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Ample literature describes the history of the association between the advances in the health and wealth of people, and mortality rates, life expectancy and adult height. Twentynine German studies with n > 200 subjects published since 1848 on menarcheal age, were reanalyzed, and 101 studies from various other European and non-European countries. On average, mean age at menarche declined since the mid-19(th) century. Historic urban samples tended to decline earlier than rural groups, upper class women earlier than working class women. In Germany, minimum values for the age at menarche were seen already between the two World Wars (Leipzig 12.6 years in 1934, Halle 13.3 years in 1939). Values for mean age and SD for age at menarche were strongly associated. With improving historic circumstances, the two parameters declined in parallel. The standard deviation for menarcheal age dropped from over 2.5 years in mid-19th century France to little more or even less than 1 year in most modern countries. In the German studies the correlation between menarcheal age and SD was almost complete with r = 0.96 (y = 0.35x - 3.53). Similar associations between mean age at menarche and SD for age were found in other European countries. The obvious and immediate effects of historic events on menarcheal age, and particularly on the age distribution, indicate that menarche is a sensitive indicator of public health and wealth, and may be an appropriate estimator for the socio-economic background of historic populations.
Recent progress in modelling individual growth has been achieved by combining the principal component analysis and the maximum likelihood principle. This combination models growth even in incomplete sets of data and in data obtained at irregular intervals. We re-analysed late 18th century longitudinal growth of German boys from the boarding school Carlsschule in Stuttgart. The boys aged 6-23 years, were measured at irregular 3-12 monthly intervals during the period 1771-1793. At the age of 18 years, mean height was 1652 mm, but height variation was large. The shortest boy reached 1474 mm, the tallest 1826 mm. Measured height closely paralleled modelled height, with mean difference of 4 mm, SD 7 mm. Seasonal height variation was found. Low growth rates occurred in spring and high growth rates in summer and autumn. The present study demonstrates that combining the principal component analysis and the maximum likelihood principle enables growth modelling in historic height data also.