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One line of enquiry in demographic research assesses whether climate affects fertility. We extend this literature by examining the ramifications of climate conditions on fertility over a period of public health crisis in a highly unequal, urban middle-income country. We use monthly data for Brazil's 5,564 municipalities and apply spatial fixed-effects models to account for unobserved municipal heterogeneity and spatial dependence. Findings suggest that increases in temperature and precipitation are associated with declines in births. We also show that changes in response to climate conditions became greater during the Zika epidemic, particularly in urban areas. Combined, findings highlight the value of understanding the intersections between climate and fertility across geographic boundaries and during this public health crisis. Epidemics have become more important in people's lives with the recurring emergence of novel infectious disease threats, such as Zika and Covid-19.

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Previous studies have found that the expansion of primary health care in Brazil following the country-wide family health strategy (ESF), one of the largest primary care programs in the world, has improved health outcomes. However, these studies have relied either on aggregate data or on limited individual data, with no fine-grained information available concerning household participation in the ESF or local supply of ESF services, which represent crucial aspects for analytical and policy purposes. This study analyzes the relationship between the ESF and health outcomes for the adult population in metropolitan areas in Brazil. We investigate this relationship through two linked dimensions of the ESF: the program's local supply of health teams and ESF household registration. In contrast with previous studies focusing on comparisons between certain definitions of "treated" versus "nontreated" populations, our results indicate that the local density of health teams is important to the observed effects of the ESF on adult health. We also find evidence consistent with the presence of positive primary health care spillovers to people not registered with the ESF. However, current ESF coverage levels in metropolitan areas have limited ability to address prevailing health inequalities. Our analysis suggests that the local intensity of ESF coverage should be a key consideration for evaluations and policy efforts related to future ESF expansion.

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This study evaluated the effects of seasons and latitude on tick counting and determined the best model to estimate genetic parameters for tick count and hair coat. Records of animals naturally exposed to ticks on farms in several Brazilian states and in Paraguay were used. The ANOVA was used to verify the effects of seasons and latitude on the tick count trait. Spring was the season with the highest average, followed by summer and autumn, which showed no differences between them. The winter presented the lowest average values. Latitude -11° had the highest mean value followed by latitude -18°. The Bayesian approach was used to evaluate tick count and hair coat and to identify a suitable model for estimating genetic parameters for use in genetic evaluations. The data were analyzed using an animal model with four different specifications for “fixed” purposes. The inference was based on a Markov chain Monte Carlo (MCMC). The criteria for selection of the Bayesian model indicated that the M1 model, which considered the breed composition in the contemporary group, was superior to the other models, both for tick count and hair coat. Heritability estimates for tick count and hair coat obtained using the M1 model were 0.14 and 0.22, respectively. The rank correlations between the models for tick count and hair coat were estimated and reordering was verified for tick count. The estimated genetic correlation between tick count and hair coat traits was negative (-0.12). These findings suggest that different genes regulate tick count and hair coat.(AU)

Os objetivos foram avaliar os efeitos das estações e latitude na contagem de carrapatos e determinar o melhor modelo para estimar parâmetros genéticos para contagem de carrapatos e pelame. Foram utilizados registros de animais expostos naturalmente a carrapatos em fazendas em vários estados brasileiros e no Paraguai. A ANOVA foi utilizada para verificar os efeitos das estações e da latitude na característica de contagem de carrapatos. A primavera foi a estação com a maior média, seguida pelo verão e outono, que não mostraram diferenças entre eles. O inverno apresentou os menores valores médios. A latitude -11° teve o maior valor médio seguido pela latitude -18°. A abordagem bayesiana foi usada para avaliar a contagem de carrapatos e o pelame e identificar o modelo adequado para estimar parâmetros genéticos e para uso em avaliações genéticas. Os dados foram analisados usando um modelo animal com quatro especificações diferentes para efeitos “fixos”. A inferência foi baseada em uma cadeia de Markov Monte Carlo (MCMC). Os critérios de seleção do modelo bayesiano indicaram que o modelo M1, que considerou a composição racial no grupo contemporâneo, foi superior aos demais modelos, tanto na contagem de carrapatos e para pelame. As estimativas de herdabilidade para contagem de carrapatos e pelame obtidas usando o modelo M1 foram de 0,14 e 0,22, respectivamente. As correlações de ranking entre os modelos para a contagem de carrapatos e pelame foram estimadas e a reordenação foi verificada para a contagem de carrapatos. A correlação genética estimada entre a contagem de carrapatos e pelame foi negativa (-0,12). Esses achados sugerem que genes diferentes regulam a contagem de carrapatos e pelame.(AU)

