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Breastfeeding has been consistently associated with higher intelligence since childhood. However, this relation could be confounded due to maternal selection bias. We estimated the association between predominant breastfeeding and intelligence in school-age children considering potential selection bias and we simulated the intelligence gap reduction between low versus higher socioeconomic status children by increasing breastfeeding. We analysed predominant breastfeeding practices (breastmilk and water-based liquids) of children 0-3 years included in the Mexican Family Life Survey (MxFLS-1). Intelligence was estimated as the z-score of the abbreviated Raven score, measured at 6-12 years in the MxFLS-2 or MxFLS-3. We predicted breastfeeding duration among children with censored data with a Poisson model. We used the Heckman selection model to assess the association between breastfeeding and intelligence, correcting for selection bias and stratified by socioeconomic status. Results show after controlling for selection bias, a 1-month increase in predominant breastfeeding duration was associated with a 0.02 SD increase in the Raven z-score (p < 0.05). The children who were predominantly breastfed for 4-6 months versus <1 month had 0.16 SD higher Raven z-score (p < 0.05). No associations were found using multiple linear regression models. Among low socioeconomic status children, increasing predominantly breastfeeding duration to 6 months would increase their mean Raven z-score from -0.14 to -0.07 SD and reduce by 12.5% the intelligence gap with high socioeconomic status children. In conclusion, predominant breastfeeding duration was significantly associated with childhood intelligence after controlling for maternal selection bias. Increased breastfeeding duration may reduce poverty-driven intelligence inequities.

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Selective outcome reporting (SOR) is a type of bias that can compromise the validity of results and affect evidence-based practice. SOR can overestimate the effect of an intervention and lead to conclusions that a treatment is effective when it is not. This study aimed to investigate the prevalence of SOR in publications of RCTs on nonsurgical periodontal therapy (NSPT) and to verify associated factors. The protocols were searched and selected on the www.clinicaltrials.gov platform up to January 16, 2022. Corresponding publications were identified, and data extraction and discrepancy analysis were performed. The risk of bias was assessed according to the RoB2 tool. One hundred forty-five studies (174 publications) were included. The prevalence of SOR was 49.7% and was unclear in nearly one third of studies (27.6%). Only 31.7% of the primary outcomes were completely described in the publications. The overall risk of bias was high in 60% of the included studies. SOR was associated with statistical significance (p < .001), and multiple publications of the same study (p = .005). Our study demonstrated the high prevalence of SOR, highlighting the need to improve the quality of reporting of RCTs on NSPT studies.

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PURPOSE: We tested the hypothesis that low institutional trust would be associated with depressive symptom elevation, with attention to potential selection bias. METHODS: The District of Columbia Area Survey (DCAS) was conducted by mail in 2018. Invitations sent to 8800 households resulted in a sample of 1061 adults. Institutional trust questions referenced nonprofit organizations, businesses, and government. Depressive symptom elevation was assessed using PHQ-9. Logistic regression model estimates were compared with and without adjustment for sociodemographic characteristics and neighborhood satisfaction; among complete cases and following multiple imputation of missing covariate data; and with and without survey weights or correction for collider selection bias. RESULTS: Of 968 participants without missing depressive symptom or trust data, 24% reported low institutional trust. Low institutional trust was associated with elevated depressive symptoms (adjusted OR following multiple imputation: 2.0; 95% CI: 1.1, 3.4), although the association was attenuated with use of survey weights (adjusted OR incorporating multiple imputation and survey weights: 1.6; 95% CI: 0.7, 3.2). CONCLUSIONS: Under contrasting scenarios where low institutional trust and depressive symptoms jointly increase nonresponse, selection bias could lead to under- or overestimation of this association. Future research could explore posited selection bias scenarios that differ in direction of bias.

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ABSTRACT: The improper disposal of pesticide packaging wastes (PPW) has posed serious harm to the environment, including groundwater and soil pollution and even health concerns to the public. To address the environmental concerns and public health issues, there is a need to recycle the pesticides packaging waste (RPPW). Though small farmers in many developing countries have joined the cooperatives to reduce the production costs and increase the product premium, how these cooperatives improve farmers' RPPW behaviors is still sparse. The current study used data collected from 725 apple farmers in Shaanxi and Gansu provinces of China to explore the phenomenon empirically. Recycling decisions and degree are used to portray the farmers' RPPW behaviors. Firstly, the Logit model was used to analyze the effect of joining cooperatives on farmers' recycling decisions. Further, to address the sample selection bias, the present study employed the propensity score matching (PSM) method for empirical analysis concerning the effect of joining cooperatives on farmers' recycling degree. Results showed that joining cooperatives positively and significantly influences farmers' recycling decisions. If farmers join a cooperative, the probability of the recycling decisions and degree will increase by 20.30% and 27.50%, respectively. Moreover, it is also found that some other factors such as education level, environmental and public health risk perception, peer effect, and relationship network also significantly influence farmers' recycling decisions. Moreover, considering the differences in farmers' gender, age, and educational attainment, the study unveiled the heterogeneous effects of joining cooperatives on farmers' RPPW behaviors. The findings revealed that gender and age variables have noticeable masking effects while education level has a typical threshold effect. The overall findings provided insights for policymakers to emphasize the development of agricultural cooperatives, improve the risk and interest linkage mechanism, and build the RPPW system. These implications are also supportive for policymakers in other developing countries.

