MPH Master pieces

the inverse care law

The inverse care law, coined by the great Julian Tudor Hart, states that those most in need of care receive it least. Here is a glimpse into one mechanism by which this law operates.

Tudor Hart served a deprived population in south Wales. Here is a paper which sums up his life's work.

period and cohort effects

After the flummery of the last two weeks, back to some solid epidemiology.

Age-period-cohort models are used to sort out whether an increase in disease incidence is due to a period effect or a cohort effect. A period effect will show itself as a sudden increase in incidence in all age bands; a cohort effect will only affect a particular age band, but throughout life, so as that cohort ages the peak in incidence will affect ever older age bands - 5 yr olds in 2005, 10 yr olds in 2010, 15 yr olds in 2015 and so on. Actually of course it is never as clear as that an period and cohort effects overlap.

The most famous example of a period effect was the increase in asthma mortality seen in the 1960s - mortality increased in all age bands over only a year or two. This was later seen as due to the introduction of a new treatment which though not obvious at the time increased mortality.

Cohort effects are less easy to exemplify but perhaps smoking will do - it became fashionable among the younger generation, who gradually aged.

If a disease is on the increase, knowing whether you are looking at a period effect or a cohort effect will help to rule in or rule out some potential causes of the increase.

So here is the worked example.

As always, note the data source: a service register. This affects ascertainment: people have to be on the register to receive special services for autism. The authors discuss the biasses which this may cause (e.g. pushier parents get on the register; poorer ones don't.)

 

 

 

Guideline development

Guideline development is part of the world we live in. Here is an interesting account from the American Cancer Society of how they will in future develop guidelines. A guideline for guidelines?

But joking apart we need to know that guidelines have been developed objectively, especially in contentious areas with clear commerical interests.

Outcome measures

Something a bit different today as there doesn't seem to be much of interest in the journals.

Suppose you were asked to come up with some indicators of how well your local health service was performing. It has already been agreed that the five domains of interest are:

1. Preventing premature death
2. Good quality of life in chronic disease
3. Speedy recovery from acute illness
4. Good experience of care
5. Patient safety

What indicators would you suggest? Here are the ones your colleagues in the Department of Health came up with.

 

A triplet of weight loss

This week not one, not two, but three trials of programmes for weight loss.

It's difficult to get one's head around so much information, but the PICO approach can help: for each paper get clear in your mind (ok, so put it on a piece of paper) the

Target Population (e.g. Body Mass Index > 30 / BMI >30 plus other risk factors)

Exact Intervention (group sessions / telephone support etc )

Comparator and

Outcome (weight loss at 3 months / 24 months etc).

Ideally you would wait for a systematic review to put it all together but we don't always have that luxury: we need to make up our own minds now.

A question raised by all of these trials is whether a weight loss of, say, 5% is worth having. In public health we tend to take the view that even small changes can add up to huge benefit at population level. The concept of '"minimal important difference" is taken very seriously in planning clinical trials: the smallest difference which is worth having from the patient's point of view.