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.

Correlates of the incidence of disability and mortality among older adult Brazilians with and without diabetes mellitus and stroke

Background: The combined effect of diabetes and stroke on disability and mortality remains largely unexplored in Brazil and Latin America. Previous studies have been based primarily on data from developed countries. This study addresses the empirical gap by evaluating the combined impact of diabetes and stroke on disability and mortality in Brazil. Methods: The sample was drawn from two waves of the Survey on Health and Well-being of the Elderly, which followed 2,143 older adults in Sao Paulo, Brazil, from 2000 to 2006. Disability was assessed via measures of activities of daily living (ADL) limitations, severe ADL limitations, and receiving assistance to perform these activities. Logistic and multinomial regression models controlling for sociodemographic and health conditions were used to address the influence of diabetes and stroke on disability and mortality. Results: By itself, the presence of diabetes did not increase the risk of disability or the need for assistance; however, diabetes was related to increased risks when assessed in combination with stroke. After controlling for demographic, social and health conditions, individuals who had experienced stroke but not diabetes were 3.4 times more likely to have ADL limitations than those with neither condition (95% CI 2.26-5.04). This elevated risk more than doubled for those suffering from a combination of diabetes and stroke (OR 7.34, 95% CI 3.73-14.46). Similar effects from the combination of diabetes and stroke were observed for severe ADL limitations (OR 19.75, 95% CI 9.81-39.76) and receiving ADL assistance (OR 16.57, 95% CI 8.39-32.73). Over time, older adults who had experienced a stroke were at higher risk of remaining disabled (RRR 4.28, 95% CI 1.53, 11.95) and of mortality (RRR 3.42, 95% CI 1.65, 7.09). However, risks were even higher for those who had experienced both diabetes and stroke. Diabetes was associated with higher mortality. Conclusions: Findings indicate that a combined history of stroke and diabetes has a great impact on disability prevalence and mortality among older adults in Sao Paulo, Brazil.

Cause-specific mortality and income inequality in Sao Paulo, Brazil

OBJECTIVE: To analyze cause-specifi c mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of Sao Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>= 0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95% CI: 2.60; 14.53); ischemic heart disease (5.47 per 10,000 [95% CI 0.76; 10.17]); HIV/AIDS (3.58 per 10,000 [95% CI 0.58; 6.57]); and respiratory diseases (3.56 per 10,000 [95% CI 0.18; 6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had signifi cantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95% CI 0.86; 4.74]), as well as among males (27.37 per 10,000 [95% CI 6.19; 48.55]) and females (15.07 per 10,000 [95% CI 3.65; 26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specifi c mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.

Cause-specific mortality and income inequality in São Paulo, Brazil

OBJECTIVE: To analyze cause-specific mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of São Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95%CI: 2.60;14.53); ischemic heart disease (5.47 per 10,000 [95%CI 0.76;10.17]); HIV/AIDS (3.58 per 10,000 [95%CI 0.58;6.57]); and respiratory diseases (3.56 per 10,000 [95%CI 0.18;6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had significantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95%CI 0.86;4.74]), as well as among males (27.37 per 10,000 [95%CI 6.19;48.55]) and females (15.07 per 10,000 [95%CI 3.65;26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specific mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.

The impact of mortality from external causes on human development in the Brazilian borderland

This article estimates the impact of mortality from external causes on the human development index (HDI) along the Brazilian borderland from 2000 to 2005. Data obtained from Brazilian government agencies were combined using the methodology defined by the United Nations Development Program, revealing the HDI according to actual conditions. Subsequently, deaths from external causes were excluded in order to estimate their impact on the index, recalculating life expectancy using the technique of competing causes. HDI showed a gradual increase from North to South, with the most developed regions concentrated in the South, consistent with studies using other sets of economic indicators. By excluding mortality from external causes, the highest gains appeared in regions where the HDI (under actual conditions) were lower, and the magnitude of gains declined towards the South.