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ABSTRACT: This study evaluated the effects of seasons and latitude on tick counting and determined the best model to estimate genetic parameters for tick count and hair coat. Records of animals naturally exposed to ticks on farms in several Brazilian states and in Paraguay were used. The ANOVA was used to verify the effects of seasons and latitude on the tick count trait. Spring was the season with the highest average, followed by summer and autumn, which showed no differences between them. The winter presented the lowest average values. Latitude -11° had the highest mean value followed by latitude -18°. The Bayesian approach was used to evaluate tick count and hair coat and to identify a suitable model for estimating genetic parameters for use in genetic evaluations. The data were analyzed using an animal model with four different specifications for "fixed" purposes. The inference was based on a Markov chain Monte Carlo (MCMC). The criteria for selection of the Bayesian model indicated that the M1 model, which considered the breed composition in the contemporary group, was superior to the other models, both for tick count and hair coat. Heritability estimates for tick count and hair coat obtained using the M1 model were 0.14 and 0.22, respectively. The rank correlations between the models for tick count and hair coat were estimated and reordering was verified for tick count. The estimated genetic correlation between tick count and hair coat traits was negative (-0.12). These findings suggest that different genes regulate tick count and hair coat.

RESUMO: Os objetivos foram avaliar os efeitos das estações e latitude na contagem de carrapatos e determinar o melhor modelo para estimar parâmetros genéticos para contagem de carrapatos e pelame. Foram utilizados registros de animais expostos naturalmente a carrapatos em fazendas em vários estados brasileiros e no Paraguai. A ANOVA foi utilizada para verificar os efeitos das estações e da latitude na característica de contagem de carrapatos. A primavera foi a estação com a maior média, seguida pelo verão e outono, que não mostraram diferenças entre eles. O inverno apresentou os menores valores médios. A latitude -11° teve o maior valor médio seguido pela latitude -18°. A abordagem bayesiana foi usada para avaliar a contagem de carrapatos e o pelame e identificar o modelo adequado para estimar parâmetros genéticos e para uso em avaliações genéticas. Os dados foram analisados usando um modelo animal com quatro especificações diferentes para efeitos "fixos". A inferência foi baseada em uma cadeia de Markov Monte Carlo (MCMC). Os critérios de seleção do modelo bayesiano indicaram que o modelo M1, que considerou a composição racial no grupo contemporâneo, foi superior aos demais modelos, tanto na contagem de carrapatos e para pelame. As estimativas de herdabilidade para contagem de carrapatos e pelame obtidas usando o modelo M1 foram de 0,14 e 0,22, respectivamente. As correlações de ranking entre os modelos para a contagem de carrapatos e pelame foram estimadas e a reordenação foi verificada para a contagem de carrapatos. A correlação genética estimada entre a contagem de carrapatos e pelame foi negativa (-0,12). Esses achados sugerem que genes diferentes regulam a contagem de carrapatos e pelame.

