A holistic approach to promoting early child development: a cluster randomised trial of a group-based, multicomponent intervention in rural Bangladesh

Abstract

IntroductionIn low- and middle-income countries, children experience multiple risks for delayed development. We evaluated a multicomponent, group-based early child development intervention including behavioural recommendations on responsive stimulation, nutrition, water, sanitation, hygiene, mental health and lead exposure prevention.MethodsWe conducted a 9-month, parallel, multiarm, cluster-randomised controlled trial in 31 rural villages in Kishoreganj District, Bangladesh. Villages were randomly allocated to: group sessions (‘group’); alternating groups and home visits (‘combined’); or a passive control arm. Sessions were delivered fortnightly by trained community members. The primary outcome was child stimulation (Family Care Indicators); the secondary outcome was child development (Ages and Stages Questionnaire Inventory, ASQi). Other outcomes included dietary diversity, latrine status, use of a child potty, handwashing infrastructure, caregiver mental health and knowledge of lead. Analyses were intention to treat. Data collectors were independent from implementers.ResultsIn July–August 2017, 621 pregnant women and primary caregivers of children<15 months were enrolled (group n=160, combined n=160, control n=301). At endline, immediately following intervention completion (July–August 2018), 574 participants were assessed (group n=144, combined n=149, control n=281). Primary caregivers in both intervention arms participated in more play activities than control caregivers (age-adjusted means: group 4.22, 95% CI 3.97 to 4.47; combined 4.77, 4.60 to 4.96; control 3.24, 3.05 to 3.39), and provided a larger variety of play materials (age-adjusted means: group 3.63, 3.31 to 3.96; combined 3.81, 3.62 to 3.99; control 2.48, 2.34 to 2.59). Compared with the control arm, children in the group arm had higher total ASQi scores (adjusted mean difference in standardised scores: 0.39, 0.15 to 0.64), while in the combined arm scores were not significantly different from the control (0.25, –0.07 to 0.54).ConclusionOur findings suggest that group-based, multicomponent interventions can be effective at improving child development outcomes in rural Bangladesh, and that they have the potential to be delivered at scale.Trial registration numberThe trial is registered in ISRCTN (ISRCTN16001234).

Making the invisible visible: Developing and evaluating an intervention to raise awareness and reduce lead exposure among children and their caregivers in rural Bangladesh

“…Loss to follow-up was not significantly different between study arms. The full trial profile and loss to follow-up data are presented in detail elsewhere ( Pitchik et al, 2021 ). Baseline socioeconomic, demographic, and exposure characteristics were similar between the intervention and control arms ( Table 2 ).…”

“…This lead-focused intervention was one part of a larger multiple component intervention addressing child development, nutrition, water, sanitation, and hygiene (WASH) ( Pitchik et al, 2021 ). The sample size calculation, overall study design, and individual intervention components are described in detail elsewhere ( Pitchik et al, 2021 ).…”

“…This lead-focused intervention was one part of a larger multiple component intervention addressing child development, nutrition, water, sanitation, and hygiene (WASH) ( Pitchik et al, 2021 ). The sample size calculation, overall study design, and individual intervention components are described in detail elsewhere ( Pitchik et al, 2021 ). Briefly, the intervention was delivered in 31 villages in the Katiadi and Kuliarchar sub-districts of Kishoreganj district in rural Bangladesh by community health workers (CHWs) between October 2017 and May 2018.…”

“…Sample size calculations were based on the primary child development outcome from the multi-component intervention and are described elsewhere ( Pitchik et al, 2021 ). We estimated differences in lead-related outcomes between intervention and control arms at endline.…”

“…For each outcome, covariates were prescreened using a likelihood ratio test, and those with p < 0.20 were included in adjusted models. We used the parametric g-formula (R package: riskCommunicator) to estimate differences in outcomes between the intervention and control arm ( Ahern et al, 2009 ; Jessica A. Grembi and McQuade, Elizabeth T. Rogawski, 2020 ; Pitchik et al, 2021 ). This approach follows four steps: 1) fit a regression model, 2) estimate counterfactuals, 3) estimate marginal differences, and 4) construct 95% confidence intervals by cluster bootstrapping resampling with 1000 replicates.…”