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Mending hearts Inequality and inequity in coronary heart disease Mending hearts Inequality and inequity in coronary heart disease County Durham & Tees.

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Presentation on theme: "Mending hearts Inequality and inequity in coronary heart disease Mending hearts Inequality and inequity in coronary heart disease County Durham & Tees."— Presentation transcript:

1 Mending hearts Inequality and inequity in coronary heart disease Mending hearts Inequality and inequity in coronary heart disease County Durham & Tees Valley Michael Fleming Clare Eynon Mark Reilly Leon Green County Durham & Tees Valley Public Health Network

2 Mending hearts Background Some context 1. Coronary heart disease still presents a large burden of disease in the community 2. A great deal is known about inequalities (variations) in coronary heart disease risk and outcome 3. Much less is known about inequity - the nature of the gap between need and provision 4. Directors of Public Health are required to develop health equity profiles & conduct audits

3 Mending hearts Main aims What did we do? 1. Described the inequalities (variations) in coronary heart disease risk and outcome 2. Measured the size of the inequity (gap between need and provision) within and between places 3. Produced PCT locality profiles of coronary heart disease using the ‘life course’ model All of this intended to provide evidence as the basis of reducing inequity systematically and effectively

4 Mending hearts Overview of methods How did we do it? Hospital episodes (HES) Deaths (PHMF) Resident population (Census) ICD10 Disease definitions 1. Coronary heart disease (ICD10 I20-I25) Angina pectoris (I20) AMI (I21) Subsequent MI (I22) Comp after AMI (I23) Other ischaemic (I24) Chronic ischaemic (I25) 2. Acute myocardial infar (ICD10 I21) Counts and rates for residents wherever treated Excludes non-residents who were treated in local hospitals

5 Mending hearts Overview of methods How did we do it? Hospital episodes (HES) Deaths (PHMF) Resident population (Census) OPCS4 interventions 1. Angiography (K63-K65) 2. Revascularisation (K40-46 and K49-50) Cor art bypass graft (CABG) (K40-K46) Perc Trans Cor Angio (PTCA) (K49-K50) Counts and rates for residents wherever treated Excludes non-residents who were treated in local hospitals

6 Mending hearts Various options to assess inequality Describing inequality Inequalities in admission coronary heart disease acute myocardial infarction Inequalities in interventions angiography revascularisation Inequalities in mortality coronary heart disease acute myocardial infarction Inequalities by: 1. Gender (Person, Male, Female) 2. Area (SHA, PCT, ward) 3. Deprivation (IMD scores/ranks) 4. Time (from 1993 to 2003)

7 16,900 binge drinkers 440 admissions (el + em) 190 angiographies 100 revascularisations 70 CHD deaths (<75 yrs) 17,100 obese 21,100 current smokers 23,700 severely deprived Iceberg of CHD risks and events Only a fraction of ‘lifecourse’ experience is visible Volumes of CHD-related events occurring every year in Derwentside 25 AMI deaths (<75 yrs)

8 Mending hearts Inequality in revascularisation between localities 1. Two-fold difference (Middlesbrough v Hartlepool) 2. Some areas below England rates Source: HES Revascularisation per 100,000 EnglandCD&TV

9 M Source: HES What about the effect of poverty on CHD admissions to hospital? Elective admissionsEmergency admissions RichPoorRichPoor Correlation r = 0.61 Correlation r = 0.21 Stronger association with deprivation No association with deprivation

10 Moments of truth Assessing the scale of inequalities (variations) in risk or treatment or death and looking at changes over time is all very well BUT it still doesn’t tell us whether the provision of health care is (in)equitable

11 Moments of truth You’ve got to look at variations in need and variations in provision and in comparable ways Equity audit flat pack

12 Mending hearts Inequality and inequity in coronary heart disease Measuring inequity 2. Measure the gap in need coronary heart disease mortality 3. Compare magnitudes of inequity admissions v coronary heart disease mortality revascularisation v coronary heart disease mortality 1. Measure the gap in provision elective admissions revascularisation

