Duševné zdravie ako komplexný dynamický systém: sieťový prístup k psychopatológii

Currently, mental disorders are usually conceptualized as a hidden causal factor, manifested by its symptoms. This notion rests upon the reflective latent model, which is implicitly at work every time complex symptomatology gets summarized by a single number or a categorical state. The present paper reflects on the quantitative, testable implications of this psychometric model and shows how its restraints are untenable for most mental disorders. The observed data are instead consistent with mental disorders being complex dynamic systems. Instead of being treated as interchangeable measures of the same latent factor, symptoms likely act as independent causal entities, directly affecting each other. In recent years, this shift in ontological stance toward psychopathology has laid a basis for adapting the network theory. Under this theory, a mental disorder is a relatively stable emergent state, which arises due to a pronounced and recurrent interaction of causally linked symptoms. It is discussed how models embedded within the network theory can help provide insight into the etiopathogenesis of mental disorders and address clinical intervention. In conclusion, limits and future challenges to the network theory are discussed.

Categorical state sequence analysis and regression tree to identify determinants of care trajectory in chronic disease: Example of end-stage renal disease

Background Patients with chronic diseases, like patients with end-stage renal disease (ESRD), have long history of care driven by multiple determinants (medical, social, economic, etc.). Although in most epidemiological studies, analyses of health care determinants are computed on single health care events using classical multivariate statistical regression methods. Only few studies have integrated the concept of treatment trajectories as a whole and studied their determinants. Methods All 18- to 80-year-old incident ESRD patients who started dialysis in Ile-de-France or Bretagne between 2006 and 2009 and could be followed for a period of 48 months after initiation of a renal replacement therapy were included ( n = 5568). Their care trajectories were defined as categorical state sequences. Associations between patients’ characteristics and care trajectories were assessed using a regression tree model together with a discrepancy analysis. Results On average, each patient experienced 1.56 different renal replacement therapies (min = 1; max = 5) during the 48 months of follow-up. About 55% of patients never changed treatment and only 1% tried three or more renal replacement therapy modalities. Twelve homogeneous care trajectory groups were identified. Covariates explained 12% of the discrepancy between groups, particularly age, regions and initiation of hemodialysis with a catheter. Conclusions Regression tree analysis of categorical state sequence highlighted geographical disparities in the care trajectory of French patients with ESRD that cannot be observed when focusing on a single outcome, such as survival. This method is an original tool to visualize and characterize care trajectories, notably in the context of chronic condition like ESRD.

Biface Reduction and the Measurement of Dalton Curation: A Southeastern United States Case Study

Stone tools were reduced during use, with implications both for classification and curation rates. Ballenger's “expended utility” (EU) is a continuous reduction measure devised for Dalton bifaces, described by its mean but also its distribution among specimens. We validate EU as a reduction measure by reference to experimental and contextual controls. We compare EU between the “special context” Dalton assemblages Sloan and Hawkins in Arkansas and Ballenger's eastern Oklahoma “occupation context” ones. Then we fit EU distributions to mathematical functions to model the curation process. Results show that Oklahoma bifaces were better curated than Arkansas ones. Fitting distributions to the Gompertz-Makeham model efficiently describes distributions' shape and scale, which are as important to know as central tendency. Curation is not a categorical state but a continuous variable whose complex variation implicates complex causes.