RESUMO

This study evaluated the effects of seasons and latitude on tick counting and determined the best model to estimate genetic parameters for tick count and hair coat. Records of animals naturally exposed to ticks on farms in several Brazilian states and in Paraguay were used. The ANOVA was used to verify the effects of seasons and latitude on the tick count trait. Spring was the season with the highest average, followed by summer and autumn, which showed no differences between them. The winter presented the lowest average values. Latitude -11° had the highest mean value followed by latitude -18°. The Bayesian approach was used to evaluate tick count and hair coat and to identify a suitable model for estimating genetic parameters for use in genetic evaluations. The data were analyzed using an animal model with four different specifications for “fixed” purposes. The inference was based on a Markov chain Monte Carlo (MCMC). The criteria for selection of the Bayesian model indicated that the M1 model, which considered the breed composition in the contemporary group, was superior to the other models, both for tick count and hair coat. Heritability estimates for tick count and hair coat obtained using the M1 model were 0.14 and 0.22, respectively. The rank correlations between the models for tick count and hair coat were estimated and reordering was verified for tick count. The estimated genetic correlation between tick count and hair coat traits was negative (-0.12). These findings suggest that different genes regulate tick count and hair coat.

Os objetivos foram avaliar os efeitos das estações e latitude na contagem de carrapatos e determinar o melhor modelo para estimar parâmetros genéticos para contagem de carrapatos e pelame. Foram utilizados registros de animais expostos naturalmente a carrapatos em fazendas em vários estados brasileiros e no Paraguai. A ANOVA foi utilizada para verificar os efeitos das estações e da latitude na característica de contagem de carrapatos. A primavera foi a estação com a maior média, seguida pelo verão e outono, que não mostraram diferenças entre eles. O inverno apresentou os menores valores médios. A latitude -11° teve o maior valor médio seguido pela latitude -18°. A abordagem bayesiana foi usada para avaliar a contagem de carrapatos e o pelame e identificar o modelo adequado para estimar parâmetros genéticos e para uso em avaliações genéticas. Os dados foram analisados usando um modelo animal com quatro especificações diferentes para efeitos “fixos”. A inferência foi baseada em uma cadeia de Markov Monte Carlo (MCMC). Os critérios de seleção do modelo bayesiano indicaram que o modelo M1, que considerou a composição racial no grupo contemporâneo, foi superior aos demais modelos, tanto na contagem de carrapatos e para pelame. As estimativas de herdabilidade para contagem de carrapatos e pelame obtidas usando o modelo M1 foram de 0,14 e 0,22, respectivamente. As correlações de ranking entre os modelos para a contagem de carrapatos e pelame foram estimadas e a reordenação foi verificada para a contagem de carrapatos. A correlação genética estimada entre a contagem de carrapatos e pelame foi negativa (-0,12). Esses achados sugerem que genes diferentes regulam a contagem de carrapatos e pelame.

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BACKGROUND: The elevated consumption of sugar-sweetened beverages (SSBs) in Mexico is an important public health concern. However, the association between SSB consumption and hyperuricemia has been scarcely studied and not well documented. OBJECTIVES: To prospectively evaluate the association between SSB consumption and risk of hyperuricemia in Mexican adults. METHODS: A longitudinal analysis was conducted using data from the Health Workers Cohort Study. Participants were followed from 2004 to 2018, with measurements every 6 y. The analysis sample consisted of 1300 adults, aged 18 to 85 y. SSB consumption during the previous year was evaluated through a semiquantitative FFQ. Hyperuricemia was defined as a concentration of uric acid ≥7.0 mg/dL in men and ≥5.7 mg/dL in women. We evaluated the association of interest using 2 methodologies: fixed-effects logistic regression and generalized estimating equations (GEEs). Potential confounders were included in both approaches. RESULTS: At baseline, median intake of SSBs was 472.1 mL/wk (IQR: 198.8-1416.4 mL/wk), and 233 participants had hyperuricemia. Uric acid was higher in participants with an SSB intake ≥7 servings/wk, compared with those with an intake <1 serving/wk (P < 0.001). Participants who changed from the lowest to the highest category of servings consumption experienced 2.6 increased odds of hyperuricemia (95% CI: 1.27, 5.26). Results from the GEE model indicated the odds of hyperuricemia increased by 44% (OR=1.44; 95% CI: 1.13, 1.84) in the 2-6 servings/wk group, and by 89% (OR=1.89; 95% CI: 1.39, 2.57) in the ≥7 servings/wk categories, compared with the <1 serving/wk category. Diet soft drinks were not associated with hyperuricemia. CONCLUSIONS: Our results suggest that the consumption of SSBs is associated with an increased risk of hyperuricemia in Mexican adults, but diet soft drink consumption is not, which supports the need to strengthen existing recommendations to reduce the intake of SSBs.The Health Workers Cohort Study (HWCS) has been approved by the Institutional Review Board of the Mexican Social Security Institute (12CEI 09 006 14), and the National Institute of Public Health of Mexico (13CEI 17 007 36).

