Socio-Economic Indexes For Areas Research Articles

Association between protein intake, diet quality, and obesity in Australian adults: A comparison of measurement units

The role of protein in decreasing the risk of cardiometabolic diseases has been proposed, yet the findings are inconsistent, possibly due to how protein intake was expressed(1). This study aimed to examine how different ways of expressing protein intake may influence its relationships with diet quality and obesity. This study used data from the Australian National Nutrition and Physical Activity Survey (NNPAS) 2011-12, focusing on adults aged ≥19 years, excluding those who were pregnant or lactating, and had both anthropometric and dietary data (n = 7637). Total protein intake was assessed by up to two 24-hour dietary recalls and reported in two measurement units, namely g/day and % of energy intake. Usual protein (g) and energy intakes (kJ) were modelled using the Multiple Source Method(2). Diet quality was assessed using the Dietary Guidelines Index (DGI)(3). Body mass index (BMI) and waist circumference were used as measures of obesity. Multiple linear regressions were performed stratified by sex, and adjusting for age, country of birth, Socio-economic Indexes for Areas, physical activity level, energy misreporting, usual energy intake (for diet quality), and non-protein energy intake (for obesity). Positive associations between protein intake and diet quality were confirmed across measurement units. Protein intake expressed in % of energy intake (β-coefficient: men = 0.97; women = 1.21, p<0.001) had a higher β-coefficient than those reported in g/day (β-coefficient: men = 0.19; women = 0.30, p<0.001). Linear regression models showed a positive association between protein intake and BMI for men and women, either expressed in g/day (β-coefficient: men = 0.02, p<0.001; women = 0.03, p = 0.001) or % of energy intake (β-coefficient: men = 0.14, p<0.001; women = 0.12, p = 0.002). The relationship between protein intake expressed in g/day and waist circumference was also statistically significant (β-coefficient: men = 0.04, p = 0.004; women = 0.05, p = 0.035). However, protein intake expressed as % of energy intake was correlated with waist circumference for men only (β-coefficient: men = 0.26, p = 0.001; women = 0.19, p = 0.075). The consistent findings across measurement units suggested that the unit of g/day and % of energy intake can be used for examining associations between protein intake and diet quality. However, associations between protein intake and measures of obesity varied between protein units and sex. These findings suggest that the selection of protein measurement units in relation to obesity outcomes might need to consider the characteristics of study population (e.g., sex, energy intake). Overall, the outcomes of this study suggest that how protein is expressed may impact the associations between protein intake, diet quality, and obesity, and therefore require further considerations when examining the role of protein in cardiometabolic health.

Access to child developmental assessment services in culturally and linguistically diverse metropolitan Sydney: a retrospective cohort analysis

BackgroundDespite the increasing prevalence of neurodevelopmental disorders (NDD), data regarding access to child development services have remained limited globally. Long wait times are a major barrier to developmental assessments, impacting on care and outcomes. The aim is to retrospectively analyse the demographic profile and prioritisation of patients seen at a child developmental assessment service (CDAS) in a vulnerable region of Sydney, and explore factors affecting wait times.MethodsData was collated and analysed for 2354 patients from 2018 to 2022. Socio-Economic Indexes for Areas (SEIFA) were collated from the Australian Bureau of Statistics. Descriptive statistics were used for demographic data and various statistical methods were used to analyse the relationships and impact of factors likely to affect wait lists.ResultsThe median age was 51 months (IQR41-61) and males comprised 73.7% of the cohort. 64% of children were from culturally and linguistically diverse backgrounds (CALD) and 47% lived in the most disadvantaged suburbs. The median wait time was 302.5 days (IQR175-379) and 70% of children were seen within 12 months. CALD patients and children over 5-years had shorter wait times. Most children with Global Developmental Delay (GDD) were from the lowest four SEIFA deciles and waited longer for an appointment. 42.6% were seen within the priority allocated time or sooner. Children with ASD and/or severe GDD were prioritised to be seen earlier. Overall, the study could not demonstrate any difference in the wait times according to the prioritisation groups.ConclusionThis study provides insights into the profile, prioritisation processes and wait lists of children seen by CDAS in South Western Sydney with high rates of social vulnerability and presents an argument to discuss benchmarking targets with service providers. It identifies the need to prioritise children living in suburbs with socioeconomic disadvantage and refine prioritisation and data collection processes to improve wait times.

