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Background: Rural Australian populations experience an increased burden of ischaemic heart disease (IHD) compared to their metropolitan counterparts, similar to other developed countries, globally. Policy and other efforts need to address and acknowledge these differences in order to reduce inequalities in health burden. This paper examines rural health policy makers’ perceptions and use of evidence in efforts to reduce the burden of IHD in rural areas. Methods: Policy makers and government advisors (n = 21) who worked with, or advised on, rural health policy at local, state and federal government levels, with specific focus on the state of Victoria (n = 9) were identified from publicly available documents and subsequent snowball sample. Semi-structured qualitative interviews were conducted in regards to the use of evidence in policy to prevent IHD and thematic analysis undertaken applying two theoretical perspectives: context-based evidence-based policy making and the conceptual framework for understanding rural and remote health. Results: The rural context, particularly low resourcing, was seen as limiting potential for evidence based policy at local government (LG) level. Lower levels of political pressure and education were seen as constraints to evidence-based policy in rural communities. Participants described the potential for policy to have a greater impact on reducing heart disease in rural areas though they felt under-resourced and out of touch with the scientific evidence. Scientific studies were less valued than local anecdote to prioritise specific policy. At all levels (local, state and federal) low self-efficacy in interpreting evidence and perceived lack of relevance inhibited development of evidence informed policy. Conclusion: The rural context constrains the use of scientific evidence in policy making for the prevention of heart disease in rural areas in Australia with multiple factors influencing the capacity for evidenced based health policy. This is similar to findings at the international scale and is for consideration across other developed countries that experience inequalities in IHD disease burden between rural and urban populations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Estimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources. Methods: Baseline survey data for 90,352 participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and over—were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (κ).The characteristics of people classified as multimorbid were compared using logistic regression modelling. Results: Agreement was found to be highest for diabetes in both hospital and claims data (κ = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data. Conclusions: Different individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions. [ABSTRACT FROM AUTHOR]