[Description and validation of a predictive model of the geographic distribution of chronic home-bound patients].
OBJECTIVE
To find a predictive model for the geographical distribution of the homebound chronically ill in function of variables corresponding to censal areas.


METHODS
A study of an ecological type.


METHODS
Raval Nord Health District, Barcelona.


METHODS
The study units are the 30 censal areas of Raval Nord. In each area researchers counted the number of homebound chronically ill recorded during the first three months of a home care programme (May 1994).


RESULTS
A multiple regression analysis was performed, with selection of the variables by the stepwise method of the SPSS-PC+ statistical package, taking as dependent variable "y" the number of homebound chronically ill per 1,000 inhabitants. The prognostic variables were taken from the 1991 municipal roll, and evaluated along with the distance in metres from each censal area to the primary care centre (PCC). The distribution of the homebound chronically ill in the different censal areas of Raval Nord was connected to 3 variables which, in order of analysis, were: distance to the PCC, the relationship with senile dependency and the index of family socio-economic capacity.


CONCLUSIONS
Distance, senile dependence and economic resources are factors which condition the number and distribution of the homebound chronically ill.
Segura Noguera
J M
JM
EAP Raval Nord, CAP Dr. Lluís Sayé, Ciutat Vella, Barcelona.
Sanz Carreras
F
F
Bastida Bastús
N
N
Martí Guadaño
N
N
González Espiñeira
C
C
Guerrero Gancedo
M
M
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease; D005260:Female; D006699:Home Care Services; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010361:Patients; D012044:Regression Analysis; D012737:Sex Factors; D012959:Socioeconomic Factors; D013030:Spain
Descripción y validación de un modelo predictivo de la distribución geográfica de los enfermos crónicos domiciliarios.