Public Health Research Digest — 2026-10-22

Daily overview

Today’s selection is dominated by decision-analytic and simulation-based economic evaluations, with cancer screening as the most common application area and a smaller number of studies addressing cardiovascular screening and health-system coverage policy. Methodologically, the set relies mainly on Markov cohort and microsimulation models, with one discrete event simulation, and typically pairs them with one-way, probabilistic and scenario sensitivity analyses. A recurring direction is the joint exploration of screening design parameters, such as eligibility thresholds, initiation age, interval and test cutoff, rather than the comparison of a single programme against no screening. Several studies adapt familiar structures to new questions, for example by accounting for risk misclassification or by treating screening as an allocation filter within a shared natural-history model. A cross-disciplinary thread links health economics with machine learning through a scoping review of economic evaluations of artificial intelligence in oncology. Within the trend snapshot, which compares a 30-day recent window with a 180-day baseline over a corpus of 45124 records, the provisional tag for cancer screening optimization appears on only 14 of 3138 recent records, so it should be read as a weak signal for this set rather than as evidence of field-wide change. Across the set, calibration and external validation are inconsistently reported, which suggests that uncertainty quantification and validation practices remain a useful focus for the research profile.

AI-generated synthesis (Claude via claude-code-action, model sonnet) of today’s selected papers and the current trend snapshot; it interprets verified metadata and does not summarize papers individually.

Recommendations

Coverage note: 6 papers met the eligibility and verification rules today (maximum 10). The list is not padded with stale or weakly related papers.

1. Data from Implementing a Risk-Stratified National Breast Screening Programme in the United Kingdom: A Model-Based Cost-effectiveness Analysis

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

Abstract Background: Risk-stratified breast screening, in which women are offered different screening intervals based on their breast cancer risk, has been proposed as a way to improve the benefits of screening in the United Kingdom National Breast Screening Programme (UK-NBSP). The aim of this study was to explore the cost-effectiveness of potential risk-stratified breast screening strategies in the UK-NBSP. Methods: An existing validated discrete event simulation model (MANC-RISK-SCREEN) was used to evaluate the cost-effectiveness of 3 risk-stratified NBSPs (risk 1, risk 2, and risk 3) compared with the current UK-NBSP, 2-yearly screening, and no screening [lifetime horizon; health service perspective; costs (£; at 2024 prices)]. MANC-RISK-SCREEN was modified to account for bias in risk prediction and allocation to risk categories (risk misclassification). Uncertainty in the results due to model inputs was quantified using probabilistic sensitivity analysis and one-way sensitivity analyses. Results: At a cost-effectiveness threshold of £20,000 per quality-adjusted life year, 2-yearly screening was most likely to be cost-effective, followed by a risk-stratified approach whereby women at high risk (>8% 10-year risk) were screened annually and women at moderate risk (5%–8% 10-year risk) biannually. When risk misclassification was accounted for, the ranking of risk-stratified screening strategies improved relative to universal strategies. Conclusions: If 2-yearly screening is infeasible due to resource constraints, implementing a risk-stratified approach in the existing NBSP in the United Kingdom would be a cost-effective use of healthcare resources. Impact: Implementation of risk-stratified breast screening in the UK national screening program has the potential to improve cancer detection at a reasonable additional resource use and cost.

Author keywords: No author keywords in verified sources

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): Cancer screening optimization, Decision-making under uncertainty

Matched emerging trend tags: Cancer screening optimization (trend score 0.35), Decision-making under uncertainty (trend score 0.39)

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): Existing method applied to a new problem. Validated discrete event simulation with lifetime horizon, health service perspective, PSA and one-way analyses, and a modification for risk misclassification. Calibration details are not given in the abstract. Contributions: Discrete event simulation extended for risk misclassification; Cost-effectiveness ranking of risk-stratified strategies.

Why it is recommended for you (AI interpretation): Directly informative for modelling risk-stratified screening strategies and for handling misclassification of risk categories in screening microsimulation.

Evidence and confidence limitations: Abstract only; the extent of the model change is not described. Breast cancer is outside the listed cancer sites. Record is a data-collection item with no journal listed. Validation is asserted by reference to an existing model rather than shown in the abstract.