13 M County Durham & Tees Valley RichPoor Rel Index Inequality 1. Measuring gap in provision - admissions Slope index inequality = 37

14 M County Durham & Tees Valley 3. Comparing the two gaps Magnitude of inequity in provision relative to need 70% RichPoor Rel Index Inequality 1. Measuring gap in provision - admissions 2. Measuring gap in need - mortality RichPoor Slope index inequality = 37 Slope index inequality = 72

15 Comparing the size of inequity Lower provision in more deprived areas Higher provision/need in more deprived areas Relative index of inequality 1. Inequalities in need between affluent and deprived areas are worse than inequalities in provision 2. Provision of care does not vary sufficiently to meet different needs and so inequity occurs 3. The inverse care law operates in Sedgefield & Easington 4. Similar results occur when using revascularisation for provision

16 Some conclusions 1. Inequalities in CHD mortality reflect differences in exposure to risk & access to appropriate healthcare 2. The provision of care for CHD is inequitable in all localities and, at the worst, some inverse care occurs 3. The ability to reduce inequity requires much more understanding of: patient care pathways; whole system engineering; and resource allocation at all levels Mending hearts What does it all mean?

17 Mending hearts Where might we start to reduce inequity? …and questions 1. Planning & commissioning Do current ‘systems’ create/entrench inequity? 2. Pathways & protocols To what extent are these influencing (in)equity? 3. Patients & professionals How does clinician-patient interaction affect equity? 4. Possible & practical What to do to reduce inequity given other constraints?

18 Reflections on the process What are we learning by studying inequity 1. Does it matter if (in)equity means different things to different people if all working toward the same end? 2. Is available ‘guidance’ for (in)equity measurement sufficient for practical needs in the real world? 3. Are we (the Public Health community) capable of ensuring that the actions we take will really reduce inequity rather than make it worse? Perceptions Techniques Proportionality of response

19 Perceptions The blind men and the elephant It’s a ropeIt’s a wallIt’s a spear

20 Perceptions The blind men and the elephant It’s all of these things and yet none of them... …and this is still an elephant.

21 Techniques They forgot to include the instructions Let me guess. Step 2, add sand More ‘instructions’ needed at every stage Illusory simplicity

22 Techniques They forgot to include the instructions The real 6 stages of audit 1. Enthusiasm 2. Despair 3. Panic 4. Search for the guilty 5. Punishment of the innocent 6. Praise & honour for those never involved

23 Proportionality of response I was wondering which of you men would spot that We need to do something to reduce inequity Wilson Yes - there is rather a lot of it about Sir

24 Proportionality of response I was wondering which of you men would spot that Is the response appropriate to the point(s) in the disease life stage where inequity is known/assumed to start? Where is inequity ‘caused’ and how does it vary within adjacent systems? Inequity in disease prevention? Inequity at diagnosis/referral? Inequity in admissions? Inequity in surgery? Inequity in rehabilitation? Knowledge is patchy & inconsistent

25 Proportionality of response Is action happening where it’s needed most? Do we know if the locality actions are proportional to the size of inequity? Could some types of inequity - like property values - get better even if there was no formal intervention?

26 Reflections on the process What are we learning by studying inequity 1. Does it matter if (in)equity means different things to different people if all working toward the same end? 2. Is available ‘guidance’ for (in)equity measurement sufficient for practical needs in the real world? 3. Are we (the Public Health community) capable of ensuring that the actions we take will really reduce inequity rather than make it worse? Not if we recognise the perceptions held Not remotely We may be capable but there is no evidence of what impact the differences in current activities will have on desired reductions in inequity

27 Thanks to those involved Report authors Michael Fleming Clare Eynon Leon Green County Durham & Tees Valley Public Health Network Steering Group Michael Fleming Alyson Learmonth Anne Low Mark Reilly Toks Sangowawa Ken Snider Equity measurement Allan Low Louise Unsworth Clinical advice Coast to Coast Network Various clinicians Mending hearts Inequality and inequity in coronary heart disease


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