Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.

Brazil was one of the countries most affected during the first year of the COVID-19 pandemic, in a pre-vaccine era, and mathematical and statistical models were used in decision-making and public policies to mitigate and suppress SARS-CoV-2 dispersion. In this article, we intend to overview the modeling for COVID-19 in Brazil, focusing on the first 18 months of the pandemic. We conducted a scoping review and searched for studies on infectious disease modeling methods in peer-reviewed journals and gray literature, published between January 01, 2020, and June 2, 2021, reporting real-world or scenario-based COVID-19 modeling for Brazil. We included 81 studies, most corresponding to published articles produced in Brazilian institutions. The models were dynamic and deterministic in the majority. The predominant model type was compartmental, but other models were also found. The main modeling objectives were to analyze epidemiological scenarios (testing interventions' effectiveness) and to project short and long-term predictions, while few articles performed economic impact analysis. Estimations of the R0 and transmission rates or projections regarding the course of the epidemic figured as major, especially at the beginning of the crisis. However, several other outputs were forecasted, such as the isolation/quarantine effect on transmission, hospital facilities required, secondary cases caused by infected children, and the economic effects of the pandemic. This study reveals numerous articles with shared objectives and similar methods and data sources. We observed a deficiency in addressing social inequities in the Brazilian context within the utilized models, which may also be expected in several low- and middle-income countries with significant social disparities. We conclude that the models were of great relevance in the pandemic scenario of COVID-19. Nevertheless, efforts could be better planned and executed with improved institutional organization, dialogue among research groups, increased interaction between modelers and epidemiologists, and establishment of a sustainable cooperation network.

Mental health outcomes in frontline healthcare workers in Brazil during the COVID-19 epidemic: Results of an online survey in four regions using respondent-driven sampling (RDS).

BACKGROUND: The COVID-19 pandemic overwhelmed health facilities and presented healthcare workers (HCWs) with a new infectious disease threat. In addition to a sanitary crisis, Brazil still had to face major political, economic, and social challenges. This study aimed to investigate mental health outcomes in frontline HCWs in different regions of the country and at different epidemic times. We also sought to identify the main risk factors associated with these outcomes. METHODS: A cross-sectional online survey using respondent-driven sampling was conducted to recruit physicians (n = 584), nurses (n = 997), and nurse technicians (n = 524) in 4 regions of Brazil (North, Northeast, Southeast, and South) from August 2020 to July 2021. We used standardized instruments to screen for common mental disorders (CMD)(SRQ-20), alcohol misuse (AUDIT-C), depression (PHQ-9), anxiety (GAD-7), and post-traumatic stress disorder (PTSD)(PCL-5). Gile's successive sampling estimator was used to produce weighted estimates. We created a three-cluster data set for each HCW category and developed a hierarchical regression model with three levels: individual characteristics; workplace-related aspects; COVID-19 personal experience. The impact of the epidemic moment on the outcomes was also studied. RESULTS: The prevalence of probable CMD was 26.8-36.9%, alcohol misuse 8.7-13.6%, depression 16.4-21.2%, anxiety 10.8-14.2%, and PTSD 5.9-8.0%. We found a stronger association between mental health outcomes and the following factors: history of psychiatric disorders, female gender, and clinical comorbidities (level 1); work overload and family isolation (level 2); sick leave (level 3). Epidemic variables, such as the number of deaths and trend of deaths by COVID-19, had almost no impact on the outcomes. CONCLUSION: An alarmingly high prevalence of depression and anxiety was found in Brazilian frontline HCWs. Individual factors were the most strongly associated with mental health outcomes. These findings indicate the need to develop programs that provide emotional support, identify professionals at risk and refer them to specialized treatment when necessary.

Impact of the COVID-19 pandemic on the mental health of frontline healthcare workers in a highly affected region in Brazil.

