Statistical analysis plan for a cluster randomised trial in Madhya Pradesh, India: community health promotion and medical provision and impact on neonates (CHAMPION2).

BACKGROUND: Neonatal mortality in India has fallen steadily and was estimated to be 24 per 1000 live births in the year 2017. However, neonatal mortality remains high in rural parts of the country. The Community Health Promotion and Medical Provision and Impact On Neonates (CHAMPION2) trial investigates the effect of a complex health intervention on neonatal mortality in the Satna District of Madhya Pradesh. METHODS/DESIGN: The CHAMPION2 trial forms one part of a cluster-randomised controlled trial with villages (clusters) randomised to receive either a health (CHAMPION2) or education (STRIPES2) intervention. Villages receiving the health intervention are controls for the education intervention and vice versa. The primary outcome is neonatal mortality. The effect of the active intervention on the primary outcome (compared to usual care) will be expressed as a risk ratio, estimated using a generalised estimating equation approach with robust standard errors that take account of clustering at village level. Secondary outcomes include maternal mortality, stillbirths, perinatal deaths, causes of death, health care and knowledge, hospital admissions of enrolled women during pregnancy or in the immediate post-natal care period or of their babies (during the neonatal period), maternal blood transfusions, and the cost effectiveness of the intervention. A total of 196 villages have been randomised and over 34,000 women have been recruited in CHAMPION2. DISCUSSION: This update to the published trial protocol gives a detailed plan for the statistical analysis of the CHAMPION2 trial. TRIAL REGISTRATION: Registry of India: CTRI/2019/05/019296. Registered on 23 May 2019. https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MzExOTg=&Enc=&userName=champion2.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development.

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.

BACKGROUND: Missing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research. OBJECTIVES: We aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes. METHODS: A systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data. RESULTS: From 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data. CONCLUSIONS: Several health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.

Markov-modulated marked Poisson processes for modeling disease dynamics based on medical claims data.

We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modeling patients' disease dynamics over time based on medical claims data. In claims data, observations do not only occur at random points in time but are also informative, that is, driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions with the health care system. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of health care interactions is governed by a continuous-time Markov chain. Its states serve as proxies for the patients' latent disease levels and further determine the distribution of additional data collected at each observation time, the so-called marks. Overall, MMMPPs jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which both depend on the underlying states. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease by modeling their drug use and the interval lengths between consecutive physician consultations. The results indicate that MMMPPs are able to detect distinct patterns of health care utilization related to disease processes and reveal interindividual differences in the state-switching dynamics.

Avaliação qualitativa das preparações servidas em um restaurante popular e os impactos do cenário pandêmico da covid-19 / Qualitative assessment of preparations served in a popular restaurant and the impacts of the COVID-19 pandemic scenario / Evaluación cualitativa de las preparaciones servidas en un restaurante popular y los impactos del escenario de pandemia COVID-19

Introdução:A chegadadapandemia da COVID-19 afetou diretamente o planejamento alimentar de instituições municipais, estaduais e de demais âmbitos, dentre eles as refeiçõesofertadas peloprograma restaurante popular, importante na garantia da segurança alimentarda população mais vulnerável.Objetivo:Avaliar de forma qualitativa as preparações do cardápio do almoço do Restaurante Popular do município de Santa Cruz-Rio Grande do Nortee os impactos do cenário pandêmico da COVID-19.Metodologia:Para tanto, avaliou-se os cardápios referentes aos meses de março, abril, maio, agosto e setembro de 2021, pelo método avaliação qualitativa das preparações do cardápio. Além disso, por meio da adaptação dométodosupracitado, designou-se as preparações de acordo com as recomendações do tipo de processamento segundo o Guia Alimentar para população Brasileira. Resultados:Identificou-se uma baixa oferta de frutas e folhosos, alta de carnes gordurosas e doce, regular de preparações ricas em enxofre e as cores iguais, todavia, quanto à fritura e ao conjunto de fritura + doce apresentou-se em apenas umdiada semana (n=109). Além disso, nos dois últimos meses de análisehouve aumento na oferta de folhosos, diminuição da monotonia, alimentos ricos em enxofre e doces, e ainda, as preparações com alimentos in natura ou minimamente processados + processados fizeram-se presentestodos os dias. Conclusões:Com a utilizaçãodométodo estudadofoipossível identificar ajustesno cardápiopara o período analisado, a fim de ofertar qualidade para os comensais.Ressalta-se que foi observadoque com a adaptação e redução dos casos da COVID-19, houve aumento na oferta de in natura e minimamente processados. Espera-se que melhorias e ajustes sejam realizadosnos cardápios, visto a necessidadede ser mais enfático em seu princípiode garantir a segurança alimentar (AU).

