1. LAA mandatory health inequalities indicator workshop Robert Kyffin & Hywell Dinsdale South East Regional Public Health Group e. robert.kyffin@dh.gsi.gov.uk t. 01483 882 264 Government Office for the South East, Guildford 25 September 2006

2. Workshop overview Workshop aim To assist the South East LAAs in interpreting the mandatory health inequalities indicator Workshop objectives To provide an overview of the main factors to be considered when interpreting the indicator To illustrate the resources available to support the analysis of the indicator To provide an opportunity for sharing experiences in interpreting the indicator

3. The mandatory indicator Background Formal guidance from DCLG for the third round LAAs states that all current and refreshed round one and two LAAs should include the following indicator: Reduce health inequalities within the local areas by narrowing the gap in all-age, all- cause mortality Formal national guidance and a clarification note issued by HIU in August and supported by a regional briefing note produced by SEPHG

4. The mandatory indicator NRF areas Four local authorities in the South East contain NRF areas – Brighton & Hove, Hastings, Portsmouth, Southampton NRF targets related to the mandatory indicator: reduce mortality rates from circulatory diseases in people aged under 75 reduce health inequalities between the most deprived neighbourhoods and the district average, using indicators that are chosen in accordance with local health priorities

5. The mandatory indicator A few points to remember The mandatory indicator supports the overall DH objective of tackling health inequalities and the life expectancy PSA Measuring health inequalities is conceptually complex and setting reduction targets presents many practical problems The guidance is there to help, but is not prescriptive – local areas have considerable freedom in interpreting the indicator

6. Workshop topics covered Which measure of mortality should be used? At what scale should the indicator be measured? Over what time period should the indicator be measured? What gap should be measured? What size reduction should be aimed for? Remember – there is no ‘right’ way to measure the indicator and you may have to be pragmatic depending on the local data and expertise available

7. Which measure to use? (1) All-age, all-cause mortality can be measured using life expectancy figures, and age- standardised rates and ratios All three are ultimately based on the same crude mortality statistics Life expectancy – arguably the best summary measure of mortality and places greater emphasis on younger deaths SMRs – require less complex calculations but do not allow direct comparisons between different areas and weight all deaths equally

8. Which measure to use? (2) The HUI guidance states that Spearhead areas should use DSRs – for other areas, the clarification note allows for local flexibility ONS have published both life expectancy figures and SMRs (for under age 85) for electoral wards for 1999-2003 DSRs – require more information to calculate than SMRs, but allow comparisons between areas; also weight all deaths equally

9. Which measure to use? (3) Templates to enable these measures to be produced locally are available from ONS; SEPHO have also produced a user-friendly life expectancy calculator (links available in SEPHG briefing) Mortality data should be locally available, but two issues: denominator data – not commonly available for small areas prior to 2001 boundary changes – make the issue of analysing trends more complicated

10. What scale to use? The DCLG guidance does not specify which ‘local’ areas should be measured Two options: wards or super output areas Can be difficult to detect statistically significant changes in mortality for small areas, so it is advisable to: combine males and females – but may be changing at different rates so advisable to monitor locally combine years – three years are common combine areas – groups of wards or SOAs

11. Over which time period? The HUI guidance suggests that the baseline for measuring progress against the mandatory indicator should be 1995-7 and the end point 2009-11 – this is consistent with Spearhead areas This end point means progress cannot be measured until after the LAA has finished – but this is the case for any target based on mortality data HUI are in negotiations with ONS about faster access to mortality data

12. And for which gap? The within-area gap in mortality can be measured by looking at the difference between: the bottom quintile and top quintile of areas, or by comparing the bottom quintile with the average for the rest of the LAA area (excluding the bottom quintile)

13. What target to aim for? The HIU guidance suggests that a 10% reduction in within-area mortality over the period 1995-7 to 2009-11 should be aimed for to ensure consistency with the Spearhead areas For areas that have already achieved this, or are projected to do so before 2009-11, a more challenging target should be set But it is important that the target should be driven by what can be achieved in the reporting time frame for the LAA and what might prove statistically significant

