RESUMO

OBJECTIVE: To determine if introducing age as another explanatory variable in an ecological regression model relating crude rates of cancer incidence and a deprivation index provides better results than the usual practice of using the standard incidence ratio (SIR) as the response variable, introducing the non-standardized index, and not including age in the model. METHODS: Relative risks associated with the deprivation index for some locations of cancer in Spain's Girona Health Region were estimated using two different models. Model 1 estimated relative risks with the indirect method, using the SIR as the response variable. Model 2 estimated relative risks using age as an explanatory variable and crude cancer rates as the response variable. Two scenarios and two sub-scenarios were simulated to test the properties of the estimators and the goodness of fit of the two models. RESULTS: The results obtained from Model 2's estimates were slightly better (less biased) than those from Model 1. The results of the simulation showed that in all cases (two scenarios and two sub-scenarios) Model 2 had a better fit than Model 1. The probability density for the parameter of interest provided evidence that Model 1 leads to biased estimates. CONCLUSIONS: When attempting to explain the relative risk of incidence of cancer using ecological models that control geographic variability, introducing age as another explanatory variable and crude rates as a response variable provides less biased results.

OBJETIVO: Determinar si la introducción de la edad como otra variable independiente en un modelo de regresión ecológica que relaciona las tasas brutas de incidencia de cáncer con un índice de carencia, ofrece mejores resultados que la práctica corriente del uso de la razón de incidencia normalizada como criterio de valoración, con introducción del índice sin normalización y sin incluir la edad en el modelo. MÉTODOS:Se calcularon los riesgos relativos asociados con el índice de carencia de algunos tipos de cáncer en la Región Sanitaria de Girona en España, mediante dos modelos diferentes. En el modelo 1 se calcularon los riesgos relativos con el método indirecto, usando la razón de incidencia normalizada como criterio de valoración. En el modelo 2 se calcularon los riesgos relativos introduciendo la edad como una variable independiente y las tasas brutas de cáncer como criterio de valoración. Se simularon dos hipótesis y dos subhipótesis con el fin de verificar las propiedades de los estimadores y la bondad del ajuste de ambos modelos. RESULTADOS: Los resultados obtenidos a partir de las estimaciones con el modelo 2 fueron un poco mejores (menos sesgados) que los resultados obtenidos con el modelo 1. Los resultados de la simulación indicaron que en todos los casos (las dos hipótesis y las dos subhipótesis) el modelo 2 exhibió un mejor ajuste que el modelo 1. La función de densidad del parámetro de interés puso en evidencia que el modelo 1 da lugar a estimaciones sesgadas. CONCLUSIONES: Cuando se intenta explicar el riesgo relativo de incidencia de cáncer mediante modelos de regresión ecológica que tienen en cuenta la variabilidad geográfica, se obtienen resultados menos sesgados cuando se introduce la edad como una de las variables independientes y se utilizan las tasas brutas de incidencia como criterio de valoración.

Assuntos

Assuntos

RESUMO

The benefits of breastfeeding for the children's health have been highlighted in many studies. The innovative aspect of the present study lies in its use of a multilevel model, a technique that has rarely been applied to studies on breastfeeding. The data reported were collected from a larger study, the Family Budget Survey-Pesquisa de Orçamentos Familiares, carried out between 2002 and 2003 in Brazil that involved a sample of 48 470 households. A representative national sample of 1477 infants aged 0-6 months was used. The statistical analysis was performed using a multilevel model, with two levels grouped by region. In Brazil, breastfeeding prevalence was 58%. The factors that bore a negative influence on breastfeeding were over four residents living in the same household [odds ratio (OR) = 0.68, 90% confidence interval (CI) = 0.51-0.89] and mothers aged 30 years or more (OR = 0.68, 90% CI = 0.53-0.89). The factors that positively influenced breastfeeding were the following: higher socio-economic levels (OR = 1.37, 90% CI = 1.01-1.88), families with over two infants under 5 years (OR = 1.25, 90% CI = 1.00-1.58) and being a resident in rural areas (OR = 1.25, 90% CI = 1.00-1.58). Although majority of the mothers was aware of the value of maternal milk and breastfed their babies, the prevalence of breastfeeding remains lower than the rate advised by the World Health Organization, and the number of residents living in the same household along with mothers aged 30 years or older were both factors associated with early cessation of infant breastfeeding before 6 months.

