from NWAHS were included in the cross-sectional study and 859 (mean ± SD age: 58.4 ± 12.1 years, 52.6% female) in the longitudinal analyses. The Center for Epidemiological Studies Depression Scale (CES-D) was used for the measurement of DepS. E-DII scores were calculated from the dietary data collected using a validated food frequency questionnaire (FFQ). Data from two stages [Stage 3 (2008–10) and North West 15 (NW15) (2015)] were used. Log- and negative binomial regression were used to assess the association between quartiles of E-DII and DepS. A recent meta-analysis was updated by including 12 publications (six cross-sectional and six cohort studies) on the association between DII and DepS. In the cross-sectional analysis, a higher E-DII score (i.e., more pro-inflammatory diet) was associated with a 79% increase in odds of reporting DepS [OR Quartile4vs1 : 1.79; 95% CI: 1.14–2.81; p = 0.01; p for trend (p trend) = 0.03]. Males with higher E-DII had a more than two-fold higher odds of DepS (OR Quartile4vs1 : 2.27; 95% CI: 1.02–5.06; p = 0.045; p trend = 0.09). Females with higher E-DII had an 81% increase in odds of DepS (OR Quartile4vs1 : 1.81; 95% CI: 1.01–3.26; p = 0.046; p trend = 0.07). These associations were consistent in the longitudinal analysis. Comparing highest to lowest quintiles of E-DII, the updated meta-analysis showed that a pro-inflammatory diet is associated with a 45% increase in odds of having DepS (OR: 1.45; 95% CI: 1.20–1.74; p < 0.01) with higher odds in females (OR: 1.53; 95% CI: 1.16–2.01; p = 0.01) compared to their male counterparts (OR: 1.29; 95% CI: 0.98–1.69; p = 0.15). The data from the NWAHS and the updated meta-analysis of observational studies provide further evidence that a pro-inflammatory diet is positively associated with increased risk of DepS. These findings support the current recommendation on consuming a less inflammatory diet to improve DepS. [ABSTRACT FROM AUTHOR]

• Studies using strict definitions to investigate the association between metabolic phenotypes (MP) and diabetes are scarce. • Participants with obesity/healthy MP were not at increased diabetes risk compared to the ones with normal weight/healthy MP. • Reduced grip strength was associated with a higher risk of diabetes incidence in the long-term. • High grip strength attenuated the risk associated with metabolically unhealthy phenotypes. The association of diabetes risk in the long-term, metabolic phenotypes (MP) and muscle strength is unclear. We aimed to investigate the association between strictly defined MP, grip strength (GS) and diabetes. MP were defined according to BMI and presence of any individual metabolic abnormality for ≥18 years, in participants of the North West Adelaide Health Study (Australia) free of diabetes at baseline. The association of MP and dominant hand GS with incident diabetes over 15-years follow-up and the moderation effect of GS on the association between diabetes and MP were investigated by logistic regression models. Of 3039 participants followed over 13.3 years (SD 2.6), 236 (7.8%) developed diabetes. Compared to the metabolically healthy (MH) normal weight phenotype, the metabolically unhealthy (MU) overweight (OR 6.15, 95%CI 2.43–15.59) and obese (OR 12.32, 95%CI 4.97–30.52) phenotypes were associated with a high risk of diabetes, but not the MU normal weight (OR 1.73, 95%CI 0.57–5.25), MH overweight (OR 1.15, 95%CI 0.31–4.31) or MH obese phenotypes (OR 0.77, 0.07–8.89). GS was inversely associated with diabetes (OR 0.97, 95% CI 0.95–0.99) and attenuated the risk associated with MU overweight (beta = −0.296, p = 0.039) and MU normal weight (beta = −0.773; p for interaction = 0.009). Strictly defined MP (rather than based on metabolic syndrome criteria) and GS, a proxy of muscle strength, might be useful for stratifying the risk of diabetes in the long-term. Improving muscle strength might be an important strategy to reduce diabetes risk. [ABSTRACT FROM AUTHOR]

