Tackling inequalities in health & wellbeing across Salford David Herne Deputy Director of Public Health Alayne Robin Consultant in Public Health

Inequalities and future local government role White paper sets out a greatly enhanced role for Local Government to promote and protect health Salford City Council’s enhanced role is likely to include: –Leading JSNAs –Supporting local voice/patient choice –Promoting joint commissioning of local NHS services social care and health improvement –Leading on local health improvement and prevention activity LG will need to understand local inequalities Have a mechanism to be able to measure them Be able to monitor impact on those inequalities

Key themes from: Strategic Review of Health Inequalities in England post Reducing health inequalities is a matter of fairness and social justice Action is needed to tackle the social gradient in health – Proportionate universalism Action on health inequalities requires action across all the social determinants of health Reducing health inequalities is vital for the economy – cost of inaction Beyond economic growth to well-being of society: sustainability and the fair distribution of health

Scope of the issue People resident in Salford are living longer but not as long as other people in England There is a difference between how long men and women live in Salford There is a difference between how long people live in different parts of Salford

WHAT ARE THE CAUSES? Insight analysis shows that for Salford the major causes contributing to the health inequalities gap when measured by life expectancy are: –Deaths from cardio vascular disease & stroke –Deaths from cancers: lung, bowel & breast –Deaths from respiratory disease –Deaths from alcohol related behaviours –Infant mortality

Causes of the causes Various research & modelling has identified that: Health care contributes 10% Health behaviours contribute 40% Socio-economic factors contribute 40% Physical environment contributes 10% to life expectancy The role that the socio-economic & physical environment has on health inequalities is shown in the Marmot Review which identifies the major causes of the causes which will require change to impact on reducing health inequalities over the next decade.

Progress so far WE HAVE: Identified the problem and formulated the question Developed a driver model which has scoped the main drivers, which on current evidence, we “think” are contributing to improving life expectancy Engaged the key stakeholders Next stage is to develop a predictive model and test it

Health Outcomes Increased life expectancy: Reduced external and internal gap. Increased healthy life expectancy Health & social care (10% of determinants) Health Behaviours (40% of determinants) Socioeconomic factors (40% of determinants) Secondary / Acute Care Infant mortality Tobacco Healthy Weight Physical Activity Alcohol use Housing incl. Fuel Poverty Social cohesion and civic society Climate change Transport Educational Attainment Employment Physical environment (10% of determinants) Built Environment Green space Infant feeding Emotional health and wellbeing Primary Care Early Years Air Quality

What needs to happen next? We don’t know what changes will make the most difference on health inequalities as measured by life expectancy? We don’t know what interventions will make those changes needed to make the most difference to health inequalities, as measured by life expectancy? We don’t know the scale of the interventions required to make those changes needed to make the most difference to health inequalities, as measured by life expectancy?

Predictive modelling of life expectancy We need a predictive model which will enable local partners, to achieve maximum health gain for investment By predicting the size of the effect on life expectancy of improving key determinants of health. This information would support investment decision and achieve outcomes

Phase 1 Identify what the most important drivers are Test the model on the Health Profile for England 2010 indicators Apply it to a selection of indicators from the driver model at Salford and Neighbourhood level. Predict the impact on life expectancy of varying each of the key indicators. Show the relationship between the indicators and demonstrate which are most relevant to life expectancy

Phase 2 If phase 1 is successful in showing the most relevant indicators, phase 2 will investigate the feasibility of building a scenario analysis tool. The tool will build a “mind map” that visualises the statistical relationships with all the indicators. Using the map it would be possible adjust the indicators to investigate their impact on life expectancy.

Phase 3 If Phase 2 is successful, the final phase is to create a scenario analysis product Show how non linear relationships work between key variable Allows us to model specific variables e.g. smoking quitters Will enable variables to be altered and their effects identified e.g. the impact of reducing prevalence of smoking by differing values which include a dashboard for the presentation of the results

What’s next To start the predictive modelling Phase 1 with NW Public Health Observatory Once Phase 1 complete, share outcomes with Place Board and NHS Salford senior team To progress work on the Driver Model and its evidence base To build the outcomes into the foundations for the Public Health Service and Health & Wellbeing Board