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Recent evidence for Mexico suggests important differences in health status between people with diagnosed and undiagnosed diabetes. However, there is at best scarce evidence on the economic consequences of diabetes, especially in contexts where the condition often remains undiagnosed, as is typically the case in low- and middle income countries. Using Mexican longitudinal and biomarker data we estimated the relationship between diabetes, as well as its time since diagnosis, and employment probabilities, wages and working hours. We further explored how these relationships differ for those with diagnosed and undiagnosed diabetes. For the longitudinal analyses, nationally representative data from 11995 men and 13858 women 15-64 years old were taken from three waves (2002, 2005, 2009) of the Mexican Family Life Survey. We estimated a fixed effects model to account for unmeasured time-invariant confounders of diabetes. We found a reduction in the probability of being employed of 7.7 and 6.3 percentage points for men and women, respectively, but no significant relationship with hours worked or wages. Employment probabilities fell gradually with each year since diagnosis for men but not for women. Using cross-sectional biomarker data, our results indicate that 68% of those exhibiting glycated hemoglobin (HbA1c) levels above the clinical diabetes threshold did not self-report a diagnosis, hence were undiagnosed. Nevertheless, regression analysis revealed that there was no association of diabetes with labour outcomes for undiagnosed women or men. This suggests that results based on self-reported diabetes cannot be extended to the (rather large) part of the population with undiagnosed diabetes, likely because of a selection of people in worse health and with a longer diabetes duration into the diagnosed population. Earlier diagnosis and improved treatment of diabetes therefore may prevent adverse health effects and related economic hardship.

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The estimation of carry-over effects is a difficult problem in the design and analysis of clinical trials of treatment sequences including cross-over trials. Except for simple designs, carry-over effects are usually unidentifiable and therefore nonestimable. Solutions such as imposing parameter constraints are often unjustified and produce differing carry-over estimates depending on the constraint imposed. Generalized inverses or treatment-balancing often allow estimating main treatment effects, but the problem of estimating the carry-over contribution of a treatment sequence remains open in these approaches. Moreover, washout periods are not always feasible or ethical. A common feature of designs with unidentifiable parameters is that they do not have design matrices of full rank. Thus, we propose approaches to the construction of design matrices of full rank, without imposing artificial constraints on the carry-over effects. Our approaches are applicable within the framework of generalized linear mixed-effects models. We present a new model for the design and analysis of clinical trials of treatment sequences, called Antichronic System, and introduce some special sequences called Skip Sequences. We show that carry-over effects are identifiable only if appropriate Skip Sequences are used in the design and/or data analysis of the clinical trial. We explain how Skip Sequences can be implemented in practice, and present a method of computing the appropriate Skip Sequences. We show applications to the design of a cross-over study with 3 treatments and 3 periods, and to the data analysis of the STAR*D study of sequences of treatments for depression.

La estimación de los efectos de arrastre es un problema difícil en el diseño y análisis de ensayos clínicos de secuencias de tratamientos, incluyendo ensayos cruzados. Excepto por diseños simples, estos efectos son usualmente no identificables y, por lo tanto, no estimables. La imposición de restricciones a los parámetros es a menudo no justificada y produce diferentes estimativos de los efectos de arrastre dependiendo de la restricción impuesta. Las inversas generalizadas o el balance de tratamientos a menudo permiten estimar los efectos principales de tratamiento, pero no resuelven el problema de estimar la contribución de los efectos de arrastre de una sequencia de tratamiento. Además, los períodos de lavado no siempre son factibles o éticos. Los diseños con parámetros no identificables comúnmente tienen matrices de diseño que no son de rango completo. Por lo tanto, proponemos métodos para la construcción de matrices de rango completo, sin imponer restricciones artificiales en los efectos de arrastre. Nuestros métodos son aplicables en un contexto de modelos lineales mixtos generalizados. Presentamos un nuevo modelo para el diseño y análisis de ensayos clínicos de secuencias de tratamientos, llamado Sistema Anticrónico, e introducimos secuencias de tratamiento especiales llamadas Secuencias de Salto. Demostramos que los efectos de arrastre son identificables sólo si se usan Secuencias de Salto apropiadas. Explicamos como implementar en la práctica estas secuencias, y presentamos un método para calcular las secuencias apropiadas. Presentamos aplicaciones al diseño de un estudio cruzado con 3 tratamientos y 3 períodos, y al análisis del estudio STAR*D de secuencias de tratamientos para la depresión.