Introduction:The arrival of the COVID-19 pandemic affected directly the food planning of municipal, state and other institutions, including the meals offered by the popular restaurant program, which is important in guaranteeing the food security of the vulnerablepopulation. Objective: To qualitatively evaluate the preparations of the lunch menu at Restaurante Popular in the municipality of Santa Cruz-Rio Grande do Norteand the impacts of the COVID-19 pandemic scenario.Methodology:To this, the menus for the months of March, April, May, August and September 2021 were evaluated by the methodqualitative evaluation of menu preparations. Furthermore, through the adaptation of the methodaformentioned, preparations were designated according to the recommendations for type of processing following the Food Guide for the Brazilian population.Results:A low supply of fruits and leafy vegetables was identified, high supply of fatty and sweet meats, regular supply of preparations rich in sulfur and the same colors. However, regarding the frying and the frying + sweet combination, it was presented in just one daythe week(n=109).Moreover, in the last two months of analysis there was an increase in the supply of hardwoods, decrease in monotony,foods rich in sulfur and sweets, and even, preparations in natura or minimally processed + processed foods were presentsevery day. Conclusions:With the use of the method studied it waspossible to adjust the menufor the analyzed periodto offer qualityto diners. It should the increase in the supply of in natura and minimally processed foods stands out with the adaptation and progression of the reduction of COVID-19cases.It is expected that improvements and adjustments will be madethe menus,given the need to be more emphatic in its principleof ensuring food safety (AU).

ntroducción: La llegada de la pandemia del COVID-19 afectó directamente la planificación alimentaria de las instituciones municipales, estatales y otras, incluyendo las comidas que ofrece el programa de restaurantes populares, importante para garantizar la seguridad alimentaria de la población más vulnerable. Objetivo:Evaluar cualitativamente las preparaciones del menú del almuerzo en el Restaurante Popular del municipio de Santa Cruz-Rio Grande do Nortey los impactos del escenario de pandemia del COVID-19.Metodología:Para ello, se evaluaron los menús de los meses de marzo, abril, mayo, agosto y septiembre de 2021 por el método evaluación cualitativa de la elaboración de menús. Además, al adaptar el métodocitado, las preparaciones fueron designadas de acuerdo con las recomendaciones del tipo de procesamiento según la Guía de Alimentos para la población brasileña.Resultados: Se identificó una baja oferta de frutas y hortalizas de hoja, alta oferta de carnes grasas y dulces, regular oferta de preparaciones ricasen azufre y los mismos colores,aún,en cuanto a la fritura y la combinación fritura + dulce, se presentó en un solo díade lasemana(n=109). Además, en los últimos dos meses de análisishubo un aumento en la oferta de maderas duras, una disminución de la monotonía, alimentos ricos en azufre y dulces, y aun, las preparaciones con alimentos in natura o mínimamente procesados + procesados presentes todos los días. Conclusiones: Con el uso del método estudió fueposible identificar ajustes en el menúpara el período analizado, con el fin de ofrecer calidad a los comensales. Cabe señalar que se observó que con la adaptación y reducción delcasosCOVID-19, hubo un aumento en la oferta de productos in natura y mínimamente procesados. Se espera que se realicenmejoras y ajustesa los menús, dada las necesidades más enfáticasen su principiode garantizar la seguridad alimentaria (AU).

