QLD branch - Multimorbidity: Measurement for Health related Quality of Life and Health service use

  • 7 May 2019
  • 5:30 PM - 6:30 PM
  • Queensland University of Technology, Gardens Point, M Block, GP-M-310

TIME: 5 pm for refreshments followed by talk at 5:30pm

TITLE: Multimorbidity: Measurement for Health related Quality of Life and Health service use

SPEAKER: Jeeva Kanesarajah, Biostatistician, University of Queensland, School of Public Health

ABSTRACT: Multimorbidity is common and becoming the norm. One in five Australian adults live with multimorbidity, and its prevalence is highest amongst women and those age 65+ years. However, there exists a substantial multimorbidity burden at mid-life, where acceleration of multimorbidity occurs.

Multimorbidity represents a significant burden to individuals, the health system and society. It is associated with lower quality of life, higher mortality and disability, polypharmacy, reduced function and increased health care utilisation. Globally, the prevalence of multimorbidity is estimated to range between 3.5% (at age 75) and nearly 100% (at age 85 years). The wide variation in the prevalence is mainly due to the challenges in multimorbidity measurement and methodology. Common methods used to measure multimorbidity do not account for disease severity or duration of disease, both which are clinically relevant and can influence quality of life. This seminar describes the development and validation of a multimorbidity index for mid-age women, which addresses this gap, using data from the Australian Longitudinal Study on Women's Health.

BIOGRAPHY: Jeeva Kanesarajah is a Biostatistician and PhD Candidate with the Australian Longitudinal Study on Women's Health, University of Queensland, School of Public Health where she is at the final stages of her doctoral research on the Burden of Multimorbidity on Australian Women. Jeeva holds a Master of Biostatistics, and a Bachelor of Mathematics and Statistics. Jeeva’s main research interests include using statistical techniques to model health data including linked data to better understand population health characteristics and its impact to the individual, health system and society.

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