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Racial disparities in education in Brazil (and elsewhere) are well documented. Because this research typically examines educational variation between individuals in different families, however, it cannot disentangle whether racial differences in education are due to racial discrimination or to structural differences in unobserved neighborhood and family characteristics. To address this common data limitation, we use an innovative within-family twin approach that takes advantage of the large sample of Brazilian adolescent twins classified as different races in the 1982 and 1987-2009 Pesquisa Nacional por Amostra de Domicílios. We first examine the contexts within which adolescent twins in the same family are labeled as different races to determine the characteristics of families crossing racial boundaries. Then, as a way to hold constant shared unobserved and observed neighborhood and family characteristics, we use twins fixed-effects models to assess whether racial disparities in education exist between twins and whether such disparities vary by gender. We find that even under this stringent test of racial inequality, the nonwhite educational disadvantage persists and is especially pronounced for nonwhite adolescent boys.

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A number of scoring systems for proficiency testing and interlaboratory comparison are in use by the metrology community. The choice of scoring system for a given study is often based on the study coordinator's experience and anecdotal knowledge, perhaps attributable to a historic lack of detailed and formal explanation about the foundation of these systems. This has influenced the development of new scoring systems, some of them departing from the well-established hypothesis testing theory. Often, different scoring systems give different results not because one may be better than the others but because, as they are documented, the user cannot control the confidence level of the test. We present a formal evaluation of seven of these systems under the fixed effects model assuming known variances. Under these sound assumptions, the systems analyzed all have the same statistical properties. Furthermore, these systems are all members of a family of systems based on strictly increasing functions in which the statistical decision problem is invariant. Under the fixed effects model with known variances, no unbiased scoring system can provide greater statistical power than the members of this family of systems. We apply these results to the lead content of water example provided in International Standard ISO 13528:2015 "Statistical methods for use in proficiency testing by interlaboratory comparisons."

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We study the relationship between environmental conditions at birth (GDP per capita and infant mortality rate) and adult stature using cohort-state level data in Brazil for the period 1950-1980. We find that GDP per capita, whose annual percentage growth rate was 4.8% during this period, not infant mortality rate, is a robust correlate of population stature in Brazil. Our results are robust to a battery of robustness checks. Using a useful bracketing property of the (state) fixed effects and lagged dependent variables (heights) estimators, we find that an increase in GDP per capita of the magnitude corresponding to that period is associated with 43-68% of the increase in adult height occurring in the same time span. Income, not disease, appears to be the main correlate of Brazilian population heights in the second half of the 20th Century.

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In this research we aimed to study environmental effects about litter size and weaning rate in Santa Ines sheep by means of generalized linear models. We used the data from experimental sheep herd of Santa Ines, of the Federal University of Piaui, Campus Professora Cinobelina Elvas, in Bom Jesus-PI, during 2008-2010. The environmental effects studied were: mating year, mating station, birth season, birth type, offspring sex, ewe age at birth, ewe weight at birth and birth weight as covariate. For statistical analysis we used the GENMOD procedure contained in SAS. Mean values for litter size and weaning rates were 1.36 and 68.18% respectively. The effect of ewe weight was significant about the litter size. There was no significant effect of birth season on the weaning rate. Correlations between productive and reproductive traits ranged from negative to positive low magnitude. The effects of ewe weight at birth and birth weight were important sources of variation for litter size and weaning rates, which reflects the need to consider these effects in models of genetic and phenotypic evaluation.

