Cost-effectiveness of screening for chronic kidney disease in the general adult population: a systematic review
Bibliographic record
Abstract
Introduction: Chronic kidney disease (CKD) is a significant public health problem, with rising incidence and prevalence worldwide, and is associated with increased morbidity and mortality. Early identification and treatment of CKD can slow its progression and prevent complications, but it is not clear whether CKD screening is cost-effective. The aim of this study is to conduct a systematic review of the cost-effectiveness of CKD screening strategies in general adult populations worldwide, and to identify factors, settings and drivers of cost-effectiveness in CKD screening. Methods: Studies examining the cost-effectiveness of CKD screening in the general adult population were identified by systematic literature search on electronic databases (MEDLINE OVID, Embase, Cochrane Library and Web of Science) for peer-reviewed publications, hand-searched reference lists and grey literature of relevant sites, focusing on the following themes: (i) CKD, (ii) screening and (iii) cost-effectiveness. Studies comprising health economic evaluations performed for CKD screening strategies, compared with no CKD screening or usual-care strategy in adult individuals, were included. Study characteristics, model assumptions and CKD screening strategies of selected studies were identified. The primary outcome of interest is the incremental cost-effectiveness ratio (ICER) of CKD screening, in cost per quality-adjusted life year (QALY) and life-year gained (LYG), expressed in 2022 US dollars equivalent. Results: Twenty-one studies were identified, examining CKD screening in general and targeted populations. The cost-effectiveness of screening for CKD was found to vary widely across different studies, with ICERs ranging from $113 to $430 595, with a median of $26 662 per QALY and from $6516 to $38 372, with a median of $29 112 per LYG. Based on the pre-defined cost-effectiveness threshold of $50 000 per QALY, the majority of the studies found CKD screening to be cost-effective. CKD screening was especially cost-effective in those with diabetes ($113 to $42 359, with a median of $27 471 per QALY) and ethnic groups identified to be higher risk of CKD development or progression ($23 902 per QALY in African American adults and $21 285 per QALY in Canadian indigenous adults), as indicated by a lower ICER. Additionally, the cost-effectiveness of CKD screening improved if it was performed in older adults, populations with higher CKD risk scores, or when setting a higher albuminuria detection threshold or increasing the interval between screening. In contrast, CKD screening was not cost-effective in populations without diabetes and hypertension (ICERs range from $117 769 to $1792 142, with a median of $202 761 per QALY). Treatment effectiveness, prevalence of CKD, cost of CKD treatment and discount rate were identified to be the most common influential drivers of the ICERs. Conclusions: Screening for CKD is especially cost-effective in patients with diabetes and high-risk ethnic groups, but not in populations without diabetes and hypertension. Increasing the age of screening, screening interval or albuminuria detection threshold, or selection of population based on CKD risk scores, may increase cost-effectiveness of CKD screening, while treatment effectiveness, prevalence of CKD, cost of CKD treatment and discount rate were influential drivers of the cost-effectiveness.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".