Caesarean sections before and during the COVID-19 pandemic in western Sydney, Australia

Background To determine the changes in emergency and elective caesarean section (CS) rates since the COVID-19 pandemic, identify the groups most affected, and examine changes in the factors associated with CS rates, and reasons for CS. Methods We conducted a retrospective cohort study using routinely collected data of 22,346 births from before the pandemic (January 2018–February 2020) and 18,597 births during the pandemic (March 2020–December 2021). Data were analysed using multinominal logistic regression. Results The CS rate increased by 4.1% (from 30.1% to 34.2%), reflecting increases of 2.3% in emergency CS (from 11.5% to 13.8%) and 1.7% in elective CS (from 18.7% to 20.4%). Large groups with notable increases were women who were nulliparous (7.2% increase), from South Asia (6.0%), obese (5.2%) and giving birth at a small hospital (6.1%). Compared to pre-pandemic, the relative risk of an emergency CS versus a vaginal delivery increased 1.36 times (adjusted relative risk ratio (aRRR) = 1.36; 95% CI = 1.27, 1.45) and the risk of having an elective CS increased 1.11 times (aRRR = 1.11; 95% CI = 1.04, 1.20). Factors associated with both emergency and elective CS were age, region of birth, reproductive history, body mass index, hypertension, diabetes, mode of antenatal care and hospital. Socio-Economic Indexes for Areas and antenatal care were only associated with elective CS. Baby gender was only associated with emergency CS. Preterm gestation at delivery was associated with reduced emergency but increased elective CS. Foetal compromise was the most common indication for emergency CS (43.2%) and increased the most (8.0%). Previous CS was the most common indication for elective CS (61.5%) and reduced the most (1.9%). Conclusions Both emergency and elective CS rates increased significantly during the pandemic, with the former increasing at a higher rate. The persistent upward trend of CS rates, exacerbated by increasing proportions of nulliparous women undergoing CSs, is concerning.

Oral Anticoagulant Use in Patients with Atrial Fibrillation at Low Risk of Stroke and Associated Bleeding Complications.

The use of oral anticoagulants (OACs) in patients with atrial fibrillation (AF) and low stroke risk might cause more harm than benefit. Little attention has been given to address its prevalence and associated consequences. This study aimed to investigate the prescription rate of OACs, identify associated factors, and describe incident bleeding events in low-risk patients. We included patients with a new diagnosis of AF between 1 January 2011 and 31 December 2018 having a low risk of stroke (CHA2DS2-VASc score of 0 for males and 1 for females) from Australian general practice data (MedicineInsight). Patients were classified as OAC users if there was a recorded prescription of an OAC within 60 days of AF diagnosis, and factors associated with the prescription of an OAC were assessed using logistic regression. Recorded incident bleeding events were identified within 6 months after AF diagnosis or after OAC initiation for OAC non-users and users, respectively. The risk of bleeding was compared between the two groups by adjusting their baseline differences using propensity score matching. The study included 2810 low-risk patients (62.3% male) with a mean age of 49.3 ± 10.8 years. Of the total, 705 (25.1%) patients had a record of OAC prescription within 60 days of diagnosis of AF. Older age (odds ratio [OR] 1.03; 95% confidence interval [CI] 1.03-1.04) and diagnosis periods (2015-2016 [OR 1.46; 95% CI 1.10-1.94] and 2017-2018 [OR 1.65; 95% CI 1.17-2.23] vs. 2011-2012) were associated with higher odds of OAC initiation. Female sex (OR 0.71; 95% CI 0.59-0.85), higher bleeding risk (ORBIT score; OR 0.80; 95% CI 0.68-0.94), and higher socioeconomic index for areas (SEIFA) quintiles (SEIFA quintiles; 2 [OR 0.65; 95% CI 0.48-0.88], 3 [OR 0.74; 95% CI 0.56-0.98], 4 [OR 0.70; 95% CI 0.52-0.94], 5 [OR 0.69; 95% CI 0.52-0.91] compared with quintile 1) were associated with lower odds of OAC prescription. A total of 52 (in 1.8% of patients) incident bleeds were identified, with 18 (2.6%) among OAC users. The rate of bleeding was not significantly different between users and non-users after matching. However, within OAC users, commencement of OAC was associated with an increased risk of bleeding compared to the period before OAC initiation (p = 0.006). One in four patients at low risk of stroke received an OAC within 60 days of AF diagnosis. Older age and the period following the widespread availability of direct-acting OACs were associated with an increased likelihood of OAC prescription. Positively, using OACs was not associated with an increased rate of bleeding compared to non-users.

