Incidence and mortality of community-acquired and nosocomial infections in Japan: a nationwide medical claims database study
Bibliographic record
Abstract
BACKGROUND: It is important to determine the prevalence and prognosis of community-acquired infection (CAI) and nosocomial infection (NI) to develop treatment strategies and appropriate medical policies in aging society. METHODS: Patients hospitalized between January 2010 and December 2019, for whom culture tests were performed and antibiotics were administered, were selected using a national claims-based database. The annual trends in incidence and in-hospital mortality were calculated and evaluated by dividing the patients into four age groups. RESULTS: Of the 73,962,409 inpatients registered in the database, 9.7% and 4.7% had CAI and NI, respectively. These incidences tended to increase across the years in both the groups. Among the patients hospitalized with infectious diseases, there was a significant increase in patients aged ≥ 85 years (CAI: + 1.04%/year and NI: + 0.94%/year, P < 0.001), while there was a significant decrease in hospitalization of patients aged ≤ 64 years (CAI: -1.63%/year and NI: -0.94%/year, P < 0.001). In-hospital mortality was significantly higher in the NI than in the CAI group (CAI: 8.3%; NI: 14.5%, adjusted mean difference 4.7%). The NI group had higher organ support, medical cost per patient, and longer duration of hospital stay. A decreasing trend in mortality was observed in both the groups (CAI: -0.53%/year and NI: -0.72%/year, P < 0.001). CONCLUSION: The present analysis of a large Japanese claims database showed that NI is a significant burden on hospitalized patients in aging societies, emphasizing the need to address particularly on NI.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".