There have been inconsistent findings on the association between dietary patterns and depressive symptoms (DepS). In addition, studies have used single analysis methods to identify dietary patterns. In the current study, we aimed to determine the association between dietary patterns, derived by principal component analysis (PCA), reduced-rank regressions (RRR) and partial least-squares (PLS), and DepS among adults using a cohort study in Australia. We examined a total of 1743 study participants (≥24 years, 48.9% males) using cross-sectional and longitudinal data from the North West Adelaide Health Study (NWAHS). The Center for Epidemiological Studies-Depression (CES-D) scale was used to assess DepS and a score ≥16 was considered as having depression. Dietary data were collected using a food frequency questionnaire. Eicosapentaenoic acid (EPA)/Docosahexaenoic acid (DHA), folate, magnesium (Mg) and zinc (Zn) densities were chosen as the response variables for RRR and PLS analyses. Dietary patterns were identified by PCA, RRR and PLS. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated across quartiles (Q) using log-binomial logistic regression to assess the association between dietary patterns and DepS. Sensitivity analyses, including a longitudinal association between dietary patterns and DepS among 859 participants, were performed. Multiple imputation was performed to investigate the effect of missing data on the estimates. In this study, 16.9% (14.2% in men and 20.8% in women) of the participants had DepS. We retained two, four and four dietary patterns captured by PCA, RRR and PLS respectively. The 'prudent' pattern determined by PCA [OR Q4VsQ1 = 0.57; 95% CI: 0.35, 0.92] and PLS [OR Q4VsQ1 = 0.66; 95% CI: 0.43, 1.00] together with the 'typical Australian' pattern determined by RRR [OR Q4VsQ1 = 0.60; 95% CI: 0.40, 0.90] were inversely associated with DepS whereas the 'western' pattern derived by PCA [OR Q4VsQ1 = 2.04; 95% CI: 1.12, 3.68] and PLS [OR Q4VsQ1 = 1.62; 95% CI: 1.05, 2.50] was positively associated with DepS. In the longitudinal analysis, the 'prudent' pattern determined by PCA [OR Q4VsQ1 = 0.52; 95% CI: 0.25, 1.09] tended to be inversely associated with DepS whereas 'western' patterns determined by PCA [OR Q4VsQ1 = 3.47; 95% CI: 1.37, 8.78] and PLS [OR Q4VsQ1 = 2.47; 95% CI: 1.24, 4.91] were positively associated with DepS. We found that a dietary pattern characterized by high intakes of fruits, vegetables, medium fat dairy, nuts, legumes, and fish was inversely associated with DepS in this population-based study. Contrary to this, a dietary pattern characterized by high intakes of processed and red meat, fast foods (snacks and takeaway foods), soft drinks, white bread and high-fat dairy products were significantly associated with DepS. Multiple imputation and sensitivity analysis identified similar patterns of association between dietary pattern and DepS. The findings indicate that the 'western' pattern was consistently associated with an increased risk, and the 'prudent' pattern tended to be associated with a reduced risk of DepS. This suggests that dietary interventions may assist with the treatment of DepS. However, current evidence on the impact of diet on DepS should be supported using further longitudinal studies with extended follow up, larger sample sizes and repeated measures. [ABSTRACT FROM AUTHOR]

Summary: Background: To determine the contribution of overweight and obesity to five chronic conditions using the population attributable risk (PAR) statistic. Method: Self-reported data were collected in Australia using a representative, ongoing monthly risk factor surveillance system using computer assisted telephone interviewing (CATI) for the period 2004–2006 (n =15,261). The PAR, adjusted for various demographic and health-related risk factors, was used to determine the contribution of overweight and obesity to diabetes, asthma, cardiovascular disease (CVD), arthritis and osteoporosis for people aged 18 years and over. Results: In total, 19.0% of South Australians aged 18 years and over were obese, and 36.0% were overweight. The PAR of overweight and obesity was highest among those with diabetes (17.3% and 28.4%). After adjusting for demographic and risk factors, overweight and obesity had a significantly high PAR for diabetes (11.3% and 23.1%), asthma (4.0% and 10.3%) and arthritis (6.7% and 8.1%). The unadjusted PAR of overweight and obesity for CVD was significant (10.8% and 11.7%) but was reduced when controlled for other factors. The PAR of overweight and obesity was significant for osteoporosis but the association was protective. Conclusions: The use of PAR contributes to the understanding of how overweight and obesity are related to various chronic conditions however the size of this association depends on the chronic disease and other socio-demographic and health-related risk factors that may or may not be modifiable. The information provides evidence of the association of overweight and obesity with chronic disease and the potential for reducing the burden of chronic disease by addressing the obesity epidemic. [Copyright &y& Elsevier]

