Estimating the costs of induced abortion in Uganda: A model-based analysis
Bibliographic record
Abstract
BACKGROUND: The demand for induced abortions in Uganda is high despite legal and moral proscriptions. Abortion seekers usually go to illegal, hidden clinics where procedures are performed in unhygienic environments by under-trained practitioners. These abortions, which are usually unsafe, lead to a high rate of severe complications and use of substantial, scarce healthcare resources. This study was performed to estimate the costs associated with induced abortions in Uganda. METHODS: A decision tree was developed to represent the consequences of induced abortion and estimate the costs of an average case. Data were obtained from a primary chart abstraction study, an on-going prospective study, and the published literature. Societal costs, direct medical costs, direct non-medical costs, indirect (productivity) costs, costs to patients, and costs to the government were estimated. Monte Carlo simulation was used to account for uncertainty. RESULTS: The average societal cost per induced abortion (95% credibility range) was $177 ($140-$223). This is equivalent to $64 million in annual national costs. Of this, the average direct medical cost was $65 ($49-86) and the average direct non-medical cost was $19 ($16-$23). The average indirect cost was $92 ($57-$139). Patients incurred $62 ($46-$83) on average while government incurred $14 ($10-$20) on average. CONCLUSION: Induced abortions are associated with substantial costs in Uganda and patients incur the bulk of the healthcare costs. This reinforces the case made by other researchers--that efforts by the government to reduce unsafe abortions by increasing contraceptive coverage or providing safe, legal abortions are critical.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".