Do established prognostic factors explain the different mortality rates in ICU septic patients around the world?

Background. The aim of this paper was to clarify if previously established prognostic factors explain the different mortality, rates observed in ICU septic patients around the world. Methods. This is a sub-study from the PROGRESS study, which was an international, prospective, observational registry of ICU patients with severe sepsis. For this study we included 10930 patients from 24 countries that enrolled more than 100 patients in the PROGRESS. The effect of potential prognostic factors on in-hospital mortality was examined using univariate and multivariate logistic regression. The complete set of data was available for 7022 patients, who were considered in the multivariate analysis. Countries were classified according to country, income, development status, and in-hospital mortality terciles. The relationship between countries' characteristics and hospital mortality mortality was evaluated using linear regression. Results. Mean in-hospital mortality was 49.2%. Severe sepsis in-hospital mortality varied widely in different countries, ranging from 30.6% in New Zealand to 80.4% in Algeria. Classification as developed or developing country was not associated with in-hospital mortality (P=0.16), nor were levels of gross national product per capita (P=0.15). Patients in the group of countries with higher in-hospital mortality, had a crude OR for in-hospital death of 2.8 (95% CI 2.5-3.1) in comparison to those in the lower risk group. After adjustments were made for all other independent variables, the OR changed to 2.9 (95% CI 2.5-3.3). Conclusion. Severe sepsis mortality varies widely, in different countries. All known markers of disease severity and prognosis do not fully, explain the international differences in mortality,. Country, income does not explain this disparity, either. Further studies should be developed to verify if other organizational or structural factors account for disparities in patient care and outcomes. (Minerva Anestesiol 2012;78:1215-25)

Anemia and dementia among the elderly: the Sao Paulo Ageing & Health Study

Background: Anemia and dementia are common diseases among the elderly, but conflicting data are available regarding an association between these two conditions. We analyzed data from the Sao Paulo Ageing & Health Study to address the relationship between anemia and dementia. Methods: This cross-sectional observational study included participants aged 65 years and older from a deprived area of the borough of Butantan, Sao Paulo, Brazil. Data about demographics, education, income, and cognitive and daily life function were collected, as well as blood samples. Anemia and dementia were defined according to WHO and DSM-IV criteria, respectively. Results: Of the 2267 subjects meeting the inclusion criteria, 2072 agreed to participate in the study; of whom 1948 had a valid total blood count and were included in the analysis. Anemia was diagnosed in 203 (10.2%) participants and dementia in 99 (5.1%). The frequency of anemia was higher in patients with dementia according to univariate analysis (odds ratio (OR) = 2.00, 95% confidence interval (CI) = 1.17-3.41, p = 0.01), but this association was not present after adjusting for age (OR = 1.33, 95% CI = 0.76-2.33, p = 0.32). Further multivariate adjustment did not change the results. Conclusion: Although anemia and dementia are frequent disorders in older people, we found their relationship to be mediated exclusively by aging in this low-income population from Sao Paulo.

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.