2. Comparing cardiovascular screening strategies in a health-examination population: a real-world markov model study

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

Cardiovascular disease (CVD) is the leading cause of death in China, yet few studies have used real-world data to systematically assess the health benefits and cost-effectiveness of different CVD screening strategies. To compare three CVD screening strategies in wide use in China, namely the China-PAR score (Strategy 1), atherosclerotic cardiovascular disease (ASCVD) risk assessment (Strategy 2), and ASCVD risk assessment combined with vascular assessment (Strategy 3), against no screening in a health-examination population, and to examine how age and sex shape screening performance. Data came from the electronic health records and the provincial cause-of-death surveillance database of the health management center of a tertiary hospital, covering 2015 to 2024. We included 14,124 people aged 40 to 80 years who had completed the examination, questionnaire, and vascular testing required for risk prediction. Rather than fitting a separate disease model for each strategy, we fitted a single covariate-adjusted three-state continuous-time Markov natural-history model (no CVD; CVD event, a composite absorbing endpoint; and non-CVD death) shared by all strategies, and represented each screening strategy as an intervention-allocation filter that determined which individuals received lifestyle intervention and, where indicated, statin plus antihypertensive therapy. Using per-person Markov cohort simulation over 10 years with 5% annual discounting, we compared each strategy against no screening. Costs comprised screening, intervention, and CVD-event costs. Outcomes were the incremental quality-adjusted life year (QALY), the number of CVD events prevented, the number needed to screen (NNS), the incremental cost-effectiveness ratio (ICER), and net monetary benefit; a cost-effectiveness acceptability frontier (CEAF) was constructed against a willingness-to-pay (WTP) threshold of ¥90,000/QALY. Probabilistic (1,000 Monte Carlo iterations), one-way, and scenario sensitivity analyses were performed, together with subgroup analyses by age group and by sex. Against no screening, the incremental QALYs per person were 0.063, 0.080, and 0.113 for Strategies 1 to 3, and the ICERs were ¥4,816, ¥9,314, and ¥12,089 per QALY, respectively, all far below the WTP threshold. Strategy 3 prevented the most CVD events (636.73 in the cohort) and had the lowest NNS to gain one QALY (8.8). In the fully incremental analysis, Strategy 2 was excluded by extended dominance, and Strategy 3 remained cost-effective against Strategy 1 (¥21,292/QALY). Age strongly shaped benefit: the incremental QALY of Strategy 3 in people aged 55 to 80 (0.224) was about 4.4 times that in the 40-to-54-year group (0.051). China-PAR (Strategy 1) modestly outperformed ASCVD risk assessment (Strategy 2) in women (incremental QALY 0.037 versus 0.035) and in adults aged 55 to 80 (0.160 versus 0.152). At the WTP threshold, Strategy 3 had about a 99% probability of being the optimal option, and every sensitivity analysis kept all strategies well below ¥90,000/QALY. All three CVD screening strategies were highly cost-effective relative to no screening. Strategy 3 was the preferred option overall and especially in adults aged 55 to 80, while China-PAR may be favored in women. Because the three strategies share a single natural-history model and differ only in intervention allocation, the differences in cost-effectiveness reflect the accuracy of risk classification rather than different disease trajectories.

Author keywords: No author keywords in verified sources

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): No tags assigned

Matched emerging trend tags: No matched trend tags

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): New combination of existing methods. Covariate-adjusted continuous-time Markov model fitted to electronic health records with cohort simulation, discounting, PSA, one-way and scenario analyses, CEAF and subgroup analyses. External validation is not described. Contributions: Shared continuous-time Markov natural-history model across screening strategies; Screening represented as intervention-allocation filter.

Why it is recommended for you (AI interpretation): Provides a clear design for comparing risk-based screening strategies using a common fitted disease model, applicable to cancer screening evaluations.

Evidence and confidence limitations: Abstract only. Application outside cancer, tobacco and infectious disease. No external validation described. Single-centre health-examination population.

