We are a diverse team of scientists quantifying the health, health equity and cost impacts of population interventions
Our vision
To improve health outcomes and reduce health inequities through decision-making that is routinely informed by health, cost and equity impacts of different interventions.
We are at a point in time where we can structure existing data and research through simulation modelling to answer the important questions of: “what health gain and cost (savings) would Intervention X lead to, and for whom?”. SHINE will achieve this by blending epidemiology, economics, data science, clinical science and health equity approaches.
Our staff
Meet our diverse team of epidemiologists, economists, clinicians, data and computer scientists and equity experts. If you are interested in joining our team, contact us.
Professor Tony Blakely, Unit Head, Epidemiologist & Public Health Medicine Specialist
Dr Kirsti Hakala Assendelft, Research Coordinator
Dr Driss Ait Ouakrim, Research Fellow in Epidemiology
Dr Tim Wilson, Simulation Modelling and Software Engineering
Dr Shiva Raj Mishra, Research Fellow in Epidemiology
Professor Vijaya Sundararajan, Clinical Epidemiologist
Hassan Andrabi, Simulation Modelling and Software Engineering
We strive to provide robust evidence on the health, health equity and cost impacts of population interventions
Our aim
SHINE is both a research program and a consulting service for the government and other agencies. The aim of SHINE is to provide robust evidence on the health, health equity, and cost impacts of population interventions, through causal inference and sophisticated simulation approaches from epidemiology, economics, and data science.
SHINE strives to be able to quantify the impact of (virtually) any intervention on health, health inequalities, health expenditure and income-earning potential of the population. We aim to answer questions like: “when applied to the population, how much health gain will intervention X achieve compared to intervention Y? Over what time period? At what cost?” Answering these questions is vital to forecasting the impact of a range of interventions and optimising policymaking for best value for money.
How we do research
We both lead projects and collaborate with other researchers. Projects we lead include COVID-19 policy modelling, a range of tobacco control projects, and the (necessary) methodological research we pioneer to build ‘next generation’ simulation modelling. Projects we collaborate on include Healthy Housing and Tobacco Endgame interventions – both as part of NHMRC Centres of Research Excellence.
Research highlights
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COVID-19 Policy Options
During the pandemic we have updated our Pandemic Tradeoffs website and tools for contemporary modelling on ‘best’ policy options.
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Tobacco Control Interventions
We have a suite of tobacco modelling, ranging from that funded by the NZ Ministry of Health to underpin their world-leading Action Plan to research with the Tobacco Endgame Centre of Research Excellence.
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Dietary Interventions
We are building dietary simulation modelling, that will build on our primary natural experiment research on health star ratings to planned analyses of Australian Obesity and Prevention Strategy targets. We have an extensive history of dietary intervention modelling in NZ, Australian and the UK – that with next generation SHINE modelling we will ‘refresh’ and extend in collaboration with other research groups.
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Housing Interventions
Much prevention modelling focuses on changing classic risk factors like tobacco and body weight. We also reach ‘upstream’ to model housing interventions. In collaboration with the Health Housing CRE, we are mapping out the likely health benefits of interventions to reduce cold and mouldy housing, and quantifying the impact of housing standards for Aboriginal and Torres Strait Islander populations and the Australian population generally.
We consult with government and other agencies, as well as business and other research groups to evaluate the health, health expenditure, and income impacts of population interventions through state-of-the-art simulation methods
Our services
SHINE quantifies the future health gains, health inequality impacts, health expenditure and income impacts of population interventions. Interventions such as denicotinising tobacco, health star rating labels on packaged food, eliminating cold housing, COVID-19 policy options. We specialize in preventive interventions, but we also tackle screening and early detection interventions, and health services and treatments. We specialize in quantifying health impacts by ‘heterogeneity’, which includes differential impacts of interventions by: sex and age; ethnicity and Indigeneity; socioeconomic status; and disease risk stratification (e.g. absolute risk of a cardiovascular event or cancer diagnosis in the next five years).