BACKGROUND: The COVID-19 pandemic had a major impact on the mental health of healthcare workers (HCWs), especially in low and middle-income countries, which had to face additional political, social, and economic challenges. We thus aimed to assess the prevalence of mental health outcomes and the associated factors in HCWs treating COVID-19 patients in one of the most affected regions in Brazil. METHODS: We used the Respondent-Driven Sampling method to assess the risks of COVID-19 infection and symptoms of mental disorders in nurses, nursing technicians, and physicians who worked on the frontline in the metropolitan region of Recife. 865 healthcare workers completed a survey regarding sociodemographic data, work-related risks, and symptoms of mental disorders - SRQ-20 for common mental disorders (CMD); AUDIT-C for problematic alcohol use; GAD-7 for anxiety; PHQ-9 for depression; PCL-5 for post-traumatic stress disorder (PTSD). Gile's successive sampling estimator was used to produce the weighted estimates by professional category. A Poisson regression model with robust variance was used to analyze factors associated with a positive screening for CMD. We will present the results of a cross-sectional analysis of the mental health outcomes after the first peak of COVID-19 - from August 2020 to February 2021. RESULTS: The prevalence ratios for a positive screening for CMD were 34.9% (95% CI: 27.8-41.9) in nurses, 28.6% (95% CI: 21.3-36.0) in physicians, and 26.6% (95% CI: 16.8-36.5) in nursing technicians. Nurses presented a higher prevalence of depressive symptoms (23%). Positive screening for problematic alcohol use (10.5 to14.0%), anxiety (10.4 to 13.3%), and PTSD (3.3 to 4.4%) were similar between the professional categories. The main factors associated with CMD in nurses and physicians were related to an intrinsic susceptibility to mental illness, such as previous or family history of psychiatric disorder, and female sex. Among nurse technicians, work-related factors, such as accidents with biological material, presented the strongest association with CMD. CONCLUSION: The mental health of HCWs fighting COVID-19 in Recife was severely affected. It is crucial that healthcare services provide adequate working conditions and psychological support, investing in programs to promote and protect HCWs mental health.

Global patterns of prescription pain medication usage in disorders of gut-brain interactions.

BACKGROUND: Forty percent of individuals globally meet Rome IV criteria for a disorder of gut-brain interaction (DGBI). The global burden of pain across these disorders has not been characterized. METHODS: Our study included 54,127 respondents from the 26 Internet survey countries. Prescription pain medication usage was selected as the proxy for pain. The associations between prescription pain medications and the environmental, sociodemographic, psychosocial, and DGBI diagnosis variables were investigated using the multivariate generalized robust Poisson regression model. KEY RESULTS: Respondents with DGBI used prescription pain medications at higher rates than those without a DGBI diagnosis with pooled prevalence rate of 14.8% (95% confidence interval [CI], 14.4-15.3%), varying by country from 6.8% to 25.7%. The pooled prevalence ratio of prescription pain medication usage in respondents with and without DGBI was 2.2 (95% CI: 2.1-2.4). Factors associated with higher prevalence of pain medication usage among respondents with a DGBI diagnosis included living in a small community, increased anxiety, depression or somatization, increased stress concern or embarrassment about bowel functioning and having more than one anatomic DGBI diagnosis. CONCLUSION: 14.8% of patients globally with at least one diagnosis of DGBI were on prescription pain medications with wide geographic variation, about twice as many as their counterparts without a diagnosis of DGBI. Environmental, sociodemographic, and individual factors may influence clinicians to consider personalized, multimodal approaches to address pain in patients with DGBI.

Modeling the impact of child vaccination (5-11 y) on overall COVID-19 related hospitalizations and mortality in a context of omicron variant predominance and different vaccination coverage paces in Brazil.