Summary: Objective: To examine the trends in the prevalence of classes I, II and III obesity between 1991 and 2006 among the South Australian adult population. In addition, to explore the association of severe (class II and III) obesity with a range of socio-demographics, chronic conditions and risk factor variables. Method: Trends of self-reported obesity prevalence were examined using representative, annual, face-to-face South Australian Health Omnibus Surveys from 1991 to 2006 (n ≈3000 per year). Biomedical data, including measured height and weight, were collected in the North West Adelaide Health (cohort) Study (NWAHS), a representative random adult sample selected from the electronic white pages (EWP) (n =4060). Results: The age standardised prevalence of self-reported class II and III obesity among those aged 18 years and over increased from 2.4% in 1991 to 8.1% in 2006. The greatest relative percentage increase over this time was seen amongst those with class III obesity (452.3%). Using biomedical data, multivariate analysis results indicated that among those who were obese, women were more than two and a half times more likely than men to be of class II and III. Among those who were obese, those aged 20–54 years and living in the low/lowest quintiles of Socioeconomic Indexes for Areas, Index of Relative Socioeconomic Disadvantage (SEIFA IRSD) were statistically significantly more likely to be class II or III obese when compared to those in the highest categories. Conclusion: The prevalence of class II and III obesity increased significantly between 1991 and 2006. The socio-demographics of those who are class II and III obesity are different from those that are normally described for obesity as a whole especially in regard to the younger age cohort. The current prevalence of severe obesity within Australia is probably underestimated and these results highlight the need to address this sub-group of the population. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to determine the trend in influenza vaccine coverage among community dwelling adults in South Australia aged 65 years and over and compare this prevalence with national and international influenza vaccine coverage. Immunisation prevalence has been measured in South Australia (SA) since 1993 using face to face surveys and Computer Assisted Telephone Interviewing (CATI). There has been an increasing trend in influenza immunisation in South Australia over the last 12 years among community dwelling adults aged 65 years and over. This indicates that policies and campaigns have impacted positively on influenza vaccine uptake. [Copyright &y& Elsevier]

Social jetlag is a term used to describe misalignment between biological and social time. Measured as the difference in sleep midpoints between work and free days, social jetlag has been associated with unhealthy lifestyle behaviours and adverse health outcomes. This study aimed to identify the prevalence of social jetlag, and its sociodemographic and behavioural correlates in 837 respondents who completed the Sleep Health Foundation Australia 2016 online survey. Binomial logistic regression models determined associations between social jetlag and self-reported lifestyle and work outcomes, excluding night, evening or rotating shift workers. One third (31.1%) of respondents experienced >1h of social jetlag. In analyses adjusted for sociodemographic variables associated with social jetlag (age, marital status, work status and metropolitan living plus the significant interaction term for age by metro living), social jetlag was associated with longer sleep duration on free days (OR = 2.8, CI = 1.9-4.1), evening preference (OR = 2.0, CI = 1.4-2.4), often staying up later than planned on work days (OR 1.9, CI = 1.3-2.9), and having a computer (OR = 1.7, CI = 1.2-2.4) or phone (OR = 1.6, CI = 1.1-2.4) in the bedroom and internet use in the hour before bed (OR = 1.7, CI 1.2-2.5). Almost twice as many working respondents with social jetlag reported going to work when they should have taken sick leave due to their state of health (OR = 1.9, CI = 1.3-3.0). In conclusion, social jetlag is prevalent in the Australian community and associated with bedtime technology use. Work attendance when in poor health is cause for concern in Australian day workers and requires further investigation. [ABSTRACT FROM AUTHOR]

Objectives: Attrition in cohort studies can cause the data to be nonreflective of the original population. Although of little concern if intragroup comparisons are being made or cause and effect assessed, the assessment of bias was undertaken in this study so that intergroup or descriptive analyses could be undertaken. Study Design and Setting: The North West Adelaide Health Study is a chronic disease and risk factor cohort study undertaken in Adelaide, South Australia. In the original wave (1999), clinical and self-report data were collected from 4,056 adults. In the third wave (2008e2010), 2,710 adults were still actively involved. Comparisons were made against two other data sources: Australian Bureau of Statistics Estimated Residential Population and a regular conducted chronic disease and risk factor surveillance system. Results: Comparisons of demographics (age, sex, area, education, work status, and income) proved to be statistically significantly different. In addition, smoking status, body mass index, and general health status were statistically significant from the comparison group. No statistically significant differences were found for alcohol risk. Conclusion: Although the third wave of this cohort study is not representative of the broader population on the variables assessed, weighting of the data and analytical approaches can account for differences. [ABSTRACT FROM AUTHOR]

Objective: To assess whether monosodium glutamate (MSG) intake is associated with sleepdisordered breathing (SDB). Methods: Data from 1227 Chinese subjects who participated in the Jiangsu Nutrition Study were analyzed. All the participants were examined at two time points (baseline in 2002 and follow-up in 2007). The MSG intake was assessed quantitatively in 2002 and a sleep questionnaire was used to assess snoring and to construct an SDB probability score in 2007. Those within the fifth quintile of the score (highest) were defined as having a high probability of SDB. Results: The MSG intake was positively associated with snoring and a high probability of SDB in participants who had a normal body weight but in those who were overweight. A comparison of the extreme quartiles of MSG intake in subjects with a body mass index lower than 23 kg/m² showed an odds ratio of 2.02 (95% confidence interval 1.02-4.00) for snoring and an odds ratio of 3.11 (95% confidence interval 1.10-8.84) for a high probability of SDB. There was a joint effect between MSG and overweight in relation to SDB. Conclusion: The intake of MSG may increase the risk of SDB in Chinese adults with a normal body weight. [ABSTRACT FROM AUTHOR]