For whatever evaluation we undertake, we then pull back to compare its health and cost impacts, and cost effectiveness, with other similarly conducted evaluations - using league tables and graphs.
SHINE-Consulting is that part of SHINE that provides services to clients in policy institutions, NGOs and the private sector. These clients likely need estimates of health, cost and cost effectiveness to aid decision making and prioritisation of resources.
SHINE also provides support to other researchers, either as part of SHINE-Consulting or a more traditional collaborative approach as outlined on the SHINE-Research page. Much research stops at the point of saying something like “this putative intervention changes X by y%”. ‘X’ varies across research projects, perhaps being blood pressure, BMI, or disease incidence or severity – meaning one research output is not directly comparable to another. SHINE can take standard research a step further, to quantifying likely health gains in a common metrics of health adjusted life years and quantifying likely health expenditure impacts, making research findings more easily comparable for both researchers and policy makers.
Testimonials
“The modelling work Tony, Driss and team did for us was extremely useful. They were able to model all the key policies we were considering for the Smokefree Aotearoa 2025 Action Plan, to show when each policy would get our population groups to the 5% goal. They were able to meet our requirement for a strong equity focus, by focusing on key population groups. The results were clearly presented in accessible graphs. The financial savings they modelled were key to making the case for these policies. This was an example of research being well designed and executed, with the crucial end result of influencing policy. And that policy will have a huge impact on improving outcomes. The results of their research will be of interest to a wide range of researchers and policy makers, around the world.”
-Katharine Good, Senior Advisor, New Zealand Ministry of Health
An independent review of 25 international tobacco control models rate the BODE3 tobacco model top. This BODE3 tobacco model was developed by Professor Blakely whilst Director of BODE3, and the model is now further evolved e.g. to include vaping) and embedded in SHINE. The SHINE PMSLT uses the same ‘back end’ as in the tobacco model, conferring a stamp of quality for any other intervention modelling SHINE does.
-Huang V, Head A, Hyseni L, O'Flaherty M, Buchan I, Capewell S, Kypridemos C. Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework. Tob Control 2022
Risk prediction to target treatments and screening
SHINE is contributing to the early stages of research planning on evaluations of interventions by risk strata, for example tobacco cessation advice and other secondary prevention interventions for people stratified by risk of developing chronic obstructive pulmonary disease.
International collaborators
SHINE works with a range of international research groups on tobacco, diet and other interventions – helping to quantify health and cost impacts.
Consulting for Government, industry and other agencies
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New Zealand Ministry of Health - tobacco
To guide the Smokefree Aotearoa 2025 Action Plan, SHINE simulated future health, cost and income impacts from denicotinisation of tobacco, 95% reduction in retail outlets, and a tobacco-free generation.
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Moderna – COVID-19 vaccine effectiveness
SHINE is being contracted to estimate the real-world COVID-19 vaccine effectiveness in Victoria, using routinely collected federal and Victorian State data.
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Asia Development Bank – COVID-19 policy options
To guide Asia Development Bank loans, SHINE was contracted to simulate COVID-19 policy options in Armenia and Pakistan.
SHINE provides free-to-user tools that will answer basic questions and 'get you started', as well as more sophisticated tools we used in consulting projections and collaborations
SHINE Free-To-User Tools
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Australia-New Zealand Health Intervention League Table (ANZ-HILT)
A data visualization tool that allows you to compare the health gains, net health costs and cost-effectiveness of 100s of evaluations already conducted and published by researchers, using highly similar ACE and PMSLT approaches – meaning comparisons are valid.
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Health Intervention Impact Calculator (HIIC)
A tool that allows you to input disease incidence, morbidity and case fatality rate changes that ‘your intervention’ will likely generate. Future iterations of HIIC will link to ANZ-HILT, and allow you to specify interventions in terms of risk factor changes.
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COVID-19 Pandemic Trade-offs
A collation of reports and interactive tools, for COIVD-19 policy response options we have modelled over the course of the pandemic.
