Analysing Mortality Rates and Data of Childhood Leukaemia by Time Series Methods

The Hungarian mortality rates were analyzed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia. The mortality data may be analyzed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of cerebrovascular diseases and analysis of the mortality rates of cancer of cervix. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. We present a new approach to analyzing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The seasonal components of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.