Assuntos

RESUMO

En ciencias de la salud, muchas variables de interés muestran cambios en el tiempo. Predecir qué valor futuro alcanzará una variable bajo determinadas condiciones iniciales constituye una importante fuente de información para la investigación básica y aplicada, al igual que para la toma de decisiones en la gestión de servicios de salud y la atención sanitaria. Los procesos estocásticos son secuencias de variables aleatorias observadas en sucesivos instantes de tiempo, y los modelos de Markov permiten estudiar la evolución temporal de cualquier proceso cuyo estado futuro dependa solo del estado en que se encuentre en el presente, pero no de su historia pasada. Desde comienzos del siglo XX sus aplicaciones en el ámbito sanitario han sido múltiples, siendo una línea de investigación de interés en la actualidad tanto a nivel teórico como aplicado. El objetivo de este trabajo es mostrar los modelos markovianos utilizados con mayor frecuencia en ciencias de la salud, prestando especial atención a los métodos de estimación, la interpretación de resultados y el software disponible para su aplicación. Desde un punto de vista práctico se abordan cuatro aspectos fundamentales para llevar a cabo análisis de datos basados en modelos de Markov, a saber: definición de los estados del proceso y los mecanismos de transición entre ellos, selección del modelo más apropiado, determinación de las probabilidades de transición entre estados y descripción de la evolución temporal del proceso.

Many variables of interest in health sciences change with time. Understanding their evolution and predicting their future status under certain initial conditions provide key information that is extremely useful in health research, health management and health care. Stochastic processes are sequences of random variables which change over time and Markov models study the evolution of any process where its future status will depend on its present status alone, but not on its past history. Since the beginning of the XX century there have been numerous applications of this type of processes in health sciences. Today, these kinds of models are an interesting theoretical and applied research line. This paper shows some of the Markov models that are currently the most widely used in the fields of health sciences. The content has been drafted with particular emphasis on the main techniques for analysis, interpretation of results, the usefulness of the models and software available for applications. From a practical standpoint, four essential aspects are taken into account in data analysis based on Markov models: definition of the process states and their transitions, selection of the best model, estimation of transition probabilities between states, and description of the process temporal evolution.

Em ciências da saúde, muitas variáveis de interesse mostram mudanças no tempo. Predizer que valor futuro alcançará uma variável sob determinadas condições iniciais constitui uma importante fonte de informação para a investigação básica e aplicada, igual que para a tomada de decisões na gestão de serviços de saúde e a atenção sanitária. Os processos estocásticos são sequências de variáveis aleatórias observadas em sucessivos instantes de tempo, e os modelos de Markov permitem estudar a evolução temporal de qualquer processo cujo estado futuro dependa somente do estado em que se encontre no presente, mas não de sua história passada. Desde o início do seculo XX suas aplicações no âmbito sanitário têm sido múltiplas, sendo uma línha de investigação de interesse na atualidade tanto a nivel teórico como aplicado. O objetivo deste trabalho é mostrar os modelos markovianos utilizados com maior frequência em ciências da saúde, prestando especial atenção aos métodos de estimação, a interpretação de resultados e o software disponível para sua aplicação. Desde o ponto de vista prático se abordam quatro aspectos fundamentais para realizar análises de dados basaedos em modelos de Markov, a saber: definição dos estados do processo e os mecanismos de transição entre eles, seleção do modelo mais apropriado, determinação das probabilidades de transição entre estados e descrição da evolução temporal do processo.

RESUMO

The aim of this work is to make known the multicentric project AMCAC, whose objective is to describe the geographical distribution of mortality from all causes in census groups of the provincial capitals of Andalusia and Catalonia during 1992-2002 and 1994-2000 respectively, and to study the relationship between the sociodemographic characteristics of the census groups and mortality. This is an ecological study in which the analytical unit is the census group. The data correspond to 298,731 individuals (152,913 men and 145,818 women) who died during the study periods in the towns of Almeria, Barcelona, Cadiz, Cordoba, Girona, Granada, Huelva, Jaen, Lleida, Malaga, Seville and Tarragona during the study periods. The dependent variable is the number of deaths observed per census group. The independent variables are the percentage of unemployment, illiteracy and manual workers. Estimation of the moderated relative risk and the study of the associations among the sociodemographic characteristics of the census groups and the mortality will be done for each town and each sex using the Besag-York-Mollie model. Dissemination of the results will help to improve and broaden knowledge about the population's health, and will provide an important starting point to establish the influence of contextual variables on the health of urban populations.

Assuntos