We are a diverse team of scientists quantifying the health, health equity and cost impacts of population interventions
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.
Through SHINE, we seek to leverage sophisticated simulation approaches from epidemiology, economics, and data science to provide robust evidence on the health, health equity, and cost impacts of population interventions.
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, Project Coordinator
Professor Vijaya Sundararajan, Clinical Epidemiologist
Dr Driss Ait Ouakrim, Research Fellow in Epidemiology
Dr Shiva Raj Mishra, Research Fellow in Epidemiology
Dr Joshua Szanyi, Public Health Medicine Registrar
Dr Tim Wilson, Simulation Modelling and Software Engineering
Hassan Andrabi, Simulation Modelling and Software Engineering
We speak routinely about impactful applications of our modelling research to prominent public health issues
In the media
We frequently provide commentary on prevailing public health issues, and how modelling techniques can be used as a tool to guide policy decisions with complex and inherently uncertain impacts. Our modelling research is regularly documented in popular news media outlets. Learn more about our work by exploring the articles below, or contact us for media enquiries
We strive to provide robust evidence on the health, health equity and cost impacts of population interventions
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. Answering these questions is vital to forecasting the impact of a range of interventions and optimising policymaking for best value for money. 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?"
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
COVID-19 Policy Options
During the pandemic we have updated our Pandemic Tradeoffs website and tools for contemporary modelling on ‘best’ policy options.View
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.View
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.View
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.View
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
SHINE Consulting 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, which we can estimate using our sophisticated proportional multistate lifetable (PMSLT) simulation modelling framework and associated data pipelines.
Starting with a proposed population intervention, SHINE consulting can help you quantify the likely health and economic outcomes associated with its implementation – ranging from future health gains, health inequality impacts, health expenditure changes, and income impacts. Critically, SHINE specialises 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). Interested in collaborating with us? Send us an inquiry using the link below.
"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, et al., Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework.
SHINE also provides support to other researchers, either as part of SHINE Consulting or a more traditional collaborative approach as outlined on our 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 by quantifying likely health gains in common and comparable metrics (such as health adjusted life years), and supplementation analyses with evaluations of likely health expenditure impacts. To this end, SHINE strives to make research findings more easily comparable for both researchers and policy makers.
Consulting for Government, industry and other agencies
New Zealand Ministry of Health: Tobacco Policy Simulation
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.View
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.View
Asia Development Bank: COVID-19 Policy Simulations
To guide Asia Development Bank loans, SHINE was contracted to simulate COVID-19 policy options in Armenia and Pakistan.View
SHINE Free-To-User Tools
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.View
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.View
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.View
We develop accessible modelling infrastructure to enable collaborators to employ advanced simulation methods within their own research
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.
We regularly publish high-quality and influential research in top peer-reviewed journals
SHINE research seeks to synthesise techniques from epidemiology, mathematics, and data science to conduct sophisticated simulation modelling analyses of public health interventions. Our research efforts are broadly grouped into five themes: SHINE infrastructure, COVID-19 modelling, dietary modelling, tobacco modelling, and housing modelling. Explore publications relevant to each theme below.
Theme 1: SHINE Infrastructure
This research theme comprises all publications that have contributed to the development of the general ecosystem of SHINE simulation modelling: from the proportional multistate lifetable (PMSLT) simulation modelling framework, to input data used in simulation models, to end-user tools that visualise and compare results of health interventions.
(1.1) Comparing health gains, costs and cost-effectiveness of 100s of interventions in Australia and New Zealand: an online interactive league table
This study compares the health gains, costs, and cost-effectiveness of hundreds of Australian and New Zealand (NZ) health interventions conducted with comparable methods in an online interactive league table designed to inform policy (ANZ-HILT).
Carvalho, Natalie, et al. "Comparing health gains, costs and cost-effectiveness of 100s of interventions in Australia and New Zealand: an online interactive league table." Population health metrics 20.1 (2022): 1-10.
(1.2) Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples
This tutorial-style methodology paper explains how SHINE proportional multistate lifetable (PMSLT) modelling quantifies intervention impacts, using comparisons between three tobacco control case studies [eradication of tobacco, tobacco-free generation i.e. the age at which tobacco can be legally purchased is lifted by 1 year of age for each calendar year) and tobacco tax].
Blakely, Tony, et al. "Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples." International journal of epidemiology 49.5 (2020): 1624-1636.
(1.3) Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data
This study estimates individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). These inputs feed into SHINE simulation modelling to generate productivity estimates for simulated interventions.
Blakely, Tony, et al. "Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data." PLoS medicine 18.11 (2021): e1003848.
