Estimates of the Number of Cancer Patients Hospitalized in a Geographic Area Using Claims Data without a Unique Personal Identifier

Objective: In French national claims databases, claims are currently anonymous i.e. not linked to individual patients. In order to improve our estimate of the medical activity related to cancer in one French region, a statistical method was developed to use claims data to assess the number of cancer patients hospitalized in acute care.

Methods: This method used the medical and administrative information available in the claims (i.e. age, primary site, length of stay) to predict an average number of stays per patient, followed by a number of patients. It was based on a two-phase study design using an internal dataset which contained personal identifiers to estimate the model parameters.

Results: The predicted number of acute care patients hospitalized in one or several health care centers in one French region was 38,109 with a 95% predictive interval (37,990; 38,228) for the first six months of 2002. A prediction error of 24% was found.

Conclusion: We provide a good estimate of the morbidity in acute care hospitals using claims data that is not linked to individual patients. This estimate reflects the medical activity and can be used to anticipate acute care needs.

Estimates of the number of cancer patients hospitalized in a geographic area using claims data without a unique personal identifier

OBJECTIVE

In French national claims databases, claims are currently anonymous i.e. not linked to individual patients. In order to improve our estimate of the medical activity related to cancer in one French region, a statistical method was developed to use claims data to assess the number of cancer patients hospitalized in acute care.

METHODS

This method used the medical and administrative information available in the claims (i.e. age, primary site, length of stay) to predict an average number of stays per patient, followed by a number of patients. It was based on a two-phase study design using an internal dataset which contained personal identifiers to estimate the model parameters.

RESULTS

The predicted number of acute care patients hospitalized in one or several health care centers in one French region was 38,109 with a 95% predictive interval (37,990; 38,228) for the first six months of 2002. A prediction error of 24 per thousand was found.

CONCLUSION

We provide a good estimate of the morbidity in acute care hospitals using claims data that is not linked to individual patients. This estimate reflects the medical activity and can be used to anticipate acute care needs.