O objetivo com esta pesquisa foi estudar efeitos ambientais sobre a prolificidade e a taxa de desmame em ovinos Santa Inês, por meio de modelos lineares generalizados. Foram utilizados dados do rebanho experimental de ovinos da raça Santa Inês, da Universidade Federal do Piauí, Campus Universitário Professora Cinobelina Elvas, em Bom Jesus-PI, no período de 2008 a 2010. Os efeitos ambientais estudados foram: ano de cobertura, Período de cobertura, Período de nascimento, tipo de nascimento, sexo da cria, idade da mãe ao parto, peso da mãe ao parto e peso ao nascer como covariável. Para análise estatística foi utilizado o procedimento GENMOD contido no programa SAS. Os valores médios de prolificidade e taxa de desmame foram respectivamente 1,36 e 68,18%. O efeito do peso da mãe ao parto foi significativo sobre a prolificidade. Observou-se efeito significativo de Período de nascimento sobre a taxa de desmame. As correlações entre características produtivas e reprodutivas variaram de negativas a positivas de baixa magnitude. Os efeitos de peso da mãe ao parto e peso ao nascer mostraram-se importantes fontes de variação para prolificidade e taxa de desmame, o que reflete a necessidade de considerar estes efeitos em modelos de avaliação genética e fenotípica.

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In this research we aimed to study environmental effects about litter size and weaning rate in Santa Ines sheep by means of generalized linear models. We used the data from experimental sheep herd of Santa Ines, of the Federal University of Piaui, Campus Professora Cinobelina Elvas, in Bom Jesus-PI, during 2008-2010. The environmental effects studied were: mating year, mating station, birth season, birth type, offspring sex, ewe age at birth, ewe weight at birth and birth weight as covariate. For statistical analysis we used the GENMOD procedure contained in SAS. Mean values for litter size and weaning rates were 1.36 and 68.18% respectively. The effect of ewe weight was significant about the litter size. There was no significant effect of birth season on the weaning rate. Correlations between productive and reproductive traits ranged from negative to positive low magnitude. The effects of ewe weight at birth and birth weight were important sources of variation for litter size and weaning rates, which reflects the need to consider these effects in models of genetic and phenotypic evaluation.

O objetivo com esta pesquisa foi estudar efeitos ambientais sobre a prolificidade e a taxa de desmame em ovinos Santa Inês, por meio de modelos lineares generalizados. Foram utilizados dados do rebanho experimental de ovinos da raça Santa Inês, da Universidade Federal do Piauí, Campus Universitário Professora Cinobelina Elvas, em Bom Jesus-PI, no período de 2008 a 2010. Os efeitos ambientais estudados foram: ano de cobertura, Período de cobertura, Período de nascimento, tipo de nascimento, sexo da cria, idade da mãe ao parto, peso da mãe ao parto e peso ao nascer como covariável. Para análise estatística foi utilizado o procedimento GENMOD contido no programa SAS. Os valores médios de prolificidade e taxa de desmame foram respectivamente 1,36 e 68,18%. O efeito do peso da mãe ao parto foi significativo sobre a prolificidade. Observou-se efeito significativo de Período de nascimento sobre a taxa de desmame. As correlações entre características produtivas e reprodutivas variaram de negativas a positivas de baixa magnitude. Os efeitos de peso da mãe ao parto e peso ao nascer mostraram-se importantes fontes de variação para prolificidade e taxa de desmame, o que reflete a necessidade de considerar estes efeitos em modelos de avaliação genética e fenotípica.