Background: Developing countries have experienced significant COVID-19 disease burden. With the emergence of new variants, particularly omicron, the disease burden in children has increased. When the first COVID-19 vaccine was approved for use in children aged 5-11 years of age, very few countries recommended vaccination due to limited risk-benefit evidence for vaccination of this population. In Brazil, ranking second in the global COVID-19 death toll, the childhood COVID-19 disease burden increased significantly in early 2022. This prompted a risk-benefit assessment of the introduction and scaling-up of COVID-19 vaccination of children. Methods: To estimate the potential impact of vaccinating children aged 5-11 years with mRNA-based COVID-19 vaccine in the context of omicron dominance, we developed a discrete-time SEIR-like model stratified in age groups, considering a three-month time horizon. We considered three scenarios: No vaccination, slow, and maximum vaccination paces. In each scenario, we estimated the potential reduction in total COVID-19 cases, hospitalizations, deaths, hospitalization costs, and potential years of life lost, considering the absence of vaccination as the base-case scenario. Findings: We estimated that vaccinating at a maximum pace could prevent, between mid-January and April 2022, about 26,000 COVID-19 hospitalizations, and 4200 deaths in all age groups; of which 5400 hospitalizations and 410 deaths in children aged 5-11 years. Continuing vaccination at a slow/current pace would prevent 1450 deaths and 9700 COVID-19 hospitalizations in all age groups in this same time period; of which 180 deaths and 2390 hospitalizations in children only. Interpretation: Maximum vaccination of children results in a significant reduction of COVID-19 hospitalizations and deaths and should be enforced in developing countries with significant disease incidence in children. Funding: This manuscript was funded by the Brazilian Council for Scientific and Technology Development (CNPq - Process # 402834/2020-8).

Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil.

We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings - school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.

Modelling optimal vaccination strategies against COVID-19 in a context of Gamma variant predominance in Brazil.

INTRODUCTION: Brazil experienced moments of collapse in its health system throughout 2021, driven by the emergence of variants of concern (VOC) combined with an inefficient initial vaccination strategy against Covid-19. OBJECTIVES: To support decision-makers in formulating COVID-19 immunization policy in the context of limited vaccine availability and evolving variants over time, we evaluate optimal strategies for Covid-19 vaccination in Brazil in 2021, when vaccination was rolled out during Gamma variant predominance. METHODS: Using a discrete-time epidemic model we estimate Covid-19 deaths averted, considering the currently Covid-19 vaccine products and doses available in Brazil; vaccine coverage by target population; and vaccine effectiveness estimates. We evaluated a 5-month time horizon, from early August to the end of December 2021. Optimal vaccination strategies compared the outcomes in terms of averted deaths when varying dose intervals from 8 to 12 weeks, and choosing the minimum coverage levels per age group required prior to expanding vaccination to younger target populations. We also estimated dose availability required over time to allow the implementation of optimal strategies. RESULTS: To maximize the number of averted deaths, vaccine coverage of at least 80 % should be reached in older age groups before starting vaccination into subsequent younger age groups. When evaluating varying dose intervals for AZD1222, reducing the dose interval from 12 to 8 weeks for the primary schedule would result in fewer COVID-19 deaths, but this can only be implemented if accompanied by an increase in vaccine supply of at least 50 % over the coming six-months in Brazil. CONCLUSION: Covid-19 immunization strategies should be tailored to local vaccine product availability and supply over time, circulating variants of concern, and vaccine coverage in target population groups. Modelling can provide valuable and timely evidence to support the implementation of vaccination strategies considering the local context, yet following international and regional technical evidence-based guidance.

PSS Health: como calcular tamanho de amostra para testes de comparação de médias de dois grupos / PSS Health: how to calculate sample sizes for mean comparison tests between two groups

Dando continuidade à série de artigos que pretendem orientar o usuário na utilização da ferramenta PSS Health para o planejamento de uma pesquisa, esta edição apresenta um passo a passo de como realizar o cálculo e de quais informações são necessárias para comparar médias: de dois grupos dependentes ou independentes, de dois grupos independentes com duas medidas repetidas (deltas), e com duas ou mais medidas repetidas. Todos os exemplos também são ilustrados e disponibilizados em vídeos no canal da Unidade de Bioestatística.

Following the series of articles aiming to guide users in using the PSS Health tool for research planning, this issue presents a step-by-step guide on how to calculate and what information is needed to compare means between 2 dependent or independent groups, 2 independent groups with 2 repeated measures (deltas), and 2 independent groups with 2 or more repeated measures. All examples are accompanied by figures and available in video on the Biostatistics Unit's channel.

