Cost-effectiveness analysis of two integrated early childhood development programs into Bangladeshi primary health-care services
Bibliographic record
Abstract
Background: This study presents results of a cost and cost-effectiveness analysis of two parenting interventions (group-based and pairs) integrated into primary health care centers in rural Bangladesh. Methods: A within-trial cost-effectiveness analysis was conducted for two trials of parenting interventions aiming to support child development through play and interactions. Eligible participants for both trials were underweight children aged 5-24 months. Participants in the control arms in both trials received standard health services. Intervention costs were estimated rom the provider perspective over the time horizon of each study (21 months for the group-based intervention; 24 months for the pair-based intervention). Incremental cost effectiveness ratios were estimated for all primary child development outcomes and presented in terms of cost per standard deviation improvements in the outcomes. A series of cost scenario analyses were conducted to assess the effect of changing cost assumptions on the cost and cost-effectiveness results. All results are presented in 2022 USD. The studies were registered with ClinicalTrials.gov (NCT02208531). Findings: Total provider costs in the within-trial analysis were US$ 67,668 for the group-based intervention and US$ 117,028 for the pair intervention. Estimated cost per child covered by the interventions was US$ 156 for the group intervention and US$ 136 for the pair intervention, reflecting likely economies of scale in delivery of the pair intervention. An additional US$ 100 expenditure on the group intervention is estimated to lead to a 0.55 SD improvement in cognition, 0.44 SD in language development and 0.33 SD in motor development. For the pair intervention, the corresponding estimates are improvements of 0.95 SD, 0.81 SD, and 0.88 SD, respectively. Under potential scale up scenarios, the economic cost per child could reduce substantially to US$ 61 and US$ 77 for group and pair interventions, respectively. Interpretation: The findings indicates that cost-efficiency and cost-effectiveness results for both interventions are comparable with the results from limited similar interventions in LMICs. However, implementation costs of the interventions will be substantially lower at scale due to lower monitoring costs, economies of scale, and full integration into the public health system. Funding: This work was supported by Grand Challenges Canada. ICDDR,B and core unrestricted support form the Government of Bangladesh and the Government of Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".