A implementação do Sistema de Rede de Bibliotecas Escolares no Paraná à luz da Lei nº 12.244/2010

Orientadora : Maria Tereza Carneiro SoaresCoorientadora : Eliane Maria StroparoResumo : O presente trabalho tem como objetivo caracterizar, dentre os programas que expressam as políticas educacionais da Secretaria de Estado da Educação (SEED), o projeto Rede de Bibliotecas Escolares e sua implementação por meio da Lei nº 12.244/2010 que dispõe sobre a universalização das bibliotecas nas instituições de ensino no país. O estudo apresenta um breve relato sobre a constituição histórica das bibliotecas no Brasil e no Estado do Paraná, destacando as mudanças ocorridas nas bibliotecas escolares paranaenses a partir da implantação da lei. Ressaltando as políticas de financiamento Federal e Estadual, o projeto de implementação do Sistema de Rede de Bibliotecas Escolares e também, os profissionais que atualmente estão atuando nas bibliotecas escolares do sistema estadual de ensino do Paraná. Constatou-se ao final que, em muitas instituições de ensino estadual estão inoperantes ou ainda, inexistentes.Abstract : This study aims to characterize, among the programs that express the educational policies of the State Department of Education (SEED), the School Library Network project and its implementation by means of Law No. 12.244 / 2010 which provides for the universalization of libraries in educational institutions in the country. The study presents a brief account of the historical development of libraries in Brazil and in Paraná, highlighting the changes in Paraná school libraries from the implementation of the law. Underscoring the federal, and state funding policies the implementation project of the School Library Network System and also the professionals who are currently working in school libraries of Paraná teaching the state system. It was noted at the end that in many state educational institutions are dead or missing.Monografia (especialização) - Universidade Federal do Paraná, Setor de Educação, Curso de Especialização em Políticas EducacionaisInclui referência

Statistical Methods to Adjust for Measurement Error in Risk Prediction Models and Observational Studies

The first part of this dissertation focuses on methods to adjust for measurement error in risk prediction models. In chapter one, we propose a nonparametric adjustment for measurement error in time to event data. Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event covariate often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for measurement error in this setting. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of Mendelian risk prediction models and multivariate survival prediction models, and illustrate our method using a data application for Mendelian risk prediction models. Results show our adjusted method corrects for measurement error mainly in two aspects; by improving calibration and total accuracy. In some scenarios discrimination is also improved. In chapter two, we use the methods proposed in chapter one to extend Mendelian risk prediction models to handle misreported family history

Using Validation Data to Adjust the Inverse Probability Weighting Estimator for Misclassified Treatment

The inverse probability weighting (IPW) estimator is widely used to estimate the treatment effect in observational studies in which patient characteristics might not be balanced by treatment group. The estimator assumes that treatment assignment, is error-free, but in reality treatment assignment can be measured with error. This arises in the context of comparative effectiveness research, using administrative data sources in which accurate procedural or billing codes are not always available. We show the bias introduced to the estimator when using error-prone treatment assignment, and propose an adjusted estimator using a validation study to eliminate this bias. In simulations, we explore the impact of the misclassified treatment assignment on the estimator, and compare the performance of our adjusted estimator to an estimate based only on the validation study. We illustrate our method on a comparative effectiveness study assessing surgical treatments among Medicare beneficiaries, diagnosed with brain tumors. We use linked SEER-Medicare data as our validation data, and apply our method to Medicare Part A hospital claims data where treatment is based on ICD9 billing codes, which do not accurately reflect surgical treatment

A Cautionary Note on the Effect of Treatment Misclassification on the Average Treatment Effect

Comparative effectiveness research often relies on large administrative data, such as claims data. Methods to estimate treatment effects assume that treatment assignment is error-free, but in reality the inaccuracy of procedural or billing codes frequently misclassifies patients into treatment groups. Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. We evaluate the impact of treatment misclassification on 1) propensity score estimation; 2) treatment effect estimation conditional on propensity score estimation and implementation. We focus on three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). We show in simulations that there is a clear relationship between the misclassification rate and the bias introduced to both the propensity score and treatment effect estimates, and that even when both specificity and sensitivity are relatively high (around 90%) the average treatment effect is biased. We briefly illustrate the impact of misclassification using SEER-Medicare data on brain cancer

Nonparametric Adjustment for Measurement Error in Time to Event Data

Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012

Evolving an Accelerated School Model through Student Perceptions and Student Outcome Data

A mixed methods convergent evaluation informed the redesign of an innovative public school that uses an accelerated model to serve grades 7-9 students who have been retained in grade level and are at risk for dropping out of school. After over 25 years in operation, a shift of practices/policies away from grade retention and toward social promotion required the school to adapt their model to best served students with high risk factors for dropping out of school who have been socially promoted, rather than retained in grade level. This study provided the qualitative (perspectives of former students (N = 8) and quantitative (demographic and outcome variables for students (N = 164) who completed the program between 2007-2009) data to ground the evolution of the school model. Five critical aspects of the school model emerged from the former students: teacher as warm demander, diverse and creative practices, being one community, student self-efficacy, and upholding diversity and equity. Quantitative analyses revealed the key finding that the number of times a student accelerated to the next grade in their courses was a positive predictor of all the high school outcomes studied. Data mixed during interpretation generated recommendations to continue strong practices and strengthen the following: have students set, monitor, and share progress; increase clear and high expectations; engage the adult community in setting, tracking and assessing goals; and increase culturally competent practices. These findings can also be used by schools serving students who may be at risk for dropping out of school

Assessing the causal effects of a stochastic intervention in time series data: Are heat alerts effective in preventing deaths and hospitalizations?

We introduce a new causal inference framework for time series data aimed at assessing the effectiveness of heat alerts in reducing mortality and hospitalization risks. We are interested in addressing the following question: how many deaths and hospitalizations could be averted if we were to increase the frequency of issuing heat alerts in a given location? In the context of time series data, the overlap assumption - each unit must have a positive probability of receiving the treatment - is often violated. This is because, in a given location, issuing a heat alert is a rare event on an average temperature day as heat alerts are almost always issued on extremely hot days. To overcome this challenge, first we introduce a new class of causal estimands under a stochastic intervention (i.e., increasing the odds of issuing a heat alert) for a single time series corresponding to a given location. We develop the theory to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on time-varying propensity scores, and derive point-wise confidence bands for these estimators. Third, we extend this framework to multiple time series corresponding to multiple locations. Via simulations, we show that the proposed estimator has good performance with respect to bias and root mean squared error. We apply our proposed method to estimate the causal effects of increasing the odds of issuing heat alerts in reducing deaths and hospitalizations among Medicare enrollees in 2817 U.S. counties. We found weak evidence of a causal link between increasing the odds of issuing heat alerts during the warm seasons of 2006-2016 and a reduction in deaths and cause-specific hospitalizations across the 2817 counties.Comment: 31 pages, 5 figures, 2 table