Australian Data Sources Worth Watching

A curated starting list for health and policy analysis in Australia.

These are useful starting points for Australian health and policy analysis.

Core Health Sources

• Australian Institute of Health and Welfare: national reports, data tables, and health and welfare statistics.
• AIHW data collections: a map of AIHW’s major data holdings.
• Australian Bureau of Statistics health statistics: surveys and official health statistics, including the National Health Survey.
• ABS Data API: programmatic access to demographic, Census, labour, economic, and regional datasets.
• Health Data Australia: research-oriented datasets and metadata.
• data.gov.au health datasets: federal, state, and local open datasets tagged as health.

Priority Datasets For Review

• Medicare statistics collection: MBS service counts, benefits, bulk billing, item-level reporting, and location summaries.
• PBS statistics: PBS and RPBS prescriptions, expenditure, supply month, item codes, patient categories, and medicine groups.
• ABS National Health Survey: chronic conditions, risk factors, disability, self-assessed health, and population subgroups.
• ABS Patient Experiences: access, affordability, avoided or delayed care, telehealth, GP, specialist, dental, and prescription barriers.
• AIHW hospitals data: emergency departments, elective surgery, admitted patient care, hospital resources, and hospital performance.
• NDIS datasets: participant numbers, plan budgets, utilisation, diagnosis, payments, providers, and regional summaries.
• GEN Aged Care Data: aged care services, places, providers, use, and interfaces with the health system.
• Australian Immunisation Register statistics: vaccine coverage summaries and trends.

Policy and Context Sources

• Productivity Commission data and reports: policy research, government services, and reform analysis.
• Report on Government Services: comparable data on health, justice, education, housing, and community services.
• Australian Institute of Family Studies: family, welfare, and social policy research.
• Australian Government data portal: broad open data catalogue.

Topic Ideas

• GP access and bulk billing by geography, socioeconomic status, remoteness, and age profile.
• Effects of recent bulk billing incentive changes on MBS item use, especially by PHN, Modified Monash area, and concession-heavy regions.
• PBS medicine use and affordability after co-payment changes, with attention to chronic disease medicine classes.
• Regional substitution between GP, specialist, emergency department, pathology, imaging, and mental health service use.
• NDIS participant growth, budget growth, utilisation, provider availability, and regional market depth.
• Predicting NDIS plan utilisation or average support budgets from age, disability group, service district, remoteness, and provider supply.
• Health service access gaps using ABS Patient Experience, MBS, PBS, and Census demographics.
• Emergency department waiting times by state, remoteness, demographics, and primary care access indicators.
• Preventive screening rates by region and socioeconomic status.
• Mental health service use before and after major policy changes.
• Aged care quality, hospital discharge pressure, and regional service availability.
• Heat, housing, and health risk in Australian cities.
• Differences between policy targets and measured outcomes in government services.

Modelling Notes

• Treat MBS and PBS as service-use and claims datasets, not direct measures of health need.
• For regional modelling, join to ABS Census and regional data for age structure, income, labour force status, remoteness, disability prevalence, language, housing, and SEIFA-style disadvantage.
• For NDIS models, distinguish participant growth, plan budget growth, payment growth, and utilisation. These are related but answer different policy questions.
• Use longitudinal validation where possible: train on earlier quarters or years, test on later periods, and keep policy-change dates explicit.
• Avoid turning prediction into allocation advice. For public-facing writing, frame models as descriptive, diagnostic, or forecasting tools with caveats.

Post Selection Checklist

Before starting an analysis, check:

• Is the question specific enough for one post?
• Is the dataset public, stable, and clearly licensed?
• Are the definitions understandable to a non-specialist reader?
• Can the analysis be reproduced from code?
• Is there a meaningful caveat section?