“A Tale of Two Worlds”

"We all have AIDS": case for reducing the cost of HIV drugs to zero BMJ 2002;324: (26 January) Now we all have AIDS. No other construction is any longer reasonable. The earth has AIDS; 36.1 million people at the end of the year In Botswana, 36 percent of adults are infected with HIV; in South Africa 20 percent. Three million humans died of AIDS in the year 2000, 2.4 million of them in sub-Saharan Africa. That is a Holocaust every two years; the entire population of Oregon, Iowa, Connecticut or Ireland dead last year, and next year, and next. More deaths since the AIDS epidemic began than in the Black Death of the Middle Ages. It is the most lethal epidemic in recorded history. Berwick DM. We all have AIDS. Washington: Washington Post, Jun :A17.

“A Tale of Two Worlds”

“A Tale of Two Worlds” shtml - 324/7332/0/ghttp://bmj.com/content/vol324/issue7332/twib. shtml - 324/7332/0/g

Leicester Warwick Medical School Health and Disease in Populations Measuring the Burden of Disease and Comparing Groups Ronald Hsu

Lecture objectives You should be able to: 1.define and differentiate between ‘incidence’ and ‘prevalence’, and describe their inter- relationship

Lecture objectives You should be able to: 2.describe the importance of systematic variation in risk of disease between groups: a.as a useful source of information b.as a nuisance which needs to be controlled for 3.explain the purpose of age/sex standardisation 4.interpret a Standardised Mortality (Morbidity) Ratio (SMR)

Objective 1 – Incidence & Prevalence How much Disease is There? The ‘amount’ of disease has two concepts: 1.The number of new cases that occurred –focuses on NEW EVENTS –useful when monitoring epidemics –e.g. the number of new cases of meningitis in 1 st year students

2.The number of people affected by the disease –counts PEOPLE with EXISTING DISEASE (both OLD and NEW cases) –describes ‘burden of disease’ –useful as a measure of need for services –e.g. cystic fibrosis in Warwickshire

Incidence Rate – measuring new cases 300 heart attacks in dye workers –is this a lot or a few? 300 heart attacks in 50,000 dye workers –considers population at risk 300 heart attacks in 50,000 dye workers between July ’94 & Dec ’95 (1½ years) –considers time dimension as well

300 heart attacks in 50,000 dye workers between July 94 & Dec 95 (1½ years) – 300 = heart attacks per worker in Jul ’94 - Dec ’95 50,000 (i.e. 6 heart attacks per 1,000 workers in 1½ years) – 300 = heart attacks per worker per year 50,000 x 1.5 i.e. 4 heart attacks per 1,000 workers per year Incidence rate = new events = events per persons per year person x time (years) Incidence Rate – measuring new cases

On 14 th February 2002, 80 patients have cancer in a population of 1,500 (Point) prevalence = 80 1,500 = 53 per 1,000 = 5.3% Prevalence is a proportion NOT a rate Denominator is persons NOT person per time Prevalence – measuring existing cases

Relationship between Incidence and Prevalence

Increase incidence?–increase prevalence Cure more patients?–lower prevalence Kill more patients?–lower prevalence Keep them alive longer?–increase prevalence P  (I x L) P = Prevalence, I = Incidence, L = Length of disease

Incidence is a measure of the population’s average risk of disease e.g.AIDS incidence 5 per 100,000 p-y heart attack incidence 10 per 1,000 p-y. But in a population not all people have the same ‘proneness’ or ‘risk’ of disease. There are variations in risk of disease between groups of people. Objective 2 – Systematic Variation a. as a useful source of information

AIDS – variation in exposure to unsafe sex or infected blood products Heart attack – variation in exposure to cigarette smoking Systematic (as opposed to random) variations in risk between people is of great interest because it can give us clues about the aetiology (cause) of disease. Useful Variations in Risk of Disease

Aetiology (Cause) of Disease We can look at the exposures in the two groups and try to identify the causal exposure. Having identified the causal exposure, it may be possible to prevent exposure and thus reduce the incidence of the disease. Useful Variations in Risk of Disease 1

Clues about the aetiology (cause) of disease: we can compare incidence rates between different groups: Incidence Rate Ratio (IRR) =Rate A Rate B if the rate in one group is higher than the rate in the other group, this implies that the two groups had different exposures which caused the difference in disease rates Useful Variations in Risk of Disease 1

