The annual cost of not breastfeeding in Indonesia: the economic burden of treating diarrhea and respiratory disease among children (< 24mo) due to not breastfeeding according to recommendation
Bibliographic record
Abstract
In Indonesia, 96% of children (< 24mo) are breastfed. However, only 42% of children (< 6mo) are exclusively breastfed, as per World Health Organization recommendations. Breastfeeding provides protective benefits such as reducing the risk of morbidity and mortality associated with diarrhea and pneumonia/respiratory disease (PRD). This study estimates the potential economic impact of not breastfeeding according to recommendation in Indonesia based on infants suffering from attributable diarrhea and PRD. A cost analysis examined both the healthcare system costs and non-medical costs for children (< 24mo) with diarrhea and PRD. Data collection took place between 2015 and 2016 and healthcare expenditures were assessed in 13 facilities, in five sites including Bandung and Tomohon City. Costs from a provider perspective were estimated using healthcare records and 26 interviews with healthcare workers. A discount rate of 3% was used. A cross-sectional survey with caregiver-child pairs ( n = 615) collected data related to out of pocket costs such transportation and opportunity costs such as wage loss. These figures were combined with the national disease prevalence rates from Indonesia Demographic and Health Survey 2012, and the relative risk of disease of not breastfeeding according to recommendation from literatures to extrapolate the financial burden of treatment. The healthcare system cost due to not breastfeeding according to recommendation was estimated at US$118 million annually. The mean healthcare system cost and out of pocket costs was US$11.37 and US$3.85 respectively. This cost consists of US$88.64 million of provider costs and US$29.98 million of non-medical patient costs. The cost of not breastfeeding according to recommendation is potentially high, therefore the Indonesian government needs to invest in breastfeeding protection, promotion and support as the potential healthcare system cost savings are significant. As suggested by other studies, the long term cost due to cognitive losses of providing not breastfeeding according to recommendation should also be taken into account to provide a complete understanding of the economic impact of not breastfeeding according to recommendation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".