Impact of the Medicare Benefits Schedule Rebate (MBSR) freeze on General Practice (GP) use: multivariable regression analysis

BackgroundIn 2015, the Australian government froze the Medicare Benefits Schedule Rebate (MBSR) for General Practitioner (GP) service use. This paper aimed to explore the impact of the MBSR freeze on the demand for GP services in Victoria, Australia, for three years, from 2014 to 2016.MethodAnnual data on GP service utilisation by the Victorian State Statistical Area Level 3 (SA3) were analysed using 2015 as the reference year (MBSR freeze year). We compared annual per-person GP service use before and after the MBSR freeze for each SA3. Socioeconomic Indexes for Areas (SEIFA) scores and regions of Victoria (Greater Melbourne and the Rest of Victoria) were used to identify the most disadvantaged SA3s in Victoria. We conducted a multivariable regression analysis for the number of GP services per patient by SA3, controlling for regions of Victoria, the number of GP services, the proportion of bulk-billed visits, age group, gender and year.FindingsAfter adjusting for age group, gender, region, SEIFA, the number of GPs and the proportion of bulk-billed GP visits, mean GP services per person per year declined steadily between 2014 and 2016, with a 3% or 0.11 visit (-0.114, 95%CI: -0.134; -0.094, P = < 0.001) reduction in mean utilisation in 2016 compared to 2014. In disadvantaged SA3s, there was a fall in the number of GP services that were bulk-billed during and after the MBSR freeze compared to 2014, and this fall was large in LOW SEIFA SA3s, with a reduction in 17% of mean bulk-billed GP services.ConclusionThe MBSR freeze for GP consultations in 2015 resulted in a reduction in the annual per capita demand for GP visits, with the impact of reduced demand more significant in lower socioeconomic and regional/rural areas. The GP funding policies must consider the demand differences by social-economic status and location.

Equitable psychiatry, telehealth, and the COVID-19 pandemic: Analysis of national data.

At the beginning of the COVID-19 pandemic, it was foreseen that the number of face-to-face psychiatry consultations would suffer a reduction. In order to compensate, the Australian Government introduced new Medicare-subsidized telephone and video-linked consultations. This study investigates how these developments affected the pre-existing inequity of psychiatry service delivery in Australia. The study analyses five and a half years of national Medicare data listing all subsidized psychiatry consultation consumption aggregated to areas defined as Statistical Area level 3 (SA3s; which have population sizes of 30 k-300 k). Face-to-face, video-linked and telephone consultations are considered separately. The analysis consists of presenting rates of consumption, concentration graphs, and concentration indices to quantify inequity, using Socio Economic Indexes for Areas (SEIFA) scores to rank the SA3 areas according to socio-economic disadvantage. There is a 22% drop in the rate of face-to-face psychiatry consultation consumption across Australia in the final study period compared with the last study period predating the COVID-19 pandemic. However, the loss is made up by the introduction of the new subsidized telephone and video-linked consultations. Referring to the same time periods, there is a reduction in the inequity of the distribution of face-to-face consultations, where the concentration index reduces from 0.166 to 0.129. The new subsidized video-linked consultations are distributed with severe inequity in the great majority of subpopulations studied. Australia-wide, video-linked consultations are also distributed with gross inequity, with a concentration index of 0.356 in the final study period. The effect of this upon overall inequity was to cancel out the reduction of inequity resulting from the reduction of face-to face appointments. Australian subsidized video-linked psychiatry consultations have been distributed with gross inequity and have been a significant exacerbator of the overall inequity of psychiatric service provision. Future policy decisions wishing to reduce this inequity should take care to reduce the risk posed by expanding telepsychiatry.

FAILURE TO MEET EXPECTATIONS OF SPORT OR RECREATION FOLLOWING TOTAL HIP ARTHROPLASTY IS ASSOCIATED WITH YOUNGER AGE, HIGH BMI, AND POOR GENERAL HEALTH

