Excesso de peso pré-gestacional e ácidos graxos poli-insaturados no leite humano: modelo teórico de causalidade / Pre-gestational overweight and polyunsaturated fatty acids in human milk: theoretical causality model

Resumo Inúmeros estudos têm se detido na avaliação da associação entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Todavia, diante da complexidade de fatores de risco potencialmente confundidores, é recomendável a utilização de ferramentas gráficas para identificar possíveis vieses. O objetivo deste artigo é propor um modelo teórico de causalidade utilizando o gráfico acíclico direcionado entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Foi realizada ampla revisão da literatura para identificar as variáveis com relações causais com a exposição e/ou desfecho. A escolha das variáveis para ajuste seguiu o algoritmo gráfico que compreende seis critérios para a seleção de um conjunto mínimo de variáveis potencialmente confundidoras. Condições socioeconômicas, intervalo interpartal, idade materna e padrão de consumo alimentar foram as variáveis ajustadas a fim de se estimar o efeito total do excesso de peso pré-gestacional sobre o conteúdo dos ácidos graxos poli-insaturados no leite humano. O conjunto mínimo de variáveis encontrado pelo presente estudo pode ser utilizado na análise de outros estudos que avaliem essa associação.

Abstract A number of studies have focused on the evaluation of the relationship between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. However, given the complexity of potentially confounding risk factors, the use of graphical tools is recommended to identify possible biases. This article aims to propose a theoretical model of causality using the directed acyclic graph between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. Methods: An extensive literature review was performed to identify variables with causal relationships with exposure and/or outcome. The choice of variables for adjustment followed the graphic algorithm that comprises six criteria for selecting a minimum set of potentially confounding variables. Socioeconomic conditions, interpartum interval, maternal age and food consumption pattern were the variables that would have to be adjusted in order to estimate the total effect of pre-pregnancy overweight on polyunsaturated fatty acids content in human milk. The minimum set of variables found in the present study can be used in the analysis of other studies that evaluate this association.

Application of directed acyclic graphs in identification and control of selection bias in causal inference / 中华疾病控制杂志

In the etiology study of epidemiology, selection bias will lead to the fact that the research sample cannot represent the general population, the association between exposure and outcome among those selected for analysis differs from the association among those eligible, and the true causal association cannot be inferred. Directed acyclic graphs (DAGs) could visualize complex causality, introduce the Collider-stratification bias using simple graphics language, provide a simple and intuitive way to identify Selection bias, different types of selection bias are verified by the graphic structure of the Collider-stratification bias. In practical studies, there may be multiple biases at the same time, improper adjustment of the collider will lead to Collider-stratification bias, open a backdoor path, even change the size and direction of the confounding bias. In order to obtain an unbiased estimate of the exposure to the outcome, it is necessary to identify the collider and avoid the adjustment to prevent the occurrence of Collider-stratification bias by using DAGs.