Mending heartsInequality and inequity in coronary heart disease

Mendinghearts

Inequality and inequityin coronary heart disease

County Durham & Tees Valley1993-2003

Michael FlemingClare EynonMark ReillyLeon Green

County Durham & Tees ValleyPublic Health Network

Mending heartsBackground

Somecontext

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

Mending heartsMain aims

What didwe 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

Mending heartsOverview of methods

How didwe do it?Hospital

episodes (HES)

Deaths (PHMF)

Residentpopulation(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 whowere treated in local hospitals

Mending heartsOverview of methods

How didwe do it?Hospital

episodes (HES)

Deaths (PHMF)

Residentpopulation(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 whowere treated in local hospitals

Mending heartsVarious options to assess inequality

Describinginequality

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)

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 eventsOnly a fraction of ‘lifecourse’ experience is visible

Volumes ofCHD-related eventsoccurring every yearin Derwentside

25 AMI deaths (<75 yrs)

Mending heartsInequality in revascularisation between localities

1. Two-fold difference (Middlesbrough v Hartlepool)2. Some areas below England rates

Source: HESSource: HESRevascularisation per 100,000

0 20 40 60 80 100 120 140 160 180 200

Middlesbrough

Langbaurgh

Darlington

North Tees

Derwentside

Sedgefield

Easington

D & C-le-S

Durham Dales

Hartlepool

England CD&TV

M

Source: HESSource: HES

What about the effect of povertyon CHD admissions to hospital?

0100200300400500600700800900

1000

0 20 40 60 80

Deprivation Score

Em

erg

ency

Ad

m

Rat

e/10

0,00

0

Correlation Co-efficient = 0.6195% CI (0.52-0.68)

Emergency AdmissionsWard by Quintile

Quintile 1 (High Emergency Admissions)Quintile 2Quintile 3Quintile 4Quintile 5 (Low Emergency Admissions)

0100200300400500600700800900

1000

0 20 40 60 80

Deprivation Score

Ele

ctiv

e A

dm

Rat

e/1

00,0

00 Correlation Co-efficient = 0.2195% CI (0.09-0.33)

Elective admissions Emergency admissions

Rich Poor Rich Poor

Correlation r = 0.61Correlation r = 0.21

Stronger associationwith deprivation

No associationwith deprivation

Moments of truth

Assessing the scale of inequalities (variations) in risk or treatment or death

and looking at changes over time

is all very wellBUT

it still doesn’t tell us whether

the provision of health care is (in)equitable

Moments of truth

You’ve got to look atvariations in need

and variations in provision

and in comparable ways

Equity audit flat pack

Mending heartsInequality and inequity in coronary heart disease

Measuringinequity

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

M

County Durham & Tees Valley

Elective Admission

0

50

100

150

200

250

300

0 0.2 0.4 0.6 0.8 1

Relative Rank

Ele

ctiv

e A

dm

iss

ion

Ra

te

RII = 23%

AffluentDeprivedRich Poor

Rel Index Inequality

1. Measuring gap in provision - admissions

Slope index inequality = 37

M

County Durham & Tees Valley

3. Comparingthe two gaps

0102030405060708090

100

Admissions Mortality

Magnitudeof inequity

in provisionrelative to need

70%

Elective Admission

0

50

100

150

200

250

300

0 0.2 0.4 0.6 0.8 1

Relative Rank

Ele

ctiv

e A

dm

iss

ion

Ra

te

RII = 23%

AffluentDeprivedRich Poor

Rel Index Inequality

1. Measuring gap in provision - admissions

CHD Mortality

020

406080

100

120140160

180200

0 0.2 0.4 0.6 0.8 1

Relative Rank

Ele

ctiv

e A

dm

iss

ion

Ra

te

RII = 93%

AffluentDeprived

2. Measuring gap in need - mortality

Rich Poor

Slope index inequality = 37

Slope index inequality = 72

Comparing the size of inequity

-50 0 50 100 150 200

Middlesbrough

North Tees

Sedgefield

Darlington

Easington

Durham Dales

Hartlepool

Langbaurgh

Derwentside

Durham & C-le-S

Admissions

Mortality

Mag 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 resultsoccur when usingrevascularisationfor provision

Someconclusions

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 heartsWhat does it all mean?

Mending heartsWhere might we start to reduce inequity?

…andquestions

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?

Reflections on the processWhat 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

PerceptionsThe blind men and the elephant

It’s a ropeIt’s a wallIt’s a spear

PerceptionsThe blind men and the elephant

It’s all of these things and yet none of them... …and this is still an elephant.

TechniquesThey forgot to include the instructions

Let me guess.Step 2, add sand

More ‘instructions’

needed at every stage

Illusory simplicity

TechniquesThey forgot to include the instructions

The real 6 stages

ofaudit

1. Enthusiasm

2. Despair

3. Panic

4. Search for the guilty

5. Punishmentof the innocent

6. Praise & honourfor those

never involved

Proportionality of responseI was wondering which of you men would spot that

We need to do somethingto reduce inequity

Wilson

Yes - there is rather a lot of it about

Sir

Proportionality of responseI 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 howdoes 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

Proportionality of responseIs action happening where it’s needed most?

Do we know if the locality actions are proportional to the size of inequity?

0 20 40 60 80 100 120 140

Durham &C-le-S

Derwentside

Langbaurgh

Hartlepool

Dur Dales

Easington

Darlington

Sedgefield

N Tees

MiddlesbroughCould some types of inequity - like property values -get better even if there was no formal intervention?

Reflections on the processWhat 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

Thanks to those involved

Report authorsMichael Fleming

Clare EynonLeon Green

County Durham & Tees ValleyPublic Health Network

Steering GroupMichael Fleming

Alyson LearmonthAnne Low

Mark ReillyToks Sangowawa

Ken Snider

Equity measurementAllan Low

Louise Unsworth

Clinical adviceCoast to Coast Network

Various clinicians

Mending heartsInequality and inequity in coronary heart disease