Efficacy of Treatment Incidence rate ratios can also be used to compare the effects of two treatments and decide which is the best. Exposure is therefore treatment A or B. (where A is the old treatment and B is the new treatment) Useful Variations in Risk of Disease 2

Drug A (old):8 deaths in 800 p-y Drug B (new):5 deaths in 1,000 p-y Rate A = 8/ 800 = 10 per 1,000 p-y Rate B = 5/1,000 = 5 per 1,000 p-y Mortality rate ratio = Rate A = 10 per 1,000 Rate B 5 per 1,000 = 2.0 Useful Variations in Risk of Disease 2

Mortality rate ratio= Rate A (old)= 2.0 Rate B (new) (N.B. – rate ratio has no units) ‘Twice as likely to die on the old treatment compared with the new treatment’ OR ‘New treatment halves the risk of death compared to the old treatment’ Useful Variations in Risk of Disease 2

The previous examples show how systematic variations in risk between groups can give us pointers to possible causes of disease and can be used to compare the effects of treatment. However, other types of systematic variation can still be IMPORTANT but not at all useful – in fact they are a nuisance. Objective 2 – Systematic Variation b. as a nuisance to be controlled for

For example: Age and sex are strong determinants of health and ill health Rate ratios for most diseases comparing Rate old are usually > 1.0 Rate young Not particularly useful for prevention: whilst it may be possible to target prevention at particular age (sex) groups, age and sex are not modifiable factors So, should we worry about these sorts of factors? Nuisance Variations in Risk of Disease

An illustration: Skin cancer mortality rate in Bournemouth vs. rest of UK: Incidence Rate Ratio (IRR) = 5.0, i.e. ‘mortality rate 5 times as high in Bournemouth than the rest of the UK’ Is sunbathing the cause? BUT people retire to the coast, so Bournemouth has on average an older population than UK old people are prone to skin cancer Nuisance Variations in Risk of Disease

? PlaceSkin cancer (Exposure)(Disease) Confounding as a Nuisance

? PlaceSkin cancer (Exposure)(Disease) associated Age Confounding as a Nuisance

? PlaceSkin cancer (Exposure)(Disease) associated Age Confounding as a Nuisance

? PlaceSkin cancer (Exposure)(Disease) associated associated Age (Confounder) Age in this situation is a CONFOUNDER Confounding as a Nuisance

Confounding can explain ALL or PART of an apparent association between an exposure and a disease. So in the illustration, the difference in age distribution between Bournemouth and the rest of the UK, gives a SPURIOUS association between living in Bournemouth and a high risk of skin cancer. Confounding as a Nuisance

In the illustration, (Crude) IRR = 5.0 BUT once age was ‘adjusted’ for, (Adjusted) IRR = 1.2 i.e. now Bournemouth seems a much safer place! Age was a CONFOUNDER in this situation. Age in this situation was a nuisance, which was getting in the way of the search for modifiable causes of disease. Confounding as a Nuisance

So how can we deal with confounding by age? Could use age-specific rate ratios, compare within each age band, e.g. are year olds in Bournemouth at higher risk than year olds in the UK? continue for all age groups… With narrow age bands, little confounding occurs – so any differences are real. BUT the results are difficult to interpret as you get too many answers: one for each age-band! Confounding as a Nuisance

Get around the problem of age/sex confounding by asking: What would the rate ratio for the two populations be IF the age/sex structure of the two populations were the same? Calculate a Standardised Mortality Ratio (SMR) – see small group session. 3 & 4 – Age/Sex Standardisation & Standardised Mortality Ratio (SMR)

SMR is a summary (single) figure which describes the mortality (morbidity) experience of a local population compared to a standard population’s experience, which takes account of age-sex confounding (indirect standardisation) Usually expressed as a percentage: 100 = same risk in local population as in the standard population >100 = higher risk in local population (sometimes expressed as being relative to 1.0) Standardised Mortality Ratio (SMR)

Incidence rate of disease Prevalence (proportion) of people affected Incidence rate ratio (IRR) comparisons Systematic variations in IRR can be: –useful in searching for causes of disease (c.f. cohort studies) treatment effects (c.f. clinical trials) –can also be a nuisance (c.f. confounding) SMRs give a single summary measure of disease corrected for age-sex confounding Summary