Socioeconomic differences in incidence and relative survival after a first acute myocardial infarction in the Basque Country, Spain

Objective To estimate the incidence and 28-day and 5-year survival rates after a first acute myocardial infarction (AMI) in relation to socioeconomic status in the Basque Country (Spain) between 1999 and 2000.MethodsData from a population-based registry of AMI were used. The study included 3,619 patients to calculate age-standardized incidence by the direct method and 2,003 patients (out-of-hospital deaths were excluded) to calculate observed and relative survival using the Kaplan-Meier and Hakulinen methods, respectively. Socioeconomic status was quantified using a deprivation index ecologically assigned to each patient according to the census tract of residence at diagnosis of AMI and was categorized into quintiles. Results Among men, the risk of AMI was higher in the lowest socioeconomic group than in the highest socioeconomic group (RR=1.17; 95%CI: 1.02-1.34). In men, a higher risk of death was observed in the middle (Q3; HR=1.60; 95%CI: 1.02-2.51) and low (Q5; HR=1.65; 95%CI: 1.02-2.69) quintiles compared with the least deprived group for age-adjusted survival during the acute phase. In the fully adjusted model, this effect was attenuated and no significant differences were observed in long-term survival. Among women, no significant differences were observed either in incidence or in short- and long-term survival. Conclusions Socioeconomic inequalities were only observed in men in incidence and in survival during the acute phase after an AMI (AU)

Objetivo Estimar la incidencia y la supervivencia a 28 días y 5 años tras un primer infarto agudo de miocardio (IAM) según la posición socioeconómica en el País Vasco entre 1999 y 2000.MétodosUtilizando datos de un registro poblacional de IAM se incluyeron 3.619 pacientes para estimar la incidencia ajustada por edad por el método directo, y 2003 (excluidas las muertes extrahopitalarias) para la supervivencia observada y relativa con el método de Kaplan-Meier y el de Hakulinen, respectivamente. El nivel socioeconómico se definió por un índice de privación ecológicamente asignado a cada paciente según la sección censal de residencia al diagnóstico del IAM, y se categorizó en quintiles. Resultados Los hombres del nivel socioeconómico más bajo tuvieron un mayor riesgo de IAM que los del más alto (RR=1,17; IC95%: 1,02-1,34). En la supervivencia en la fase aguda ajustada por edad, los hombres de los quintiles medio (Q3; HR=1,60; IC95%: 1,02-2,51) y bajo (Q5; HR=1,65; IC95%: 1,02-2,69) presentaron un mayor riesgo de muerte en comparación con el grupo más favorecido. Este efecto se vio atenuado en los modelos completamente ajustados, y no hubo diferencias significativas en la supervivencia a largo plazo. En la mujeres no se hallaron diferencias significativas en la incidencia ni en la supervivencia a corto y largo plazo. Conclusiones Sólo se han observado desigualdades socioeconómicas en los hombres en la incidencia y la supervivencia durante la fase aguda (AU)

Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries.

BACKGROUND: The use of hospital discharge administrative data (HDAD) has been recommended for automating, improving, even substituting, population-based cancer registries. The frequency of false positive and false negative cases recommends local validation. METHODS: The aim of this study was to detect newly diagnosed, false positive and false negative cases of cancer from hospital discharge claims, using four Spanish population-based cancer registries as the gold standard. Prostate cancer was used as a case study. RESULTS: A total of 2286 incident cases of prostate cancer registered in 2000 were used for validation. In the most sensitive algorithm (that using five diagnostic codes), estimates for Sensitivity ranged from 14.5% (CI95% 10.3-19.6) to 45.7% (CI95% 41.4-50.1). In the most predictive algorithm (that using five diagnostic and five surgical codes) Positive Predictive Value estimates ranged from 55.9% (CI95% 42.4-68.8) to 74.3% (CI95% 67.0-80.6). The most frequent reason for false positive cases was the number of prevalent cases inadequately considered as newly diagnosed cancers, ranging from 61.1% to 82.3% of false positive cases. The most frequent reason for false negative cases was related to the number of cases not attended in hospital settings. In this case, figures ranged from 34.4% to 69.7% of false negative cases, in the most predictive algorithm. CONCLUSIONS: HDAD might be a helpful tool for cancer registries to reach their goals. The findings suggest that, for automating cancer registries, algorithms combining diagnoses and procedures are the best option. However, for cancer surveillance purposes, in those cancers like prostate cancer in which care is not only hospital-based, combining inpatient and outpatient information will be required.