(1.4) Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities
Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. This study develops the heterogeneity disaggregation algorithm used in SHINE modelling, that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step.
Andersen, Patrick, et al. "Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities." Population health metrics 20.1 (2022): 1-17.
(1.5) Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand
The objective of this study was to use statistical methods to disaggregate all publicly funded health expenditure by disease, disease phase, and comorbidities. SHINE modelling implements these relativities by disease phase to disaggregate Australian and other country disease expenditure for inputting to PMSLT modelling.
Blakely, Tony, et al. "Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand." PLoS medicine 16.1 (2019): e1002716.
Theme 2: COVID-19 Modelling
This research theme comprises applications of our simulation modelling to the COVID-19 pandemic in Australia, and internationally. Research efforts in this sphere have occurred in collaboration with various government and private organisations, both domestically and abroad. These efforts are developed simultaneously with our online pandemic trade-offs interactive tools to visualise and communicate key modelling results.
(2.1) An integrated epidemiologic and economic model to assess optimal COVID-19 pandemic policy (preprint)
This study employs an agent-based model to estimate morbidity, mortality, and costs over 18 months from 1 April 2022 for for 44 policy packages (two levels of stringency of public health and social measures [PHSMs]; providing respirators during infection surges; 11 vaccination schedules of current and next-generation vaccines), and across 64 future SARS-CoV-2 variants (combinations of transmissibility, virulence, immune escape, and incursion date). Policies were ranked on cost-effectiveness (health system only and health system plus GDP perspectives), deaths and days exceeding hospital occupancy thresholds.
Szanyi, Joshua, et al. "An integrated epidemiologic and economic model to assess optimal COVID-19 pandemic policy." medRxiv (2022).
(2.2) A log-odds system for waning and boosting of COVID-19 vaccine effectiveness
This study describes a log odds system for generating vaccine effectiveness by number of doses, time since last dose, and severity (i.e. protection against any infection through to death). This method is at the heart of our recent SHINE-COVID-19 policy modelling:
Szanyi, Joshua, et al. "A log-odds system for waning and boosting of COVID-19 vaccine effectiveness." Vaccine (2022).
(2.3) The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study (preprint)
This study quantifies the morbidity (years lived with disability; YLDs) of long COVID in Australia during the 2021-2022 Omicron BA.1/BA.2 wave, with an aim to compare long COVID YLDs with: acute SARS-CoV-2 infection YLDs; years of life lost (YLLs) from SARS-CoV-2; and health loss from other diseases.
Howe, Samantha, et al. "The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study." medRxiv (2022).
(2.4) 2022 will be better: COVID-19 Pandemic Tradeoffs modelling (report)
This study employs an agent-based model to simulate virus transmission, and assess how it varies with factors we cannot control, such as the true reproductive rate (R0) of the virus, and factors we can at least partially control, such as our public-health response.
Blakely, Tony, et al. "2022 will be better: COVID-19 Pandemic Tradeoffs Modelling". Population Interventions Unit, Melbourne School of Population and Global Health.
(2.5) Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia
This study analyses 4 policy responses to the COVID-19 pandemic (aggressive elimination, moderate elimination, tight suppression, and loose suppression) n the state of Victoria, Australia, to identify the policy response with the least health losses and is the most cost-effective of.
Blakely, Tony, et al. "Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia." JAMA Health Forum. Vol. 2. No. 7. American Medical Association, 2021.
(2.6) The probability of the 6-week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS-CoV-2
This study estimates the probability of achieving elimination of community transmission of COVID-19, in Victoria, Australia. This study was pivotal in strengthening the case for Australasia to follow an elimination strategy in 2020:
Blakely, Tony, et al. "The probability of the 6‐week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS‐CoV‐2." Medical Journal of Australia 213.8 (2020): 349-351.
Theme 3: Diet
This research theme encompasses our extensive research efforts with dietary modelling in New Zealand, Australia, and the United Kingdom. The next generation of SHINE modelling with extend these analyses in collaboration with other research groups.
(3.1) The effect of food taxes and subsidies on population health and health costs: a modelling study
This study estimates the health and cost impacts of various food taxes and subsidies in New Zealand, and summarises 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:
Blakely, Tony, et al. "The effect of food taxes and subsidies on population health and health costs: a modelling study." The Lancet Public Health 5.7 (2020): e404-e413.
