Logistic regression analysis of potential prognostic factors for pulmonary thromboembolism

Objective: To identify potential prognostic factors for pulmonary thromboembolism (PTE), establishing a mathematical model to predict the risk for fatal PTE and nonfatal PTE.Method: the reports on 4,813 consecutive autopsies performed from 1979 to 1998 in a Brazilian tertiary referral medical school were reviewed for a retrospective study. From the medical records and autopsy reports of the 512 patients found with macroscopically and/or microscopically,documented PTE, data on demographics, underlying diseases, and probable PTE site of origin were gathered and studied by multiple logistic regression. Thereafter, the jackknife method, a statistical cross-validation technique that uses the original study patients to validate a clinical prediction rule, was performed.Results: the autopsy rate was 50.2%, and PTE prevalence was 10.6%. In 212 cases, PTE was the main cause of death (fatal PTE). The independent variables selected by the regression significance criteria that were more likely to be associated with fatal PTE were age (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.00 to 1.03), trauma (OR, 8.5; 95% CI, 2.20 to 32.81), right-sided cardiac thrombi (OR, 1.96; 95% CI, 1.02 to 3.77), pelvic vein thrombi (OR, 3.46; 95% CI, 1.19 to 10.05); those most likely to be associated with nonfatal PTE were systemic arterial hypertension (OR, 0.51; 95% CI, 0.33 to 0.80), pneumonia (OR, 0.46; 95% CI, 0.30 to 0.71), and sepsis (OR, 0.16; 95% CI, 0.06 to 0.40). The results obtained from the application of the equation in the 512 cases studied using logistic regression analysis suggest the range in which logit p > 0.336 favors the occurrence of fatal PTE, logit p < - 1.142 favors nonfatal PTE, and logit P with intermediate values is not conclusive. The cross-validation prediction misclassification rate was 25.6%, meaning that the prediction equation correctly classified the majority of the cases (74.4%).Conclusions: Although the usefulness of this method in everyday medical practice needs to be confirmed by a prospective study, for the time being our results suggest that concerning prevention, diagnosis, and treatment of PTE, strict attention should be given to those patients presenting the variables that are significant in the logistic regression model.

Body size and flight distance in stingless bees (Hymenoptera: Meliponini): inference of flight range and possible ecological implications

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Buffalos milk yield analysis using random regression models

Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed), daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genetic de Bubalinos (PROMEBUL) and from records of EMBRAPA Amazonia Oriental - EAO herd, located in Belem, Para, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre's polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre's polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. © 2013 American Dairy Science Association.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.

An assessment of the economic impact of climate change on the health sector In Jamaica

