Resumen

PEREZ-FARINOS, Napoleón et al. A sampling design for a Sentinel General Practitioner Network. Gac Sanit [online]. 2009, vol.23, n.3, pp.186-191. ISSN 0213-9111.

Objective: To construct a design for probabilistic sampling of reporting physicians in sentinel networks. Methods: We performed a multi-stage sample selection study. Data on primary care physicians and their patients were obtained from the Madrid Health Institute for 2005. The geographical unit of reference was the basic health area. A factorial analysis was performed on the basis of demographic, socio-cultural and socio-occupational variables. A cluster analysis was conducted to group the 247 basic health areas into homogeneous strata, which were then tested using a discriminant analysis. The general practitioners and pediatricians needed in each stratum were selected by simple random sampling. The representativeness of the population monitored by the selected physicians was studied with respect to the population of Madrid. Results: Factorial analysis yielded five factors. Using these, 14 strata were obtained, which were shown to be homogeneous and mutually different by discriminant analysis. The minimum population that needed to be monitored consisted of 146,946 adults and 24,518 children, proportionally distributed among the respective strata. Eighty-eight general practitioners and 32 pediatricians were selected, who respectively covered populations of 154,610 and 31,336 persons representative of the general population. Conclusions: Obtaining samples through suitable designs improves the accuracy of the information gathered by health sentinel networks in epidemiologic surveillance. Ensuring the representativeness of the study population vis-à-vis the general population is essential; cluster analysis and simple random sampling are methods that meet this need. Selecting physicians by means of probabilistic methods enables the accuracy of estimates to be ascertained.

Palabras clave : Sentinel surveillance; Sampling studies; Primary health care; Cluster analysis.

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