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Record W3157559929 · doi:10.1210/jendso/bvab048.1485

Cost-Effectiveness Analysis of an Interdisciplinary Lifestyle Intervention Targeting Women With Obesity and Infertility in Comparison to Usual Care

2021· article· en· W3157559929 on OpenAlexaffabout
Matea Bélan, Belina Carranza‐Mamane, Youssef Ainmelk, Marie-Hélène Pesant, Farrah Jean-Denis, Marie‐France Langlois, Thomas G. Poder, Jean‐Patrice Baillargeon

Bibliographic record

VenueJournal of the Endocrine Society · 2021
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsInfertilityMedicineFertilityRandomized controlled trialObesityPregnancyChildbirthIntervention (counseling)Pregnancy rateGynecologyFamily medicineObstetricsPopulationNursingEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Abstract Although lifestyle modification is considered as the first-line treatment for women with obesity and infertility, these women generally do not have access to a program supporting them in adopting healthy habits that is integrated to fertility care. Implementing such a program requires to demonstrate its efficiency. The purpose of this study was to conduct a cost-effectiveness analysis (CEA) of an interdisciplinary lifestyle intervention (Fit-for-Fertility (FFF) program) for women with obesity and infertility, in comparison with the usual care protocol, i.e. fertility treatments. Methods: A CEA was conducted alongside a randomized controlled trial, recruiting women at the fertility clinic of the Centre hospitalier universitaire de Sherbrooke. Women were randomized to: i) the intervention group (IG): FFF program alone for 6 months (individual follow-ups every 6 weeks and 12 group sessions), and in combination with usual care for infertility after 6 months if not pregnant; or ii) control group (CG): usual care from the outset. Data were collected in both groups, during 18 months or until the end of the pregnancy for those who became pregnant. Costs related to the management of infertility, obesity, pregnancy and childbirth, and the FFF program were considered and collected by self-reported questionnaires, review of medical records and administrative databases. Live birth (LB) rate was used to assess effectiveness. The CEA’s parameter of interest was the incremental cost-effectiveness ratio (ICER), calculated by non-parametric bootstrap with 5,000 iterations. All costs are in Canadian dollars, 2019. Results: A total of 130 women were randomized (65 CG, 65 IG). We present results for the 108 women (57 CG, 51 IG) who completed at least 6 months in the study. We observed an absolute difference of 14.2% (p=0.328) in LB rate between groups (IG: 51.0%; CG: 36.8%). Total mean costs per patient were significantly higher in the IG vs the CG for healthcare system’s ($5,660 ± $3,200 vs $3,631 ± $3,389; p=0.002) and society’s ($9,745 ± $5,899 vs $6,898 ± 7,021; p=0.026) perspectives. We observed an ICER of $12,633 per additional LB [$5,319-$19,947] from the healthcare system’s perspective, and $5,980 [$3,086-$8 874] from the patients’ perspective. Overall, the ICER for the society’s perspective, which includes both previous perspectives, was estimated at $24,393 per additional LB [$15,509-$33,276]. Conclusion: According to our results, a lifestyle intervention may be clinically more effective than the usual protocol of care for women with obesity and infertility, but generates higher costs as well, resulting in a positive ICER (of $12,600 per additional life birth for the healthcare system). Such an intervention could be considered efficient compared to the usual standard of care, but studies are needed to assess the willingness to pay of stakeholders for this type of intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.009
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.028
GPT teacher head0.392
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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