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In this research we aimed to study environmental effects about litter size and weaning rate in Santa Ines sheep by means of generalized linear models. We used the data from experimental sheep herd of Santa Ines, of the Federal University of Piaui, Campus Professora Cinobelina Elvas, in Bom Jesus-PI, during 2008-2010. The environmental effects studied were: mating year, mating station, birth season, birth type, offspring sex, ewe age at birth, ewe weight at birth and birth weight as covariate. For statistical analysis we used the GENMOD procedure contained in SAS. Mean values for litter size and weaning rates were 1.36 and 68.18% respectively. The effect of ewe weight was significant about the litter size. There was no significant effect of birth season on the weaning rate. Correlations between productive and reproductive traits ranged from negative to positive low magnitude. The effects of ewe weight at birth and birth weight were important sources of variation for litter size and weaning rates, which reflects the need to consider these effects in models of genetic and phenotypic evaluation.(AU)

O objetivo com esta pesquisa foi estudar efeitos ambientais sobre a prolificidade e a taxa de desmame em ovinos Santa Inês, por meio de modelos lineares generalizados. Foram utilizados dados do rebanho experimental de ovinos da raça Santa Inês, da Universidade Federal do Piauí, Campus Universitário Professora Cinobelina Elvas, em Bom Jesus-PI, no período de 2008 a 2010. Os efeitos ambientais estudados foram: ano de cobertura, Período de cobertura, Período de nascimento, tipo de nascimento, sexo da cria, idade da mãe ao parto, peso da mãe ao parto e peso ao nascer como covariável. Para análise estatística foi utilizado o procedimento GENMOD contido no programa SAS. Os valores médios de prolificidade e taxa de desmame foram respectivamente 1,36 e 68,18%. O efeito do peso da mãe ao parto foi significativo sobre a prolificidade. Observou-se efeito significativo de Período de nascimento sobre a taxa de desmame. As correlações entre características produtivas e reprodutivas variaram de negativas a positivas de baixa magnitude. Os efeitos de peso da mãe ao parto e peso ao nascer mostraram-se importantes fontes de variação para prolificidade e taxa de desmame, o que reflete a necessidade de considerar estes efeitos em modelos de avaliação genética e fenotípica.(AU)

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Partindo do estudo dos potenciais resultados do Programa Mais Médicos, procurou-se analisar o impacto do aumento do número de médicos sobre indicadores de saúde. O estudo usou dados em painel de 2008 a 2010 para os municípios brasileiros, a estimação foi feita utilizando o modelo de efeitos fixos. Como variável dependente foi utilizada a taxa de mortalidade infantil, e a variável independente é o número de médicos para cada 1000 habitantes, além dos controles. Os resultados sugerem que, isoladamente, o aumento do número de médicos não é capaz de gerar melhoras na taxa de mortalidade infantil. É necessário que o aumento no número de médicos seja acompanhado por melhoras na infraestrutura, saneamento e aumento na disponibilidade de aparelhos e medicamentos.

Based on the study of the potential outcomes of Programa Mais Médicos, we tried to analyze the impact of increasing the number of physicians on health indicators. The study used data from 2008 to 2010 panel to the municipalities, the estimation was done using the fixed effects model. As the dependent variable infant mortality rate was used, and the independent variable is thenumber of physicians per 1000 population, in addition to controls.The results suggest that, alone, increasing the number of doctors is not able to generate improvements in the infant mortality rate. It is necessary that the increase in the number of doctors is accompanied by improvements in infrastructure, sanitation and increased availability of equipment and drugs.

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Conditional Poisson models have been used to analyze vaccine safety data from self-controlled case series (SCCS) design. In this paper, we derived the likelihood function of fixed effects models in analyzing SCCS data and showed that the likelihoods from fixed effects models and conditional Poisson models were proportional. Thus, the maximum likelihood estimates (MLEs) of time-varying variables including vaccination effect from fixed effects model and conditional Poisson model were equal. We performed a simulation study to compare empirical type I errors, means and standard errors of vaccination effect coefficient, and empirical powers among conditional Poisson models, fixed effects models, and generalized estimating equations (GEE), which has been commonly used for analyzing longitudinal data. Simulation study showed that both fixed effect models and conditional Poisson models generated the same estimates and standard errors for time-varying variables while GEE approach produced different results for some data sets. We also analyzed SCCS data from a vaccine safety study examining the association between measles mumps-rubella (MMR) vaccination and idiopathic thrombocytopenic purpura (ITP). In analyzing MMR-ITP data, likelihood-based statistical tests were employed to test the impact of time-invariant variable on vaccination effect. In addition a complex semi-parametric model was fitted by simply treating unique event days as indicator variables in the fixed effects model. We conclude that theoretically fixed effects models provide identical MLEs as conditional Poisson models. Because fixed effect models are likelihood based, they have potentials to address methodological issues in vaccine safety studies such as how to identify optimal risk window and how to analyze SCCS data with misclassification of adverse events.