Climate change is a naturally occurring phenomenon in which the earth‘s climate goes through cycles of warming and cooling; these changes usually take place incrementally over millennia. Over the past century, there has been an anomalous increase in global temperature, giving rise to accelerated climate change. It is widely accepted that greenhouse gas emissions from human activities such as industries have contributed significantly to the increase in global temperatures. The existence and survival of all living organisms is predicated on the ability of the environment in which they live not only to provide conditions for their basic needs but also conditions suitable for growth and reproduction. Unabated climate change threatens the existence of biophysical and ecological systems on a planetary scale. The present study aims to examine the economic impact of climate change on health in Jamaica over the period 2011-2050. To this end, three disease conditions with known climate sensitivity and importance to Jamaican public health were modelled. These were: dengue fever, leptospirosis and gastroenteritis in children under age 5. Historical prevalence data on these diseases were obtained from the Ministry of Health Jamaica, the Caribbean Epidemiology Centre, the Climate Studies Group Mona, University of the West Indies Mona campus, and the Meteorological Service of Jamaica. Data obtained spanned a twelve-year period of 1995-2007. Monthly data were obtained for dengue and gastroenteritis, while for leptospirosis, the annual number of cases for 1995-2005 was utilized. The two SRES emission scenarios chosen were A2 and B2 using the European Centre Hamburg Model (ECHAM) global climate model to predict climate variables for these scenarios. A business as usual (BAU) scenario was developed using historical disease data for the period 2000-2009 (dengue fever and gastroenteritis) and 1995-2005 (leptospirosis) as the reference decades for the respective diseases. The BAU scenario examined the occurrence of the diseases in the absence of climate change. It assumed that the disease trend would remain unchanged over the projected period and the number of cases of disease for each decade would be the same as the reference decade. The model used in the present study utilized predictive empirical statistical modelling to extrapolate the climate/disease relationship in time, to estimate the number of climate change-related cases under future climate change scenarios. The study used a Poisson regression model that considered seasonality and lag effects to determine the best-fit model in relation to the diseases under consideration. Zhang and others (2008), in their review of climate change and the transmission of vector-borne diseases, found that: ―Besides climatic variables, few of them have included other factors that can affect the transmission of vector-borne disease….‖ (Zhang 2008) Water, sanitation and health expenditure are key determinants of health. In the draft of the second communication to IPCC, Jamaica noted the vulnerability of public health to climate change, including sanitation and access to water (MSJ/UNDP, 2009). Sanitation, which in its broadest context includes the removal of waste (excreta, solid, or other hazardous waste), is a predictor of vector-borne diseases (e.g. dengue fever), diarrhoeal diseases (such as gastroenteritis) and zoonoses (such as leptospirosis). In conceptualizing the model, an attempt was made to include non-climate predictors of these climate-sensitive diseases. The importance of sanitation and water access to the control of dengue, gastroenteritis and leptospirosis were included in the Poisson regression model. The Poisson regression model obtained was then used to predict the number of disease cases into the future (2011-2050) for each emission scenario. After projecting the number of cases, the cost associated with each scenario was calculated using four cost components. 1. Treatment cost morbidity estimate. The treatment cost for the number of cases was calculated using reference values found in the literature for each condition. The figures were derived from studies of the cost of treatment and represent ambulatory and non-fatal hospitalized care for dengue fever and gastroenteritis. Due to the paucity of published literature on the health care cost associated with leptospirosis, only the cost of diagnosis and antibiotic therapy were included in the calculation. 2. Mortality estimates. Mortality estimates are recorded as case fatality rates. Where local data were available, these were utilized. Where these were unavailable, appropriate reference values from the literature were used. 3. Productivity loss. Productivity loss was calculated using a human capital approach, by multiplying the expected number of productive days lost by the caregiver and/or the infected person, by GDP per capita per day (US$ 14) at 2008 GDP using 2008 US$ exchange rates. 4. No-option cost. The no-option cost refers to adaptation strategies for the control of dengue fever which are ongoing and already a part of the core functions of the Vector Control Division of the Ministry of Health, Jamaica. An estimated US$ 2.1 million is utilized each year in conducting activities to prevent the post-hurricane spread of vector borne diseases and diarrhoea. The cost includes public education, fogging, laboratory support, larvicidal activities and surveillance. This no-option cost was converted to per capita estimates, using population estimates for Jamaica up to 2050 obtained from the Statistical Institute of Jamaica (STATIN, 2006) and the assumption of one expected major hurricane per decade. During the decade 2000-2009, Jamaica had an average inflation of 10.4% (CIA Fact book, last updated May 2011). This average decadal inflation rate was applied to the no-option cost, which was inflated by 10% for each successive decade to adjust for changes in inflation over time.

Ecologia da ictiofauna e análise ecossistêmica das áreas de influência direta da UHE Coaracy Nunes, Ferreira Gomes - AP