3. Defining value-based PLCOm2012 eligibility thresholds for low-dose CT lung cancer screening.

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

22 Background: Annual low-dose computed tomography (LDCT) reduces lung cancer mortality, but US screening eligibility remains categorical along age and smoking history. This approach may screen some adults with limited expected benefit while missing others with high individualized risk. We estimated the PLCOm2012 6-year lung cancer risk threshold at which LDCT becomes high value and compared risk-based screening with a USPSTF-equivalent strategy. Methods: We developed a decision-analytic Markov model from a US Medicare payer perspective for adults aged 50–80 years. Annual LDCT was evaluated across PLCOm2012 6-year risk thresholds from 0.5% to 5.0%. A 1.2% PLCOm2012 threshold was modeled as a USPSTF-equivalent proxy strategy. Health states included no diagnosed lung cancer, stage-specific lung cancer, survivorship, lung cancer death, and other-cause death. LDCT effectiveness was informed by NLST, with NELSON tested in scenario analysis. Stage distribution and survival were derived from SEER lung and bronchus cancer data. Costs were estimated from 2026 Medicare payment files and published SEER-Medicare phase-of-care costs. Outcomes included costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and net monetary benefit (NMB) at $100,000/QALY. We stress-tested less favorable LDCT effectiveness, screening/follow-up costs, treatment-cost offsets, and false-positive quality-of-life harms. Results: LDCT became cost-effective at approximately 0.78% PLCOm2012 6-year risk. Screening was not cost-effective at 0.5% risk but was cost-effective at thresholds ≥1.0%. At the 1.5% threshold, LDCT increased costs by $2,245 and QALYs by 0.0632 versus no screening, yielding an ICER of $35,544/QALY and NMB of $4,071. Compared with the USPSTF-equivalent proxy strategy, PLCOm2012 screening at 1.5% generated approximately $1,697 greater incremental NMB per screened person. At 1.5%, LDCT remained cost-effective when screening costs increased by 30% ($52,641/QALY), follow-up testing costs increased by 30% ($48,571/QALY), treatment costs decreased by 30% ($55,006/QALY), and false-positive harms were included ($61,175/QALY). Results were most sensitive to LDCT effectiveness; a low-effectiveness scenario increased the ICER to $104,540/QALY. Conclusions: Risk-based LDCT eligibility may improve the value of lung cancer screening delivery. Here, LDCT was high value at PLCOm2012 thresholds >1.0%, aand the 1.5% threshold outperformed a USPSTF-equivalent proxy strategy by NMB. These findings support PLCOm2012-guided screening as a practical value-based approach for optimizing LDCT allocation. Cost-effectiveness of annual LDCT screening across PLCOm2012 6-year lung cancer risk thresholds. Strategy ICER vs No Screening NMB at $100K/QALY PLCOm2012 1.0% Favorable Value USPSTF proxy / 1.2% ref ref PLCOm2012 1.5% $35,544/QALY +$4,071

Author keywords: No author keywords in verified sources

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): Cancer screening optimization

Matched emerging trend tags: Cancer screening optimization (trend score 0.35)

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): Existing method applied to a new problem. Decision-analytic Markov model with stage-specific health states, NLST-informed effectiveness, SEER-derived stage and survival, and deterministic stress tests. Probabilistic analysis and validation are not mentioned. Contributions: Value-based risk threshold analysis for screening eligibility.

Why it is recommended for you (AI interpretation): Directly relevant to lung screening eligibility optimization using risk-based thresholds and net monetary benefit.

Evidence and confidence limitations: Abstract only. Conference abstract with limited methods detail. insufficient methods information in abstract

4. Economic evaluation of prostate health index for prostate cancer screening and diagnostic pathway in Chinese men by varying screening initiation ages, intervals, and cutoffs

