Use of maternal and early life risk factors to predict childhood overweight and obesity: a systematic review. (November 2017) - Ziauddeen, Nida; Roderick, Paul J; Macklon, Nicholas S; Alwan, Nisreen A
Abstract: Background: Childhood obesity is a serious public health challenge, and identification of high-risk populations for early intervention to prevent its development is a priority. We aimed to systematically review prediction models for childhood overweight–obesity and critically assess the methodology of their development, validation, and reporting. Methods: Medline and EMBASE were searched from dates of inception to Dec 31, 2016, for studies published in English describing the development, validation, or both, of a model that could predict the development of overweight–obesity between 1 and 13 years using maternal and early life factors. We used the following search terms: {Pediatric Obesity/ OR Fetal Macrosomia/ OR [(child or childhood or children or p#ediatric* or infant* or toddler or embry* or prenatal* or neonat*).mp. AND (obes*.mp. OR overnutrition/ or obesity/ or overweight/ OR overweight.mp. OR over weight.mp.)]} AND [exp causality/ OR ((Reinforc* or Enabl* or predispos*) and factor*).mp. OR (risk* or predict* or causal* or prognos* or causation).mp.] AND [exp Maternal Behavior/ OR maternal.mp. OR mother*.mp. OR early life.mp.]. Data were extracted with the Cochrane CHARMS checklist. The TRIPOD statement was used to assess transparency in reporting. Findings: Ten studies were identified that developed (one), developed and validated (seven), or externally validated an existing (two) prediction model. A median of 23 TRIPOD items (IQR 22–24) out of 37 (31 for derivation or validation alone) were reported. Models, apart from one, were developed with automated variable selection methods. Only four studies included complete cases, and two studies used multiple imputation to handle missing data. Maternal body-mass index, birthweight, and sex were the most commonly included predictors. Median area under the receiver operating characteristic curve was 0·78 in development and internal validation and 0·71 in external validation. Interpretation: Owing to considerable model heterogeneity, it was not possible to combine the results. Some included models have not been externally validated or compared with existing models to assess performance. New methods are needed to combine findings from existing prediction models. Future prediction models need to be developed, validated, and recalibrated to target populations using standard robust methods to refine the applicability of the resulting scores. Funding: NZ is supported by a University of Southampton PhD studentship.