SHINE

Research Overview

Answering these questions is vital to forecasting the impact of a range of interventions and optimising policy-making for best value-for-money.

We call this program of impact-driven research SHINE: Scalable Health Intervention Evaluation.  We aim to scale SHINE’s influence in three ways: out to include multi-country evaluations (e.g., Australia, NZ, SE Asian countries); down to quantity intervention impacts by sub-populations (e.g. health inequality impacts on Aboriginal and Torres Strait Islander populations); and up to examine packages of interventions (e.g., an education campaign and a new health app launched simultaneously).

SHINE’s state-of-the-art forecasting toolkit

Core components of SHINE include:

SHINE-Infrastructure : Building and evolving next-generation simulation models and supporting tools such as.

• Australia New Zealand Health Intervention League Table (ANZ-HILT): A world-first online interactive tool that allows users to compare 100s of interventions on their health gains (i.e. quality-adjusted life-years gained or disability-adjusted life-years averted), health systems costs and cost-effectiveness.
  • Who can use this, what expertise is required and perhaps a quick demo video so people can see what it looks like/how it works quickly
• Health Intervention Impact Calculator (HIIC) : a freeonline webtool, about to be launched as a protype that will allow the user to input what they estimate will be the change in disease incidence rates by time into the future for a given intervention. The tool then outputs expected: gains in health adjusted life years, changes in health expenditure and gains in workforce income (i.e. productivity).  Future iterations of HIIC will allow the user to input changes in risk factors arising from an intervention (e.g. the percentage point change in tobacco consumption or BMI distribution), and run scenarios for multiple countries.
  • Who can use this, what expertise is required and perhaps a quick demo video so people can see what it looks like/how it works quickly

SHINE-Research : a suite of thematic programs of research that tackles ‘big issues’ (e.g. COVID-19, tobacco, housing and other interventions) that are either:

• led by the Population Interventions Unit
• undertaken collaboratively between the PI Unit and other researchers.

SHINE-Consulting : our consulting arm undertakes evaluations of the health gains (e.g. in health adjusted life years), health expenditure impacts, productivity gains among the working age population and cost effectiveness of – virtually – any population intervention. Potential clients are diverse, ranging across:

• academic researchers wanting to sub-contract SHINE to undertake an evaluation of intervention(s)
• policy makers wanting a range of interventions evaluated to inform decision making
• NGOs and other parties wanting independent and robust estimates of the future health gains and cost impacts of population interventions.

Research Publications

Infrastructure

1. Blakely T, Moss R, Collins J, et al. Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples. Int J Epidemiol 2020; 49(5): 1-13. A tutorial-style paper explaining PMSLT modelling as used in SHINE.
2. Blakely T, Sigglekow F, Irfan M, et al. Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data. Plos Med in press. Income loss by disease, used in our PMSLT modelling to estimate productivity gains.
3. Andersen P, Mizdrak A, Wilson N, Davies A, Bablani L, Blakely T. Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities. medRxiv 2021 and in press Pop H Metrics. Describes our heterogeneity module that disaggregates populations by strata (e.g. SES, CVD absolute risk) for modelling of health gains and costs.
4. Blakely T, Kvizhinadze G, Atkinson J, Dieleman J, Clarke P. Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand. PLoS Med 2019; 16(1): e1002716. 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

1. Blakely T, Wilson T, Andrabi H, Thompson J. 2022 will be better: COVID-19 Pandemic Tradeoffs Modelling. : .Population Interventions Unit, Melbourne School of Population and Global Health, University of Melbourne, 2021. A report summarizing COVID-19 Pandemic Tradeoffs scenarios for ‘living with the virus’ in 2022.
2. Blakely T, Thompson J, Bablani L, 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 2021; 2(7). 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.
3. Blakely T, Thompson J, Carvalho N, Bablani L, Wilson N, Stevenson M. The probability of the 6-week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS-CoV-2. Med J Aust 2020; 213(8): 349-51 e1. A pivotal paper that strengthened the case for Australasia to follow an elimination strategy in 2020.

Diet

1. Blakely T, Cleghorn C, Mizdrak A, et al. The effect of food taxes and subsidies on population health and health costs: a modelling study. The Lancet Public Health 2020; 5(7): e404-e13. 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.
2. Bablani L, Ni Mhurchu C, Neal B, Skeels CL, Staub KE, Blakely T. 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. PLoS Med 2020; 17(11): e1003427. A natural experiment analysis of the Health Star Rating system’s impact on industry reformulation of food, in both Australia and New Zealand.

Tobacco

1. Singh A, Petrović-van der Deen FS, Carvalho N, Lopez AD, Blakely T. Impact of tax and tobacco-free generation on health-adjusted life years in the Solomon Islands: a multistate life table simulation. Tobacco Control 2019; 29(4): 388-97. 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.
2. Petrovic-van der Deen FS, Wilson N, Crothers A, Cleghorn CL, Gartner C, Blakely T. Potential Country-level Health and Cost Impacts of Legalizing Domestic Sale of Vaporized Nicotine Products. Epidemiol 2019; 30(3): 396-404. An example of PMSLT modelling applied to e-cigarette liberalization in New Zealand.
3. Singh A, Wilson N, Blakely T. Simulating future public health benefits of tobacco control interventions – A systematic review of models. Tob Control 2020; Online ahead of print. A critique of existing tobacco intervention modelling, that lays out the criteria we look for in robust and comparable modelling.