RESUMO: O descarte inadequado de resíduos de embalagens de pesticidas (PPW) tem causado sérios danos ao meio ambiente, incluindo a poluição das águas subterrâneas e do solo e até mesmo problemas de saúde pública. Para abordar as preocupações ambientais e questões de saúde pública, há a necessidade de reciclar os resíduos de embalagens de pesticidas (RPPW). Embora pequenos agricultores, em muitos países em desenvolvimento, tenham se unido às cooperativas para reduzir os custos de produção e aumentar o prêmio do produto, ainda é escassa a forma como essas cooperativas melhoram os comportamentos de RPPW dos agricultores. O estudo atual usou dados coletados de 725 produtores de maçã nas províncias de Shaanxi e Gansu da China para explorar o fenômeno empiricamente. Decisões e grau de reciclagem são usados para retratar os comportamentos de RPPW dos agricultores. Primeiramente, o modelo Logit foi utilizado para analisar o efeito da adesão às cooperativas nas decisões de reciclagem dos agricultores. Além disso, para abordar o viés de seleção da amostra, o presente estudo empregou o método Propensity Score Matching (PSM) para análise empírica sobre o efeito da associação de cooperativas no grau de reciclagem dos agricultores. Os resultados mostraram que a adesão às cooperativas influencia positiva e significativamente as decisões de reciclagem dos agricultores. Se os agricultores aderirem a uma cooperativa, a probabilidade das decisões de reciclagem aumentará em 20,30%, e o grau de reciclagem aumentará em 27,50%. Além disso, também se constata que alguns outros fatores como nível de escolaridade, percepção de risco ambiental e de saúde pública, efeito de pares e rede de relacionamento também influenciam significativamente as decisões de reciclagem dos agricultores. Além disso, considerando as diferenças de gênero, idade e escolaridade dos agricultores, o estudo também revelou os efeitos heterogêneos da adesão às cooperativas sobre os comportamentos de RPPW dos agricultores. Os resultados revelaram que as variáveis de gênero e idade têm efeitos de mascaramento perceptíveis, enquanto o nível de escolaridade tem um efeito limiar típico. As descobertas gerais fornecem insights para os formuladores de políticas enfatizarem o desenvolvimento de cooperativas agrícolas, melhorar o mecanismo de vinculação de risco e interesse e construir o sistema RPPW. Essas implicações também são favoráveisaos formuladores de políticas em outros países em desenvolvimento.

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Athletes born closer to an arbitrary cut-off date are more likely to reach an elite level in sport, which is supported by a phenomenon called the relative age effect (RAE). It is important to determine whether this phenomenon is present in a sport to minimize this selection bias. This study aimed to investigate the prevalence of RAE in elite volleyball athletes, considering the influence of gender, the playing position (Setter, Middle, Libero, Opposite, and Outside Hitter) and the performance level (attack points, aces, and block points). The sample comprised 203 male and 193 female athletes competing in the Superliga A in the 2020/2021 season, which was equivalent to all of the teams of the championship. The data collection was performed during May and June, 2021. Athletes were organized according to gender, the playing position, and performance in the Superliga. For performance variables, athletes were separated based on the median value (90.0), and classified as high- or low-performance. Chi-squared tests were performed to verify differences between birth date distributions in relation to the aforementioned variables. Results indicated overrepresentation of relatively older male athletes (Q1 = 35.96%; Q2 = 27.59%; Q3 = 19.21%; Q4 = 17.24%), especially in Middles, Opposites, and Outside Hitters, regardless of their performance level. Considering females, no differences were found. Our findings suggest that RAE operates differently for men and women in elite Brazilian volleyball. The characteristics of the games played by male and female elite athletes may lead to different talent selection processes, affecting the likelihood of RAE prevalence.

