ArticleCancer research and treatment2018
Strategic Distributional Cost-Effectiveness Analysis for Improving National Cancer Screening Uptake in Cervical Cancer: A Focus on Regional Inequality in South Korea.
Article in Cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological heterogeneity and equity challenges of distributional cost-effectiveness analysis in healthcare: a systematic review from 2017 to 2025.Archives of public health = Archives belges de sante publique · 2026Pooled it
- Incorporating Equity Concerns in Cost-Effectiveness Analyses: A Systematic Literature Review.PharmacoEconomics · 2022Pooled it
- Incorporating health equity into value assessment: frameworks, promising alternatives, and future directions.Journal of managed care & specialty pharmacy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to conduct a cost effectiveness analysis of strategies designed to improve national cervical cancer screening rates, along with a distributional cost effectiveness analysis that considers regional disparities. MATERIALS AND
methodsCost effectiveness analysis was conducted using a Markov cohort simulation model, with quality adjusted life years as the unit of effectiveness. The strategies considered were current (biennial Papanicolaou smear cytology of females aged 20 or above), strong screening recommendation by mail to target regions (effect, 12% increase in screening uptake; cost, 1,000 Korean won per person), regular universal screening recommendation by mail (effect, 6% increase in screening uptake; cost, 500 Korean won per person), and strong universal screening recommendation by mail (effect, 12% increase in screening uptake; cost, 1,000 Korean won per person). Distributional cost effectiveness analysis was conducted by calculating the cost effectiveness of strategies using the Atkinson incremental cost effectiveness ratio.
resultsAll strategies were under the threshold value, which was set as the Korean gross domestic product of $25,990. In particular, the 'strong screening recommendation to target regions' strategy was found to be the most cost effective (incremental cost effectiveness ratio, 7,361,145 Korean won). This was also true when societal inequality aversion increased in the distributional cost effectiveness analysis.
conclusionThe 'strong screening recommendation to target regions' strategy was the most cost effective approach, even when adjusting for inequality. As efficiency and equity are objectives concurrently sought in healthcare, these findings imply a need to develop appropriate economic evaluation methodologies to assess healthcare policies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.