Proactive management of extreme prematurity: disagreement between obstetricians and neonatologists

Objective: To verify, in extremely preterm infants, if disagreement between obstetricians and neonatologists regarding proactive management is associated with early death. Study Design: Prospective cohort of 484 infants with 23(0/7) to 26(6/7) weeks, without malformations, born from January 2006 to December 2009 in eight Brazilian hospitals. Pro-active management was defined as indication of >= 1 dose of antenatal steroid or cesarean section (obstetrician) and resuscitation at birth according to the international guidelines (neonatologist). Main outcome was neonatal death in the first 24 h of life. Result: Obstetricians and neonatologists disagreed in 115 (24%) patients: only neonatologists were proactive in 107 of them. Disagreement between professionals increased 2.39 times the chance of death in the first day (95% confidence interval 1.40 to 4.09), adjusted for center and maternal/neonatal clinical conditions. Conclusion: In infants with 23 to 26 weeks of gestation, disagreement between obstetricians and neonatologists, translated as lack of antenatal steroids and/or vaginal delivery, despite resuscitation procedures, increases the odds of death in the first day. Journal of Perinatology (2012) 32, 913-919; doi:10.1038/jp.2012.28; published online 29 March 2012

The Real Importance of Pre-Existing Comorbidities on Long-Term Mortality after Acute Kidney Injury

Background: The causes of death on long-term mortality after acute kidney injury (AKI) have not been well studied. The purpose of the study was to evaluate the role of comorbidities and the causes of death on the long-term mortality after AKI. Methodology/Principal Findings: We retrospectively studied 507 patients who experienced AKI in 2005-2006 and were discharged free from dialysis. In June 2008 (median: 21 months after AKI), we found that 193 (38%) patients had died. This mortality is much higher than the mortality of the population of Sao Paulo City, even after adjustment for age. A multiple survival analysis was performed using Cox proportional hazards regression model and showed that death was associated with Khan's index indicating high risk [adjusted hazard ratio 2.54 (1.38-4.66)], chronic liver disease [1.93 (1.15-3.22)], admission to non-surgical ward [1.85 (1.30-2.61)] and a second AKI episode during the same hospitalization [1.74 (1.12-2.71)]. The AKI severity evaluated either by the worst stage reached during AKI (P=0.20) or by the need for dialysis (P=0.12) was not associated with death. The causes of death were identified by a death certificate in 85% of the non-survivors. Among those who died from circulatory system diseases (the main cause of death), 59% had already suffered from hypertension, 34% from diabetes, 47% from heart failure, 38% from coronary disease, and 66% had a glomerular filtration rate <60 previous to the AKI episode. Among those who died from neoplasms, 79% already had the disease previously. Conclusions: Among AKI survivors who were discharged free from dialysis the increased long-term mortality was associated with their pre-existing chronic conditions and not with the severity of the AKI episode. These findings suggest that these survivors should have a medical follow-up after hospital discharge and that all efforts should be made to control their comorbidities.

Propensity score matching approach to test the association of income inequality and mortality in Sao Paulo, Brazil

Background Support for the adverse effect of high income inequality on population health has come from studies that focus on larger areas, such as the US states, while studies at smaller geographical areas (eg, neighbourhoods) have found mixed results. Methods We used propensity score matching to examine the relationship between income inequality and mortality rates across 96 neighbourhoods (distritos) of the municipality of Sao Paulo, Brazil. Results Prior to matching, higher income inequality distritos (Gini >= 0.25) had slightly lower overall mortality rates (2.23 per 10 000, 95% CI -23.92 to 19.46) compared to lower income inequality areas (Gini <0.25). After propensity score matching, higher inequality was associated with a statistically significant higher mortality rate (41.58 per 10 000, 95% CI 8.85 to 73.3). Conclusion In Sao Paulo, the more egalitarian communities are among some of the poorest, with the worst health profiles. Propensity score matching was used to avoid inappropriate comparisons between the health status of unequal (but wealthy) neighbourhoods versus equal (but poor) neighbourhoods. Our methods suggest that, with proper accounting of heterogeneity between areas, income inequality is associated with worse population health in Sao Paulo.