Application of the Australian Bureau of Statistics Socio-Economic Indexes for Areas in cardiovascular disease research: a scoping review identifying implications for research.

Objective To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods The Joanna Briggs Institute's scoping review methodology was used. Results The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research.

The application of spatial measures to analyse health service accessibility in Australia: a systematic review and recommendations for future practice.

BACKGROUND: Australia's inequitable distribution of health services is well documented. Spatial access relates to the geographic limitations affecting the availability and accessibility of healthcare practitioners and services. Issues associated with spatial access are often influenced by Australia's vast landmass, challenging environments, uneven population concentration, and sparsely distributed populations in rural and remote areas. Measuring access contributes to a broader understanding of the performance of health systems, particularly in rural/remote areas. This systematic review synthesises the evidence identifying what spatial measures and geographic classifications are used and how they are applied in the Australian peer-reviewed literature. METHODS: A systematic search of peer-reviewed literature published between 2002 and 2022 was undertaken using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Search terms were derived from three major topics, including: [1] Australian population; [2] spatial analysis of health service accessibility; and [3] objective physical access measures. RESULTS: Database searches retrieved 1,381 unique records. Records were screened for eligibility, resulting in 82 articles for inclusion. Most articles analysed access to primary health services (n = 50; 61%), followed by specialist care (n = 17; 21%), hospital services (n = 12; 15%), and health promotion and prevention (n = 3; 4%). The geographic scope of the 82 articles included national (n = 33; 40%), state (n = 27; 33%), metropolitan (n = 18; 22%), and specified regional / rural /remote area (n = 4; 5%). Most articles used distance-based physical access measures, including travel time (n = 30; 37%) and travel distance along a road network (n = 21; 26%), and Euclidean distance (n = 24; 29%). CONCLUSION: This review is the first comprehensive systematic review to synthesise the evidence on how spatial measures have been applied to measure health service accessibility in the Australian context over the past two decades. Objective and transparent access measures that are fit for purpose are imperative to address persistent health inequities and inform equitable resource distribution and evidence-based policymaking.

Are changes in depressive symptoms, general health and residential area socio-economic status associated with trajectories of waist circumference and body mass index?

OBJECTIVE: This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). METHODS: A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative location factor, was derived from hedonic regression models using residential property features but blind to location. Growth curve models with latent variables were fitted to data. RESULTS: Waist circumference, BMI and depressive symptoms increased over time. General health and relative location factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative location factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only. CONCLUSIONS: Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.

Comparison of general and cardiac care-specific indices of spatial access in Australia.

OBJECTIVE: To identity differences between a general access index (Accessibility/ Remoteness Index of Australia; ARIA+) and a specific acute and aftercare cardiac services access index (Cardiac ARIA). RESEARCH DESIGN AND METHODS: Exploratory descriptive design. ARIA+ (2011) and Cardiac ARIA (2010) were compared using cross-tabulations (chi-square test for independence) and map visualisations. All Australian locations with ARIA+ and Cardiac ARIA values were included in the analysis (n = 20,223). The unit of analysis was Australian locations. RESULTS: Of the 20,223 locations, 2757 (14% of total) had the highest level of acute cardiac access coupled with the highest level of general access. There were 1029 locations with the poorest access (5% of total). Approximately two thirds of locations in Australia were classed as having the highest level of cardiac aftercare. Locations in Major Cities, Inner Regional Australia, and Outer Regional Australia accounted for approximately 98% of this category. There were significant associations between ARIA+ and Cardiac ARIA acute (χ2 = 25250.73, df = 28, p<0.001, Cramer's V = 0.559, p<0.001) and Cardiac ARIA aftercare (χ2 = 17204.38, df = 16, Cramer's V = 0.461, p<0.001). CONCLUSIONS: Although there were significant associations between the indices, ARIA+ and Cardiac ARIA are not interchangeable. Systematic differences were apparent which can be attributed largely to the underlying specificity of the Cardiac ARIA (a time critical index that uses distance to the service of interest) compared to general accessibility quantified by the ARIA+ model (an index that uses distance to population centre). It is where the differences are located geographically that have a tangible impact upon the communities in these locations-i.e. peri-urban areas of the major capital cities, and around the more remote regional centres. There is a strong case for specific access models to be developed and updated to assist with efficient deployment of resources and targeted service provision. The reasoning behind the differences highlighted will be generalisable to any comparison between general and service-specific access models.

Do relationships between environmental attributes and recreational walking vary according to area-level socioeconomic status?

