Biomedical subjects

F F Nobre

Publications and source records attributed to F F Nobre.

9 recordsLinked to original sources

PubMed1999

Multi-criteria decision making--an approach to setting priorities in health care.

The objective of this paper is to present a multi-criteria decision making (MCDM) approach to support public health decision making that takes into consideration the fuzziness of the decision goals and the behavioural aspect of the decision maker. The approach is used to analyse the process of health technology procurement in a University Hospital in Rio de Janeiro, Brazil. The method, known as TODIM, relies on evaluating alternatives with a set of decision criteria assessed using an ordinal scale. Fuzziness in generating criteria scores and weights or conflicts caused by dealing with different viewpoints of a group of decision makers (DMs) are solved using fuzzy set aggregation rules. The results suggested that MCDM models, incorporating fuzzy set approaches, should form a set of tools for public health decision making analysis, particularly when there are polarized opinions and conflicting objectives from the DM group.

Brazil↗

PubMed1999

[Mathematical location models applied in the spatial organization of health units].

Mathematical location models have been increasingly applied in the health services at the international level. In Brazil, although incipient, there exists an enormous potential for the use of such models in the area of public health. In this paper several location models that can be applied to public health are presented initially, and the location of non-emergency services, of emergency services and of services hierarchically related are analysed. A hierarchical model is then applied to the location of maternal and perinatal assistance in the municipality of Rio de Janeiro. In this part, after presenting some related data for the municipality, a four-level hierarchical model (location of out-patient units, maternity hospitals, neonatal hospitals and general hospitals) is proposed and the impact that the adoption of this methodology would have as compared with that of the present system is analysed.

Emergency Medical Services↗

PubMed1998

The path analysis approach for the multivariate analysis of infant mortality data.

PURPOSE: This paper reviews the use of the Path Analysis (PA) methodology in health determinants modeling, with special reference to infant mortality modeling. METHODS: A review of the literature on PA applications in the modeling of infant mortality and similar problems is presented, together with a discussion of the conceptual basis of PA and its relation to other multivariate statistical techniques. Important aspects of the technique are discussed: 1) criteria for path formulation; 2) parameter estimation methods; 3) direct, indirect, spurious, and joint effects; and 4) goodness-of-fit and modification indices. RESULTS AND CONCLUSION: The review of the literature suggests that PA represents a methodological improvement regarding multivariate techniques used in modeling some health-related issues. PA allows investigation of more complex models, providing information that could have been previously overlooked, such as how the interrelations among independent variables in a model affect the dependent ones.

Humans↗

PubMed1997

GISEpi: a simple geographical information system to support public health surveillance and epidemiological investigations.

One important question for the implementation of a surveillance system concern the type of instrument that can provide timely information on the course of diseases and other health events. This may facilitate prompt implementation of prevention and intervention efforts, such as strengthening control action in one specific area or initiation of epidemiological investigation. Since health related variables of interest are often spatially distributed they require special tools for representation and analysis. Owing to their inherent ability to manage spatial information, geographical information systems (GIS) provide an excellent framework for the design of surveillance systems. This paper presents a simple information system, based on the concepts of GIS, designed for representation and elementary analysis of epidemiological data. An example of its potential use to support malaria control activities in Brazil is discussed.

Geography↗

PubMed1994

A monitoring system to detect changes in public health surveillance data.

One task faced by public health surveillance practitioners is the timely identification of data patterns that might suggest the onset of an epidemic period. Many available techniques for analysis of surveillance data are based on sequential procedures, which predict expected numbers of cases and compare this estimate with observed values. To detect changes in the reported occurrence of a disease (increase, decrease, or change in trend), we used exponential smoothing and transformation of the difference between the observed and estimated data to calculate a function called the probability index. We illustrate this procedure using weekly provisional data for measles cases in the US reported through the National Notifiable Diseases Surveillance System to the Centers for Disease Control and Prevention (CDC). The method is potentially useful in public health surveillance to facilitate prompt intervention and prevention efforts, since it can be used at the national and regional levels without the requirement for sophisticated computing.

