The Interplay Between Continuity of Care, Multimorbidity, and Adverse Events in Patients With Diabetes
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of continuity of care and multimorbidity on health outcomes in patients with diabetes. RESEARCH DESIGN: Using a US claims database of insured patients, we identified those with incident diabetes between 2004 and 2008 and followed them until death, disenrollment, or December 31, 2010. Continuity of care was defined using Breslau's Usual Provider of Continuity (UPC; proportion of visits to the usual or predominant provider within 2 y of diabetes diagnosis). Multivariable logistic regression was used to determine the association between UPC in the first 2 years after diabetes diagnosis and subsequent 1-year composite primary outcome of all-cause hospitalization or death in year 3 in patients with/without multimorbidity. RESULTS: Of the 285,231 patients with incident diabetes, 74% had multimorbidity; their average age was 53 years (SD=10.5) and 49% were female. A total of 77,270 (27%) individuals had a mean UPC≥75% in the first 2 years. During year 3 of follow-up, 33,632 (12%) patients died or were hospitalized for any cause. Greater continuity of care (UPC≥75%) was associated with reduced risk of subsequent death or hospitalization [7.2% vs. 13.5%; adjusted odds ratio (aOR)=0.72; 95% CI, 0.70-0.75]. Although multimorbidity was independently associated with an increased risk of our primary composite endpoint (13.4% vs. 7.2%; aOR=1.26; 95% CI, 1.21-1.30), the association between greater continuity and better outcomes was similar in those with multimorbidity (aOR=0.71; 95% CI, 0.69-0.71) as in those without (aOR=0.75; 95% CI, 0.71-0.80). CONCLUSIONS: In patients with incident diabetes, greater continuity of care is associated with improved outcomes, irrespective of whether or not they have multimorbidity.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".