Carga de enfermedad en el Perú. Estimación de los años de vida saludables perdidos 2019 / Burden of disease in Peru. Estimated healthy years of life lost 2019

En ese sentido, el estudio de carga de enfermedad en el Perú del 2019 nos presenta las principales enfermedades y lesiones que han aportado mayor número de años de vida saludables perdidos en el país, reconociendo no solo aquellas que producen muerte prematura, sino también discapacidad. De esta manera, podemos disponer de información valiosa para la toma de decisiones en salud pública y dirigir las acciones hacia un mayor beneficio para la población de nuestro país. Los años de vida ajustados por discapacidad -AVISA- estiman la carga de la enfermedad, de acuerdo a las distintas causas o problemas de salud consideradas. Un AVISA corresponde a un año de vida sana perdido y la medición de la carga de enfermedad por este indicador significaría la brecha existente entre la situación actual de salud de una población y la ideal en la que cada miembro de esa población podría alcanzar la vejez libre de enfermedad y discapacidad.

A hidden Markov model for continuous longitudinal data with missing responses and dropout.

We propose a hidden Markov model for multivariate continuous longitudinal responses with covariates that accounts for three different types of missing pattern: (I) partially missing outcomes at a given time occasion, (II) completely missing outcomes at a given time occasion (intermittent pattern), and (III) dropout before the end of the period of observation (monotone pattern). The missing-at-random (MAR) assumption is formulated to deal with the first two types of missingness, while to account for the informative dropout, we rely on an extra absorbing state. Estimation of the model parameters is based on the maximum likelihood method that is implemented by an expectation-maximization (EM) algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.

Combining estimators in interlaboratory studies and meta-analyses.

Many statistical methods (estimators) are available for estimating the consensus value (or average effect) and heterogeneity variance in interlaboratory studies or meta-analyses. These estimators are all valid because they are developed from or supported by certain statistical principles. However, no estimator can be perfect and must have error or uncertainty (known as estimator uncertainty). For a given dataset, the consensus value and heterogeneity variance given by different estimators can often differ significantly. Consequently, the choice of different estimators can affect the conclusion of an interlaboratory study or meta-analysis. However, there is no universally accepted metric for determining which estimator is optimal among a set of candidate estimators. Instead of selecting and using a single estimator, this paper proposes an estimator-averaging approach to combine a set of individual estimators. The final averaged estimator is a linear combination of individual estimators, which accounts for three sources of uncertainties including the estimator uncertainty. Monte Carlo simulations were performed to examine the long-run performance of four individual estimators and the proposed averaged estimators. A case study: the determination of the Newtonian constant of gravitation is presented, where 10 individual estimators (eight frequentist weighted average methods and two Bayesian methods) are combined using the proposed estimator-averaging approach.

Telehealth as a Tool to Improve Access and Reduce No-Show Rates in a Large Safety-Net Population in the USA.

Low-income populations are at higher risk of missing appointments, resulting in fragmented care and worsening disparities. Compared to face-to-face encounters, telehealth visits are more convenient and could improve access for low-income populations. All outpatient encounters at the Parkland Health between March 2020 and June 2022 were included. No-show rates were compared across encounter types (face-to-face vs telehealth). Generalized estimating equations were used to evaluate the association of encounter type and no-show encounters, clustering by individual patient and adjusting for demographics, comorbidities, and social vulnerability. Interaction analyses were performed. There were 355,976 unique patients with 2,639,284 scheduled outpatient encounters included in this dataset. 59.9% of patients were of Hispanic ethnicity, while 27.0% were of Black race. In a fully adjusted model, telehealth visits were associated with a 29% reduction in odds of no-show (aOR 0.71, 95% CI: 0.70-0.72). Telehealth visits were associated with significantly greater reductions in probability of no-show among patients of Black race and among those who resided in the most socially vulnerable areas. Telehealth encounters were more effective in reducing no-shows in primary care and internal medicine subspecialties than surgical specialties or other non-surgical specialties. These data suggest that telehealth may serve as a tool to improve access to care in socially complex patient populations.

