A Mobile Phone–Based Support Intervention to Increase Use of Postabortion Family Planning in Cambodia: Cost-Effectiveness Evaluation
Bibliographic record
Abstract
BACKGROUND: Despite progress over the last decade, there is a continuing unmet need for contraception in Cambodia. Interventions delivered by mobile phone could help increase uptake and continuation of contraception, particularly among hard-to-reach populations, by providing interactive personalized support inexpensively wherever the person is located and whenever needed. OBJECTIVE: The objective of this study was to evaluate the cost-effectiveness of mobile phone-based support added to standard postabortion family planning care in Cambodia, according to the results of the MOTIF (MObile Technology for Improved Family planning) trial. METHODS: A model was created to estimate the costs and effects of the intervention versus standard care. We adopted a societal perspective when estimating costs, including direct and indirect costs for users. The incremental cost-effectiveness ratio was calculated for the base case, as well as a deterministic and probabilistic sensitivity analysis, which we compared against a range of likely cost-effectiveness thresholds. RESULTS: The incremental cost of mobile phone-based support was estimated to be an additional US $8160.49 per 1000 clients, leading to an estimated 518 couple-years of protection (CYPs) gained per 1000 clients and 99 disability-adjusted life-years (DALYs) averted. The incremental cost-effectiveness ratio was US $15.75 per additional CYP and US $82.57 per DALY averted. The model was most sensitive to personnel and mobile service costs. Assuming a range of cost-effectiveness thresholds from US $58 to US $176 for Cambodia, the probability of the intervention being cost-effective ranged from 11% to 95%. CONCLUSIONS: This study demonstrates that the cost-effectiveness of the intervention delivered by mobile phone assessed in the MOTIF trial lies within the estimated range of the cost-effectiveness threshold for Cambodia. When assessing value in interventions to improve the uptake and adherence of family planning services, the use of interactive mobile phone messaging and counselling for women who have had an abortion should be considered as an option by policy makers. TRIAL REGISTRATION: ClinicalTrials.gov NCT01823861; https://clinicaltrials.gov/ct2/show/NCT01823861.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".