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Introduction With population ageing the prevalence of multi-morbidity (the co-occurrence of two or more chronic medical conditions) is increasing. Objectives and Approach Our goal was to use data linkage to obtain clinically validated data on the incidence of a range of common chronic conditions developed by participants in the Australian Longitudinal Study on Women’s Health (57,000 women) and hence to track the cumulative incidence of multi-morbidity over time. Results The major data sources differed for different conditions, e.g. the Pharmaceutical Benefits Scheme was important for identifying musculoskeletal conditions, whereas hospital admission data was crucial for identifying stroke. The most common combinations of conditions differed for women at different ages, e.g., mental health, musculoskeletal and respiratory conditions were most common for women born in 1989-95, whereas heart disease was a prominent part of multi-morbidity for women born in 1921-26. Among these older women about 50% had 3 or more chronic conditions in 2002 (when they were aged 76-81) and this increased to over 80% by 2015 (when they were 89-93). For comparison, among women born in 1973-78 fewer than 5% had 2 or more chronic conditions in 2002 (when they were aged 24-29) and this increased to about 15% in 2016 (when they were 38-43). Conclusion / Implications Record linkage from multiple data sources, repeated over a long time span, is a powerful method for studying the development of multi-morbidity patterns in cohorts.

ABSTRACT Objectives To examine the trend of the healthcare cost of arthritis in a population-based cohort of older women and to estimate the mean adjusted incremental healthcare costs, and selected percentiles. Approach This is a healthcare cost study based on individual-level data. Data included health survey and linked administrative data, from 2003 to 2009, from the Australian Longitudinal Study on Women’s Health. The Medicare Australia datasets include the Pharmaceutical Benefits Scheme (unit record data on claims for government-subsidized prescription medicines) and the Medicare Benefits Schedule (listing of health services subsidized by the Australian Government) datasets; they were the source for all healthcare utilization and cost data in this study. The main outcome measure was the incremental healthcare cost of arthritis (estimated from the Australian Government’s cost perspective) expressed as dollars per person per year. All costs were expressed in 2012 Australian dollars. Regression models were used to estimate the adjusted incremental costs of arthritis. The mean adjusted incremental healthcare cost of arthritis was computed using GLMs with a logarithmic-link function and a gamma distribution for costs. The adjusted incremental costs at the 25th, 50th, 75th, 90th and 95th percentiles were computed using Quantile Regression. These percentiles were chosen because cost data are skewed to the right and it was expected that there would be smaller differences between the lower percentiles but bigger differences between upper adjacent percentiles. Results Data from 4287 women were included in the analysis. Adjusted incremental healthcare cost of arthritis did not increase significantly from 2003 to 2009. However, there were indications that costs at the lower percentiles decreased slightly over the study period while costs at higher (above 50th) percentiles increased. The estimated median cost was $480 (95% CI: $498 - $759) per person per year in 2009. However, ten percent of women had more than 300% higher cost than the “average person” with arthritis. Conclusion Healthcare cost of arthritis represents a substantial burden. However, considering only overall cost does not provide a detailed picture of expenditure. Our results suggest that higher cost patients had different experiences in arthritis cost over time, compared to patients with lower costs, although overall cost has not increased over time. As healthcare spending is concentrated in the high-cost patients, characterising these patients and formulating initiatives that target them could have a considerable impact on improving care and lowering health expenditure due to arthritis.

ABSTRACT Objectives Anticholinergic medicines burden is common, can have negative impacts, and is problematic to identify. Many medicines used by older women have anticholinergic effects. Importantly for older women, where multimorbidity and use of multiple medicines is common, even when anticholinergic effect of an individual medicine is small, the anticholinergic effects of multiple medicines may be additive, constituting cumulative anticholinergic burden. This study describes medicines contributing to and predictors of anticholinergic burden among community-dwelling older Australian women. Approach Retrospective longitudinal analysis of data from the Australian Longitudinal Study on Women’s Health linked to Pharmaceutical Benefits Scheme medicines data from 1 January 2008 to 30 December 2010; for 3694 women born in 1921–1926.Anticholinergic medicines were assigned anticholinergic potency levels 0 to 3, according to the Anticholinergic Drug Scale. Anticholinergic Drug Scale ratings for all medicines used by each woman were summed across each six months to give an Anticholinergic Drug Scale score. Commonly used medicines were identified for women with high ADS scores (defined as 75th percentile of scores). Predictors of high ADS scores were analysed using generalised estimating equations. Results During 2008-2010, 1126 (59.9%) of women used at least one anticholinergic medicine. Median Anticholinergic Drug Scale score was 4. Most anticholinergic medicines used by women who had a high anticholinergic burden (Anticholinergic Drug Scale score > 9) had a low anticholinergic potency (Anticholinergic Drug Scale level 1). Increasing age, cardiovascular disease, and number of other medicines used were predictive of a higher anticholinergic burden. Conclusion High anticholinergic medicines burden in this group was driven by use of multiple lower anticholinergic potency medicines rather than use of higher potency medicines. While we might expect that doctors would readily identify anticholinergic burden risk for those using high potency medicines, they may be less likely to identify this risk for users of multiple low potency anticholinergic medicines. The paper will also discuss how GPs view these findings, and how to translate them into the prescribing setting.