Objective In public health and health services research, the inclusion of geographic information in data sets is critical. Because of concerns over the re-identification of patients, data from small geographic areas are either suppressed or the geographic areas are aggregated into larger ones. Our objective is to estimate the population size cutoff at which a geographic area is sufficiently large so that no data suppression or further aggregation is necessary.

Design The 2001 Canadian census data was used to conduct a simulation to model the relationship between geographic area population size and uniqueness for some common demographic variables. Cutoffs are computed for geographic area population size, and prediction models are developed to estimate the appropriate cutoffs.

Measurements Re-identification risk is measured using uniqueness. Geographic area population size cutoffs are estimated using the maximum number of possible values in the data set and a traditional entropy measure.

Results The model which predicted population cutoffs using the maximum number of possible values in the data set had R² values around 0.9, and relative error of prediction less than 0.02 across all regions of Canada. The models are then applied to assess the appropriate geographic area size for the prescription records provided by retail and hospital pharmacies to commercial research and analysis firms.

Conclusion To manage re-identification risk, the prediction models can be used by public health professionals, health researchers and research ethics boards to decide when the geographic area population size is sufficiently large.