PSS Health: como calcular tamanho de amostra para testar relações de variáveis com um desfecho binário / PSS Health: how to calculate the sample size to test variables relationships with a binary outcome

Dando continuidade à série de artigos que pretendem orientar o usuário na utilização da ferramenta PSS Health para o planejamento de uma pesquisa, esta edição apresenta um passo a passo de como realizar o cálculo para tamanho de amostra e de quais informações são necessárias para testar relações estatísticas entre variáveis e um desfecho binário: comparação de proporções entre grupos independentes (dois ou mais), comparação de duas proporções dependentes e regressão logística. Todos os exemplos também são ilustrados e disponibilizados em vídeos no canal da Unidade de Bioestatística.

Following the series of articles that aim to guide the user in using the PSS Health tool for planning research, this issue presents a step-by-step guide on how to perform the sample size calculation and what information is needed to test statistical relationships between variables and a binary outcome: comparison of proportions between independent groups (two or more), comparison of two dependent proportions, and logistic regression. All examples are also illustrated and available in videos on the Biostatistics Unit's channel.

Association between diet quality index and cardiometabolic risk factors in adolescents: Study of Cardiovascular Risks in Adolescents (ERICA).

OBJECTIVE: This study evaluated the association between diet quality, assessed by the Diet Quality Index for Adolescents adapted for Brazilians (DQIA-BR), and cardiometabolic markers in adolescents. METHODS: The DQIA-BR and cardiometabolic markers were assessed in 36 956 Brazilian adolescents (12-17 y old) enrolled in the Study of Cardiovascular Risks in Adolescents (ERICA), a national school-based cross-sectional multicenter study in Brazil. For analyses, the sample was stratified by sex and nutritional status. Multiple linear regressions were used to investigate the association between DQIA-BR and cardiometabolic markers (total cholesterol, HDL-c, LDL-c, triglycerides, fasting glucose and HOMA-IR). Adjusted models were constructed with two input levels of covariates. The first model was adjusted for sex, age, and socioeconomic status; in the second model, total energy intake, physical activity, and sedentary behavior were included. RESULTS: A higher DQIA-BR score was associated with a better cardiometabolic profile in girls with normal weight; however, no association was observed in those with overweight/obesity. In boys with overweight/obesity, a better quality of diet was associated with lower concentrations of total cholesterol (ß = -0.338, 95% confidence interval [CI]: -0.611 to -0.066) and LDL-c (ß = -0.227, 95% CI: -0.448 to -0.005), but only LDL-c remained significant in those with normal weight (ß = -0.115, 95% CI: -0.224 to 0.005). CONCLUSION: The effects of diet quality on cardiometabolic risk factors differ according to sex and the presence of overweight/obesity. Overall, DQIA-BR is a suitable tool to evaluate the association between diet quality and cardiometabolic markers in normal-weight adolescents, but not for adolescents, especially girls, with overweight/obesity.

Validation of the WHOQOL-Bref instrument in Brazilian sign language (Libras).

PURPOSE: The recognition of the Brazilian Sign Language (Libras) as the official language of the Brazilian deaf, in 2002, reaffirms the linguistic and cultural particularities of the deaf population. Therefore, there is a lack of a validated instrument for assessing the Quality of Life of deaf people using Libras. With authorization from the World Health Organization (WHO), a version of the WHOQOL-Bref in Libras was developed, called WHOQOL-Bref/Libras. However, its psychometric properties have not been examined as yet. Therefore, the purpose of this work is to perform the psychometric validation of the WHOQOL-Bref/Libras. METHODS: WHOQOL-Bref/Libras and a sociodemographic questionnaire were applied to 311 deaf people from the five Brazilian regions. To assess temporal stability, the questionnaire was readministered to 52 deaf people, over an interval of 2 weeks. RESULTS: WHOQOL-Bref/Libras demonstrated satisfactory psychometric values for reliability, discriminant and construct validity, temporal stability, and internal consistency. Cronbach's alpha coefficient showed satisfactory values for each of the WHOQOL-Bref domains: Physical health (0.641), Psychological (0.705), Environment (0.710), and Overall-Bref domains (0.873). The WHOQOL-Bref/Libras is the appropriate option to assess the quality of life of deaf people who communicate through Libras. CONCLUSION: WHOQOL-Bref/Libras had a satisfactory psychometric performance; therefore, it is a valid option that will provide autonomous participation for the deaf in quality of life investigations.

