OBJECTIVES: Identify factors explaining variability in prescribing costs after reviewing ecological data related to costs and socio-demographic characteristics of the health care zones in the autonomous region of Valencia, and explore the usefulness of using the model to set prescribing budgets in basic healthcare zones. MATERIALS AND METHODS: An ecological analysis of the value socio-demographic characteristics and use of healthcare services to explain prescribing costs in 1997. Development of a prediction model based on multiple linear regression in data for prescribing costs in 1997 and validation in data for 1998. RESULTS: Factors that correlated positively with prescribing costs were the percentage of inhabitants over the age of 80, the death rate, the percentage of inhabitants with only primary education or less, the percentage of inhabitants between the ages of 65 and 79 and the distance from the capital city. A multivariate model including the death rate, the percentage of inhabitants 80 years of age and older, the number of cars per 100 inhabitants and number of visits per inhabitant accounted for 44.5% of the variations in prescribing costs in 1997 and 32% in 1998. CONCLUSIONS: Socio-demographic factors and certain variables associated with health care utilization can be applied, within certain limitations, to set prescribing budgets in basic healthcare zones.
OBJECTIVES: Identify factors explaining variability in prescribing costs after reviewing ecological data related to costs and socio-demographic characteristics of the health care zones in the autonomous region of Valencia, and explore the usefulness of using the model to set prescribing budgets in basic healthcare zones. MATERIALS AND METHODS: An ecological analysis of the value socio-demographic characteristics and use of healthcare services to explain prescribing costs in 1997. Development of a prediction model based on multiple linear regression in data for prescribing costs in 1997 and validation in data for 1998. RESULTS: Factors that correlated positively with prescribing costs were the percentage of inhabitants over the age of 80, the death rate, the percentage of inhabitants with only primary education or less, the percentage of inhabitants between the ages of 65 and 79 and the distance from the capital city. A multivariate model including the death rate, the percentage of inhabitants 80 years of age and older, the number of cars per 100 inhabitants and number of visits per inhabitant accounted for 44.5% of the variations in prescribing costs in 1997 and 32% in 1998. CONCLUSIONS: Socio-demographic factors and certain variables associated with health care utilization can be applied, within certain limitations, to set prescribing budgets in basic healthcare zones.
Authors: Juan C Ruiz; María A Ariza; Belén Aguilera; Mariano Leal; Ramón Gómez; José Abellán Journal: Aten Primaria Date: 2012-01-31 Impact factor: 1.137
Authors: Amaia Calderón-Larrañaga; Chad Abrams; Beatriz Poblador-Plou; Jonathan P Weiner; Alexandra Prados-Torres Journal: BMC Health Serv Res Date: 2010-01-21 Impact factor: 2.655
Authors: Silvia Calzón; Juan José Mercader; Juan Carlos Montero; Carmen Sánchez-Cantalejo; Raquel Valencia Journal: Aten Primaria Date: 2012-11-28 Impact factor: 1.137