Prevalence of comorbidities associated with sickle cell disease among non-elderly individuals with commercial insurance–A retrospective cohort study
Bibliographic record
Abstract
Sickle cell disease (SCD) is a severe monogenic disease associated with high morbidity and mortality and a disproportionate burden on Black communities. Few population-based studies have examined the prevalence of comorbidities among persons with SCD. We estimated the prevalence of comorbidities experienced by individuals with SCD enrolled in employer-based health insurance plans in the US over their non-elderly lifetimes (0-64 years of age) with a retrospective cohort design using Truven Health MarketScan commercial claims data from 2007-2018. ICD-9/10 codes were used to identify individuals with SCD using a previously published algorithm. For this cohort, comorbidities associated with SCD were identified across 3 age categories (<18, 18-45, 46-64 years-old), based on the CMS Chronic Comorbidities Warehouse or SCD-specific diagnosis codes, when applicable. The total number of SCD patients available for analysis in each age category was 7,502 (<18 years), 10,183 (18-45 years) and 4,459 (46-64 years). Across all ages, vaso-occlusive pain, infections (non-specific), and fever were the most common comorbidities. Vaso-occlusive pain and infection were the most prevalent conditions for persons age <18- and 18-45-year-olds, while in the 46-54-year-old age group, infection and cardiovascular including pulmonary hypertension were most prevalent. Compared to persons <18 years old, the prevalence of vaso-occlusive pain, fever, and acute chest syndrome claims declined in older populations. The comorbidity burden of SCD is significant across all age groups. SCD patients experience comorbidities of age such as chronic pain, cardio-vascular conditions including pulmonary hypertension and renal disease at far higher rates than the general population. Novel disease modifying therapies in development have the potential to significantly reduce the comorbidity burden of SCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".