There are few published studies about the effects of inadequate solid waste collection on the health of the population exposed to this situation. The objective of the present work was to describe this association in a sample of children under 5 years of age living in seven low-income neighborhoods and favelas in the city of Belo Horizonte, state of Minas Gerais, Brazil. We defined as "exposed" those children whose families were not served by waste collection; "not-exposed" were children who lived in areas with waste collection. The study employed data collected in 1994 and organized as a database by the municipal department of health. We employed a cross-sectional design, in which a "case" was defined as a child whose outpatient clinic record indicated a diagnosis of diarrheal, parasitic, or dermatological disease. Other diagnoses for the same age group composed the control group. Our epidemiological study revealed an association between the absence of domestic solid waste collection and public health. Our results suggest that the children exposed to the absence of solid waste collection have a 40% higher odds (OR = 1.40) of presenting diarrheal, parasitic, and dermatological diseases than not-exposed children. In addition, the calculation of attributable risk revealed that the presence of waste collection could prevent (based on the 1995 situation) 512 cases in the neighborhoods studied and (based on the 1994 conditions) 2316 cases among children in the entire city of Belo Horizonte.
The presence of sanitary or controlled landfill in urban areas and its implications for the health of the population that lives in its bordering area has been little investigated. The presence of these deposits, with design or operational problems, can end up providing a likely location for the proliferation of diverse vectors and favouring uncontrolled emissions, which may affect health, both for the people that work and live in these places and those that live nearby. This paper describes a study carried out in the city of Belo Horizonte, Brazil, with the objective of associating the presence of the BR 040 sanitary landfill, located in the urban environment, and the health of the neighbouring population. There were 475 possibilities of occurrences studied, covering 19 different combinations for association between the exposure factor and the groupings of researched diseases (respiratory diseases, diarrhoea and skin diseases). However, of this total only 33 results were shown to be significant in showing the existence of an association. For these results, the variable exposures identified as a risk factor showed an odds ratio ranging between 1.20 and 13.75. Although without an evidenced relationship, respiratory diseases appeared as a relevant outcome in the study, as they were present in 23 of the 33 studied combinations in which the results were significant.
Chemical analyses of groundwater often present data sets with censored values, i.e., below the detection limit (LOD). When the proportion of censored values is significant, descriptive (mean, median and standard deviation) or exploratory geochemical analysis may be impaired. Ignoring such data or replacing them with some predetermined value is not always the recommended alternative. Thus, the objective of this research is to investigate the applicability of four methods in estimating censored chemical data from an area with contaminated groundwater. Three statistical methods were used: parametric (Maximum Likelihood Estimation, MLE), non-parametric (Kaplan-Meier, KM) and robust (Order Regression Methods, ROS), in addition to the traditional method of direct replacement of censored data, using LOD/2. The MLE, assuming a Gaussian distribution of the data (MLE-no), yielded allowable substitution factors, close to 0.5, similarly to the traditional substitution method (LOD/2). Validation with complete datasets with the same estimation methods and considering three artificial LOD attested to the good results of MLE-no and ROS with 25% and 50% of censored data, respectively, as well as LOD/2. The first two methods are preferable to LOD/2 as they are statistically based. It is recommended in future studies that such estimation methods be combined with other geostatistical treatments to improve the spatial analysis of hydrochemical datasets.
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