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tions or extremes in specific climate variables, historical patterns are compared with those of ecosystem changes or disease outbreaks, with an appropriate time lag sometimes factored in. In recent years this approach has been widely used to study the effects of ENSO phenomenon, especially El Niño events, on infectious disease patterns.

Typically, this analytical approach is employed to look at a single region, so spatial or spatio-temporal variations in the climate/disease relationship are usually not considered. One critical limiting feature of this retrospective approach is that many years of comparable environmental and disease incidence data are usually needed to have confidence that apparent patterns are not occurring by chance. Often, disease surveillance data are inadequate, reducing the applicability of this approach. In addition, the observational nature of this method makes it difficult to separate the influences of other ecological or social changes from those of climate.

For all of these reasons one must be cautious about interpreting the findings of these analog studies and extrapolating the results beyond the specific context of any particular situation. For instance, studies of the effects of an El Niño event do show some of the ways that short-term climate variations can affect epidemic disease, but they are not necessarily a good analog of future long-term climate change. In general, though, this analytical approach does hold potential for improving forecasts of how short-term variability may alter epidemic risk; and if consistent relationships are found over a long time period or in many different places, more confidence can be gained in using these relationships to forecast future changes.