PSS Health: como calcular tamanho de amostra para estimar média, proporção e correlação / PSS Health: how to calculate a sample size to estimate means, proportions, and correlations

A ferramenta PSS Health (Power and Sample Size for Health Researchers) foi desenvolvida com o propósito de facilitar o cálculo do tamanho amostral e do poder de testes de hipóteses para diferentes objetivos de estudo, usando interface amigável e terminologia comum à área da saúde. Este é o primeiro de uma série de artigos que pretendem orientar o usuário na utilização da ferramenta PSS Health para o planejamento de uma pesquisa. Neste artigo, se ensina como utilizar o PSS Health quando o objetivo principal do estudo é estimar uma média, estimar uma proporção (prevalência ou incidência) ou estimar uma correlação. São disponibilizados vídeos demonstrando o uso da ferramenta em cada um dos contextos citados. (AU)

The PSS Health (Power and Sample Size for Health Researchers) tool was developed with the purpose of facilitating the calculation of sample size and power of hypothesis tests for different study objectives, based on a user-friendly interface and common health care terminology. This is the first in a series of articles intending to guide the user in how to use the PSS Health tool for planning a research project. This article teaches how to use PSS Health when the main objective of the study is to estimate means, proportions (prevalence or incidence), or correlations. Videos showing how to use the tool in each of the mentioned contexts are available. (AU)

Making Decisions and Motor Actions with Technical Biomechanical Classifications in Male Judo Weight Categories.

The aim of the study was to compare motor action variables of judo combat phases and technical biomechanical assessment of the seven weight categories. The sample was composed of 638 bouts (176 of extra lightweight <66 kg, 289 of half lightweight 66 > 73 kg, 180 of lightweight 73 > 81 kg, 244 of half middleweight 81 > 90 kg, 174 of middleweight 81 > 90 kg, 151 of half heavyweight 90 > 100 kg and 142 of heavyweight >100 kg) during motor actions of approach, gripping, attack, defense and groundwork combat phases, verifying the interactions between them by Markov chains and comparisons by Kruskall-Wallis and Dunn post hoc tests (p ≤ .05). The results demonstrated that lighter weight categories showed higher approach phases than the heavyweight category, and the lightweight group demonstrated higher defensive action frequencies than the half lightweight category and heavyweight athletes. Heavyweight and middleweight categories demonstrated higher groundwork combat frequencies than lighter weights, and the heavyweight category showed higher pause frequencies than the lightweight category. Regarding the decision-making model, the main transitions presented by the Markov chains showed higher prevalence of the following combinations: gripping occurring before the lever and length attacks, lever attack followed by the length attack, and length attack occurring earlier than the groundwork phase.

Diet quality index for Brazilian adolescents: the ERICA study.

PURPOSE: This study aimed to assess the dietary patterns of adolescents using a food-based diet quality index and their compliance with a healthy dietary guideline METHODS: Participants included 71,553 Brazilian adolescents (12-17 years old) from the Study of Cardiovascular Risks in Adolescents (ERICA), a cross-sectional school-based multicenter study.. Dietary intake was measured by one 24-h recall. A second recall was collected in a random subsample (~ 10%) to correct within-person variability. The Diet Quality Index for Adolescents adapted for Brazilians (DQIA-BR) was used to measure the overall quality of the dietary intake. The National Cancer Institute method was applied to estimate usual dietary intake. The DQIA-BR and the distribution of its components (quality, diversity, and equilibrium) were analyzed according to sex, geographical area, and type of school RESULTS: The mean (SD) DQIA-BR scores were 14.8% (6.1%) for females and 19.0% (6.3%) for males. All analyzed strata revealed low scores of DQIA-BR and its components. The median usual intake was five to sevenfold below the recommendations for vegetables and fruits and approximately twofold below the recommendations for dairy. The highest DQIA-BR mean scores were found in the northern region [17.0% (6.4%), females; 20.7% (6.3%), males]. Adolescents in both types of schools had relatively similar median intakes of snacks (~ 85 g) and sugared drinks (~ 600 ml) CONCLUSIONS: The overall diet quality of Brazilian adolescents is inadequate based on evaluated parameters in all regions and socioeconomic backgrounds.

