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The differences in health of the people of Quebec are determined by their station in life and where they live. People at the bottom of the socioeconomic scale and living in the most complex environments have the most mental and physical problems. This theory is demonstrated with a model using statistics from the 1987 Sante Quebec survey. The text of the article is in French.

This paper is based upon the results of two research projects that took place in very dissimilar places and contexts, at different situations in the epidemiological transition process, but lead to some important common conclusions. In both studies, a geo-systemic or socio-spatial classificatory model was first constructed, comprising a series of environmental (independent) variables. Second, this model was used to reveal, measure, and try to explain the health disparities existing in the area under study, as measured by certain health indicators (dependent variables). Two socio-spatial models were constructed. The first used 26 environmental variables, measured in the 78 municipios of Puerto Rico. The dependent variables were mortality rates. The second employed 28 environmental variables, measured in the 32 community health department territories (DSCs) of Quebec. The dependent variables were physical and mental health indices. The most significant common conclusion of these studies is that the socio-economic level per se, or its classical principal defining elements (income, education level) are not the main determinants of the observed health disparities. A certain stability in the physical and social environment, the person's sense of understanding of his environment and of control over the course and setting of his own life (what is called ambiocontrol) that is induced by this stability appear to be the most important determinants of health. On the other hand, centrality, complexity and instability of the environment are the most pathogenic characteristics of the milieu.

The Thailand 'Rural Data Base' includes a number of variables in the social, economic, services, housing, agriculture and health fields. This data base comes from a survey at the village level. There are weaknesses in the data base - inaccuracies, missing data, etc. . . . that make it difficult to discover the structures that theoretically exist in the data using direct analysis methods, like correlation analysis for instance. The 2383 villages of the province of Ubon-Ratchathani, in Thailand, are the units of this study. The objective is to uncover the hypothetical health disparities that exist among the villages and to devise a typology of villages that will show classes of villages that have significant problems and, also, classes of villages that could suggest concrete solutions and be used as models. Some variables of the data base are considered dependent (health indicators) and others are considered independent; they are used to build a model called 'geo-systemic' that is used to expose mortality differentials in Ubon-Ratchathani.