MPH Master pieces

Cervical cancer after three negative smears

The results of this  paper seem obvious once you know the answer. The question is this: if a woman has three consecutive negative smears, can you give her the all-clear and say she doesn't need to come again?

The details of method are quite complicated but the essence is simple - a very large national database in the Netherlands with results of smear tests and follow up data on which women developed cancer.

Before we answer that question, let's look at something which may at first sight appear rather similar. Thirty years ago, it was standard practice to do a test for blood in the stool (faecal occult blood) on patients suspected of having bowel cancer. One might be negative by chance; so how many tests to do before giving patients the all-clear? Each test seemed cheap, and sometimes the fifth or even sixth test was the first to be positive so to be on the safe side it was suggested that six consecutive negative tests were needed to rule out cancer. The problem was that finding a cancer with the sixth test was so rare that it worked out as costing $47m per extra cancer detected (i.e a marginal cost of $47m).  

D Neuhauser and A M Lweicki What do we gain from the sixth stool guaiac?The New England journal of medicine 293 (5), 226-8 (31 Jul 1975)

 

But this is not the same situation as cervical cancer screening.

 

The six tests for cancer are done in a short time frame - essentially repeat tests of the same situation. Cervical cancer screening every three years is to check if something has developed since the last test.

 

The finding that women can get cancer after three negative smears should not surprise us: cervical cancer is caused by human papilloma virus, so if the woman remains sexually active, she remains at risk of getting it.

Post traumatic stress disorder

 This is a good study from the US military looking at risk factors for PTSD. I particularly liked the careful definition of PTSD, based on questionnaire response but rooted in the DSM. The Diagnostic and Statistical Manual of Mental Disorders is the bible of psychiatric epidemiology. If the researchers don't use DSM criteria to make their diagnoses, you really have no idea of what they are choosing to call PTSD (or depression or schizophrenia or whatever).

The conclusion of the study may seem obvious with hindsight - that people with poorer health before deployment are more likely to develop PTSD - but it does enable military health services to target preventive action. More than two thirds of new cases of PTSD were in people with the lowest 15% of physical and mental health scores on the SF36 at baseline. 

Alcohol in Slovenia

The statistical analysis in this paper is rather tricky but the basic design is simple - a study of the number of suicides before and after a change in law to restrict alcohol sales and consumption. 

The authors use the correct technique (ARIMA) to study change in the daily count of suicide. Time series analysis is very tricky because of a problem called serial autocorrelation: if the numbers are up one day they are likely to be slightly up the next day too. This happens because natural ups and downs which are continuous are sliced by us humans into counts which start afresh every midnight as a daily count. The same is true for daily counts of, say, hospital admissions during an epidemic (and at other times).

So the count for each day is correlated with the day before and the day after. As you know, independence of the observations (i.e. no correlation between them) is really important for routine statistical analyses: hence the need for a special technique to cope - ARIMA.

The message of the study seems clear, however: 1. Alcohol kills and 2. laws work.

Slovenia' start position was 17 litres alcohol per head  - the highest in Europe. By 2003, according to the WHO European office database it was down to about 9 litres per head: similar to France and the UK, but the French trend is down and ours is up.

A large report from SCHARR looked at the economics of alcohol consumption. Based on evidence of price elasticity (i.e. what happens when you put the price up) the CMO's call for a minimum price of 50p would preferentially reduce drinking among poor and young people i.e. do exactly what is needed. Gordon Brown dismissed it out of hand. But perhaps CMO will outlast Gordon and try again.  

Survival after cardiac surgery

I wonder what you think about this one. The dataset is large and the basic method simple, but I can't say I agree with the authors' headline.

The paper looks at survival after cardiac surgery, and the issue highlighted by the authors is deprivation: patients from deprived areas fare worse. But if you look at the numbers it seems that the effects of deprivation are dwarfed by the effects of smoking and diabetes.

It's always good to look at basic data, before any statistical manipulations are done. There is a table sorting the patients into quartiles by deprivation score, and tabulating the number of survivors at 3500 days after surgery:

n in quartile     n alive at 3500 days

10 803            536

10 854            554

10 872            568

10 748            532

Which looks a pretty small difference to me. Numerically the difference is reported as 2.4% increased mortality for a one unit increase in Carstairs score (i.e. a hazard ratio of 1.024 in a Cox proportional hazard model). This doesn't mean much if you don't know about Carstairs score, so you need to look for the range of Carstairs scores. Elsewhere the interquartile range for Carstairs is given as -2.2 to +2.3: a 4.1 point difference in score, which equates to a 4.1 * 2.4% i.e. a 9.2% difference in mortality. Some of the deprivation effect is due to more smoking, obesity and diabetes in the higher deprivation categories; after adjusting for this in a multivariate analysis, the deprivation effect drops to 1.7% per unit of Carstairs score. 

But the effect of diabetes is stated to be a 31% increase in mortality, and of smoking to be 29% (current smoker) or 25% (ex smoker). So surely the headline of the paper should have been about diabetes and smoking? Or have I misunderstood the numbers badly?

A parenting trial

 Here is a good trial which the BMJ short-changed by printing in its hard copy a ridiculously short summary. You need to read the full write-up online.

Little comment needed from me, except perhaps to point out that parenting trials, though important, measure us a proxy result - better parenting - rather than the thing we want - better (i.e. happier and healthier) children. On the other hand, as the authors point out, there is good evidence that poor bonding between mother and infant reliably predicts trouble later on.