Estimand in benefit-risk assessment.

ICH E9(R1) introduces the estimand framework to strengthen dialogues between sponsors and regulators during drug development. A well-structured benefit-risk assessment (BRA) framework also intends to facilitate communication among stakeholders. However, the estimand in ICH E9(R1) is written mainly from the perspective of a single measure of treatment effect in clinical trials. There is lack of systematic discussion on estimand in the context of BRA. This paper initiates the BRA discussion under the estimand framework. By identifying two types of BRA approaches, we summarize and discuss completed clinical trials, using the estimand language for BRA. Benefits and challenges of using estimand for BRA are also discussed.

Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model.

We examined the setting in which a variable that is subject to missingness is used both as an inclusion/exclusion criterion for creating the analytic sample and subsequently as the primary exposure in the analysis model that is of scientific interest. An example is cancer stage, where patients with stage IV cancer are often excluded from the analytic sample, and cancer stage (I to III) is an exposure variable in the analysis model. We considered two analytic strategies. The first strategy, referred to as "exclude-then-impute," excludes subjects for whom the observed value of the target variable is equal to the specified value and then uses multiple imputation to complete the data in the resultant sample. The second strategy, referred to as "impute-then-exclude," first uses multiple imputation to complete the data and then excludes subjects based on the observed or filled-in values in the completed samples. Monte Carlo simulations were used to compare five methods (one based on "exclude-then-impute" and four based on "impute-then-exclude") along with the use of a complete case analysis. We considered both missing completely at random and missing at random missing data mechanisms. We found that an impute-then-exclude strategy using substantive model compatible fully conditional specification tended to have superior performance across 72 different scenarios. We illustrated the application of these methods using empirical data on patients hospitalized with heart failure when heart failure subtype was used for cohort creation (excluding subjects with heart failure with preserved ejection fraction) and was also an exposure in the analysis model.

A Realistic Evaluation of Methods for Handling Missing Data When There is a Mixture of MCAR, MAR, and MNAR Mechanisms in the Same Dataset.

The impact of missing data on statistical inference varies depending on several factors such as the proportion of missingness, missing-data mechanism, and method employed to handle missing values. While these topics have been extensively studied, most recommendations have been made assuming that all missing values are from the same missing-data mechanism. In reality, it is very likely that a mixture of missing-data mechanisms is responsible for missing values in a dataset and even within the same pattern of missingness. Although a mixture of missing-data mechanisms and causes within a dataset is a likely scenario, the performance of popular missing-data methods under these circumstances is unknown. This study provides a realistic evaluation of methods for handling missing data in this setting using Monte Carlo simulation in the context of regression. This study also seeks to identify acceptable proportions of missing values that violate the missing-data mechanism assumed by the method used to handle missing values. Results indicate that multiple imputation (MI) performs better than other principled or ad-hoc methods. Different missing-data methods are also compared via the analysis of a real dataset in which mixtures of missingness mechanisms are created. Recommendations are provided for the use of different methods in practice.

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models.

The measurement of latent traits and investigation of relations between these and a potentially large set of explaining variables is typical in psychology, economics, and the social sciences. Corresponding analysis often relies on surveyed data from large-scale studies involving hierarchical structures and missing values in the set of considered covariates. This paper proposes a Bayesian estimation approach based on the device of data augmentation that addresses the handling of missing values in multilevel latent regression models. Population heterogeneity is modeled via multiple groups enriched with random intercepts. Bayesian estimation is implemented in terms of a Markov chain Monte Carlo sampling approach. To handle missing values, the sampling scheme is augmented to incorporate sampling from the full conditional distributions of missing values. We suggest to model the full conditional distributions of missing values in terms of non-parametric classification and regression trees. This offers the possibility to consider information from latent quantities functioning as sufficient statistics. A simulation study reveals that this Bayesian approach provides valid inference and outperforms complete cases analysis and multiple imputation in terms of statistical efficiency and computation time involved. An empirical illustration using data on mathematical competencies demonstrates the usefulness of the suggested approach.