Total hip arthroplasty (THA) has high rates of patient satisfaction; however patient expectations for recreational and sporting activities are not always met. Our study aimed to identify preoperative factors that predict whether patient expectations for sporting or recreational activity are met 12 months following THA.Patient reported outcome measures (PROMs) were collected prospectively from 2015-2018 at one private hospital in Sydney. Age, gender, postcode, weight, and height were recorded preoperatively. Included participants underwent primary THA by one of the investigating surgeons. Univariable and multivariable analyses were performed with an expectation fulfilment score used as the primary outcome variable. Preoperative predictor variables included: age, gender, BMI, Socio-economic Indexes for Areas (SEIFA), Oxford Hip Score, Hip Osteoarthritis Outcome Score, EQ-5D-5L and EQ Visual Analogue Scale (EQ VAS).1019 participants were eligible and included. 13% reported that preoperative expectations of sport or recreation were not met at 12 months. Younger age, lower preoperative EQ VAS, and higher BMI were associated with failed expectations on multivariable analysis. Odds of failed expectations increased by 2% for every one year younger in age (OR= 0.98, 95% CI = 0.96 to 1.00, p=.048), by 2% for every one point lower on EQ VAS (OR=0.98, 95% CI = 0.98 to 0.99, p=.002), and by 4% for every one-point increase in BMI (OR = 1.04, 95% CI = 1.00 to 1.09, p=.042).Failure to have expectations met for sporting or recreational activity was associated with younger age, poorer general health, and high BMI. With a rise in younger patients who likely have higher physical demands, a tailored preoperative education is preferable to generic models to better manage patient expectations.Younger age, higher BMI, and poorer health may predict unmet expectations for sport and recreation after THA. Tailored education in these groups should be considered.

The Feasibility of Deriving the Electronic Frailty Index from Australian General Practice Records

Frailty is a prevalent condition in older adults. Identification of frailty using an electronic Frailty Index (eFI) has been successfully implemented across general practices in the United Kingdom. However, in Australia, the eFI remains understudied. Therefore, we aimed to (i) examine the feasibility of deriving an eFI from Australian general practice records and (ii) describe the prevalence of frailty as measured by the eFI and the prevalence with socioeconomic status and geographic remoteness. This retrospective analysis included patients (≥70 years) attending any one of >700 general practices utilizing the Australian MedicineInsight data platform, 2017-2018. A 36-item eFI was derived using standard methodology, with frailty classified as mild (scores 0.13-0.24); moderate (0.25-0.36) or severe (≥0.37). Socioeconomic status (Socio-Economic Indexes for Areas (SEIFA) index)) and geographic remoteness (Australian Statistical Geography Standard (ASGC) remoteness areas) were also examined. In total, 79,251 patients (56% female) were included, mean age 80.0 years (SD 6.5); 37.4% (95% CI 37.0-37.7) were mildly frail, 16.7% (95% CI 16.4-16.9) moderately frail, 4.8% (95% CI 4.7-5.0) severely frail. Median eFI score was 0.14 (IQR 0.08 to 0.22); maximum eFI score was 0.69. Across all age groups, moderate and severe frailty was significantly more prevalent in females (P < 0.001). Frailty severity increased with increasing age (P < 0.001) and was strongly associated with socioeconomic disadvantage (P < 0.001) but not with geographic remoteness. Frailty was identifiable from routinely collected general practice data. Frailty was more prevalent in socioeconomically disadvantaged groups, women and older patients and existed in all levels of remoteness. Routine implementation of an eFI could inform interventions to prevent or reduce frailty in all older adults, regardless of location.

Household expenditure of smokers and ex-smokers across socioeconomic groups: results from a large nationwide Australian longitudinal survey

BackgroundCountries with best practice tobacco control measures have experienced significant reductions in smoking prevalence, but socioeconomic inequalities remain. Spending on tobacco products, particularly by low-income groups can negatively affect expenditure on other goods and services. This study aims to compare the household expenditure of adults who smoke tobacco products and those who formerly smoked across socioeconomic groups.MethodsDaily smokers and ex-smokers were compared using the Household, Income and Labour Dynamics in Australia Survey, over 7 waves. Adults who never smoked were not included. Participants were continuing sample members across waves. Mean number of participants per wave was 2505, 25% were smokers and 75% ex-smokers. The expenditure variables investigated included tobacco products, alcohol, motor vehicle fuel, health practitioners, insurance, education, and meals eaten out. Regression models using the generalized estimating equation technique were employed to compare expenditure data aggregated across the waves by Socioeconomic Index for Areas (SEIFA) quintiles of relative socio-economic advantage/disadvantage while accounting for within-participant autocorrelation. Quintiles are ranked by information such as the income, occupation and access to material and social resources of the residents.ResultsSmokers from all quintiles spent significantly less per year on meals out, education and insurance than ex-smokers (p < 0.001). Smokers from quintiles 2–5 spent less on groceries, medicines, and health practitioners (p < 0.01). Smokers from quintiles 1 and 2 (most disadvantaged), spent less on motor vehicle fuel than ex-smokers ($280;95%CI: $126–$434), ($213;95%CI: $82–$344). Smokers from quintiles 2 and 3 spent more on alcohol ($212;95%CI: $86–$339), ($231.8;95%CI: $94–$370) than ex-smokers. Smokers from the least disadvantaged groups spent less on clothing than ex-smokers ($348;95%CI: $476–$221), ($501; 95%CI: $743–$258). Across the whole sample, smokers spent more than ex-smokers on alcohol ($230;95%CI:$95–$365) and less on meals out ($361;95%CI:$216–$379), groceries ($529;95%CI:$277–$781), education ($456;95%CI:$288–$624), medicine ($71;95%CI:$38–$104), health practitioners ($345;95%CI:$245–$444) and insurance ($318;95%CI:$229–$407).ConclusionsSmoking cessation leads to reallocation of spending across all socioeconomic groups, which could have positive impacts on households and their local communities. Less spending on alcohol by ex-smokers across the whole sample could indicate a joint health improvement associated with smoking cessation.

