Humanistic and Economic Burden of Patients with Cardiorenal Metabolic Conditions: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Diabetes is associated with significant economic burden. Moreover, cardiovascular disease (CVD), including heart failure, and chronic kidney disease (CKD) are common comorbidities, leading to premature mortality. We conducted a systematic review to assess the humanistic and economic burden of cardio-renal-metabolic (CRM) conditions in individuals ≥ 18 years with CVD, CKD, and type 2 diabetes mellitus. METHODS: databases from 2011 to January 10, 2022 for English publications reporting humanistic and economic burden outcomes from observational studies, real-world evidence, and economic model studies. Intervention and validation studies were excluded. Study quality was assessed using the Newcastle-Ottawa Scale. Abstracts/posters were identified from four conferences (2020-2022). RESULTS: Of 1804 studies identified, 22 (including four conference publications) were selected involving 351,296,930 participants (one modeled the US population); eight reported healthcare resource utilization (HCRU), seven only cost data, six HCRU and cost data, one reported quality-of-life data (11/18 and 7/18 had estimated low and medium risk of bias, respectively). Participants were predominantly ≥ 65 years and identified as having White ethnicity. Higher costs and HCRU were observed in patients with all three conditions compared to those with two or none. Urban/metropolitan and insured patients had higher healthcare expenditure and service utilization compared to uninsured and racial/ethnic minority populations. Comorbidities were associated with increased hospitalizations, higher costs, and more emergency department visits. In general, patients identified as having Black ethnicity had low odds of using healthcare services, possibly due to disparities in healthcare access and distrust in the system. Limitations included no adjustment for inflation and a predominance of retrospective studies. CONCLUSIONS: This review showed a greater economic burden for patients with CRM conditions, with a clear trend between increasing numbers of comorbidities and increasing healthcare costs/resource use. Comparisons between countries are complicated and the scarcity of evidence from minority racial and ethnic groups and lack of data from non-US geographies warrant further investigation.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".