Assessing progress towards achieving the transport dimension of the SDGs in China.

Transport is an important service industry in the national economy. Sustainable transport is central to sustainable development. Currently, investigating the sustainable development process and trade-offs in China's transport sector is urgent. In this study, 11 transport indicators were selected and constructed for the sustainable development goals (SDGs) under the UN indicator framework. The scores of each indicator were calculated, and spatiotemporal patterns and interactions were analyzed. The results revealed that China's transport infrastructure performed well in large transportation volumes and guaranteed traffic safety and strict land use control, with scores above 75. However, China's transport sector currently faces a challenge in using clean energy, and a more balanced development of bus ownership among the provinces is expected. The interaction analysis revealed three pairs of indicators with synergy (ρ > 0.5), but both the significant negative and positive relationships among the selected indicators accounted for approximately half, indicating the development of sustainable transport in China would move in zigzags. Road accessibility was an indicator interacting with most sustainable transport indicators. We suggest that more SDG indicators with indirect impacts should be included in future sustainable transport research.

Infinite hidden Markov models for multiple multivariate time series with missing data.

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach.

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

A comparison of statistical methods for animal oncology studies.

In pre-clinical oncology studies, tumor-bearing animals are treated and observed over a period of time in order to measure and compare the efficacy of one or more cancer-intervention therapies along with a placebo/standard of care group. A data analysis is typically carried out by modeling and comparing tumor volumes, functions of tumor volumes, or survival. Data analysis on tumor volumes is complicated because animals under observation may be euthanized prior to the end of the study for one or more reasons, such as when an animal's tumor volume exceeds an upper threshold. In such a case, the tumor volume is missing not-at-random for the time remaining in the study. To work around the non-random missingness issue, several statistical methods have been proposed in the literature, including the rate of change in log tumor volume and partial area under the curve. In this work, an examination and comparison of the test size and statistical power of these and other popular methods for the analysis of tumor volume data is performed through realistic Monte Carlo computer simulations. The performance, advantages, and drawbacks of popular statistical methods for animal oncology studies are reported. The recommended methods are applied to a real data set.

O inventário de envolvimento paterno revisado: inclusão de pais com filhos pré-escolares / The inventory of father involvement revised: inclusion of fathers with preschool children / El inventario de envolvimiento paterno revisado: inclusión de padres de preescolares

Apesar da importância do envolvimento paterno, sua avaliação persiste desafiadora. No Brasil, o Inventário de Envolvimento Paterno (IFI-BR) vem se mostrando adequado para uso com pais de crianças de 5 a 10 anos. Entretanto, do ponto de vista do desenvolvimento infantil e de intervenções preventivas, seria importante avaliar o envolvimento paterno quando as crianças são mais novas. Assim, este trabalho teve como objetivos: identificar limitações do IFI-BR, quando usado com pais de crianças entre 2 e 10 anos, e avaliar itens para o IFI-BR-revisado. No Estudo 1, 434 pais com filhos no Ensino Infantil ou Fundamental 1 responderam a um questionário sociodemográfico e ao IFI-BR. Com base em análises de dados omissos, estrutura interna e precisão, modificações foram sugeridas, visando à manutenção da estrutura interna original do instrumento. No Estudo 2, 572 pais com filhos na mesma faixa etária responderam a um questionário sociodemográfico e à versão modificada do IFI-BR. Foram comparadas as frequências de dados omissos e estimativas de precisão para os itens originais e modificados, selecionando aqueles que melhor representavam essa amostra de pais para compor a versão revisada do IFI-BR. Esses resultados indicaram evidências adequadas de validade, com base no conteúdo da versão revisada do IFI-BR, quando utilizada para avaliar a qualidade do envolvimento paterno de pais brasileiros com filhos do Ensino Infantil ao Fundamental 1. Após verificadas evidências de validade adicionais, essa versão revisada do IFI-BR poderá ser utilizada, por exemplo, em estudos longitudinais e na avaliação de intervenções precoces com pais.(AU)