Communicable Disease Control↗

PubMed1991

[Occurrence of meningococcal meningitis in a Southern region of Brazil, from 1974 to 1980, using the point event model].

Descriptive statistical techniques and point event model methods were used to investigate the temporal series of cases of meningococcal meningitis which occurred in 100 municipalities in the Rio Grande do Sul State, Brazil, during the period 1974-1980. The data were grouped by epidemiological state (epidemic or endemic), and separated into 5 groups according to the municipal population. The number of cases of the disease notified weekly was analysed by means of incidence coefficients, with the purpose of studying the epidemic threshold for the state. The time interval between events was analysed in the light of their probability density functions and expected density functions, with the objective of studying the relationship and dependences among events. The analysis of the epidemic threshold suggests that there should not be only one threshold value for detection of outbreak of the disease throughout the state. Analysis of the expected density function extracted from inter-event intervals of the epidemic state showed a correlational structure indicating dependence between events occurring up to 14 weeks apart. No significant correlation for the endemic state, taking as reference model the shuffled version of the original intervals, was observed.

Brazil↗

PubMedDate not supplied

Spatial partitioning using multivariate cluster analysis and a contiguity algorithm.

Spatial analysis of epidemiological data can be a useful tool for identifying patterns of disease occurrence and can provide substantial support for prevention and control strategies. To obtain the greatest spatial resolution, it is important to use the smallest available areal units with homogeneous population. However, small areas usually have a small population, introducing spurious variability in the chosen indicators of disease occurrence. This paper describes an approach for combining small geographical units to stabilize mortality rates by pooling information across areas according to specified risk profiles. The procedure is based on a principal component analysis, followed by a cluster analysis of social-economic indicators to classify the risk profile of each small area. The classification is used in an algorithm to join neighbouring areas with similar profiles until an estimated population size is achieved. We applied this method to two Administrative Regions of the city of Rio de Janeiro, Brazil, using the census tracts as the basic areal unit. Census tracts were classified according to four socioeconomic categories distributed spatially as a mosaic, where tracts of differing categories neighbour each other. The aggregation algorithm produced a new partition of the region studied, with the created areal units preserving the internal socioeconomic homogeneity.

Adult↗

PubMedDate not supplied

Feasibility of contour mapping epidemiological data with missing values.

Data of epidemiologic interest often occur as spatial information during each of several time periods. In most cases data are available from a set of regions or localities which can be viewed as points in a plane. Although contour mapping is useful for displaying these data, the lack of data for all data points in a region may lead to erroneous interpretation. In this paper we use stimulation to investigate the impact of missing data points for contour mapping using two distinct simulated spatial-time distributions for epidemiologic variables. A model for the occurrence of malaria in localities randomly distributed in one region is chosen as the prototype for data generation.

Bayes Theorem↗

PubMedDate not supplied

[Voronoi s Diagram for defining catchment areas for public hospitals in the Municipality of Rio de Janeiro].

One of the most important pieces of information for health resources planning is the definition of catchment areas for health units. Voronoi Diagrams are a potential technique for this purpose. They are polygons with the property whereby adjacent polygons have their borders located within the same distance of the respective generator points. One possible adjustment to the catchment areas thus defined is the use of weighted Voronoi Diagrams, which result in an improved representation of a health unit's actual capacity. In this study, the 21 public general hospitals in the city of Rio de Janeiro, Brazil, were used as generator points for Voronoi Diagrams. Non-weighted Voronoi Diagrams were initially implemented and then used as the basis for obtaining weighted Voronoi Diagrams, using as weights the annual admission rates estimated for each unit. In the classic Voronoi Diagram case, some catchment areas had similar sizes, although their respective health units had different characteristics. In the weighted case the areas were modified in a way that appeared closer to the actual functioning of the units. The method appeared simple to implement, used easy-to-access data, and did not rely on geopolitical considerations such as existing administrative areas. It thus provided a more realistic picture of a unit's capacity to support basic health programs.

Algorithms↗