14. Other factors to consider Historical trends – achieving a reduction in within-area inequalities might first entail the reversal of a trend towards increasing inequalities Nursing homes – concentrations of nursing and residential homes in a small population will lower life expectancy and increase mortality rates, potentially affecting any apparent health inequalities gaps

15. Supporting indicators The mandatory health inequalities indicator should be supported by a range of other measured which reflect local health priorities May include both process and outcome indicators Examples included in the DCLG guidance include circulatory disease mortality, RTA mortality, four week smoking quitters May also be appropriate to focus on those already with disease eg. increase uptake of aspirin, beta blocker and statin therapy in those with CVD

16. To recap… (1) What measure? – all-age, all-cause mortality can be measured using several methods, but the main issue is availability of denominator data prior to 2001 What scale? – can use wards or SOAs, but may be necessary to combine males and females, years and areas What time period? – suggested baseline is 1995-7 and end point is 2009-11, but analysing the pattern prior to 2001 may be difficult due to data issues

17. To recap… (2) Which gap? – can look at the within-area gap in different ways What target? – a 10% reduction in within- area mortality over the period 1995-7 to 2009-11 is suggested

18. Top vs. bottom quintile gaps LAA area Population (2001) No. of wards Wards per quintile (rounded up) Average LE (top quintile) ULLL Average LE (bottom quintile) ULLLGapULLL Relative gap (to top quintile) ULLL Medway24924022579.980.779.175.676.474.84.35.43.25.4%6.8%4.0% Bracknell Forest10963018480.982.179.776.878.075.64.15.72.55.1%7.1%3.0% West Berkshire14448030682.683.681.677.078.076.05.67.04.26.8%8.5%5.0% Reading14481016481.982.980.975.876.874.86.17.54.77.4%9.2%5.7% Slough12079014380.181.279.075.576.674.44.66.23.05.7%7.7%3.8% Windsor and Maidenhead13357023581.682.780.577.678.776.54.05.52.54.9%6.7%3.1% Wokingham15033025583.684.682.677.778.776.75.97.34.57.1%8.8%5.4% Milton Keynes20855023580.581.479.675.876.774.94.75.93.55.8%7.3%4.3% Brighton and Hove25017021580.481.279.675.676.474.84.85.93.76.0%7.4%4.6% Portsmouth18805014380.281.179.375.176.074.25.16.43.86.4%7.9%4.8% Southampton21979016480.381.179.575.075.874.25.36.54.16.6%8.1%5.1% Isle of Wight131600481082.183.281.076.877.975.75.36.83.86.5%8.3%4.6% Buckinghamshire4785401082283.484.082.877.377.976.76.16.95.37.3%8.3%6.3% East Sussex4930101012183.283.882.676.777.376.16.57.35.77.8%8.8%6.8% Hampshire12407802495083.083.482.677.177.576.75.96.45.47.1%7.7%6.5% Kent13291402825782.683.082.276.076.475.66.67.16.18.0%8.6%7.4% Oxfordshire6067401362883.283.782.776.877.376.36.47.15.77.7%8.6%6.8% Surrey10591102064283.183.582.777.678.077.25.56.14.96.6%7.3%5.9% West Sussex7549301452982.683.182.177.177.676.65.56.24.86.7%7.4%5.9% Source: ONS ward level life expectancy 1999-2003 (experimental statistics), Census 2001