Despite the importance assigned to father involvement, evaluating this construct remains a challenge. In Brazil, the Inventário de Envolvimento Paterno (IFI-BR) has showed satisfactory evidence of validity for fathers of children between 5 and 10 years old. From the perspective of child development and preventive interventions, however, evaluating father involvement with younger children is essential. Hence, this study sought to: identify limitations of the IFI-BR for fathers of children between 2 and 10 years old, and evaluate items for a revised IFI-BR. In Study 1, 434 fathers of children in early childhood and primary school settings answered a sociodemographic questionnaire and the IFI-BR. Based on analyses of missing data, internal structure, and reliability, modifications were suggested to maintain the original internal structure. In Study 2, 572 fathers of children in the same age range answered a sociodemographic questionnaire and the modified IFI-BR. After comparison between values for missing data and reliability of the original and modified items, the items that best represented the broader sample of fathers were selected to compose the revised IFI-BR. Results indicated adequate evidence of content validity for the revised IFI-BR when used to assess the involvement of Brazilian fathers with children in early childhood education and primary school settings. After additional evidence has been verified, this revised IFI-BR can be used, for example, in longitudinal studies and to evaluate early interventions with fathers.(AU)

La participación paterna es importante, pero su evaluación sigue siendo desafiadora. En Brasil, el Inventário de Envolvimento Paterno (IFI-BR) demuestra ser adecuado para aplicar a padres de niños de 5 a 10 años de edad. No obstante, desde la perspectiva del desarrollo infantil y de las intervenciones preventivas, sería importante evaluar la participación de los padres de niños más jóvenes. Este estudio tuvo como objetivos: identificar limitaciones del IFI-BR cuando se aplica a padres de niños entre los 2 y 10 años y evaluar ítems para el IFI-BR-revisado. En Estudio 1, 434 padres con hijos en el jardín de infantes o escuela primaria respondieron un cuestionario sociodemográfico y el IFI-BR. Con base en el análisis de datos faltantes, estructura interna y exactitud, se sugirieron modificaciones para mantener la estructura interna original del instrumento. En Estudio 2, 572 padres respondieron un cuestionario sociodemográfico y la versión modificada del IFI-BR. Se compararon las frecuencias de datos faltantes y estimaciones de exactitud para los ítems originales y modificados, seleccionando aquellos que representaban mejor a esta muestra de padres para la versión revisada del IFI-BR. Estos resultados indicaron evidencia adecuada de validez, basada en el contenido de la versión revisada del IFI-BR, cuando se utilizó para evaluar la calidad de la participación de padres brasileños con niños en el jardín de infantes y en la escuela primaria. Después de verificada la evidencia adicional de validez, la versión revisada del IFI-BR se puede utilizar, por ejemplo, en estudios longitudinales y en la evaluación de intervenciones precoz con los padres.(AU)

Antibiotic Prescription in Endodontics: A Transversal Observational Study Comparing Dental Students and General Dental Practitioners in Rio de Janeiro, Brazil

ABSTRACT Objective: To evaluate and compare antibiotic prescriptions for endodontic infections of last-year undergraduate Dentistry students and dental surgeons from Brazil. Material and Methods: A questionnaire comprised of 15 questions was applied, including a series of hypothetical clinical cases through multiple choice questions. The survey respondents had the option to respond in favor or against the prescription of antimicrobials in each case. Afterwards, the commented answer keys of all topics contained in the survey form were released for educational purpose. Results: A total of 42 undergraduates and 115 dental surgeons answered the survey. Regarding the questions about antimicrobial prescriptions, 91.3% of the professional dentists and 69.8% of the dentistry students indicated that they prescribe antibiotics for a limited number of patients. However, when they responded about drug recommendations, most professionals (76.5%) and students (76.7%) chose to recommend antimicrobials without any clinical recommendation. Conclusion: In the groups herein evaluated, many antibiotic prescriptions could have been avoided. The rational use of these drugs is still a conduct that needs further disclosure and commitment among prescribers.