Estimating Prevalence of Diabetesand Other Chronic Diseases for Small Geographic Areas

Peter Congdon, Geography, QMUL

Major chronic diseases are leading source of morbidity and health service costs in developed societies with ageing populations

Age Effect compounded by high diabetes rate among relatively young immigrant/ethnic group populations (e.g. South Asian community in England)

Diabetes also increasing in developing societies (e.g. China)

Trends

Prevalence Trends have to be distinguished from Trends in Mortality (e.g. Important for CHD)

Diabetes relatively small as direct cause of death but important as risk factor for CHD, stroke, etc

Trends in Diabetes Prevalence: US Data shows upward trend and this also applies for England

HSE 1993 to 2003 (ages 16+) Males 3% 4.3% Females 2% 3.4%

Risk Factors for DiabetesDiabetes type 2 (adult onset) incidence varies

considerably by age, ethnic group and income group

Diabetes linked to socio-economic deprivation: diabetic patients with lower education level less likely to follow advice on lifestyle/medicines; less likely to attend their GP for a review of their condition.

HSE also shows higher prevalence for males

HSE 2004 Diabetes Rates by Ethnic Group & Age

Indirect Area Estimates Can model gradients over these demographic categories

using Health Survey for England; use logit regression to develop prevalence rate profile by age, ethnicity, and gender.

Apply prevalence rate profile to census populations disaggregated according to these categories

Census table ST101 cross-tabulates age, ethnicity and gender down to electoral ward level (approx. 8000 wards in England)

Deprivation gradient also applied. Gradient in prevalence over IMD quintiles, as ratio to average prevalence, is (0.76, 0.84, 0.91, 1.13, 1.37) for males, (0.80, 0.84, 0.93, 1.07, 1.36) for females.

Relevance of Quality Outcomes Framework (QOF Registers)

QOF Registers also supply estimates of prevalence but geographic profile limited (PCTs only, not local authorities or electoral wards - at least in terms of publicly available data)

Also subject to registration biases-work in Jrnl of Public Health (Sigfrid et al, Vol 28(3)) shows exception reporting increased for deprived practices - means prevalence gradient by deprivation flatter than should be

Compare Prevalence to Adverse Events

Thous. %Lower Limb Amputations

Ketoacidosis & Coma

Amput-ations

DKA & Coma

England 1529 3.1 3.4 27.5 13.5 9.1Highest % Prevalence Birmingham 91 4.0 2.8 32.4 7.9 8.5North East London 59 4.0 4.5 28.4 10.8 7.3North West London 60 3.4 2.0 24.0 5.9 6.9West Yorkshire 71 3.4 2.3 25.3 8.1 7.5Lowest % Prevalence Avon Gloucs Wilts 59 2.7 3.5 26.1 16.9 9.9Hampshire & IOW 48 2.7 4.8 26.2 21.4 10.0Beds & Herts 42 2.6 3.2 24.4 14.1 9.4Thames Valley 52 2.4 2.6 24.3 11.1 10.0* 2000-1 & 2001-2 (Two years)

Comparison of Adverse Outcomes to Prevalence

England and StHAs

Adverse Hospital Outcomes*

Age Standardised rates per 100,000 popn

Rates per 1000 Prevalent Popn

Prevalence (Persons, 1 &

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Prevalence % (Persons)

Diabetic Coma Rate per1000 Prevalent Cases

Highest & Lowest Ward Level% Prevalence

Ward Name LA P M FLatimer Leicester UA 7.4 8.2 6.6Spitalfields and Banglatown Tower Hamlets LB 7.3 8.6 5.9Sutton on Sea North East Lindsey CD 7.1 7.8 6.4Handsworth Birmingham MCD 6.9 7.3 6.5Sparkbrook Birmingham MCD 6.9 7.5 6.2Southall Broadway Ealing LB 6.7 7.3 6.1Small Heath Birmingham MCD 6.7 7.5 6.0Blakenhall Wolverhampton MCD 6.7 7.2 6.1West Bromwich Central Sandwell MCD 6.6 7.2 6.0Sparkhill Birmingham MCD 6.5 7.2 5.9LowestWellington Rushmoor CD 1.2 1.1 1.4Bicester South Cherwell CD 1.2 1.2 1.1South Chafford Thurrock UA 1.1 1.2 1.0Carfax Oxford CD 1.1 1.0 1.2The Lower Tarrants North Dorset CD 1.1 0.9 1.3Scotton Richmondshire CD 1.1 0.9 1.4Whitehill Pinewood East Hampshire CD 1.0 1.0 1.0Heslington York UA 1.0 0.9 1.1Tidworth Perham Down Kennet CD 0.9 0.9 1.0Hipswell Richmondshire CD 0.7 0.6 0.9

Other Applications Same principles can be applied to other major

chronic diseases (CHD, Serious Mental Illness)Potential to link prevalence estimation to

development of IMD indicator domainsPrevalence forecasts linked to mortality forecastsPrevalence forecasts linked to ethnic population

estimates & projectionsCompare indicative prevalence (indirect

approaches) with QOF registrations - assess under or over-registration