Ali Al-Zahrani1, Ali Baomer, Nasser Al-Hamdan, Gamal Mohamed. 1. Department of Biostatistics, Epidemiology and Scientific Computing, Research Centre, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
Abstract
BACKGROUND: The 1994 Saudi National Cancer Registry (NCR), a population-based registry, showed a crude incidence rate (CIR) of 39/100,000 for all cancers in the Saudi population. The low CIR suggested possible under-reporting, especially during the early years of operation. This study as aimed at estimating the number of missed cases due to under-reporting, and to assess the validity of reported data from a major public referral hospital in Riyadh city. MATERIALS AND METHODS: We compared cancer cases from the three data sources: medical records (MR), which were the original source of NCR cases; pathology reports (PR); and death certificates (DC). We estimated the missing cancer cases using the capture-recapture method with log-linear models of Fienberg to correct for interdependency between the three data sources. To assess the validity of the data, we reabstracted records of about 8% (39/4760 of previously reported cases from the same hospital. RESULTS: A total of 811 cancer cases were reported through the three sources, i.e., MR 611 (75.3%), PR 639 (78.8%) and DC 204 (25.2%). After fitting a series of log-linear models to the three sources of data, the three sources were found to be statistically dependent. Capture-repcapture method indicated that 384 cases were missed, giving an estimation of 1195 cancer cases to be reported. Using these 1195 estimated cases; the estimated ascertainment rates were 51% for medical records, 53% for pathology reports, 17% for death certificates, and 68% for the aggregated registry. In the validity assessment, major disagreement between the abstracted and reabstracted data was found to be highest for stage of disease (44%), followed by histology code and behavior (25.6%). Minor disagreements were most common for date of diagnosis (36%) and grade (36%). Overall, agreements were highest for laterality (95%), followed by primary site codes (90%) and basis of diagnosis (85%). Agreement of tumor description variables (site, histology, behavior, and stage) was 57%. CONCLUSION: Cancer registration will require substantial improvements in both completeness of reporting and data quality at the hospital level. Use of multiple data sources and estimation of missed cases will help ensure completeness of case registration.
BACKGROUND: The 1994 Saudi National Cancer Registry (NCR), a population-based registry, showed a crude incidence rate (CIR) of 39/100,000 for all cancers in the Saudi population. The low CIR suggested possible under-reporting, especially during the early years of operation. This study as aimed at estimating the number of missed cases due to under-reporting, and to assess the validity of reported data from a major public referral hospital in Riyadh city. MATERIALS AND METHODS: We compared cancer cases from the three data sources: medical records (MR), which were the original source of NCR cases; pathology reports (PR); and death certificates (DC). We estimated the missing cancer cases using the capture-recapture method with log-linear models of Fienberg to correct for interdependency between the three data sources. To assess the validity of the data, we reabstracted records of about 8% (39/4760 of previously reported cases from the same hospital. RESULTS: A total of 811 cancer cases were reported through the three sources, i.e., MR 611 (75.3%), PR 639 (78.8%) and DC 204 (25.2%). After fitting a series of log-linear models to the three sources of data, the three sources were found to be statistically dependent. Capture-repcapture method indicated that 384 cases were missed, giving an estimation of 1195 cancer cases to be reported. Using these 1195 estimated cases; the estimated ascertainment rates were 51% for medical records, 53% for pathology reports, 17% for death certificates, and 68% for the aggregated registry. In the validity assessment, major disagreement between the abstracted and reabstracted data was found to be highest for stage of disease (44%), followed by histology code and behavior (25.6%). Minor disagreements were most common for date of diagnosis (36%) and grade (36%). Overall, agreements were highest for laterality (95%), followed by primary site codes (90%) and basis of diagnosis (85%). Agreement of tumor description variables (site, histology, behavior, and stage) was 57%. CONCLUSION:Cancer registration will require substantial improvements in both completeness of reporting and data quality at the hospital level. Use of multiple data sources and estimation of missed cases will help ensure completeness of case registration.