School Age Developmental Outcomes of Children Conceived by IVF Compared with Controls: A Population Linkage Study

Background: There has been increasing interest in assessing longer term developmental and health outcomes in IVF-conceived offspring compared with those born after natural conception. So far, the findings have been conflicting. The Australian Early Developmental Consensus (AEDC) assesses children in their first year of primary school across five domains; physical health and wellbeing, social competence, emotional maturity, language and cognitive skills, and communication skills and general knowledge. Aim: To compare school entry (5-7 years of age) outcomes in IVF-conceived children in Victoria with naturally conceived controls. Method: We undertook a statewide data linkage study, with perinatal data (births 2005-2014) linked to data from major IVF providers in Victoria and the AEDC. Our approach to analysis included: complete case analysis, multiple imputation of missing data, consideration of clustering (siblings) and inverse probability weighted modeling to adjust for covariates. Our primary outcome was an AEDC score indicative of developmental vulnerability in two or more domains. We adjusted for the child’s age at assessment, sex, highest level of maternal education, maternal age, parity, SEIFA (Socio-Economic Indexes for Areas) quintile, language background other than English, and Aboriginal and Torres Strait Islander (ATSI) status. Results: The linked dataset comprised 163,418 children, including 4,441 IVF-conceived children. The IVF conceived population had older, more highly educated mothers who lived in more affluent areas and were less likely to be from non-English speaking backgrounds or identify as ATSI. IVF-conceived children were less likely to be developmentally vulnerable, in both unadjusted (RR 0.59, 95%CI: 0.52-0.67, p&lt;0.001) and adjusted analyses (aRR 0.72, 95%CI: 0.58-0.88, p&lt;0.001). Conclusion: Children conceived by IVF were less likely to be developmentally vulnerable in their first year of schooling, compared with peers conceived naturally. Further research should aim to understand if similar patterns are seen in other education and health domains and the potential mechanisms for these differences.

Vision screening outcomes of 4-5 year-olds reflect the social gradient

ABSTRACT Clinical relevance Children in socioeconomically disadvantaged communities often do not access follow-up eye care services when referred from vision screenings; whether this is due to lack of availability is not known. This paper highlights the need for vision and eye care for vulnerable children with practicing clinical optometrists well placed to provide vision care. Background Vision impairments develop from a young age and may inhibit learning experiences and impact life outcomes. Vision screening to detect and refer vision abnormalities supports children in their education and prevents minor vision impairments from worsening. This research describes outcomes from a vision screening programme for 4– to 5-year-olds delivered in Queensland, Australia. Methods The programme involved all prep children from participating schools in Queensland. Vision screening was conducted with the Parr 4 m Visual Acuity Test and Welch Allyn Spot Vision Screener. A cross-sectional study design was adopted. Descriptive data analyses explored the frequency of vision screening and referral outcomes. Inferential analyses examined associations between vision screening and referral outcomes with socio-economic indexes for areas (SEIFA) scores . Results Of 71,003 prep students screened, 4,855 (6.8%) received a referral recommendation. A higher proportion of children who received a referral recommendation was from more disadvantaged locations (?2 = 109.16, p < 0.001). Of the students referred, 3,017 were seen by an eye health professional. Further vision assessment of students by an eye health professional revealed that 43.3% of the referred children were diagnosed with a vision abnormality, 18.9% had no vision abnormality and 37.7% had an ‘undetermined’ diagnosis. A higher proportion of children confirmed with a vision abnormality were from more disadvantaged locations (?2 = 52.27, p < 0.001). Conclusion It is important that vision screening programmes target disadvantaged populations and support families of children who require further health assessment to access health services.