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

To evaluate the cost-effectiveness of integrating the Prostate Health Index (PHI) into prostate cancer (PCa) screening and diagnostic pathway in China, and to explore the impacts of varying screening initiation ages, intervals, and PHI cutoffs. A Markov microsimulation model was developed to compare two strategies: (1) Prostate Specific Antigen (PSA) strategy: Magnetic Resonance Imaging (MRI) for PSA 4–10 ng/mL, then biopsy for Prostate Imaging Reporting and Data System (PI-RADS) ≥ 3; (2) PHI strategy: PHI testing for PSA 4–10 ng/mL, followed by MRI for PHI > cutoff, and then biopsy for PI-RADS ≥ 3. Sensitivity analyses were performed. Compared with the PSA strategy, PHI strategy (initiation age 50, biennial, PHI cutoff of 35) reduced diagnostic costs by $643,398 (30.1%) and total costs by $1,115,276 (6.4%), while decreasing unnecessary biopsies by 1,815 (62.9%) and QALYs by 32 but increasing missed cases of csPCa by 6 and deaths by 8 per 10,000 men. The incremental cost-utility ratio (ICUR) was $34,852/QALY. Higher screening initiation ages, longer intervals, or higher PHI cutoffs reduced costs and unnecessary biopsies, increased missed diagnoses of csPCa, deaths, and a loss of health utility. Among the secondary analyses, initiation at age 50 with a 4-year screening interval showed favorable cost-effectiveness among the evaluated age/interval scenarios, while a PHI cutoff of 35 showed favorable cost-effectiveness in the cutoff analysis. The PHI strategy demonstrates favorable cost-effectiveness, with age 50 initiation, quadrennial screening, and a PHI cutoff of 35 showing favorable results. Not applicable.

Author keywords: No author keywords in verified sources

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): Cancer screening optimization

Matched emerging trend tags: Cancer screening optimization (trend score 0.35)

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): Existing method applied to a new problem. Markov microsimulation comparing two screening and diagnostic pathways, with sensitivity analyses and secondary scenarios on age, interval and cutoff. Calibration and validation are not described. Contributions: Microsimulation of biomarker-based screening pathway; Joint exploration of start age, interval and cutoff.

Why it is recommended for you (AI interpretation): Directly relevant to screening strategy optimization via microsimulation and shows trade-offs between cost savings and missed cases.

Evidence and confidence limitations: Abstract gives few modelling details. Abstract only. insufficient methods information in abstract

5. Economic evaluation of artificial intelligence in oncology: a scoping review of literature

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

Artificial intelligence (AI) has significant potential to address pressing challenges in healthcare, particularly in improving access to, and quality, and efficiency of cancer care. An increasing number of AI-based applications are being developed and implemented across various cancers, and in domains such as diagnostics, immunotherapy, radiation therapy, and research. This scoping review assesses the current literature on health economic evaluations of AI in oncology, examines methodological approaches used, and explores potential economic benefits and risks associated with integrating AI into cancer care. Literature on health economic evaluation (EE) of AI in oncology was reviewed from 1 January 2019 to April 2026. Papers were included if they included data on clinical effectiveness, resource utilisation, costs, or other relevant parameters related to AI systems. The key information from the papers was extracted, including the author, year, country, study design, population, care pathway phase (e.g. screening, diagnostic, planning, delivery). A total of 29 health economic evaluations were reviewed. Of these studies, 9 (31%) focused on breast cancer, 6 (21%) on colorectal cancer and 5 (17%) on lung cancer. AI-based technologies were most commonly evaluated in cancer screening, accounting for 22 (76%) studies. The studies were conducted primarily in Western developed countries, with 8 (28%) studies from Asia and 1 (3%) from Middle East. No studies were identified from Africa, Latin America, or other regions. Cost-utility analysis was the predominant evaluation approach, and Markov modelling was the most frequently used modelling technique. Most studies adopted a healthcare perspective. Overall, the evaluations reported economic benefits associated with AI implementation due to improved accuracy, labour cost reduction, and improved workflow. Results suggest that AI has potential to be both cost-effective and cost-saving, particularly in screening and diagnostic phases of cancer care. However, uncertainty remains regarding applicability of these results to real-world settings. The predominance of model-based evaluations using cost-utility and cost-effectiveness frameworks indicates that AI interventions are largely being assessed within traditional health technology assessment paradigms. However, this approach often overlooks critical aspects unique to AI, such as algorithmic adaptability, data dependency, and product lifecycle costs. Future evaluations should incorporate real-world performance data, dynamic learning costs, and implementation outcomes.

Author keywords: No author keywords in verified sources

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): AI-assisted health economic modelling

Matched emerging trend tags: AI-assisted health economic modelling (trend score 0.32), Cancer screening optimization (trend score 0.35)

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): Not a methods paper. Scoping review of economic evaluations of AI in oncology with descriptive extraction of design, population and care pathway phase. Search and appraisal details are not in the abstract.