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Resumen Los resultados de diversos hallazgos de investigación han sido objeto de crítica, en especial en los últimos años, debido a presencia de errores sistemáticos (sesgos), los que ponen en duda la validez interna de los resultados obtenidos. Estos sesgos pueden ocurrir en cualquier etapa del curso de una investigación, es decir, desde la planificación del estudio hasta la presentación y publicación de sus resultados. Los sesgos se han clasificado de diferentes formas, intentado agruparlos bajo dimensiones conceptuales, objeto de organizar de mejor forma la información existente, que además es considerable. Los sesgos pueden ocurrir por diversos motivos, pero en general, los más frecuentes son aquellos originados por el observador (él o los que miden), por lo que es observado (sujeto en estudio); y aquello con lo que se observa (instrumento de medición). Por otra parte, varios de los múltiples sesgos existentes, se pueden agrupar en: sesgos de selección, de medición o información, y de confusión. El objetivo de este manuscrito fue comentar la importancia de los sesgos más comunes en la investigación quirúrgica, y su relación con algunos diseños de investigación; así como, conocer las estrategias existentes para minimizar su ocurrencia.

The results of many research findings have come under scrutiny in recent years due to the introduction of systematic errors (biases), which can occur at any stage during an investigation, from planning to presentation of results and their presentation and further publication. Biases have been classified in different ways, trying to group them under conceptual dimensions to better organize the existing information, which is considerable. Biases can occur for various reasons, but in general, the most frequent are those originated by the observer, what is observed; and what is observed with. I.e., the subject that is measured, who measures it and with what it measures it. On the other hand, several of the multiple biases can be grouped into selection, measurement or information, and confounding biases. The aim of this manuscript was to comment on the importance of the most common biases in surgical research, and their relationship with some research designs; as well as know the existing strategies to reduce its occurrence.

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Abstract: Correctly recognizing gender identity in population-based surveys is essential to develop effective public health strategies to improve the living conditions of transgender and gender-diverse populations, as well as to adequately collect data on cisgender individuals. This study aims to present the two-step measure as the best strategy for assessing gender identity in Brazilian surveys, thus we performed two separate analyses. Firstly, we conducted a systematic review concerning HIV-related care among Brazilian transgender and gender-diverse populations to assess the strategy used to identify participants' gender identity. Secondly, we re-analyzed data from a recent survey that included Brazilian transgender populations, comparing characteristics and health outcomes from the sample identified by single-item and by the two-step measure. Concerning the systematic review, from 6,585 references, Brazilian research teams published seven articles, and only one study used the two-step measure. Regarding this survey, the two-step measure recognized 567 cisgender and 773 transgender and gender diverse participants among the 1,340 participants who answered the questionnaire, whereas the single-item measure was able to recognize only 540 transgender and gender diverse people. Furthermore, 31 transgender women self-identified as "transgender men" on the single-item measure. Therefore, although scarcely used in Brazil, the two-step measure is a more accurate strategy to recognize gender identity.

Resumo: O reconhecimento correto da identidade de género em inquéritos populacionais é essencial para desenvolver estratégias eficazes de saúde pública para melhorar as duras condições de vida das populações transgênero e não binárias, além de coletar dados adequados sobre pessoas cisgênero. O estudo tem como objetivo apresentar a medida de dois passos como a melhor estratégia para avaliar a identidade de gênero em inquéritos brasileiros. Para tanto, fizemos duas análises separadas. Primeiro, realizamos uma revisão sistemática sobre a assistência relacionada ao HIV em populações transgênero e não binárias brasileiras para avaliar a estratégia aplicada ao reconhecimento da identidade de gênero dos participantes. Depois, reanalisamos os dados de um inquérito recente que incluiu populações transgênero brasileiras, comparando as características e os desfechos de saúde na amostra identificada com as medidas de item único e de dois passos, respectivamente. Quanto à revisão sistemática, entre 6.585 referências, sete artigos foram publicados por pesquisadores brasileiros, e apenas um estudo aplicou a medida de dois passos. Com relação ao inquérito, a medida de dois passos reconheceu 567 pessoas cisgênero e 773 pessoas transgênero e não binárias entre os 1.340 participantes que iniciaram o questionário, enquanto a medida de item único reconheceu somente 540 pessoas transgênero e não binárias. Além disso, 31 mulheres transgênero se identificaram como "homens transgênero", quando foi usada a medida de item único. Portanto, embora a medida de dois passos seja pouco aplicada no Brasil, é uma estratégia mais precisa para reconhecer a identidade de gênero.

