Another of Durkheim’s achievements, in making the case for a sociological approach to human behaviour, was that he also laid the groundwork for sociological medicine, what we now call epidemiology. He wasn’t the only one of course – the German states, Austria and Sweden had all begun to collect data for this purpose in the eighteenth century. But social medicine, epidemiology, was also born in the great industrial cities as people struggled to cope with unprecedented problems and experiences, not least in regard to hygiene. One of the first in Britain, who scored a notable early success, and acted as a model for others, was Sir John Snow, who took a statistical/sociological approach to cholera. In 1854, there was in London a terrible outbreak of cholera which had caused over five hundred deaths in fewer than ten days. In going through the lists of deceased and afflicted persons, Snow noted that most cases had occurred in the neighbourhood of Broad Street. ‘Upon interviewing members of the families of the deceased, Snow was able to isolate a single common factor, namely the Broad Street [water] pump, from which victims had drunk in every case. Corroborating evidence was made from the observation that in the local workhouse, also in the Broad Street area, only a few inmates had contracted cholera and that in every case they had contracted it before being admitted to the workhouse. Snow hypothesised (and found) that the workhouse drew water from a separate well . . . The pay-off for Snow’s careful investigation occurred when, finally convinced that impure water from the Broad Street pump was the cause of the cholera, Snow appealed to the authorities to have the pump closed.’ This brought the outbreak to an end. Though it had little immediate effect, the episode subsequently became a legend. What makes the investigation doubly unusual is the fact that the cholera bacillus was not discovered, by Robert Koch, until some twenty-eight years after Snow’s investigation.46
The germ theory of disease did not emerge fully until the 1880s. At much the same time that Snow made his deductions, Ignaz Semmelweis, a Hungarian, observed that cases of childbed fever could be reduced by having surgeons wash their hands between deliveries. Joseph Lister went further in 1865, advocating the use of carbolic acid (phenol) on patients’ wounds during surgery. But it was not until Louis Pasteur noticed that weakened bacteria could be used to provide immunity from diseases they provoked at full strength, that the idea of vaccination was conceived and quickly used for a widening number of ailments which proliferated in cities – tuberculosis, diptheria, cholera.47
The problems of urbanisation also prompted the British to establish a decennial census, beginning in 1851. The aim here was to provide a simple but empirical basis for the social dimensions of modern Britain. In turn, the census stimulated the first systematic attempts to assess the dimensions of poverty and of the housing problem. This, says Roger Smith, ‘transformed the political and moral consciousness of the country’.48
The census reflected a growth of interest in statistics. The British Association for the Advancement of Science, itself a new organisation, founded in 1831, established a statistical section in the same year. The Manchester Statistical Society was founded two years later, and the London Society a year after that. It was by now taken as read that collecting figures on morbidity, say, or the incidence of crime or insanity, or the facts of nutrition, would comprise the empirical basis both for social policy on the part of government, and for social science in the universities. All of a sudden, then, or so it seemed, a mass of data became available, describing life in Britain and elsewhere. It was the sheer volume of this detail that provoked more sophisticated statistical analysis, rather than simple counting. The first two types of statistical approach concentrated on the distribution of measurements of any particular aspect of life, while the second looked at the correlation between measurements. Besides having policy implications, these techniques had two further effects. They showed how certain different phenomena tended to go together, throwing up fresh questions, and they revealed the extent to which correlations were invariably less than perfect. Because measurements varied (along a distribution) questions began to be asked about the indeterminacy of the world, a preoccupation which loomed large in the twentieth century, even in hard sciences, like physics.49
More formal statistics began with the Belgian astronomer L.-A.-J. Quetelet (1796–1874). He went to Paris in 1823 to study astronomy and while there he encountered the theories of probability conceived by Pierre-Simon Laplace, then in his seventies (he died in 1827). And this is where we come back to the survey by Delambre and Méchain, in developing an accurate measure for the metre. Ken Alder, in his book on the survey, notes that the two men were very different in their working methods. Delambre wrote everything down in ink, in notebooks with numbered pages: any errors he made were there for all to see. Méchain, on the other hand, used separate sheets, often just scraps of paper, and wrote in pencil, which might fade or could be rubbed out or lost. Whether these working techniques were symptomatic, it certainly became clear to Delambre, when the two men came to compare notes, that his colleague had fudged a lot of his data, mainly to conform to expectations. One of the reasons these ‘discrepancies’ arose was because, in fact, the earth is a more irregular body than Méchain believed, meaning that meridians vary slightly, and so gravity varies slightly too at certain points, affecting the plumb lines they were using. But Méchain thought he had obtained anomalous results because he had miscalculated his readings of the stars in his triangulation exercises. Now, by then the exact position of the stars had become almost a classical problem, in both astronomy and mathematics. On the face of it, determining the exact location of a star (and its apparent motion) seems simple, but in fact it isn’t simple at all. By the time of the metre survey it was well known that, even with the latest telescopes, the exact location of distant stars was difficult to pin down. Observations tended to produce a range of results. To begin with, the arithmetic mean of these observations was taken as the ‘true’ answer. Then it emerged that people differed systematically in their readings and so teams of researchers were used to eliminate this bias. But many mathematicians still weren’t satisfied: they felt that observations nearer the mean should have more validity, more weight, than observations further away. This gave rise to two important developments. First, Adrien-Marie Legendre devised the method of least squares to do just this. Under this method, the best fit of any set of observations was held to be that ‘which minimised the square of the value of the departure of each data point from the curve’.50 From our point of view, the important point is that Legendre came up with his theory and first worked it out on Delambre and Méchain’s data.
This work by Laplace, Quetelet and Legendre was built on by Karl Friedrich Gauss (1777–1855), who made the second advance. Essentially, the astronomical techniques had shown that when observations by different astronomers were plotted on a graph, they were found to be, in the formal phrase, ‘regularly distributed’. This regular distribution was found to apply to a number of other phenomena and so the phrase was changed to ‘standard distribution’ (about a mean). The idea was further refined in the 1890s by the English mathematician Karl Pearson (1857–1936), who introduced the term ‘normal distribution curve’, what became known as the bell(-shaped) curve. And this was, perhaps, the most influential idea of all, at least at that time, because the bell-shaped curve was used by Quetelet to produce what he called l’homme moyen, the average man.51 It was this notion which caught the imagination of many and before long it was made wide use of – for example, by writers, marketing people, and manufacturers. In addition to that, however, there were questions raised by this discovery that seemed to pose more fundamental issues regarding human nature. Was the average man the ideal? Or was he the most mediocre? Were people at the edges of the distribution exotic or degenerate? Did l’homme moyen represent what was essential about man?52