Using ALSWH data to support policy decisions
Cross-sectional data (like the Australian Census) provides a snapshot in time. Repeated cross-sections can show broad population trends. In comparison, longitudinal data from the Study is repeatedly collected from the same participant over time. By linking an individual’s survey data with administrative data from Medicare, MBS, cancer registries and more we build a rich tapestry. It becomes possible to analyse the complex threads – the physical, psychological, social, behavioural, and demographic factors – that influence women’s health and use of healthcare services across the course of their lives.
Linked administrative data
Accurate estimations of prevalence where national registry data does not exist or medical testing isn’t appropriate
How does longitudinal data support policy decisions?
The policy approach to manage an issue will vary greatly depending if it is entrenched or likely to change, for example a period of short term ill-health versus chronic ill health. For example, data from the study shows that:
- women who have poor mental health in their 20s are more likely to have poor health in their 30s
- Women who are caregivers for another adult generally have worse physical and mental health than those who are not undertaking a caregiving role. Additional support is important for the wellbeing of the X percent of women acted as caregivers between the ages of xx and xxx. Insight from longitudinal data shows that this is a highly transitory period of women’s lives, with only xxx % providing care for this entire period.
The short term health impacts of domestic violence are well documented. ALSWH is the first study to look at the long-term impacts of domestic violence. Even after 16 years, women in the study who experienced domestic violence showed consistently worse physical and mental health than those who hadn’t6. Most services support women during an immediate crisis. Unfortunately, for the one in four women impacted by domestic violence, the effects could last a lifetime.
Programs are necessarily limited and need to focus resources on factors that will have the most impact on outcomes. Statistical analysis and modelling can be used to quantify the relative strength of the different factors influencing an outcome.
For example, in another first, research from the Study linked experiencing domestic violence with a higher risk of early menopause. Early menopause (before the age of 45) increases women’s risk of heart disease, osteoporosis, and diabetes. However, smoking (which is a known risk factor for early menopause) accounts for more than one-third of this link. Women in the study who experienced domestic abuse were also twice as likely to be heavy smokers (more than 20 cigarettes/day). In light of this, our researchers recommended tailoring specific quit smoking programs to support domestic violence survivors7.
Data linkage with a broad range of state and national administrative data sets can provide policymakers with insight into women’s health service use and related costs. It is possible to see whether services are accessed or if policies have had an impact.
- The Better Access Scheme (BAS) was introduced in 2006 to improve access to health care for people with mental health concerns. We can see that the service works because the mental health scores of women who use it improve. However, it is underutilised. In the 1989-95 cohort, 13% of women used the services. Unfortunately, an additional 30% reported depression or anxiety but had not accessed BAS services4.
- Women who access in home aged care services enter residential care later
- Smoking rates vs alcohol rates.
Comparing obesity rates across the four ALSWH cohorts reveals a worrying trend. Each new generation of women in the study is heavier than the one before and gaining weight faster1. In 2015, 26% of Australian women were obese. If we continue to gain weight on our current trajectories, a staggering 40% of Australian women will be obese by 20351. Public health interventions will have the potential to improve quality of life for generations to come and will save billions of dollars in taxpayer-funded health care for fertility issues, heart disease, diabetes, arthritis, and cancer.