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Customize Your Product
For evaluations beyond those that can be retrieved from the above tools or where the user prefers us to generate the findings, SHINE undertakes bespoke quantification of intervention impacts and other services.
We develop accessible modelling infrastructure to enable collaborators to employ advanced simulation methods within their own research
Tools for contract and collaborative research projects
It is not possible to make all of SHINE’s tools and infrastructure fully publicly available. SHINE uses these tools in consulting and research projects, for and with clients and collaborators.
Proportional Multistate Lifetable Modelling. At the heart of SHINE is a proportional multistate life table (PMSLT) model. This model has evolved from Excel-based models developed initially by ACE-Prevention and evolved in the BODE3 program. SHINE’s PMSLT is coded in Python in collaboration with the Institute of Health Metrics and Evaluation (IHME, the home of the Global Burden of Disease (GBD)) for greater robustness, functionality, and re-useability (see methods paper). It includes the following features:
- Data pipelines from GBD data, to automatically input business-as-usual disease incidence, morbidity and case fatality rates (and future projections), and all-cause mortality and morbidity. For any country.
- Data processing tools to disaggregate any country’s data by heterogeneity (socioeconomic position, indigeneity, disease risk strata) given user inputs of relative risk differences in disease parameters by strata of heterogeneity.
- Disease-related health expenditure for Australia, disaggregated by disease phase (year of diagnosis, last year of life, prevalent) using NZ estimates. We plan to also include such disease-related health expenditure for many other countries, using algorithms under development. This data – when integrated with the PMSLT – automatically outputs the change in health expenditure by time into the future for any intervention.
- Disease-related income loss for NZ , purchase power parity adjusted to Australia and other countries. This data – when integrated with the PMSLT – automatically outputs the change in future income earnings due to preventing death and morbidity. That is, productivity impacts of health interventions, an output of intense interest to Treasury in decision making societal impacts of health interventions.
- A range of ‘interventions models’, for example our smoking-vaping life history, risk factor (e.g. BMI) intervention models linked to changing disease incidence, and our COVID-19 agent-based model. Over time, a growing suite of such intervention models will be built that can be re-used in future similar projects.
For an overview of SHINE Infrastructure and collaboration opportunities, see this presentation.
The SHINE team is constantly producing high-quality, influential research, which you can read about in the following peer-reviewed publications
Key SHINE Publications
SHINE Infrastructure
A tutorial-style paper explaining PMSLT modelling as used in SHINE:
Income loss by disease, used in our PMSLT modelling to estimate productivity gains:
Describes our heterogeneity module that disaggregates populations by strata (e.g. SES, CVD absolute risk) for modelling of health gains and costs:
A whole of country panel study to estimate excess health expenditure by disease phase (first year of diagnosis, prevalent, last year of life if dying of disease); we use these relativities by disease phase to disaggregate Australian and other country disease expenditure for inputting to PMSLT modelling:
COVID-19
A report summarizing COVID-19 Pandemic Tradeoffs scenarios for ‘living with the virus’ in 2022:
Our COVID agent-based model linked to our PMSLT model to estimate HALYs and costs for policy options to manage COVID-19 in 2020 in Victoria, Australia:
A pivotal paper that strengthened the case for Australasia to follow an elimination strategy in 2020:
Diet
A paper summarizing a large body of NZ work in the Burden of Disease Epidemiology, Equity and Cost-Effectiveness Program (BODE3) on food taxes and subsidies, propagated through PMSLT modelling to quantify health gains and costs:
A natural experiment analysis of the Health Star Rating system’s impact on industry reformulation of food, in both Australia and New Zealand:
Tobacco
A ‘proof of principle’ paper applying tobacco PMSLT modelling to the Solomon Islands, drawing on Global Burden of Disease data – an example of ‘scaling out’ in SHINE:
An example of PMSLT modelling applied to e-cigarette liberalization in New Zealand:
A critique of existing tobacco intervention modelling, that lays out the criteria we look for in robust and comparable modelling: