OBJECTIVE: To study the geographical variations in Disability Free Life Expectancy (DFLE) at birth (DFLEb) and at 65 years (DFLE65) in Spain and to identify the main factors that explain these variations. DESIGN: Ecological study with the 50 provinces of Spain as the units of analysis. Sullivan's method is used to calculate DFLE for each province based on information from the death registry and the survey on disabilities, impairments and handicaps. Information on the independent variables-socioeconomic level, factors related with the health system and risk factors-was taken from various sources. MAIN OUTCOME MEASURE: Simple correlation coefficients were obtained between each dependent variable (DFLEb and DLFE65) and the independent variables. Two multiple linear regression models were fit to obtain the best set of factors that explain the geographical distribution of DFLEb and DLFE65. RESULTS: Both DFLEb and DLFE65 vary widely among provinces. The multiple linear regression analysis shows that the illiteracy rate, the percentage of the unemployed and the percentage of smokers in the population were the main factors associated with the geographical variation of DFLE. The models explained approximately 40% of the variance for DFLEb and 30% for DLFE65. CONCLUSIONS: The results obtained show the influence of education, the unemployment rate and smoking on the geographical differences of DFLE. The DFLE indicators are shown to be valid for use in health policy.
OBJECTIVE: To study the geographical variations in Disability Free Life Expectancy (DFLE) at birth (DFLEb) and at 65 years (DFLE65) in Spain and to identify the main factors that explain these variations. DESIGN: Ecological study with the 50 provinces of Spain as the units of analysis. Sullivan's method is used to calculate DFLE for each province based on information from the death registry and the survey on disabilities, impairments and handicaps. Information on the independent variables-socioeconomic level, factors related with the health system and risk factors-was taken from various sources. MAIN OUTCOME MEASURE: Simple correlation coefficients were obtained between each dependent variable (DFLEb and DLFE65) and the independent variables. Two multiple linear regression models were fit to obtain the best set of factors that explain the geographical distribution of DFLEb and DLFE65. RESULTS: Both DFLEb and DLFE65 vary widely among provinces. The multiple linear regression analysis shows that the illiteracy rate, the percentage of the unemployed and the percentage of smokers in the population were the main factors associated with the geographical variation of DFLE. The models explained approximately 40% of the variance for DFLEb and 30% for DLFE65. CONCLUSIONS: The results obtained show the influence of education, the unemployment rate and smoking on the geographical differences of DFLE. The DFLE indicators are shown to be valid for use in health policy.
Authors: L Ferrucci; G Izmirlian; S Leveille; C L Phillips; M C Corti; D B Brock; J M Guralnik Journal: Am J Epidemiol Date: 1999-04-01 Impact factor: 4.897
Authors: Shengwei Wang; Songbo Hu; Pei Wang; Yuhang Wu; Zhitao Liu; Huilie Zheng Journal: Int J Environ Res Public Health Date: 2021-04-27 Impact factor: 3.390