Power and sample size for health researchers: uma ferramenta para cálculo de tamanho amostral e poder do teste voltado a pesquisadores da área da saúde / Power and sample size for health researchers: a tool for calculating sample size and statistical power designed for health researchers

Nas próximas edições da seção de Bioestatística da revistaClinical & Biomedical Researchuma nova série de artigos será publicada abordando um assunto de grande importância ao planejar uma pesquisa: o tamanho de amostra mínimo necessário para atingir os objetivos do estudo. Nessa série será apresentado como calcular o tamanho de uma amostra usando a ferramenta PSSHealth(Power and Sample Size for Health Researchers), construído em linguagem R por meio do pacote Shiny, para diferentes tipos e objetivos de estudo, direcionado à pesquisadores da área da saúde, utilizando termos e conceitos comumente utilizados nesta área. Além disso, o pacote fornece uma sugestão de texto com as informações consideradas no cálculo, e como devem ser descritas, com a finalidade de minimizar problemas de interpretação por parte dos pesquisadores. Neste primeiro artigo será apresentada essa ferramenta desenvolvida pela Unidade de Bioestatística do Grupo de Pesquisa e Pós-Graduação do Hospital de Clínicas de Porto Alegre, que permite calcular não apenas o tamanho de amostra, mas também o poder de um teste de hipóteses. (AU)

In the next issues ofClinical and Biomedical Research, the Biostatistics section will introduce a new series of articles addressing a very important subject for research planning: the minimum sample size to achieve the aim of a study. This series will show how to calculate sample size using PSS Health (Power and Sample Size for Health Researchers). This tool was built using R language through the Shiny package. It can be used for different types of study and is designed for health researchers by using terms and concepts commonly used in this area. PSS Health also suggests a text with information considered in the calculation to minimize problems of interpretation by the researchers. In this first article, a general overview of PSS Health will be presented. This tool, which was developed by the Research and Graduate Group Biostatistics Unit of the Hospital de Clínicas de Porto Alegre, is useful not only to calculate sample size but also to determine power of a hypothesis test. (AU)

Equívocos estatísticos: perguntas que você sempre quis fazer, mas nunca teve coragem / Statistical misconceptions: questions you've always wanted to ask, but never dared

Este artigo visa elucidar algumas dúvidas enfrentadas ou equívocos estatísticos cometidos por pesquisadores de diversas áreas. São explanados os temas: "tradução não é validação", "análise fatorial exploratória ou confirmatória", "nem todo estudo com dois grupos tem delineamento caso-controle", "teste ou ajuste de Bonferroni", "tamanho de amostra para teste de hipóteses e/ou para intervalo de confiança", e "testes ou dados paramétricos". A abordagem é realizada em uma linguagem acessível ao público leigo, utilizando exemplos e sugerindo referências para aprofundar o conhecimento.(AU)

This article aims to answer some questions and elucidate statistical misconceptions of researchers from different fields. The following topics are addressed: "translation is not validation", "exploratory or confirmatory factor analysis", "not every study with two groups is a case-control study", "Bonferroni test or adjustment", "sample size for testing hypotheses and/or for confidence intervals", and "parametric data or tests". The topics are explained in lay terms, using examples and suggesting references to advance knowledge.(AU)

Performance of the Gail and Tyrer-Cuzick breast cancer risk assessment models in women screened in a primary care setting with the FHS-7 questionnaire.

Breast cancer (BC) risk assessment models base their estimations on different aspects of a woman's personal and familial history. The Gail and Tyrer-Cuzick models are the most commonly used, and BC risks assigned by them vary considerably especially concerning familial history. In this study, our aim was to compare the Gail and Tyrer-Cuzick models after initial screening for familial history of cancer in primary care using the FHS-7 questionnaire. We compared 846 unrelated women with at least one positive answer to any of the seven FHS-7 questions (positive group) and 892 unrelated women that answered negatively (negative group). Concordance between BC risk estimates was compared by Bland-Altman graphics. Mean BC risk estimates were higher using the Tyrer-Cuzick Model in women from the positive group, while women from the negative group had higher BC risk estimates using the Gail model. With increasing estimates, discordance also increased, mainly in the FHS-7 positive group. Our results show that in women with a familial history of cancer, the Gail model underestimates risk and the Tyrer-Cuzick seems to be more appropriate. FHS-7 can be a useful tool for the identification of women with higher breast cancer risks in the primary care setting.

