Guerra-Tort Carla, López-Vizcaíno Esther, Santiago-Pérez María I, Rey-Brandariz Julia, Candal-Pedreira Cristina, Varela-Lema Leonor, Schiaffino Anna, Ruano-Ravina Alberto, Perez-Rios Monica
Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
Servizo de Difusión e Información, Instituto Galego de Estatística, Xunta de Galicia, Santiago de Compostela, Spain.
Tob Induc Dis. 2023 Sep 1;21:112. doi: 10.18332/tid/169683. eCollection 2023.
Small-area estimation methods are an alternative to direct survey-based estimates in cases where a survey's sample size does not suffice to ensure representativeness. Nevertheless, the information yielded by small-area estimation methods must be validated. The objective of this study was thus to validate a small-area model.
The prevalence of smokers, ex-smokers, and never smokers by sex and age group (15-34, 35-54, 55-64, 65-74, ≥75 years) was calculated in two Spanish Autonomous Regions (ARs) by applying a weighted ratio estimator (direct estimator) to data from representative surveys. These estimates were compared against those obtained with a small-area model applied to another survey, specifically the Spanish National Health Survey, which did not guarantee representativeness for these two ARs by sex and age. To evaluate the concordance of the estimates, we calculated the intraclass correlation coefficient (ICC) and the 95% confidence intervals of the differences between estimates. To assess the precision of the estimates, the coefficients of variation were obtained.
In all cases, the ICC was ≥0.87, indicating good concordance between the direct and small-area model estimates. Slightly more than eight in ten 95% confidence intervals for the differences between estimates included zero. In all cases, the coefficient of variation of the small-area model was <30%, indicating a good degree of precision in the estimates.
The small-area model applied to national survey data yields valid estimates of smoking prevalence by sex and age group at the AR level. These models could thus be applied to a single year's data from a national survey, which does not guarantee regional representativeness, to characterize various risk factors in a population at a subnational level.
在调查样本量不足以确保代表性的情况下，小区域估计方法是基于直接调查估计的一种替代方法。然而，小区域估计方法得出的信息必须经过验证。因此，本研究的目的是验证一个小区域模型。
通过对具有代表性的调查数据应用加权比率估计器（直接估计器），计算了两个西班牙自治区（AR）按性别和年龄组（15 - 34岁、35 - 54岁、55 - 64岁、65 - 74岁、≥75岁）划分的吸烟者、曾经吸烟者和从不吸烟者的患病率。将这些估计值与应用于另一项调查（即西班牙国家健康调查）的小区域模型所获得的估计值进行比较，该调查按性别和年龄对这两个自治区不具有代表性。为了评估估计值的一致性，我们计算了组内相关系数（ICC）以及估计值之间差异的95%置信区间。为了评估估计值的精度，获得了变异系数。
在所有情况下，ICC均≥0.87，表明直接估计值与小区域模型估计值之间具有良好的一致性。估计值之间差异的十分之八点多的95%置信区间包含零。在所有情况下，小区域模型的变异系数均<30%，表明估计值具有良好的精度。
应用于国家调查数据的小区域模型能够有效估计自治区层面按性别和年龄组划分的吸烟患病率。因此，这些模型可应用于国家调查的单一年度数据（该数据不能保证区域代表性），以描述国家以下层面人群中的各种风险因素。
