Actividad asistencial y costes en los últimos 3 meses de vida de pacientes fallecidos con cáncer en Euskadi / Care costs and activity in the last three months of life of cancer patients who died in the Basque Country (Spain)

Objetivo: Analizar el consumo de recursos sanitarios y su coste en los últimos meses de vida de la población fallecida por neoplasia maligna en la Comunidad Autónoma del País Vasco. Método: Estudio retrospectivo observacional sobre una población con diagnóstico de neoplasia maligna fallecida en el País Vasco (2010 y 2011). Fuente de datos: Conjunto Mínimo Básico de Datos y Registro de Mortalidad. Variables: sexo, edad, lugar de defunción, localización del tumor, actividad asistencial y costes en los últimos 3 meses de vida. Análisis descriptivo de la actividad asistencial y costes. Regresiones lineales multivariadas para obtener los costes medios ajustados según sexo, edad y lugar de defunción. Resultados: Se identificaron 9333 fallecidos/as por neoplasia maligna durante 2010 y 2011. El 65,4% eran hombres, el 61,5% tenían 70 o más años de edad, la edad media era de 72,9 años y el 71,1% falleció en el hospital. Las personas fallecidas en el hospital tuvieron un coste medio de casi el doble con respecto a las fallecidas en el domicilio (14.794 euros y 7.491 euros, respectivamente; p <0,001), y un 31,3% superior al de la residencia (11.269 euros; p <0,001). Conclusiones: Es necesaria una mayor capacidad de intervención al final de la vida en el nivel comunitario, reforzando la capacidad de atención desde la atención primaria, tanto desde su capacitación como desde el soporte de equipos expertos con el fin de cambiar el perfil actual de atención hacia una mayor atención extrahospitalaria que permita un menor consumo de recursos y una mayor atención en el domicilio (AU)

Objective: To analyse the use of health resources and its budget in the last months of life of the population who died from malignant neoplasm in the Basque Autonomous Country (Spain). Method: Retrospective observational study of a population with a diagnosis of malignancy deceased in the Basque Country (2010 and 2011). Data source: MDS and Mortality Register. Variables: gender, age, place of death, tumour location, clinical activity data and costs in the last three months of life. We performed a descriptive analysis of clinical activity and costs, and lineal multivariate regressions to obtain the adjusted mean costs by gender, age and place of death. Results: 9,333 deaths from malignancy were identified in 2010 and 2011. 65.4% were men, 61.5% aged 70 or over, mean age 72.9 years, 71.1% died in hospital. People who died in the hospital had an average cost of about double that of the people who died at home (euros14,794 and euros7,491, respectively; p <0.001) and 31.3% higher than in the nursing home (euros11,269; p <0.001). Conclusions: Greater interventions at the end of life at the community level are necessary, strengthening the care capacity of primary health care, both from training and support from expert teams in order to change the current care profile to a more outpatient care that allows a lower consumption of resources and greater care at home (AU)

[Care costs and activity in the last three months of life of cancer patients who died in the Basque Country (Spain)]. / Actividad asistencial y costes en los últimos 3 meses de vida de pacientes fallecidos con cáncer en Euskadi.

OBJECTIVE: To analyse the use of health resources and its budget in the last months of life of the population who died from malignant neoplasm in the Basque Autonomous Country (Spain). METHOD: Retrospective observational study of a population with a diagnosis of malignancy deceased in the Basque Country (2010 and 2011). DATA SOURCE: MDS and Mortality Register. VARIABLES: gender, age, place of death, tumour location, clinical activity data and costs in the last three months of life. We performed a descriptive analysis of clinical activity and costs, and lineal multivariate regressions to obtain the adjusted mean costs by gender, age and place of death. RESULTS: 9,333 deaths from malignancy were identified in 2010 and 2011. 65.4% were men, 61.5% aged 70 or over, mean age 72.9 years, 71.1% died in hospital. People who died in the hospital had an average cost of about double that of the people who died at home (€14,794 and €7,491, respectively; p <0.001) and 31.3% higher than in the nursing home (€11,269; p <0.001). CONCLUSIONS: Greater interventions at the end of life at the community level are necessary, strengthening the care capacity of primary health care, both from training and support from expert teams in order to change the current care profile to a more outpatient care that allows a lower consumption of resources and greater care at home.

[Development of a predictive risk model for unplanned admissions in the Basque Country]. / Desarrollo de un modelo de predicción de riesgo de hospitalizaciones no programadas en el País Vasco.

BACKGROUND: Hospitalizations are undesirable events that can be avoided to some degree through proactive interventions. The objective of this study is to determine the capability of models based on Adjusted Clinical Groups (ACG), in our milieu, to identify patients who will present unplanned admissions in the following months to their classification, in both the general population and in subpopulations of chronically ill patients (diabetes mellitus, chronic obstructive pulmonary disease and heart failure). METHODS: Cross-sectional study which analyzes data from a two year period, of all residents over 14 years old in the Basque Country (N = 1,964,337). Data from the first year (demographic, deprivation index, diagnoses, prescriptions, procedures, admissions and other contacts with the health service) were used to construct the independent variables; hospitalizations of the second year, the dependent ones. We used the area under the ROC curve (AUC) to evaluate the capability of the models to discriminate patients with hospitalizations and calculated the positive predictive value and sensitivity of different cutoffs. RESULTS: In the general population, models for predicting admission at 6 and 12 months, as well as long-term hospitalizations showed a good performance (AUC> 0.8), while it was acceptable (AUC 0.7 to 0.8) in the groups of chronic patients. CONCLUSION: A hospitalization risk stratification system, based on ACG, is valid and applicable in our milieu. These models allow classifying the patients on a scale of high to low risk, which makes possible the implementation of the most expensive preventive interventions to only a small subset of patients, while other less intensive ones can be provided to larger groups.