Population-Wide Screening for Chronic Kidney Disease—A Step Toward Health Equity?
Bibliographic record
Abstract
Could widespread screening for chronic kidney disease (CKD) be a step toward health equity?One in seven US adults lives with CKD, but 90% of them are unaware of their diagnosis.1 Moreover, minoritized racial and ethnic groups in the United States, including Alaska Native, American Indian, Black, Latinx, Native Hawaiian, and Pacific Islander individuals, experience a disproportionate burden of advanced CKD and kidney failure, 1,2 underscoring the urgent need to develop processes that not only reduce the overall burden of CKD but also specifically address and mitigate health inequities.3 A report from a National Institute of Diabetes and Digestive and Kidney Diseases workshop recently highlighted the importance of promoting interventions aimed at addressing structural racism and improving access to care. 4 However, to date there has been no consensus on the implementation of CKD screening programs by US (or any other nation's) health systems or governments, although an update is in progress with respect to the US Preventive Services Task Force recommendations on this topic.Persons living with CKD strongly advocate for early detection and screening, 5 and the Kidney Disease: Improving Global Outcomes (KDIGO) 2024 Clinical Practice Guideline for the Evaluation and Management of CKD "strongly support efforts aimed at the early detection and treatment of CKD among people at high risk for CKD." 6 The aging population, coupled with rapidly escalating health care costs of CKD to individuals, health care systems, and society, emphasize the urgent need for CKD prevention.Recent exciting advances in nephrology, such as sodium-glucose cotransporter 2 (SGLT2) inhibition, indicate the potential for significant reductions in CKD progression in those at risk, but could screening for CKD also help eliminate kidney health disparities?Elsewhere in JAMA Network Open, Cusick and colleagues 7 aimed to determine the health outcomes, costs, and cost-effectiveness of population-wide one-time or periodic CKD screening by modeling disease progression among adults in Hispanic, non-Hispanic Black, non-Hispanic White, and additional (non-Hispanic Asian, multiracial, and other) racial and ethnic groups.Using population-based data from the National Health and Nutrition Examination Survey, the authors reported every 5-year screening from 55 to 75 years of age combined with SGLT2 inhibitor use was projected to result in reductions in incidence of kidney failure requiring kidney replacement therapy, gains in discounted and quality-adjusted life-years, and overall reduced health care costs at a population level, while also reducing kidney health disparities across all included racial and ethnic groups.In light of these results, the authors concluded that population-wide screening for CKD was cost-effective, would result in improved kidney health, and may have even greater health benefits to non-Hispanic Black adults if begun at earlier ages.In recent years, federal health institutions such as the US Centers for Disease Control and Prevention and the US Preventive Services Task Force have committed to reaching health equity.The findings of this important study highlight how population screening for CKD could potentially resultin equitable access to early detection of disease and intervention with the goal of achieving optimal kidney health at a population level.CKD is responsible not only for substantial morbidity and increased cardiovascular and all-cause mortality, but also negatively impacts employment and education status.8 Living with CKD, particularly in advanced stages, can result in significant socioeconomic deprivation for individuals and their caregivers by restricting their ability to work and care for family members, which in turn results in poorer health outcomes.As such, addressing the +
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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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".