SIPHER Decision Support Tool (DST)

Assisting effective policy-making for better health and wellbeing

In the dynamic landscape of policy-making, where decisions at national, regional, and city levels impact the lives of many, the need for strategic insight into the whole system's performance has never been more critical.

SIPHER aims to help policy teams to identify the best leverage points for intervention, when the aim is to reduce health inequalities and promote wellbeing in the general population.

Working with policy partners, we have developed the SIPHER Decision Support Tool to help policy-makers navigate the complexities of crafting effective, impactful policies.

The SIPHER-DST builds upon and integrates existing SIPHER computer models that provide projections for health and wellbeing outcomes.

The DST enables policy-makers to: 

  • consider multiple and potentially conflicting outcomes and performance metrics at the same time; 
  • search and navigate through an otherwise unwieldy number of potential leverage points and timings within the system; 
  • evaluating and comparing these leverage points to find and visualise the best set of trade-offs with respect to the outcomes of interest, the levels of stimulus involved, and the uncertainty in the model forecasts.

SIPHER Glossary For clarification of our terminology and use of acronyms.

Contact: sipher@glasgow.ac.uk

Resources

  • SIPHER Glossary For clarification of our terminology and use of acronyms.

 

 

News

  • SIPHER Decision Support Tool Trials  - October 2023

We are grateful to SIPHER partners at Scottish Government and Public Health Scotland for their support in trialling early versions of the Decision Support Tool.  Their constructive feedback has contributed to the refinement and continued development of the DST, playing a crucial role in shaping the tool for real-world application.

  • Conference Abstract  - September 2023

    Revealing how the SIPHER Decision Support Tool can help shape effective health policies Shraddha Ghatkar, Decision Support (Workstrand 7) Research Associate presented "A Multi-Objective Optimization Framework for Effective CrossSectoral Policy Making to Improve Population Health and Reduce Health Inequalities" abstract to the Operational Research Society Annual Conference (OR65) in Bath in September 2023.  Read at:  OR65 Abstract  - Pg 142

Techincal Information

Provides technical details of the characteristics including strengths and limitations for this tool. 

CharacteristicDetails
Main Perspective From Individual Level (Micro) to Population Level (Macro)
Purpose The decision support tool is not a model in itself. Rather, it uses the available SIPHER models to provide decision support to policy analysts.
Strengths Can search over many thousands of different intervention options (e.g. local communities, socio-demographic sub-groups, levels of intervention) to reveal trade-offs between outcomes.
Limitations The decision support tool is dependent on SIPHER models and therefore subject to the limitations of these underlying models. Synthetic Population, Dynamic Systems Model and Dynamic Microsimulation can all be integrated but their limitations will then apply to the resulting decision support tool. It is important to note that the decision support tool is not intended to be used as a decision making tool. Rather the tool will provide a range of possible answers reflecting the trade-offs associated with potential decisions. The tool does not make any decisions - this responsibility rests with the user.
Geography Adopts the same geographical perspective as the SIPHER models that have been integrated - typically it is matched to the needs of the policy partner (so we have created Sheffield, Greater Manchester, Scotland (and Scottish LA) versions of the tool).
Time Period Adopts the same time period as the SIPHER models that have been integrated. Corresponds to the period covered in the underlying Synthetic Population, for example based on Understanding Society wave k (2019-2021) up to 2025/2026.
Adjustments / Extensions Potential adjustments include characteristics of the underlying models as well as features and the geographical granularity of the reported outcomes.
Data Requirements The decision support tool requires results from other SIPHER models. In addition, information on the intervention as well as cost-effectiveness assumptions are required.
Applications Applications include local community interventions on components of wellbeing; spatial targeting of job creation schemes; impact of targeted employment stimuli on health outcomes.
Modelling Assumptions Inherits the assumptions of the SIPHER models that have been integrated. In addition, assumptions on the costs and effectiveness of interventions are required.
User Options Geographical and temporal focus. Intervention configuration options.
User Type(s) Modellers, decision makers
Examples / Link with Other Models and Data The decision support tool uses the synthetic population, the systems dynamic model, the static and dynamic microsimulations, and the equivalent income utility function.
Software Requirement(s) Python
Options for Extension Alternative policy/intervention configurations.