Santos Iolanda Karla Santana Dos, Conde Wolney Lisbôa
Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brasil.
Fundação Universidade Federal do ABC, Santo André, Brasil.
Cad Saude Publica. 2020 Jun 26;36(6):e00167219. doi: 10.1590/0102-311X00167219. eCollection 2020.
This study aimed to describe the estimated means for weight, height, and body mass index (BMI) according to two imputation methods, using data from Vigitel (Risk and Protective Factors Surveillance System for Chronic Non-Communicable Diseases Through Telephone Interview). This was a cross-sectional study that used secondary data from the Vigitel survey from 2006 to 2017. The two imputation methods used in the study were hot deck and Predictive Mean Matching (PMM). The weight and height variables imputed by hot deck were provided by Vigitel. Two models were conducted with PMM: (i) explanatory variables - city, sex, age in years, race/color, and schooling; (ii) explanatory variables - city, sex, and age in years. Weight and height were the outcome variables in the two models. PMM combines linear regression and random selection of the value for imputation. Linear prediction is used as a measure of distance between the missing value and the possible donors, thereby creating the virtual space with the candidate cases for yielding the value for imputation. One of the candidates from the pool is randomly selected, and its value is assigned to the missing unit. BMI was calculated by dividing weight in kilograms by height squared. The result shows the means and standard deviations for weight, height, and BMI according to imputation method and year. The estimates used the survey module from Stata, which considers the sampling effects. The mean values for weight, height, and BMI estimated by hot deck and PMM were similar. The results with the Vigitel data suggest the applicability of PMM to the set of health surveys.
本研究旨在利用通过电话访谈进行的慢性非传染性疾病风险与保护因素监测系统（Vigitel）的数据，根据两种插补方法描述体重、身高和体重指数（BMI）的估计均值。这是一项横断面研究，使用了2006年至2017年Vigitel调查的二手数据。该研究中使用的两种插补方法是热卡填充法和预测均值匹配法（PMM）。热卡填充法插补的体重和身高变量由Vigitel提供。使用PMM进行了两个模型分析：（i）解释变量——城市、性别、年龄（岁）、种族/肤色和受教育程度；（ii）解释变量——城市、性别和年龄（岁）。体重和身高是这两个模型中的结果变量。PMM将线性回归与用于插补的值的随机选择相结合。线性预测被用作缺失值与可能的供体之间距离的度量，从而创建具有产生插补值的候选案例的虚拟空间。从候选池中随机选择一个候选者，并将其值赋给缺失单元。BMI通过将体重（千克）除以身高的平方来计算。结果显示了根据插补方法和年份得出的体重、身高和BMI的均值及标准差。这些估计使用了Stata的调查模块，该模块考虑了抽样效应。热卡填充法和PMM估计的体重、身高和BMI的均值相似。Vigitel数据的结果表明PMM适用于健康调查数据集。