O reservatório da UHE Coaracy Nunes no rio Araguari esta localizado entre os municípios de Ferreira Gomes e Porto Grande no estado do Amapá-Brasil, distando 200 km do Oceano Atlântico. A usina Coaracy Nunes foi a primeira hidrelétrica a ser construída na Amazônia brasileira, tendo suas obras iniciadas em 1967. O rio Araguari e o principal rio do estado do Amapá e representa fonte de geração de renda através da pesca, atividades agropecuárias em sua várzea, navegação, mineração, geração de energia e lazer. O presente estudo teve por objetivo avaliar as alterações impostas pela construção do reservatório da UHE Coaracy Nunes, através das assembleias de peixes de quatro áreas de influencia direta desta usina. Para isso, no período de maio de 2009 a julho de 2010, foram realizadas coletas bimensais, de peixes, com redes de malhas padronizadas variando de 1,0 a 10,0 cm entre nos adjacentes e outras técnicas auxiliares. A partir destas coletas, no capitulo 1 foi verificado a composição, abundancia (CPUEn) e biomassa (CPUEp) relativas da ictiofauna, eficiência amostral (curva do coletor, curva de rarefação e Jacknife) e descritores ecológicos de comunidades (riqueza, diversidade, equitabilidade e dominância) das assembleias das quatro áreas. Foram efetuadas analises de variância (ANOVA: bifatorial), Kruskal-Wallis, teste-T e Mann-Whitney para verificar se havia diferenças significativas dos descritores entre as áreas e períodos sazonais. Estas análises foram corroboradas por analises multivariadas de agrupamento (cluster), ordenamento (MDS), Anosim e Simper. No capitulo 2, os estados ecológicos das quatro áreas foram verificados utilizando como indicadores: curvas espécie abundancia, curvas K-dominância e curvas ABC, assim como modelos espécie-abundancia serie geométrica, log serie, log normal e broken stick, e modelo de regressão linear de espectros de tamanho. No capitulo 3, a estrutura trófica foi estimada a partir da categorização das espécies de cada área em 5 guildas: piscívora, onívora, detritívora, carnívora e herbívora. A abundancia, biomassa e índices ecológicos destas guildas foram estimados e verificados suas variações espaço-temporais, por analises de variância (ANOVA: bifatorial e Kruskal-Wallis) e teste t. No capitulo 4, a dieta das espécies mais abundantes das assembleias de cada área foi verificada e suas variações espaço-temporais detectadas por analise de variância (ANOVA: bifatorial e Kruskal-Wallis). Também foram estimados a amplitude e sobreposição de nicho das espécies mais abundantes, assim como a existência de competição entre as espécies através de modelagem nula. No capitulo 5 foi realizada a avaliação ecossistêmica das quatro áreas através de modelos de fluxo de biomassa na rede trófica do ecossistema, usando como instrumento de modelação o software Ecopath. Essas análises tinham por objetivo descrever as variações dos atributos ecológicos que quantificam as propriedades de maturidade, estabilidade e resiliência ecossistêmica que pudessem refletir os estados ecológicos dessas áreas. O modelo incluiu compartimentos funcionais desde produtores primários ate predadores de topo. No geral, todas as análises indicaram sensíveis alterações na ictiofauna atribuídas a implantação da UHE Coaracy Nunes, que se refletem nos três níveis de organização: ecossistema, comunidade (assembleia) e guilda. Os resultados indicaram a captura de 1.977 peixes distribuídos em 2 classes, 9 ordens, 23 famílias, 73 gêneros e 108 espécies. As curvas de acumulação de espécies e curvas de rarefação individualizadas demonstraram que houve suficiência amostral nas áreas Reservatório e Lacustre. Os resultados mostraram que a área Jusante foi mais rica, diversa e equitativa em relação as demais e que a sazonalidade não influenciou na variação destes índices. A abundancia relativa (CPUEn) foi superior nas áreas Reservatório e Lacustre e a biomassa relativa (CPUEb) foi superior na área Jusante, não havendo diferenças sazonais para esses descritores em todas as áreas. As analises de agrupamento (cluster) e ordenamento (MDS) da ictiofauna permitiram identificar a formação de três assembleias distintas: Jusante, Montante e uma assembleia que compreende as áreas Reservatório e Lacustre, ratificando a similaridade dessas duas áreas. Os resultados das curvas whitakeplot, ABC e K-dominância, assim como o ajuste satisfatório do modelo broken stick e os padrões das curvas de espectro de tamanho para a assembleia da área a jusante indicam que esta área foi a mais equilibrada em termos ecológicos. Nas áreas Lacustre e Reservatório, os resultados tanto do ajuste ao modelo serie geométrica, quanto os resultados das curvas whitake-plot, ABC e K-dominância e o espectro de tamanho, assim como os resultados das curvas e ajustes ao modelo série e menor espectro de tamanho para a assembleia da área Reservatório, refletem que os peixes destas áreas, em sua maioria, são indivíduos pequenos com elevada dominância e baixa equitabilidade, caracterizando comunidades típicas de áreas impactadas. A estrutura trófica das assembleias de peixes das áreas represadas (Reservatório e Lacustre) foram formatadas em função do barramento do rio, que isolou e fragmentou o ambiente, determinando sua modificação física, impondo o estabelecimento de uma ictiofauna de espécies pré-adaptadas as condições ambientais de represamento, diferente, em parte, da estrutura da ictiofauna fluvial pre-barramento, destacando as piscívoras, onívoras e detritívoras, que foram as mais ricas e abundantes em função da disponibilidade, nas duas áreas, dos recursos alimentares de sua preferencia. Os resultados demonstraram que as dietas das assembleias de todas as áreas foram similares quanto ao predomínio do consumo de peixes e detritos, seguidos de alimento vegetal aloctone, revelando um padrão com poucos nichos amplos e uma concentração maior de espécies com nichos mais estreitos. Contudo, o padrão de baixa amplitude trófica foi evidenciado pelo predomínio da guilda piscívora, somada as guildas detritívora e herbívora. A sazonalidade pouco influenciou na alimentação da maioria das espécies em todas as áreas. Os padrões comparativos da dieta entre as áreas Montante e Jusante com as áreas Reservatório e Lacustre indicam que a maioria das espécies das áreas represadas pertenciam as guildas piscívora, onívora e detritívora antes do barramento do rio, que colonizaram estes ambientes, influenciadas, principalmente, pela abundancia dos recursos alimentares de suas preferencias e das condições físicas ambientais favoráveis. Interações competitivas foram evidenciadas pelos modelos nulos, sugerindo que a competição também foi um fator importante na estruturação das assembleias. Ecossistemicamente, os quatro modelos de fluxo de biomassa representam ecossistemas com elevada produção primaria oriunda da floresta riparia e algas filamentosas, que são utilizadas parcialmente. A cadeia trófica baseada em detrito apresentou ser mais importante que a que tem como base a produção primaria nas áreas Reservatório e Lacustre. A maioria dos fluxos ocorre nos compartimentos de níveis tróficos baixos. As propriedades ecossistêmicas da área Jusante indicam que este ambiente se encontra mais desenvolvido e maduro em relação aos outros, caracterizado por resiliência e entropia altas. As áreas represadas (Reservatório e Lacustre) apresentaram atributos ecossistêmicos que lhe conferiram características de menos resiliente e menos maduro que as áreas de rio. A área Montante apresentou um padrão intermediário de resiliência, estabilidade e maturidade. Esses resultados evidenciam que apos quarenta anos da construção da barragem do reservatório de Coaracy Nunes, a fragmentação do ambiente proporcionou alterações ecossistêmicas negativas, refletidas nas assembleias de peixes das áreas acima do barramento e na analise ecossistêmica, evidenciando que a área jusante apresenta características de ambiente em bom estado ecológico, com baixa alteração de origem antrópica e capaz de suportar distúrbios.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Method validation using weighted linear regression models for quantification of UV filters in water samples

