Cost-Effectiveness Analysis of Non-invasive Prenatal Testing for Down Syndrome in China
Bibliographic record
Abstract
OBJECTIVES: There is little evidence in China regarding the cost-effectiveness of non-invasive prenatal testing (NIPT) for Down syndrome (DS). This study aims to evaluate the cost-effectiveness of NIPT and provide evidence to inform decision-making. METHODS: To determine the cost-effectiveness of NIPT for DS, a decision-analytic model was developed using the TreeAge Pro software from a societal perspective in a simulated cohort of 10 000 pregnant women. Main indicators were based on field surveys from sampled hospitals in four locations in China and a literature review. RESULTS: The conventional maternal serum screening (CMSS) strategy, contingent screening strategy (NIPT delivered to high risk pregnant women after CMSS), and universal screening strategy could prevent 3.02, 7.53, and 9.97 DS births, respectively. NIPT would decrease unnecessary invasive procedures, resulting in fewer procedure-related miscarriages. The cost-effectiveness ratio of the contingent screening strategy was the lowest. When compared with the CMSS strategy, the incremental cost per DS birth averted by the contingent screening strategy and universal screening strategy were USD 20,160 and 352,388, respectively. One-way sensitivity analysis showed that, if the cost of NIPT could be decreased to USD 76.92, the cost-effectiveness ratio of the universal screening strategy would be lower than the CMSS strategy. CONCLUSIONS: Although NIPT has the merits of greater effectiveness and safety, CMSS is unlikely to be replaced by NIPT at this time because of NIPT's higher cost. Contingent screening may be an appropriate strategy to balance the effectiveness and cost factors of the new genetic testing technology.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| 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".