Suicide among hospitality workers in Australia, 2006–2017

PurposeSuicide among hospitality workers has recently attracted attention in the media. To date, little is known about suicide among hospitality workers in Australia.MethodsSuicide data were obtained from the National Coronial Information System (NCIS). Occupational suicide rates were calculated using the Australian Bureau of Statistics population-level data from the 2011 census. Negative binomial regression, univariate logistic regression, and multivariate logistic regression were used to estimate the association between suicide and employment as a hospitality worker over the period 2006–2017, compared to all other occupations.ResultsSuicide rates for chefs was significantly higher than for persons in non-hospitality occupations [incidence rate ratio (IRR), 3.93; 95% CI 2.53–5.79; P < 0.001]. The interaction between occupation and sex was examined with follow-up testing. Suicide rates for female chefs were significantly higher than for females in non-hospitality occupations (IRR, 3.93; 95% CI 2.60–5.94). Suicide rates for male chefs were also significantly higher than males in non-hospitality occupations (IRR, 1.38; 95% CI 1.14–1.67). Compared with non-hospitality occupations, hospitality workers who died by suicide had significantly greater odds of being female (OR 0.63, 95% CI 0.50–0.79), residing in residential Socio-Economic Indexes for Areas (SEIFA) classified as most disadvantaged (OR 1.62, 95% CI 1.19–2.20), and being born outside of Australia (OR 1.74, 95% CI 1.34–2.25).ConclusionResults indicate the need for targeted prevention of suicide by Australian hospitality workers. Overall, results suggest that specific hospitality occupations present a higher risk of suicidal behaviour than other non-hospitality occupations.

Spatio-temporal distribution of vector borne diseases in Australia and Papua New Guinea vis-à-vis climatic factors.

Weather and climate are directly linked to human health including the distribution and occurrence of vector-borne diseases which are of significant concern for public health. In this review, studies on spatiotemporal distribution of dengue, Barmah Forest Virus (BFV) and Ross River Virus (RRV) in Australia and malaria in Papua New Guinea (PNG) under the influence of climate change and/ or human society conducted in the past two decades were analysed and summarised. Environmental factors such as temperature, rainfall, relative humidity and tides were the main contributors from climate. The Socio-Economic Indexes for Areas (SEIFA) index (a product from the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage) was important in evaluating contribution from human society. For future studies, more emphasis on evaluation of impact of the El Niño-Southern Oscillation (ENSO) and human society on spatio-temporal distribution of vector borne diseases is recommended to highlight importance of the environmental factors in spreading mosquito-borne diseases in Australia and PNG.

Measuring area-level disadvantage in Australia: development of a locally sensitive indicator

Background In Australia, the Socio-Economic Indexes for Areas (SEIFA), which includes the Index of Relative Socioeconomic Disadvantage (IRSD), captures the socioeconomic characteristics of areas. Because SEIFA rankings are relative to the country or state, the decile categorisations may not reflect an area’s socioeconomic standing relative to areas nearby.&#x0D; Aims The aim of the research was to explore whether IRSD rankings could be re-ranked to become locally sensitive.&#x0D; Data and methods Using existing SEIFA data to redistribute the membership of current decile IRSD groups, we tested three methods to re-rank all SA1 areas relative to the nearest areas capped at: (1) the nearest 99 neighbours, (2) a population threshold of 50,000 (3) a distance threshold of 10 km.&#x0D; Results The reclassification of SEIFA IRSD deciles was largest (up to 8 decile points of change) when comparing the nearest neighbour and population threshold local methods to current state-based rankings. Moreover, compared to using current national and state SEIFA IRSD rankings, the use of local rankings resulted in more evenly distributed deciles between cities, regional, and remote areas.&#x0D; Conclusions Because SEIFA IRSD rankings are used to allocate resources and health services, we encourage the combined use of a state and local ranking to refine areas considered the most disadvantaged.

