I have been concerned about the study that appeared in the BMJ recently about the association between industry affiliation and position on cardiovascular risk. Basically there was a clear and strong link between the orientations of authors’ expressed views on roiglitazone and their financial conflicts of interest: a drug that has concerned trusttheevidence previously.

In addition to this, an evaluation of solutions to sponsorship bias of more than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results.

What is alarming is that only half of the articles analysed in the BMJ had competing interest statements. However, this is much better that the 3% that was reported in 1998 by Stelfox. Authors at this time who supported the use of calcium-channel antagonists were significantly more likely than neutral or critical authors to have financial relationships with manufacturers of calcium-channel antagonists.

So in pondering this problem I think my solution is for primary studies, conflicts of interest should be disclosed when the article is published. However, for editorials and reviews where there is a direct conflict of interest (viz the researcher has received direct cash payments in the last five years) they should not be published by peer reviewed journals and should be left to those with an impartial viewpoint to publish in order to clear the muddy waters.

Understanding bias in clinical studies can help identify some of the reasons why we reach the wrong conclusions about the effects of interventions. Some of the biases we will be looking out for in 2010 include:

1. Publication bias: Positive findings are more likely to be published in medical journals than negative findings (Tamiflu). Media coverage of health issues often tends to be biased towards publication of stories which will grab headlines (swine flu).
2. Citation bias: This one is even more scary than publication bias: published articles tend to cite other articles that support their views rather than those articles which refute their views, and so have a negative impact on the scientific truth.
3. Selection bias: If the population studied is not representative of the population we want to draw conclusions about, then the study has a selection bias. The Framingham Heart study, for example, studied coronary heart disease in volunteers from a largely white, middle class population, and so we cannot necessarily draw conclusions about heart disease in blacks or other ethnic groups, or in populations in other countries.
4. Spectrum bias: A spectrum bias occurs when we overestimate how good a test is at picking up or excluding disease, because the test was evaluated in a biased sample of patients. The monofilament is a special tool, used to test whether diabetics have lost sensation in their feet. If it was only tested in people with mild sensory loss, then the monofilament may not be as good at picking up or excluding sensory loss, when it is used on people with severe sensory loss.
5. Information bias: If the way in which we measure an outcome or an exposure within a study is flawed, then we have an information bias. Data regarding carbon emissions rely on the integrity of countries and companies in their reporting. However, there is some evidence to suggest that such self-reporting (surprise surprise) is leading to gross under-reporting of emissions.
6. Recall bias: A special case of information bias is “recall bias”, where the ability of subjects to recall an exposure affects the results of a study. The difference between the amount of alcohol that people think that they drink and their actual alcohol consumption is a very good example.
7. Measurement bias: It is important to know whether the outcome measurement of interest is inaccurate. This can be due to inaccuracy in the measurement instrument or bias in the study participants expectations or responses. Often the way round the latter of these is to ensure adequate blinding.
8. Funding bias: an evaluation of solutions to sponsorship bias of more than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results. In 2010 elimination of such sponsorship bias should be a priority.

The final two biases are personal, in that when recognized, it may be possible to do something about them.

• Cognitive bias: is the tendency to make errors in judgment based on the way we think. In terms of diagnosis expertise is not a matter of acquiring an all-inclusive reasoning strategy, as several strategies may lead to the same diagnosis. These diagnoses are often correct; however, Clinicians tend to under-appreciate the likelihood their diagnoses are wrong and this tendency to overconfidence is related to both intrinsic and systemically reinforced factors.
• Reader Bias: Systematic errors of interpretation made during assumption by the user or reader of clinical information. These biases are due to the factors we put down to expertise: clinical experience, tradition, credentials, prejudice and human nature.

The last of these references by Richard Owen on reader bias is well worth a read, as it includes a whole host of further biases including: rivalry bias; personal habit bias, moral bias, clinical practice bias, do something bias. (The converse, do nothing bias, is common among academics), favoured design bias, prestigious journal bias, prominent author bias, famous institution bias (The converse: unrecognized institution bias), flashy title bias, friendship bias and my favourite “I am an epidemiologist" bias - Alternatively called bias bias - and is defined as repudiating a study containing any flaw in its design, analysis.

By Ami Banerjee and Carl Heneghan

Around Christmas, we are often more aware of the scale of poverty than at other times of the year. Everybody is talking about social inequality and its impact on health, from the World Health Organisation (WHO) to our own UK policymakers , from researchers and journalists to health professionals . The WHO defines social determinants as “...the conditions in which people are born, grow, live, work and age, including the health system. These circumstances are shaped by the distribution of money, power and resources at global, national and local levels, which are themselves influenced by policy choices. The social determinants of health are mostly responsible for health inequities - the unfair and avoidable differences in health status seen within and between countries.”

The difficulty, of course, is how to tease out what is most important in causing ill health- poverty, education, childhood social environment or family situation. If we don’t understand what are the causes and effects of these inequalities, then it becomes very hard to design coherent policies to address them. The link between childhood intelligence and health is a great example. Previous studies have suggested that intelligence in childhood and early adulthood is associated with illness and death. Note the wording: an “association” or “link” does not necessarily imply “causation”; i.e. we cannot say whether differences in childhood intelligence actually cause changes in health in later life.

There are two competing hypotheses to explain this association or link: firstly, that early IQ is the fundamental explanation for socioeconomic differences in health; and secondly, that socioeconomic conditions in childhood and adulthood are responsible for health differences linked to early IQ. In the first hypothesis, we are assuming a true association. In the second hypothesis, early IQ may be a confounder of the relationship between socioeconomic conditions and health. Whereas bias involves error in the measurement of a variable (in this case childhood IQ or mortality), confounding involves error in the interpretation of what may be an accurate measurement. When we look at an association, we have to consider whether the results could be due to bias, confounding, or chance (considered in lesson 2 of “Understanding EBM in 4 days”), before we conclude that it is a true association.

In a Swedish study of 1500 children who were followed up over their lifetimes after taking an IQ test, this relationship between childhood IQ and mortality was examined. As with previous studies, they found that increased educational attainment was associated with reduced mortality, with a 9% reduction in men and a 12% reduction in women for each additional year in school. These findings were unchanged after adjustment for the childhood IQ, and so the researchers concluded: “mortality differences among participants by own educational attainment were not explained by childhood IQ, neither for men nor women. Hence, our results do not suggest that differences in early IQ can explain why people with longer education live longer.”

They also found that childhood IQ was independently linked to male adult mortality, and so “higher cognitive ability also seems to have an additional direct protective effect”. In women, the researchers found that the mortality risk was highest in those with the highest childhood IQ, and this risk was greatest in women over the age of 60. They explained this finding as follows: “intelligent women may have a greater underlying risk of dying that is masked by their having on average a longer education and, supposedly, a lower mortality risk than women who spent less time in education”. These findings make the first hypothesis for a direct effect of childhood IQ on health implausible, and it seems much more likely that “the link between IQ and mortality involves the social and physical environment rather than simply being a marker of a healthy body to begin with”.