[Factors conditioning primary care services utilization. Empirical evidence and methodological inconsistencies]

In Spain, the degree and characteristics of primary care services utilization have been the subject of analysis since at least the 1980s. One of the main reasons for this interest is to assess the extent to which utilization matches primary care needs. In fact, the provision of an adequate health service for those who most need it is a generally accepted priority.

The evidence shows that individual characteristics, mainly health status, are the factors most closely related to primary care utilization. Other personal characteristics, such as gender and age, could act as modulators of health care need. Some family and/or cultural variables, as well as factors related to the health care professional and institutions, could explain some of the observed variability in primary care services utilization. Socioeconomic variables, such as income, reveal a paradox. From an aggregate perspective, income is the main determinant of utilization as well as of health care expenditure. When data are analyzed for individuals, however, income is not related to primary health utilization.

The situation is controversial, with methodological implications and, above all, consequences for the assessment of the efficiency in primary care utilization. Review of the literature reveals certain methodological inconsistencies that could at least partly explain the disparity of the empirical results. Among others, the following flaws can be highlighted: design problems, measurement errors, misspecification, and misleading statistical methods.Some solutions, among others, are quasi-experiments, the use of large administrative databases and of primary data sources (design problems); differentiation between types of utilization and between units of analysis other than consultations, and correction of measurement errors in the explanatory variables (measurement errors); consideration of relevant explanatory variables (misspecification); and the use of multilevel models (statistical methods).