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OBJECTIVES: To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. METHODS: A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4,079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. RESULTS: The model successfully identified 99.71% of the fraudulent medical authorizations and 99.86% of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59%, while its negative predictive value was 99.97%. CONCLUSIONS: The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chile's public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.

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OBJETIVOS: Desarrollar un modelo para detectar casos de fraude planificado en Chile a partir de los datos contenidos en los formularios de licencia médica curativa (LMC) y establecer la contribución relativa de esos datos a su detección. MÉTODOS: SE aplicó un modelo de regresión logística binominal a partir de cuatro variables contenidas en el formulario legal de LMC exigido nacionalmente para justificar las ausencias al trabajo por motivos de enfermedad: el número de licencias médicas asignadas a una misma persona, el número de días de licencia médica otorgados por el médico tratante, el monto total a pagar por la enfermedad y una variable dicotómica que refleja si el diagnóstico es comprobable o no. Se analizaron 4 079 LMC presentadas el año 2003 a una institución privada de salud previsional, de las cuales 356 estaban ya clasificadas como fraudulentas por un panel de médicos expertos en fraude. RESULTADOS: El modelo logró identificar correctamente 99,71 por ciento de las licencias médicas fraudulentas y 99,86 por ciento de las no fraudulentas según el criterio del panel de expertos en fraude. Tres de las variables empleadas presentaron un poder predictivo independiente estadísticamente significativo. El valor predictivo positivo del modelo propuesto fue de 98,59 por ciento, mientras el valor predictivo negativo fue de 99,97 por ciento. CONCLUSIONES: El modelo logístico binomial desarrollado, basado en cuatro variables de uso universal en los formularios de LMC utilizados por todas las entidades aseguradoras de Chile, tanto públicas como privadas, permite discriminar de forma precisa y más rápidamente y con menor costo que los paneles de expertos las solicitudes fraudulentas de las no fraudulentas.

OBJECTIVES: To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. METHODS: A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4 079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. RESULTS: The model successfully identified 99.71 percent of the fraudulent medical authorizations and 99.86 percent of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59 percent, while its negative predictive value was 99.97 percent. CONCLUSIONS: The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chile's public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.

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BACKGROUND: The issue of medically justified work absenteeism has a great relevance in Chile at the present moment. AIM: To analyze sick leaves among people working in hospitals, mines, automotive industry and universities. MATERIAL AND METHODS: Analysis of 14 thesis and research papers about absenteeism in Chile. The incapacity rate (number of days with sick leave per worker per year, the frequency rate (number of sick leaves per year per worker) and the severity rate (mean duration of sick leaves) were calculated. The diseases causing the highest rates of absenteeism were also recorded. RESULTS: The mean age of the studied populations was 36 years old and the most common diseases causing absenteeism were respiratory, rheumatologic and trauma. Hospital workers had the highest incapacity rate with 14.3 days of sick leave per worker per year, followed by mining industry with 12 days, automotive industry with 7.1 days and universities with 6 days. CONCLUSIONS: In Chile, respiratory diseases are the main cause of sick leaves and hospital workers have the highest incapacity rate.

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La mortalidad de origen cardiovascular ha descendido en Chile desde 1980 a razón de -0,94 por ciento anual, disminución que alcanza a -4,5 por ciento si las tasas son ajustadas por edad. En nuestro país, el descenso de la mortalidad circulatoria se inició y ha continuado antes que se produjera un gran envejecimiento poblacional. En la actualidad, la mortalidad se genera mayoritariamente por enfermedad cerebrovascular, enfermedad coronaria y otras cardiopatías, ocurriendo de preferencia después de los 70 años. Las variaciones registradas entre las regiones chilenas se asocian significativamente al riesgo global de muerte y a la proporción de población senescente. La comparación del riesgo en los años 1970 y 1999 permite apreciar que las muertes han descendido en todos los grupos de edad y que la caída depende en 42 por ciento de la disminución de la mortalidad por accidentes vasculares cerebrales, en 25 por ciento del descenso de la enfermedad coronaria, otro 25 por ciento en afecciones arteriales y 5 por ciento en la cardiopatía reumática. Se comentan las posibles causas del descenso

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