Resumen: Reconocer correctamente la identidad de género en las encuestas basadas en población es esencial para desarrollar estrategias públicas de salud efectivas, con el objeto de mejorar las duras condiciones de vida de las poblaciones transgénero y de género diverso, así como recabar adecuadamente datos sobre personas cisgénero. El objetivo de este estudio es presentar un modelo de dos etapas como la mejor estrategia para evaluar la identidad de género en encuestas brasileñas. Para tal fin, se realizaron dos análisis separados. En primer lugar, se llevó a cabo una revisión sistemática relacionada con los cuidados recibidos por la población transgénero brasileña con VIH y poblaciones de género diverso, con el fin de evaluar la estrategia aplicada para reconocer la identidad de género de los participantes. En segundo lugar, volvimos a analizar los datos de una encuesta reciente, que incluyó a poblaciones transgénero brasileñas, comparando características y resultados de salud de la muestra identificada por un modelo de ítem único y por el modelo de dos etapas. Respecto a la revisión sistemática, de las 6.585 referencias se publicaron siete artículos por parte de equipos de investigación brasileños, y solamente un estudio donde se aplicó el modelo de dos etapas. Respecto a esta encuesta, el modelo de dos etapas reconoció a 567 cisgénero y 773 transgénero, así como a participantes de género diverso, entre los 1.340 participantes que realizaron el cuestionario, aunque el modelo de un único ítem fue capaz de reconocer solo a 540 personas transgénero y de género diverso. Además, 31 mujeres transgénero se autoidentificaron como "hombres transgéneros" en el modelo de un único ítem. Por consiguiente, a pesar de que se aplicó escasamente en Brasil, el modelo de dos etapas es una estrategia más precisa para reconocer la identidad de género.

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Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and five-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.

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BACKGROUND: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population. OBJECTIVE: We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018). METHODS: We used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested. RESULTS: MRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates. CONCLUSION: The MRP is a promising strategy to predict subpopulations' physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations.

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Abstract: Using data collected by the Brazilian National Household Sample Survey - COVID-19 (PNAD-COVID19) and semi-Bayesian modelling developed by Wu et al., we have estimated the effect of underreporting of COVID-19 cases in Brazil as of December 2020. The total number of infected individuals is about 3 to 8 times the number of cases reported, depending on the state. Confirmed cases are at 3.1% of the total population and our estimate of total cases is at almost 15% of the approximately 212 million Brazilians as of 2020. The method we adopted from Wu et al., with slight modifications in prior specifications, applies bias corrections to account for incomplete testing and imperfect test accuracy. Our estimates, which are comparable to results obtained by Wu et al. for the United States, indicate that projections from compartmental models (such as SEIR models) tend to overestimate the number of infections and that there is considerable regional heterogeneity (results are presented by state).

Resumo: Estimamos o efeito da subnotificação de casos de COVID-19 no Brasil até dezembro de 2020, com base nos dados coletados pela Pesquisa Nacional de Amostra de Domicílios sobre COVID-19 (PNAD-COVID19) e a modelagem semi-bayesiana desenvolvida por Wu et al. O número total de indivíduos infectados é cerca de 3 a 8 vezes o número de casos notificados, a depender do estado do país. No final de 2020, os casos confirmados representavam 3,1% da população total, enquanto nossa estimativa aponta para quase 15% dos cerca de 212 milhões de brasileiros no mesmo período. O método de Wu et al., que adotamos com pequenas modificações nas especificações, aplica correções de vieses para compensar pela testagem incompleta e pela acurácia imperfeita dos testes. Nossas estimativas, que são comparáveis aos resultados obtidos por Wu et al. para os Estados Unidos, indicam que projeções a partir de modelos compartimentais (tais como modelos SEIR) tendem a superestimar o número de infecções, e que há uma heterogeneidade regional considerável (resultados apresentados por estado).

Resumen: Usando los datos recogidos por la Encuesta Nacional por Muestra de Domicilios - COVID-19 (PNAD-COVID19) y un modelado semibayesiano desarrollado por Wu et al., hemos estimado el efecto del subregistro de casos de COVID-19 en Brasil en diciembre de 2020. El número total de individuos infectados es de entre 3 a 8 veces más el número de casos informados, dependiendo del estado. Los casos confirmados son un 3,1% del total de población y nuestra estimación del total de casos es al menos un 15% de aproximadamente 212 millones de brasileños en 2020. El método que se tomó fue el de Wu et al., con leves modificaciones en las especificaciones previas, es aplicable a las correcciones de sesgo para tener en cuenta los test incompletos y la imprecisión de los tests. Nuestras estimaciones, que son comparables a los resultados obtenidos por Wu et al. para los Estados Unidos, indican las proyecciones de los modelos compartimentales (tales como los modelos SEIR), que tienden a sobreestimar el número de infecciones, así como la considerable heterogeneidad regional (los resultados se presentan por estado).

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