Evaluating Predictors of Geographic Area Population Size Cut-offs to Manage Re-identification Risk
Evaluating Predictors of Geographic Area Population Size Cut-offs to Manage Re-identification Risk
复制标题
DOI：
10.1197/jamia.m2902
复制
发表时间：
2009-03-01
影响因子：
6.4
通讯作者：
AbdelMalik, Philip
中科院分区：
文献类型：
--
作者：
El Emam, Khaled;Brown, Ann;AbdelMalik, Philip
目标：在公共卫生和卫生服务研究中，将地理信息纳入数据集至关重要。由于对患者重新识别的担忧，来自小地理区域的数据要么被抑制，要么将地理区域聚合到更大的地理区域中。我们的目标是估计人口规模截止在一个地理区域是足够大的，使没有数据抑制或进一步的聚合是necessary.Design：2001年加拿大人口普查数据进行模拟模型的地理区域人口规模和独特性之间的关系，一些常见的人口变量。截止计算的地理区域人口规模，预测模型的开发，以估计适当的cut-offs.Measurements：重新识别的风险进行了测量使用的独特性。地理区域人口规模截止估计使用的最大数量的可能值在数据集和传统的熵measure.Results：该模型预测人口截止使用的最大数量的可能值在数据集的R-2值约为0.9，预测的相对误差小于0.02在加拿大的所有地区。然后，该模型被应用于评估适当的地理区域大小的处方记录提供的零售和医院药房的商业research and analysis firm.Conclusions：管理重新识别风险，预测模型可用于公共卫生专业人员，健康研究人员和研究伦理委员会，以决定当地理区域的人口规模足够大。
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 cut-off at which a geographic area is sufficiently large so that no data suppression or further aggregation is necessary.Design: The 2001 Canadian census data were used to conduct a simulation to model the relationship between geographic area population size and uniqueness for some common demographic variables. Cut-offs were computed for geographic area population size, and prediction models were developed to estimate the appropriate cut-offs.Measurements: Re-identification risk was measured using uniqueness. Geographic area population size cut-offs were estimated using the maximum number of possible values in the data set and a traditional entropy measure.Results: The model that predicted population cut-offs using the maximum number of possible values in the data set had R-2 values around 0.9, and relative error of prediction less than 0.02 across all regions of Canada. The models were 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.Conclusions: 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.