The costs of suboptimal breastfeeding in Ontario, Canada, and potential healthcare resource impacts from improving rates: a pediatric health system costing analysis
Bibliographic record
Abstract
Abstract Background Human milk from the breast is the healthiest option for infants. Other sources of nutrition pose some risk to child, maternal, and environmental health. There are significant costs to suboptimal rates of breastfeeding for children, families and society. Over 92% of mothers in Ontario, Canada initiate breastfeeding, yet exclusivity and duration rates decline over time. This study estimates potential pediatric healthcare cost savings from increased exclusive breastfeeding. Methods We conducted a cost-effectiveness analysis to compare healthcare savings from enhanced breastfeeding rates against current practices by estimating pediatric healthcare costs associated with suboptimal breastfeeding and potential savings from improved rates. Savings are calculated from reduced incidence of childhood illnesses associated with breastfeeding, including lower respiratory tract infections (LRTI), gastrointestinal infections (GII), acute otitis media (AOM), acute lymphoblastic leukemia (ALL), necrotizing enterocolitis (NEC), childhood obesity, and asthma. Cost data were drawn from Canadian healthcare sources, supplemented with data from the UK and other international studies. We used initiation and exclusive breastfeeding rates at hospital discharge and six months postpartum. The study assumes that the incidence of preventable conditions like LRTI, GII, and AOM is directly related to breastfeeding rates at these time points. A six-month threshold for exclusive breastfeeding, recommended by the World Health Organization, was selected for analysis. Partial breastfeeding rates were not separately modeled due to data limitations. Results Improving exclusive breastfeeding (EBF) rates at six months to match rates at hospital discharged and initiation rates could result in 47,114–91,457 fewer cases of LRTI, GII, and AOM, prevent 3,685–7,096 hospitalizations, and reduce 22,043–47,621 outpatient visits. Increased EBF rates could prevent cases of NEC (37–67), ALL (3–6), childhood obesity (1,199–2,661), and asthma (970–2,111). Suboptimal breastfeeding at 6 months for infants born in Ontario in 2019 cost the healthcare system US $72.2 million annually for treating four childhood illnesses and US $61.0 million for long-term conditions (ALL, obesity, and asthma). Increasing breastfeeding rates could save US $32–63 million in annual treatment costs and US $23.6–51.6 million in long-term healthcare costs. Conclusions Suboptimal breastfeeding rates impose a burden on the health of families and Ontario’s healthcare system. Supporting breastfeeding through evidence-based interventions could reduce this burden through lowering pediatric healthcare demands.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".