Economic evaluation and decision analytic modelling

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Diet and physical activity based interventions in pregnancy have potential to alter maternal and child outcomes.

To assess whether the effects of diet and lifestyle interventions vary in subgroups of women, based on maternal BMI, age, parity, Caucasian ethnicity and underlying medical condition, by undertaking an individual patient data (IPD) meta-analysis. We also evaluated the association of gestational weight gain with adverse pregnancy outcomes, and assessed the cost effectiveness of the interventions.

Data sources:
MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, and Health Technology Assessment Database (October 2013 to March 2015).

Review methods:
Researchers from the International Weight Management in Pregnancy (i-WIP) Collaborative Network shared the primary data. For each intervention type and outcome, we performed a two-step IPD random-effects meta-analysis, for all women (except underweight) combined and for each subgroup of interest, to obtain summary estimates of effects and 95% confidence intervals, and synthesised the differences in effects between subgroups. In the first stage, we fitted a linear regression adjusted for baseline (for continuous outcomes) or a logistic regression model (for binary outcomes) in each study separately; estimates were combined across studies using random-effects meta-analysis models. We quantified the relationship between weight gain and complications, and undertook a decision-analytic model-based economic evaluation to assess the cost effectiveness of the interventions.

Diet and lifestyle interventions reduced gestational weight gain by an average of 0.71 70 kg (95% CI: -0.92 to -0.4948, 32 33 studies, 9082 9320 women). The effects on composite maternal outcome (summary aOROR 0.8990, 95% CI: 0.798 to 1.032, 241 studies, 8852061 women) and composite fetal / neonatal outcome (summary aOROR 01.9402, 95% CI: 0.9830 to 1.0814, 20 18 studies, 84579815 women) were not significant. The effect did not vary with baseline BMI, age, ethnicity, parity or underlying medical conditions for gestational weight gain, and composite maternal and fetal outcomes. The reductions in individual maternal complications were not significant, for gestational diabetes (aOROR 0.89, 95% CI: 0.72 to 1.10), pre-eclampsia or pregnancy induced hypertension (aOROR 0.95, 95% CI: 0.78 to 1.16), caesarean section (aOROR 0.91, 95% CI: 0.83 to 01.9900) and preterm birth (aOROR 0.941, 95% CI: 0.76 78 to 1.1309). There was no significant effect on fetal outcomes. The interventions were not cost effective.

Gestational weight gain, including adherence to Institute of Medicine (IOM) recommended targets, was not associated with reduction in complications. Predictors of gestational weight gain were maternal age (summary estimate -0.10 kg, 95% CI -0.14 to -0.06) and multiparity (summary estimate: -0.72 73 kg, 95% CI -1.22 24 to -0.2123).

The findings were limited by the lack of standardisation in the components of intervention, across studies, residual heterogeneity in effects across studies for most analyses, and the unavailability of IPD in some studies.

Diet and lifestyle interventions should be offered to all pregnant mothers, irrespective of risk factors, to reduce excess gestational weight gain.
Original languageEnglish
Title of host publicationEffects of antenatal diet and physical activity on maternal and fetal outcomes
Subtitle of host publicationIndividual patient data (IPD) meta-analysis and health economic evaluation
PublisherNIHR Health Technology Assessment Programme
Publication statusPublished - Aug 2017

Bibliographical note

This work was part of a study registered as PROSPERO CRD42013003804, funded by the National Institute for Health Research Health Technology Assessment programme. The final report is published as a journal article: Rogozińska E, Marlin N, Jackson L, Rayanagoudar G, Ruifrok AE, Dodds J, et al. Effects of antenatal diet and physical activity on maternal and fetal outcomes: individual patient data meta-analysis and health economic evaluation. Health Technol Assess 2017;21(41). . Non commercial use


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