(3.2) The impact of voluntary front-of-pack nutrition labelling on packaged food reformulation: A difference-in-differences analysis of the Australasian Health Star Rating scheme
Front-of-pack nutrition labelling (FoPL) of packaged foods can promote healthier diets. Australia and New Zealand (NZ) adopted the voluntary Health Star Rating (HSR) scheme in 2014. This study estimates the impact of voluntary adoption of health star rating (HSR) on food reformulation relative to unlabelled foods and examined differential impacts for more-versus-less healthy foods.
Jones, Alexandra, et al. "The performance and potential of the Australasian Health Star Rating system: a four‐year review using the RE‐AIM framework." Australian and New Zealand journal of public health 43.4 (2019): 355-365.
Theme 4: Tobacco
This research theme encompasses the applications of our proportional multistate lifetable (PMSLT) simulation modelling framework to tobacco interventions in New Zealand. Research in this domain has occurred in collaboration with international government agencies, and is currently being adapted to different countries.
(4.1) Tobacco endgame intervention impacts on health gains and Māori:non-Māori health inequity: a simulation study of the Aotearoa-New Zealand Tobacco Action Plan
This study seeks to estimate the health gains and Māori:non-Māori health inequality reductions of the Aotearoa/New Zealand Government’s proposed endgame strategy, reporting on modelling commissioned by the NZ Government of forecast heath gains and health inequality impacts of the world-leading Aotearoa New Zealand tobacco endgame strategy (denicotinising tobacco, massive reductions in retail, tobacco-free generation).
Ouakrim, Driss Ait, et al. "Tobacco endgame intervention impacts on health gains and Māori: non-Māori health inequity: a simulation study of the Aotearoa-New Zealand Tobacco Action Plan." medRxiv (2022).
(4.2) Impact of tax and tobacco-free generation on health-adjusted life years in the Solomon Islands: a multistate life table simulation
This study represents a ‘proof of principle’, applying tobacco PMSLT modelling to the Solomon Islands, drawing on Global Burden of Disease data – an example of ‘scaling out’ in SHINE. In this study, we seek to estimate health-adjusted life years (HALY) gained in the Solomon Islands for the 2016 population over the remainder of their lives, for three interventions: hypothetical eradication of cigarettes; 25% annual tax increases to 2025 such that tax represents 70% of sales price of tobacco; and a tobacco-free generation (TFG).
Singh, Ankur, et al. "Impact of tax and tobacco-free generation on health-adjusted life years in the Solomon Islands: a multistate life table simulation." Tobacco Control 29.4 (2020): 388-397.
(4.3) Potential Country-level Health and Cost Impacts of Legalizing Domestic Sale of Vaporized Nicotine Products
This study presents an application of PMSLT modelling to e-cigarette liberalisation in New Zealand. In this study, we seek to estimate the net impact on population health and health system costs of vaporised nicotine products. We model, with uncertainty, the health and cost impacts of liberalising the vaporised nicotine market for a high-income country, New Zealand (NZ).
Petrovic-van der Deen, Frederieke S., et al. "Potential country-level health and cost impacts of legalizing domestic sale of vaporized nicotine products." Epidemiology 30.3 (2019): 396-404.
(4.4) Simulating future public health benefits of tobacco control interventions: a systematic review of models
This study presents a critique of existing tobacco intervention modelling, thereby identifying the criteria we look for in robust and comparable modelling. We applied a Medline search with keywords intersecting modelling and tobacco, and undertook a systematic review of tobacco intervention simulation models to assess model structure and input variations that may render model outputs non-comparable.
Singh, Ankur, Nick Wilson, and Tony Blakely. "Simulating future public health benefits of tobacco control interventions: a systematic review of models." Tobacco Control 30.4 (2021): 460-470.
Theme 5: Housing
This research theme encompasses applications of our proportional multistate lifetable (PMSLT) simulation modelling framework to housing interventions domestically, and abroad. Research in this domain has occurred in collaboration with the Healthy Housing CRE.
(5.1) The Health Gains and Cost Savings of Eradicating Cold Housing in Australia (preprint)
This study applies the SHINE proportional multistate lifetable (PMSLT) simulation modelling framework to estimate the health, health inequality, health expenditure and income impacts of lifting the temperature in living areas of the home to 18 degrees Celsius in cold homes in the south-eastern States of Australia (N= 17 million).
Mishra, Shiva Raj, et al. "The Health Gains and Cost Savings of Eradicating Cold Housing in Australia." Available at SSRN 4165606.
Get in touch
If you wish to collaborate with SHINE on a project or learn more about our work, please click the 'contact us' button below, and submit an inquiry. You should receive a reply within 48 hours of submitting your message to us.
Address: level 3, 207 Bouverie St, Carlton VIC 3053. Melbourne School of Population and Global Health, Centre of Epidemiology and Biostatistics.