Obesity Policy Research Unit, Population, Policy and Practice, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
PLoS One. 2022 Jan 31;17(1):e0263043. doi: 10.1371/journal.pone.0263043. eCollection 2022.
Simulated interventions using observational data have the potential to inform policy and public health interventions where randomised controlled trials are not feasible. National childhood obesity policy is one such area. Overweight and obesity are primarily caused by energy-rich and low-nutrient diets that contribute to a positive net energy imbalance. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we investigated whether causal modelling techniques could be applied to simulate the potential impact of policy-relevant calorie-reduction interventions on population prevalence and inequalities in obesity in childhood.
Predicted probabilities of obesity at age 11 (UK90 cut offs) were estimated from logistic marginal structural models (MSM) accounting for observed calorie consumption at age 7 and confounding, overall and by maternal occupational social class. A series of population intervention scenarios were modelled to simulate daily calorie-reduction interventions that differed in effectiveness, targeting mechanism and programme uptake level.
The estimated effect of maternal social class on obesity after accounting for confounding and observed calorie intake was provided by the controlled direct effect (CDE), in which, 18.3% of children were living with obesity at age 11 years,. A universal simulation to lower median intake to the estimated average requirement (EAR) (a 6.1% reduction in daily calories) with 75% uptake reduced overall obesity prevalence by 0.6%; there was little impact on inequalities. A targeted intervention to limit consumption to the EAR for children with above average intake reduced population obesity prevalence at 11 years by 1.5% but inequalities remained broadly unchanged. A targeted intervention for children of low-income families reduced prevalence by 0.7% and was found to slightly reduce inequalities.
MSMs allow estimation of effects of simulated calorie-reduction interventions on childhood obesity prevalence and inequalities, although estimates are limited by the accuracy of reported calorie intake. Further work is needed to understand causal pathways and opportunities for intervention. Nevertheless, simulated intervention techniques have promise for informing national policy where experimental data are not available.
利用观察性数据进行模拟干预有可能为无法进行随机对照试验的政策和公共卫生干预提供信息。国家儿童肥胖政策就是这样一个领域。超重和肥胖主要是由富含能量和低营养的饮食引起的，这些饮食导致净能量失衡呈阳性。本研究利用阿冯纵向研究父母和儿童（ALSPAC）的数据，探讨了因果建模技术是否可用于模拟与政策相关的卡路里减少干预对儿童肥胖人群流行率和不平等的潜在影响。
使用逻辑边际结构模型（MSM），从观察到的 7 岁时卡路里摄入量和混杂因素中，估计了 11 岁时肥胖的预测概率（UK90 切点），并分别按总体和母亲职业社会阶层进行了估计。模拟了一系列人群干预情景，以模拟不同效果、靶向机制和方案参与水平的每日卡路里减少干预。
在校正混杂因素和观察到的卡路里摄入量后，母亲社会阶层对肥胖的估计影响是通过受控直接效应（CDE）提供的，11 岁时，有 18.3%的儿童肥胖。一项降低中位数摄入量至估计平均需求量（EAR）（每天卡路里减少 6.1%）的全民模拟，有 75%的参与率，将总体肥胖流行率降低了 0.6%；对不平等影响不大。针对摄入量高于平均水平的儿童，将摄入量限制在 EAR 以下的靶向干预可使 11 岁儿童的人群肥胖流行率降低 1.5%，但不平等状况基本保持不变。针对低收入家庭儿童的靶向干预使流行率降低了 0.7%，且发现该干预略微降低了不平等程度。
MSM 可用于估计模拟卡路里减少干预对儿童肥胖流行率和不平等的影响，尽管估计结果受到报告卡路里摄入量准确性的限制。需要进一步研究以了解因果途径和干预机会。尽管如此，模拟干预技术在没有实验数据的情况下，有望为国家政策提供信息。