Why it is recommended for you (AI interpretation): Background on how AI interventions are currently evaluated economically and where methods fall short, relevant to screening-focused modelling.

Evidence and confidence limitations: Abstract only. Review of the literature; no new model. insufficient methods information in abstract

6. Cost-utility analysis and budget impact of policy options for kidney replacement therapy coverage for end-stage kidney disease in Kenya.

Abstract (original abstract, verbatim from OpenAlex (reconstructed from its inverted index))

Objectives To evaluate the cost-utility and budget impact of alternative kidney replacement therapy (KRT) modalities and policy options for patients with end-stage kidney disease (ESKD) in Kenya. Design Cost-utility and budget impact analyses were conducted using a decision-analytic Markov model. Deterministic one-way and probabilistic sensitivity analyses assessed parameter uncertainty. Model face validity was assessed through consultation with nephrology experts and external validation compared model-predicted survival among kidney transplant recipients with observed survival data. Budget impact was assessed over 5 years from the perspective of the Social Health Authority (SHA). Setting Kenya Participants Adults receiving KRT for ESKD. Interventions Three KRT modalities, that is, peritoneal dialysis (PD), haemodialysis (HD) and kidney transplantation (KT) were compared. Compared with current practice (scenario 1), four policy scenarios were initiating all incident ESKD patients on PD (scenario 2), expanding KT coverage to achieve an additional 10% coverage within 5 years (scenario 3), redistributing coverage to 68% HD, 20% PD and 12% KT (scenario 4), and providing HD three times per week to 20% of patients currently receiving HD two times per week (scenario 5). Primary and secondary outcome measures Primary outcomes were incremental cost-effectiveness ratio and incremental net monetary benefit. Secondary outcomes included costs, life years and quality-adjusted life years (QALYs) over 5-, 10-, 15-year and lifetime horizons. Results PD had the highest lifetime costs compared with HD and KT. At a willingness-to-pay threshold of 120 000 Kenya shillings per QALY gained, KT was dominant compared with PD over the lifetime horizon and compared with HD over 10-year and 15-year horizons. Scenario 3 was dominant over 5-, 10- and 15-year horizons and had the lowest 5-year budget impact. Conclusions Expanding access to KT appears economically efficient for improving ESKD coverage in Kenya, although decisions should consider uncertainty in key model parameters and health systems requirements for KT expansion.

Author keywords: HEALTH ECONOMICS; Health economics; Health policy; Nephrology

MeSH terms: No MeSH terms in verified sources

AI-assigned topic tags (AI-generated classification, not author keywords): No tags assigned

Matched emerging trend tags: No matched trend tags

Score breakdown (weighted sum computed in Python; scoring version scoring-7474ce1172)

Methodological assessment (AI interpretation, preliminary — abstract only): Incremental application. Decision-analytic Markov model with one-way and probabilistic sensitivity analyses, expert face validity and external validation of transplant survival. Calibration details and reproducibility materials are not visible in the abstract. Contributions: Markov cost-utility model with external survival validation; 5-year budget impact of coverage scenarios.

Why it is recommended for you (AI interpretation): Useful as a worked example of combining cost-utility, budget impact and policy scenarios with model validation; limited direct relevance to the profile’s disease areas.

Evidence and confidence limitations: Application area is not cancer, tobacco or infectious disease. Assessment based on abstract only. Calibration approach and code availability are not stated in the abstract.

Methodology and coverage

Publication horizon: 30 days before 2026-10-22; retrieval overlap 7 days; trend windows 30/180 days.

Providers: crossref: ok; europe_pmc: ok; medrxiv: ok; openalex: ok; pubmed: ok.

Versions: pipeline 0.1.0, scoring scoring-7474ce1172, rubric rubric-83f3f3d704, prompts 1.0, trend snapshot trend-v1-db2b77f24b, configuration d4e4816448.

Source metadata (titles, authors, dates, identifiers, abstracts, keywords, MeSH) comes verbatim from the named providers. Assessments, tags and the overview marked as AI interpretation are generated by Claude from that metadata and may be wrong; novelty judgements are preliminary when based on abstracts only.