Migrant residential concentrations and socio-economic disadvantage in two Australian gateway cities

This article explores the relationship of residential concentrations of non-Anglophone migrants with socio-economic disadvantage at the suburb (SA2) level. We look at two main Australian gateway cities, Sydney and Melbourne. We use the ‘person-counts’ of the latest available (2016) Australian Census data, matching them with the socio-economic data provided by the Australian Bureau of Statistics ‘socio-economic indexes for areas’ (SEIFA). Our analysis shows that despite decades of careful filtering of migrants for skills and language, socio-economic disadvantage in migrant concentrations persists in the main gateway cities, being more pronounced in Melbourne than in Sydney. The article employs an original quantitative analysis in order to advance the understanding of relationship between ethnicity, socio-economic position and residential location. We seek to contribute to the ongoing scholarly and policy debate about migrant concentration areas in large immigrant-receiving cities.

1295School age outcomes in IVF-conceived children compared with controls: a population linkage study

Abstract Background There is interest in longer term outcomes in IVF-conceived offspring compared with those conceived naturally. So far, the findings have been conflicting. The Australian Early Developmental Consensus (AEDC) assesses children in their first year of primary school across five domains. Methods To compare school entry outcomes in IVF-conceived children with naturally conceived controls, we undertook a statewide data linkage study, with perinatal data (2005-2014) linked to data from IVF providers in Victoria and the AEDC. Our approach to analysis included: complete case analysis, multiple imputation of missing data, consideration of clustering (siblings) and inverse probability weighted modeling to adjust for covariates. Our primary outcome was an AEDC score indicative of developmental vulnerability in two or more domains. We adjusted for confounders: child’s age at assessment, sex, highest level of maternal education, maternal age, parity, SEIFA (Socio-Economic Indexes for Areas) quintile, language background other than English, and Aboriginal and Torres Strait Islander (ATSI) status. Results The linked dataset comprised 163,418 children, including 4,441 IVF-conceived children. The IVF-conceived population had older, more highly educated mothers who lived in more affluent areas and were less likely to be from non-English speaking backgrounds or identify as ATSI. IVF-conceived children were less likely to be developmentally vulnerable, in both unadjusted (RR 0.59, 95%CI:0.52-0.67, p &amp;lt; 0.001) and adjusted analyses (aRR 0.72, 95%CI:0.58-0.88, p &amp;lt; 0.001). Conclusions IVF-conceived were less likely to be developmentally vulnerable in their first year of schooling, compared with peers conceived naturally.

The biomedical, socioeconomic and demographic predictors of heart failure readmissions

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Sydney Local Health District Introduction Heart failure hospitalisations and readmissions pose a significant clinical and economic burden internationally. Current studies support a range of biomedical and sociodemographic factors as predictors of HF readmissions; however, there are a paucity of studies synthesising the research on this topic. Purpose The purpose of this systematic review was to identify biomedical, socioeconomic and demographic predictors of heart failure-related readmissions in adults with heart failure. Methods Electronic databases (Medline, Embase and Scopus) were searched using a structured search strategy. The particular risk factors of interest were left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) function class, brain natriuretic peptide (BNP), heart rate, blood pressure, employment status, income, age, sex, ethnicity, indigenous status, marital status, and socioeconomic indexes for areas (SEIFA) scores. After the removal of duplicates, the article selection process was conducted by two independent authors according to an inclusion and exclusion criteria. The articles were first filtered by title and abstract against the inclusion and exclusion criteria. The remaining articles were then assessed by reviewing the studies in full text. The relevant Critical Appraisal Skills Programme (CASP) tools were then used to appraise the final articles included for review. Results In total 1,516 papers were retrieved from the search. Of the 1,516 papers, 666 were from Medline, 567 were from CINAHL, 282 were from Embase, and one paper was from an independent search. After the removal of duplicates, 1296 papers were filtered according to title and abstract and 1183 papers were excluded from this process. 95 papers were assessed by reading them in full and of the 95 papers, 77 were excluded. This resulted in eighteen studies being included for review. Unemployment was the only socioeconomic factor predictive of HF readmissions (HR = 1.09; 95%CI= 1.05-1.14; p = 0.03) and SEIFA scores did not predict HF readmissions in adults with HF (p &amp;gt; 0.05). Indigenous status was a risk factor of HF readmissions according to one study (p &amp;lt; 0.05). Age or sex did not affect the risk of HF readmissions in adults with HF (p &amp;gt; 0.05). NYHA function class, BNP levels, and heart rate predicted HF readmissions in the population of interest. LVEF and blood pressure were non-significant predictors of HF readmissions (p &amp;gt; 0.05). Conclusions Current HF guidelines have a strong biomedical focus in the management of HF patients. This review however, shows there are sociodemographic factors that are predictive of HF readmissions in addition to the biomedical predictors that have been more extensively studied. Future studies should examine their combined effect on readmission risk and the early interventions to prevent readmissions.

