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Trends in factors such as rates of disease and death, as well as behaviours such as smoking, are often used by public health professionals to assist in healthcare needs assessments, service planning, and policy development. Examining data over time also makes it possible to predict future frequencies and rates of occurrence. The most obvious first step in assessing a trend is to plot the observations of interest by year or some other time period deemed appropriate. The observations can also be examined in tabular form.
These steps form the basis of subsequent analysis and provide an overview of the general shape of the trend, help identify any outliers in the data, and allow the researcher to become familiar with the rates being studied. Detailed knowledge of the statistical methods used in analysis is beyond the scope of MFPH Part A, but methods include:.
Time series analysis refers to a particular collection of specialised regression methods that illustrate trends in the data. It involves a complex process that incorporates information from past observations and past errors in those observations into the estimation of predicted values. Briefly, there are three types of modelling used to analyse time series data: autoregressive AR models, integrated I models and moving average MA models.
Autoregression is based on the premise that past observations have an effect on the current, and the number of previous observations that contribute to the current observation can be varied in the model.
For example, in a first-order autoregressive model — AR 1 — the current observation is only predicted by the immediately preceding value, and in a second-order model — AR 2 — the current observation is predicted by the previous two observations, etc. Moving average models are slightly different. Here, instead of using past observed values as predictors, we instead use the errors of previous forecasts. Again, the number of previous forecasts used in the model can be set, so an MA 1 model only uses the error of the previous forecast.
An assumption in ARMA models is that the time series is stationary i. Addition of an integrated I term helps account for any underlying trends i.
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