LAUSR

Seasonality of demographic data has been of great interest. The seasonality depends mainly on climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyse the number of births (deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, non-significant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. The estimated monthly models chosen are approximately orthogonal and they have little influence on the parameter estimates. Exact orthogonality should be obtained if the data are equidistant, that is, if the months are of equal length (e.g. 30 days), corresponding to 30∘. Exactly equidistant data can be observed when circadian rhythms (24 h) are studied. In this study, we compare seasonal models with models with exact orthogonality.