RESUMO

The objectives of this study were to compare four linear or logistic regression models and to determine their effects on the level of significance and parameter estimates, using the data from a study on seroprevalence of brucellosis in goats. Information on 5 114 does tested during 2002-2003 from 79 herds in the Bajio region in Michoacan, Mexico was used. The models were: the prevalence of seropositive animals per herd (V1), analyzed by a general linear model (GLM), herds with at least one seropositive animal, analyzed by standard logistic regression (SLRH); V1 analyzed by standard logistic regression (SLR), assuming independence among results within a same herd (SLRA); and V1 analyzed by mixed LR, considering the herd as random effect (MLR). The risk factors included in the four models were: the presence of abortions the year previous to the study, cleanness of the corral (hygiene) and length of lactation. The V1 variable transformed to arcsine-square root did not show a normal distribution. SLRH model (SLR assuming the herd as the unit of interest) and MLR were not compared because they were not nested models. MLR model adjusted the data better than the SLRA model. The deviance (-2LL) from model SLRH (70.6) was similar to their degrees of freedom (75), suggesting that the model adjusted the data very well. Levels of significance for the risk factors were different, depending of the model used. GLM and SLRH models showed significant effects (P < 0.02) only for the presence of abortions; SLRA model showed significant effect (P < 0.05) for the three risk factors, and MLR, effects of the presence of abortions and lactation length, but not for hygiene. The values for the odd ratios (OR) for the SLRA and MLR models were different; the narrowest confidence intervals corresponded to the SLRA model, and the widest to the SLRH model.

Los objetivos de este estudio fueron comparar cuatro modelos de regresión lineal o logística y determinar sus efectos sobre los niveles de significancia y parámetros, utilizando los datos de un estudio de seroprevalencia de brucelosis en cabras. Se utilizó la información de 5 114 cabras en 79 hatos de la región del Bajío, en Michoacán, México, durante 2002-2003. Los modelos fueron: la prevalencia de animales seropositivos por hato (V1), analizados mediante un modelo lineal general (MLG); hatos con al menos un animal seropositivo, analizados mediante regresión logística estándar (RLEH), V1 analizada mediante RLE, suponiendo independencia entre resultados dentro de un mismo hato (RLEA) y V1 analizada mediante RL mixta, considerando al hato como efecto aleatorio (RLM). Los factores de riesgo incluidos en los cuatro modelos fueron: presencia de abortos el año anterior al estudio, limpieza del corral (higiene) y duración de la lactancia. La variable V1 transformada a arcoseno-raíz cuadrada no mostró distribución normal. El modelo RLEH (RL estándar considerando al hato como la unidad de interés) y RLM no se compararon por no ser modelos anidados. El modelo RLM ajustó mejor los datos que el modelo RLEA. La deviance (-2LL) del modelo RLEH (70.6) fue similar a sus grados de libertad (75), ello sugiere que este modelo ajustó estadísticamente bien los datos. Se encontraron niveles de significancia diferentes para los factores de riesgo, según el modelo estadístico utilizado. Los modelos MLG y RLEH mostraron efectos significativos (P < 0.02) sólo de presencia de abortos; el modelo RLEA mostró efecto significativo (P < 0.05) para los tres factores de riesgo, y el RLM, efectos de presencia de abortos y duración de la lactancia, pero no de higiene. Los valores de la razón de momios (OR) para los modelos RLEA y RLM fueron diferentes; los intervalos de confianza más estrechos correspondieron al modelo RLEA, y los más amplios, al RLEH.