Specialist Healthcare Intervention and Follow-up Trends in Post-Acute COVID-19 Hospitalization as Compared to Other Respiratory Infections
Bibliographic record
Abstract
INTRODUCTION: Post-acute sequelae of COVID-19, often referred to as "long COVID," have raised concerns about increased healthcare utilization following hospitalization. Whether these patterns differ significantly from those observed after other acute respiratory infections (ARIs) remains unclear. This study aimed to compare post-discharge healthcare use between patients hospitalized for COVID-19 and those with other ARIs in Lombardy, Italy. METHODS: We conducted a population-based cohort study using 2021 administrative healthcare data from the Lombardy Region. Patients aged ≥ 40 years hospitalized for COVID-19 or other ARIs were followed for 12 months post-discharge. We evaluated specialist consultations, rehospitalizations, diagnostic testing, and new chronic drug treatment initiations. Logistic regression models adjusted for age, sex, and comorbidities (Drug-Derived Complexity Index) were used to assess differences. RESULTS: Among 57,795 patients, 35,458 were hospitalized for COVID-19 and 21,375 for other ARIs. Patients with COVID-19 were younger and had lower comorbidity burden and post-discharge mortality (10.7% vs. 33.5%). A higher proportion received at least one specialist visit (75.8% vs. 70.3%), though with a longer median time to first visit (63 vs. 45 days, p < 0.0001). Patients with COVID-19 had more frequent imaging and spirometry but initiated fewer chronic drug treatments overall. However, a higher prescription rate for antidiabetics (OR 1.42), psychoanaleptic (OR 1.21), and genitourinary/hormonal drugs (OR 1.29) emerged after COVID-19 hospitalizations: this rate remained statistically higher for antidiabetics even after excluding subjects who died in the year following discharge. Hospitalizations for causes other than COVID-19 were more frequent in patients with ARI. CONCLUSIONS: Compared to other ARIs, COVID-19 survivors exhibited distinct post-discharge healthcare patterns, with delayed but focused specialist care and selective increases in diagnostic and pharmacological interventions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".