Residents of areas with lower socioeconomic status (SES) are known to be less physically active during leisure time. Neighborhood walkability has been shown to be related to recreational walking equally in low and high SES areas. This cross-sectional study tested whether associations of specific environmental attributes, measured objectively and subjectively, with walking for recreation were moderated by area-level SES. The data of the North West Adelaide Health Study collected in 2007 (n = 1500, mean age 57) were used. Self-reported walking frequency was the outcome of the study. Environmental exposure measures included objectively measured walkability components (residential density, intersection density, land use mix, and net retail area ratio) and perceived attributes (access to destinations, neighborhood esthetics, walking infrastructure, traffic/barriers, and crime safety). Participants' suburbs were categorized into low and high SES areas using an indicator of socioeconomic disadvantage. Low SES areas had lower scores in residential density, neighborhood esthetics, walking infrastructure, traffic/barriers, and crime safety. Recreational walking was associated with residential density, access to destinations, esthetics, traffic/barriers, and crime safety. Effect modification was observed for two attributes (out of nine): residential density was associated with walking only in low SES areas, while walking infrastructure was associated with walking only in high SES areas. The associations of neighborhood environmental attributes with recreational walking were largely consistent across SES groups. However, low SES areas were disadvantaged in most perceived environmental attributes related to recreational walking. Improving such attributes in low SES neighborhoods may help close socioeconomic disparities in leisure time physical activity.

Public open spaces and walking for recreation: moderation by attributes of pedestrian environments.

OBJECTIVE: This study examined whether attributes of pedestrian environments moderate the relationships between access to public open spaces (POS) and adults' recreational walking. METHODS: Data were collected from participants of the North West Adelaide Health Study in 2007. Recreational walking was determined using self-reported walking frequency. Measures of POS access (presence, count, and distance to the nearest POS) were assessed using a Geographic Information System. Pedestrian environmental attributes included aesthetics, walking infrastructure, barrier/traffic, crime concern, intersection density, and access to walking trails. Regression analyses examined whether associations between POS access and recreational walking were moderated by pedestrian environmental attributes. RESULTS: The sample included 1574 participants (45% men, mean age: 55). POS access measures were not associated with recreational walking. However, aesthetics, walking infrastructure, and access to walking trail were found to moderate the POS-walking relationships. The presence of POS was associated with walking among participants with aesthetically pleasing pedestrian environments. Counter-intuitively, better access to POS was associated with recreational walking for those with poorer walking infrastructure or no access to walking trails. CONCLUSION: Local pedestrian environments moderate the relationships between access to POS and recreational walking. Our findings suggest the presence of complex relationships between POS availability and pedestrian environments.

Area-level socioeconomic characteristics, prevalence and trajectories of cardiometabolic risk.

This study examines the relationships between area-level socioeconomic position (SEP) and the prevalence and trajectories of metabolic syndrome (MetS) and the count of its constituents (i.e., disturbed glucose and insulin metabolism, abdominal obesity, dyslipidemia, and hypertension). A cohort of 4,056 men and women aged 18+ living in Adelaide, Australia was established in 2000-2003. MetS was ascertained at baseline, four and eight years via clinical examinations. Baseline area-level median household income, percentage of residents with a high school education, and unemployment rate were derived from the 2001 population Census. Three-level random-intercepts logistic and Poisson regression models were performed to estimate the standardized odds ratio (SOR), prevalence risk ratio (SRR), ratio of SORs/SRRs, and (95% confidence interval (CI)). Interaction between area- and individual-level SEP variables was also tested. The odds of having MetS and the count of its constituents increased over time. This increase did not vary according to baseline area-level SEP (ratios of SORs/SRRs ≈ 1; p ≥ 0.42). However, at baseline, after adjustment for individual SEP and health behaviours, median household income (inversely) and unemployment rate (positively) were significantly associated with MetS prevalence (SOR (95%CI) = 0.76 (0.63-0.90), and 1.48 (1.26-1.74), respectively), and the count of its constituents (SRR (95%CI) = 0.96 (0.93-0.99), and 1.06 (1.04-1.09), respectively). The inverse association with area-level education was statistically significant only in participants with less than post high school education (SOR (95%CI) = 0.58 (0.45-0.73), and SRR (95%CI) = 0.91 (0.88-0.94)). Area-level SEP does not predict an elevated trajectory to developing MetS or an elevated count of its constituents. However, at baseline, area-level SEP was inversely associated with prevalence of MetS and the count of its constituents, with the association of area-level education being modified by individual-level education. Population-level interventions for communities defined by area-level socioeconomic disadvantage are needed to reduce cardiometabolic risks.

