What Is the Economic Cost of Unplanned Pregnancy Following Hysteroscopic Sterilization in the US? A New National Estimate Based on Essure<sup>®</sup> Procedure Prevalence, Failure Rates and Workforce Productivity
Bibliographic record
Abstract
OBJECTIVE: Although hysteroscopic sterilization (HS) (Essure ®) has been available in the US since 2002, there is disagreement regarding its efficacy, and there has been no study of the economic impact of HS failure. Our investigation examined the economic consequences of contraceptive failure with Essure in the US. METHODS: Contraceptive failure rates (CFR) of 5.7%, 7.7% and 9.6% were applied to the US cohort of HS patients (n = 600,000). Direct economic impact of productivity losses resulting from unplanned conceptions after HS was calculated by factoring Essure failure rate, the exposed population, US female labour force participation, unemployment rate, time away from work owing to vaginal delivery or pregnancy termination and weekly wages. RESULTS: For the 9.6% CFR scenario, US workforce productivity loss from unplanned pregnancy and delivery was estimated at 771,065 days (2,112 years). Productivity loss secondary to conception and subsequent termination of pregnancy after Essure was approximately 23,725 days (65 years). Assuming CFR at 5.7%, livebirth delivery with total time missed from work at 65 days, this was associated with an aggregate economic impact of $49.2M in lost annual wages. Direct economic impact of unplanned pregnancy after Essure irrespective of outcome (terminations and deliveries) was estimated to result in US productivity losses valued at ~$130M. CONCLUSION: Although not all unplanned pregnancy costs are attributable to failed HS, estimates derived from earlier surveys have not considered this contraceptive method, and the economic consequences of unplanned pregnancy after Essure are not trivial. Quantifying the economic consequences of HS failure would be improved with specific ICD-10 coding for Essure-associated symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".