Effects of health decentralization, financing and governance in Mexico

OBJECTIVE: To identify the effects of decentralization on health financing and governance policies in Mexico from the perspective of users and providers. METHODS: A cross-sectional study was carried out in four states that were selected according to geopolitical and administrative criteria. Four indicators were assessed: changes and effects on governance, financing sources and funds, the final destination of resources, and fund allocation mechanisms. Data collection was performed using in-depth interviews with health system key personnel and community leaders, consensus techniques and document analyses. The interviews were transcribed and analyzed by thematic segmentation. RESULTS: The results show different effectiveness levels for the four states regarding changes in financing policies and community participation. Effects on health financing after decentralization were identified in each state, including: greater participation of municipal and state governments in health expenditure, increased financial participation of households, greater community participation in low-income states, duality and confusion in the new mechanisms for coordination among the three government levels, absence of an accountability system, lack of human resources and technical skills to implement, monitor and evaluate changes in financing. CONCLUSIONS: In general, positive and negative effects of decentralization on health financing and governance were identified. The effects mentioned by health service providers and users were related to a diversification of financing sources, a greater margin for decisions around the use and final destination of financial resources and normative development for the use of resources. At the community level, direct financial contributions were mentioned, as well as in-kind contributions, particularly in the form of community work.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.