19. Top vs. bottom quintile gaps Source: ONS ward level life expectancy 1999-2003 (experimental statistics), Census 2001

20. Bottom quintile vs. rest gaps SE LAA areas Populatio n (2001) No of wards Wards per quintile (rounde d up) Average LE (rest) ULLL Average LE (bottom quintile) ULLLGapULLL Relative gap (to rest) ULLL Medway24924022578.378.777.975.676.474.82.73.61.83.5%4.6%2.4% Bracknell Forest10963018479.680.279.076.878.075.62.84.21.53.6%5.2%1.9% West Berkshire14448030680.781.280.277.078.076.03.74.82.54.6%6.0%3.1% Reading14481016479.780.379.275.876.874.83.95.12.84.9%6.4%3.5% Slough12079014378.879.478.275.576.674.43.34.62.04.2%5.8%2.6% Windsor and Maidenhead13357023580.581.079.977.678.776.52.94.01.73.5%5.0%2.1% Wokingham15033025581.481.980.877.778.776.73.74.82.54.5%5.9%3.1% Milton Keynes20855023579.279.678.775.876.774.93.44.32.44.2%5.5%3.0% Brighton and Hove25017021578.979.378.475.676.474.83.34.12.44.1%5.2%3.0% Portsmouth18805014378.178.677.675.176.074.23.04.02.03.9%5.1%2.6% Southampton21979016478.979.378.575.075.874.23.94.83.05.0%6.1%3.8% Isle of Wight131600481080.180.679.576.877.975.73.34.52.14.1%5.6%2.6% Buckinghamshire4785401082281.181.480.977.377.976.73.84.53.24.7%5.5%3.9% East Sussex4930101012180.881.180.676.777.376.14.14.83.55.1%5.9%4.3% Hampshire12407802495081.081.280.877.177.576.73.94.33.54.8%5.3%4.3% Kent13291402825780.280.380.076.076.475.64.24.63.85.2%5.7%4.7% Oxfordshire6067401362881.081.280.776.877.376.34.24.83.65.2%5.9%4.5% Surrey10591102064281.381.581.177.678.077.23.74.13.24.5%5.1%4.0% West Sussex7549301452980.981.180.677.177.676.63.84.33.34.7%5.3%4.0% Source: ONS ward level life expectancy 1999-2003 (experimental statistics), Census 2001

21. Bottom quintile vs. rest gaps Source: ONS ward level life expectancy 1999-2003 (experimental statistics), Census 2001

22. Discussion points raised Population denominators – the lack of pre- 2001 small area population estimates for many parts of the South East was raised as a major concern by several attendees. One suggestion was to use the age-sex profiles contained in the 2001 mid-year estimates as the basis for approximations for earlier years Lifestyle indicators – the problems in obtaining local data for supporting lifestyle indicators such as alcohol consumption and physical activity were raised. It was emphasised that synthetic estimates are not well-suited for longitudinal monitoring

23. Key data and resource links ONS ward life expectancy figures www.statistics.gov.uk/statbase/Product.asp?vlnk=14466 www.statistics.gov.uk/statbase/Product.asp?vlnk=14466 ONS life expectancy calculator www.statistics.gov.uk/downloads/theme_health/LE_templ ate.xls www.statistics.gov.uk/downloads/theme_health/LE_templ ate.xls SEPHO life expectancy calculator www.sepho.org.uk/Pages/viewResource.aspx?id=8943 www.sepho.org.uk/Pages/viewResource.aspx?id=8943 ONS mid-year population estimates for wards and SOAs www.statistics.gov.uk/statbase/Product.asp?vlnk=14357 www.statistics.gov.uk/statbase/Product.asp?vlnk=14357

24. Workshop attendees Anne Morrison, South East Public Health Group Charlie Manicom, NHS South East Coast Dan King, Southampton City PCT David Sheehan, South East Public Health Group Helen Atkinson, South East Public Health Group Jackie Wilderspin, Oxford City PCT Maddy Knott, Portsmouth City PCT Mary Linton, Berkshire West PCT Nicola Holttum, Southampton City PCT Nigel Owen, West Berkshire Council Pam Naylor, South East Public Health Group Sid Beauchant, Berkshire Public Health Network Sue Sylvester, Hampshire County Council Terry Blair-Stevens, Brighton & Hove City PCT