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Objectives: Record linkage between data sets is relatively simple when unique, universal, permanent, and common variables exist in each data set. This situation occurs infrequently; thus, there is a need to apply probabilistic methods to identify corresponding records. DataLink has been tested to determine if the use of clustering techniques will improve performance with a minimum decrease in accuracy.Methods: The study uses cancer registry data which includes hospital discharge and pathology reports from two hospitals in the Murcia Region for the years 2002-2003. These data are standardized prior to running DataLink. The original version of DataLink compares all of the records one by one, and in two later versions of the software clustering is applied which filters for one or more variables. Computing time and the proportion of detected matches have been investigated with each version.Results: The clustering versions achieve 96.1% and 96.2% accuracy, respectively. An improvement in the computational time of 97.3% and 98.6% is achieved for the two clustering versions compared with the original. The clustering versions lose 0.36% and 1.07% of real duplicates, respectively.Conclusions: DataLink implements deterministic and probabilistic record linkage to eliminate duplicates and to merge new information with existing cases. The standardization of variables to a common format has been adapted to the characteristics of Spanish language data. Clustering techniques minimize computational time and maximize accuracy in the detection of corresponding records. [ABSTRACT FROM AUTHOR]

Objectives: To identify the impact of immigration on health services by comparing hospital discharges, case-mix, and economic effects between immigrants and the native population., Methods: We performed a retrospective longitudinal study of acute-care hospital admissions in Murcia (Spain) registered in the Minimum Data Set from 2004-2005. The groups to be compared, established on the basis of country of birth, were <>, <> and <>. Diagnoses were codified using the ICE-9-CM and were grouped by means of the All Patient-Diagnosis Related Groups (AP-DRG) version 18. Utilization rates were calculated by the time of medical insurance. Economic effects were calculated through DRG weights for 2004., Results: There were 196,275 discharges, with 2,590,376 person-years of insurance. The frequency of discharges was 75.8 per thousand among Spaniards, 64.3 per thousand among immigrants from Europe-25 and 73.8 per thousand among immigrants from the remaining countries. The most frequent causes of admission were related to pregnancy, childbirth and the puerperium. Cost per admission was 3,529 euro in Spaniards, 3,231 euro in persons from Europe-25 and 2,423 euro in persons from the remaining countries. The average cost per year of insurance was 263 euro for Spaniards, 217 euro for immigrants from Europe-25 and 219 euro for those from the remaining countries., Conclusions: Hospital utilization and costs per admission and for person-year of insurance are higher in Spaniards than in immigrants, especially the group from <>. In this group, case-mix is also different.

Objectives: To validate the ability of a hospital administration data set (minimum data set [MDS]) to detect incident cases of colorectal cancer using the Murcia Cancer Registry (MCR) as the gold standard and to measure agreement between the MDS and registration of colorectal cancer., Material and Method: A cross sectional validation study of the MDS of the main hospital in the region of Murcia (Spain) was conducted. The study population consisted of incident cases of colorectal cancer in 2000 obtained from the MCR and cases in the MDS of the above-mentioned hospital for the same year with an ICD-9 diagnostic code between 153.0 and 154.1, eliminating readmissions. During the process, two analyses were performed: one analysis with the principal diagnosis only and another with all the diagnostic codes. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and agreement was calculated with their 95% confidence intervals (CI)., Results: With the first diagnosis only, the MDS detected 80% of the incident cases of colorectal cancer with a PPV of 75%. With all the diagnoses, the MDS detected 85% of the cases with a PPV of 64%. The agreement in codification was high at three digits (kappa 88% [95% CI, 0.79-0.97] first diagnosis, 89% [95% CI, 0.80-0.97] all diagnoses) as well as at four digits (kappa 77% [IC, 0.68-0.85] first diagnosis, 78% [95% CI, 0.70-0.86] all diagnoses) in both analyses., Conclusions: Because of its high sensitivity, the MDS is a good source for detecting incident cases of cancer. The high agreement found in the site of colorectal cancer helps to consolidate the MDS as a data source for cancer registration.