Subjects' Perception in Quantifying Printed and Digital Photos of Food Portions.

Although digital photos have the potential to improve the precision of reported portions in dietary assessment, there are few studies investigating its accuracy in comparison to printed photos. The aim of this study was to evaluate the perception of adults in quantifying food portion sizes using printed and digital photos, displayed on computer-screens and tablets. In total, 1165 evaluations were performed for 60 photos of portion sizes in Brazil. Each participant (n = 58) attended two sessions in the study center, with an interval of at least one week. In each session, twelve food portions were prepared and randomly evaluated by each participant in its printed and digital forms. The mean error (difference between the estimated and true portions) was not significantly different between the printed photos (2.1 g ± 47.2) and the digital ones (-6.4 g ± 53.7). The agreement on using the printed and digital photos was 91% and 90%, respectively. Furthermore, the use of the tablet was more prone to underestimation when compared to printed and computer-screen photos (p < 0.001). Overall, participants did not present major difficulties in perceiving the portion sizes using the printed and digital photos, but the use of tablets led to less accurate results, indicating that this needs to be further evaluated.

Bioestatística e epidemiologia: perguntas que você sempre quis fazer, mas nunca teve coragem / Biostatistics and epidemiology: questions you always wanted to ask but never had the courage to

Dando continuidade aos artigos da série "Perguntas que você sempre quis fazer, mas nunca teve coragem", que tem como objetivo responder e sugerir referências para o melhor entendimento das principais dúvidas estatísticas levantadas por pesquisadores da área da saúde, este terceiro artigo aborda o contexto epidemiológico. Neste contexto, foram diferenciadas as principais medidas como prevalência, incidência, Odds Ratio (OR), Risco Relativo (RR), Razão de Prevalência (RP) e Hazard Ratio (HR), foi esclarecido o uso de análises por intenção de tratar e análise por protocolo, e também discutidos alguns dos termos comumente utilizados e pouco compreendidos como tipo de amostra, nível de evidência, relevância clínica e estatística, entre outros. (AU)

Continuing the series of articles "Questions you always wanted to ask but never had the courage to," which aims to answer key statistical questions raised by health researchers and suggest references for a better understanding, this third article addresses the epidemiological context. In this context, important measures such as prevalence, incidence, odds ratio (OR), relative risk (RR), prevalence ratio (PR) and hazard ratio (HR) were differentiated; the use of intention-to-treat analysis and per-protocol analysis was clarified; and some terms commonly used and poorly understood were discussed, such as type of sample, level of evidence, clinical and statistical relevance, among others. (AU)

Modelagem estatística: perguntas que você sempre quis fazer, mas nunca teve coragem / Statistical modeling: questions you have always wanted to ask but never had the courage to

Dando continuidade aos artigos da série "Perguntas que você sempre quis fazer, mas nunca teve coragem", que tem como objetivo responder e sugerir referências para o melhor entendimento das principais dúvidas dos pesquisadores do Hospital de Clínicas de Porto Alegre sobre estatística, este quarto artigo se propõe a responder às principais dúvidas levantadas sobre modelagem estatística. São discutidas questões referentes à classificação de variáveis em independentes e dependentes, diferenças entre correlação, associação e regressão, os principais tipos de regressão e quais etapas são necessárias na construção de modelos. Os conceitos são abordados numa linguagem acessível ao público leigo e diversas referências são sugeridas para os curiosos em relação ao tema. (AU)

Continuing the series of articles "Questions you have always wanted to ask but never had the courage to," which aims to answer the most common questions of researchers at Hospital de Clínicas de Porto Alegre regarding statistics and to suggest references for a better understanding, this forth article addresses the topic of statistical modeling. Questions about classification of variables as dependent or independent, differences between correlation, association and regression, types of regression and steps for statistical modeling are discussed. The concepts are explained in plain language for lay readers and several references are suggested for those curious about the topic. (AU)