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Image-Guided Cryoablation of the Spine in a Swine Model: Clinical, Radiological, and Pathological Findings with Light and Electron Microscopy

This study was designed to present the feasibility of an in vivo image-guided percutaneous cryoablation of the porcine vertebral body. Methods The institutional animal care committee approved this study. Cone-beam computed tomography (CBCT)-guided vertebral cryoablations (n = 22) were performed in eight pigs with short, 2-min, single or double-freezing protocols. Protective measures to nerves included dioxide carbon (CO2) epidural injections and spinal canal temperature monitoring. Clinical, radiological, and pathological data with light (n = 20) or transmission electron (n = 2) microscopic analyses were evaluated after 6 days of clinical follow-up and euthanasia. Results CBCT/fluoroscopic-guided transpedicular vertebral body cryoprobe positioning and CO2 epidural injection were successful in all procedures. No major complications were observed in seven animals (87.5 %, n = 8). A minor complication was observed in one pig (12.5 %, n = 1). Logistic regression model analysis showed the cryoprobe-spinal canal (Cp-Sc) distance as the most efficient parameter to categorize spinal canal temperatures lower than 19 °C (p<0.004), with a significant Pearson’s correlation test (p < 0.041) between the Cp-Sc distance and the lowest spinal canal temperatures. Ablation zones encompassed pedicles and the posterior wall of the vertebral bodies with an inflammatory rim, although no inflammatory infiltrate was depicted in the surrounding neural structures at light microscopy. Ultrastructural analyses evidenced myelin sheath disruption in some large nerve fibers, although neurological deficits were not observed. Conclusions CBCT-guided vertebral cryoablation of the porcine spine is feasible under a combination of a short freezing protocol and protective measures to the surrounding nerves. Ultrastructural analyses may be helpful assess the early modifications of the nerve fibers.

A general threshold stress hybrid hazard model for lifetime data

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

Estimativa de densidade amostral para elaboração de mapas de produtividade

Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD) and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.

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.