Access to cardiac rehabilitation does not equate to attendance.

BACKGROUND/AIMS: Timely access to appropriate cardiac care is critical for optimizing positive outcomes after a cardiac event. Attendance at cardiac rehabilitation (CR) remains less than optimal (10%-30%). Our aim was to derive an objective, comparable, geographic measure reflecting access to cardiac services after a cardiac event in Australia. METHODS: An expert panel defined a single patient care pathway and a hierarchy of the minimum health services for CR and secondary prevention. Using geographic information systems a numeric/alpha index was modelled to describe access before and after a cardiac event. The aftercare phase was modelled into five alphabetical categories: from category A (access to medical service, pharmacy, CR, pathology within 1 h) to category E (no services available within 1 h). RESULTS: Approximately 96% or 19 million people lived within 1 h of the four basic services to support CR and secondary prevention, including 96% of older Australians and 75% of the indigenous population. Conversely, 14% (64,000) indigenous people resided in population locations that had poor access to health services that support CR after a cardiac event. CONCLUSION: Results demonstrated that the majority of Australians had excellent 'geographic' access to services to support CR and secondary prevention. Therefore, it appears that it is not the distance to services that affects attendance. Our 'geographic' lens has identified that more research on socioeconomic, sociological or psychological aspects to attendance is needed.

Area-level socioeconomic characteristics and incidence of metabolic syndrome: a prospective cohort study.

BACKGROUND: The evidence linking socioeconomic environments and metabolic syndrome (MetS) has primarily been based on cross-sectional studies. This study prospectively examined the relationships between area-level socioeconomic position (SEP) and the incidence of MetS. METHODS: A prospective cohort study design was employed involving 1,877 men and women aged 18+ living in metropolitan Adelaide, Australia, all free of MetS at baseline. Area-level SEP measures, derived from Census data, included proportion of residents completing a university education, and median household weekly income. MetS, defined according to International Diabetes Federation, was ascertained after an average of 3.6 years follow up. Associations between each area-level SEP measure and incident MetS were examined by Poisson regression Generalised Estimating Equations models. Interaction between area- and individual-level SEP variables was also tested. RESULTS: A total of 156 men (18.7%) and 153 women (13.1%) developed MetS. Each percentage increase in the proportion of residents with a university education corresponded to a 2% lower risk of developing MetS (age and sex-adjusted incidence risk ratio (RR)=0.98; 95% confidence interval (CI) =0.97-0.99). This association persisted after adjustment for individual-level income, education, and health behaviours. There was no significant association between area-level income and incident MetS overall. For the high income participants, however, a one standard deviation increase in median household weekly income was associated with a 29% higher risk of developing MetS (Adjusted RR=1.29; 95%CI=1.04-1.60). CONCLUSIONS: While area-level education was independently and inversely associated with the risk of developing MetS, the association between area-level income and the MetS incidence was modified by individual-level income.

Relative residential property value as a socio-economic status indicator for health research.

BACKGROUND: Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value.Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between location and residential property value, and six cardiometabolic disease risk factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score. METHODS: RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models. RESULTS: A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group. CONCLUSIONS: This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.

Neighborhood walkability and the walking behavior of Australian adults.

BACKGROUND: The physical attributes of residential neighborhoods, particularly the connectedness of streets and the proximity of destinations, can influence walking behaviors. To provide the evidence for public health advocacy on activity-friendly environments, large-scale studies in different countries are needed. Associations of neighborhood physical environments with adults' walking for transport and walking for recreation must be better understood. METHOD: Walking for transport and walking for recreation were assessed with a validated survey among 2650 adults recruited from neighborhoods in an Australian city between July 2003 and June 2004, with neighborhoods selected to have either high or low walkability, based on objective measures of connectedness and proximity derived from geographic information systems (GIS) databases. The study design was stratified by area-level socioeconomic status, while analyses controlled for participant age, gender, individual-level socioeconomic status, and reasons for neighborhood self-selection. RESULTS: A strong independent positive association was found between weekly frequency of walking for transport and the objectively derived neighborhood walkability index. Preference for walkable neighborhoods moderated the relationship of walkability with weekly minutes, but not the frequency of walking for transport--walkability was related to higher frequency of transport walking, irrespective of neighborhood self-selection. There were no significant associations between environmental factors and walking for recreation. CONCLUSIONS: Associations of neighborhood walkability attributes with walking for transport were confirmed in Australia. They accounted for a modest but statistically significant proportion of the total variation of the relevant walking behavior. The physical environment attributes that make up the walkability index are potentially important candidate factors for future environmental and policy initiatives designed to increase physical activity.