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Melioidosis-a disease of socioeconomic disadvantage.

There is growing recognition of the contribution of the social determinants of health to the burden of many infectious diseases. However, the relationship between socioeconomic status and the incidence and outcome of melioidosis is incompletely defined. All residents of Far North Queensland, tropical Australia with culture-proven melioidosis between January 1998 and December 2020 were eligible for the study. Their demographics, comorbidities and socioeconomic status were correlated with their clinical course. Socioeconomic status was determined using the Socio-Economic Indexes for Areas (SEIFA) Index of Relative Socio-economic Disadvantage score, a measure of socioeconomic disadvantage developed by the Australian Bureau of Statistics. Socioeconomic disadvantage was defined as residence in a region with a SEIFA score in the lowest decile in Australia. 321 eligible individuals were diagnosed with melioidosis during the study period, 174 (54.2%) identified as Indigenous Australians; 223/321 (69.5%) were bacteraemic, 85/321 (26.5%) required Intensive Care Unit (ICU) admission and 37/321 (11.5%) died. 156/321 (48.6%) were socioeconomically disadvantaged, compared with 56603/269002 (21.0%) of the local general population (p<0.001). Socioeconomically disadvantaged patients were younger, more likely to be female, Indigenous, diabetic or have renal disease. They were also more likely to die prior to hospital discharge (26/156 (16.7%) versus 11/165 (6.7%), p = 0.002) and to die at a younger age (median (IQR) age: 50 (38-68) versus 65 (59-81) years, p = 0.02). In multivariate analysis that included age, Indigenous status, the presence of bacteraemia, ICU admission and the year of hospitalisation, only socioeconomic disadvantage (odds ratio (OR) (95% confidence interval (CI)): 2.49 (1.16-5.35), p = 0.02) and ICU admission (OR (95% CI): 4.79 (2.33-9.86), p<0.001) were independently associated with death. Melioidosis is disease of socioeconomic disadvantage. A more holistic approach to the delivery of healthcare which addresses the social determinants of health is necessary to reduce the burden of this life-threatening disease.

Factors associated with successful return to work in young heart failure and ischaemic heart disease patients following index hospital admission

Abstract Funding Acknowledgements Type of funding sources: None. Background Factors influencing return to remunerated work following an acute cardiac illness are poorly defined. We wished to compare the factors in our cohorts following first presentation of acute coronary syndrome(ACS) and decompensated heart failure(HF). Methods Prospectively identified subjects, aged 18-65years, from a rehabilitation population for ACS and HF during 2018-2019 underwent a survey. Results Of 133cases meeting inclusion criteria, 84 completed the survey(41 HF, 80% male, mean age 55years; 43 ACS, 86% male, mean age 57years). Socio-economic indexes for Areas(SIEFA) index were similar for HF(900) &amp; ACS(909) groups, which represents 11th and 14th percentile for Australia respectively. Cardiovascular risk factors were similar except hypercholesterolemia(37% v 60%; p = 0.029) was more common in ACS. Many subjects did not continue beyond Yr12, (54% HF v 30% ACS; p = 0.029). A majority of ACS cases returned to work as compared with HF(70% v 44%; p = 0.017)(Figure). On multivariate analysis, male gender[p = 0.031;OR 13.71 (1.28-147.36)]; access to financial benefits[p &amp;lt; 0.001;OR 22.75 (4.31-119.99)] and a desire to return to work [p = 0.014;OR 12.1 (1.67-87.82)] were associated with successful return to work (Table). Limitations Our study has small numbers so will be difficult to generalise to a wider population. We do show a signal towards the complex interplay of the social and individual factors in determining return to work. Further larger studies are required to tease out the differences between the individual factors to help predict return to work in the Australian context. Conclusion Successful return to work for patients with first presentation of ACS or HF could not be reliably predicted. Patients with ACS returned to work more often than HF. In HF patients who do n to return to work, recurrent symptoms, individual motivation, social support and access to financial benefits have a complex interplay. Predictors of return to work Predictor P value OR (95% CI) Diagnosis (heart failure) 0.095 0.29 (0.07, 1.24) Gender (male) 0.031 13.71 (1.28, 147.36) Access to benefit (none) &amp;lt;0.001 22.75 (4.31, 119.99) Desire to RTW (yes) 0.014 12.1 (1.67, 87.82) Abstract Figure. Rates of return to work in the 2 groups