Avoidable Hospitalizations in Ages 0-17. What do Current Information Flows tell us?
Bibliographic record
Abstract
Introduction: Potentially avoidable hospitalizations can be used as indicators of access and quality of primary care. Several criteria are reported in the literature to identify these cases. We have used the criteria proposed by the US Agency for Healthcare Research and Quality integrated by three further conditions monitored in Italy by S. Anna Institute of Pisa and the National Outcome Plan. The study reports on the characteristics of potentially avoidable hospitalizations, in the age group 0-17 years in the province of Trento – Italy, in the year 2018. The study also explores the possible role of some maternal and perinatal factors. Materials and Methods: The cases of interest were extracted from the computerized archive of hospital discharges relating to subjects residing in the province of Trento, for the age group 0-17 years, considering both discharges from provincial institutions and that from institutions outside the province of Trento. We followed the selection and exclusion criteria indicated by the reference institutions. Many socio-demographic and care variables were considered among those present in the hospital discharge form. The hospitalization rate was calculated for all the cases identified and for the individual conditions. The hospitalization rate by age group was also calculated. We compared the hospitalization rate in Italians vs. foreigners and in relation to the area of residence. By linking the hospital discharge archive with that of the Childbirth, we explored the role of some maternal-perinatal factors. Results: In 2018, 413 potentially avoidable hospital admissions were identified in the 0-17 age group representing 6.8% of the total hospitalizations. Admissions for tonsillectomy represent almost 60% of cases. Males predominate over females. The 0-4 age group comprises 43.5% of hospitalizations, 86.2% of cases are Italian citizens, 19.8% reside in an urban area and 80.2% in a rural area. 57.0% of the total cases have been hospitalized in day hospital/day surgery; urgent hospitalizations represent 68.7% of cases and only 11.4% of hospitalizations take place over the weekend. All cases are discharged to their home for an overall average hospital stay of 3.6 days. Hospitalization takes on a decreasing trend with increasing age. A higher hospitalization rate emerges in foreigners and also in residents in rural areas. There is an excess of subjects with low qualifications among the mothers of cases with avoidable hospitalization. Discussion: The use of hospital data to describe the quality of primary care is widespread although it has various limitations. One limitation is represented by the quality of hospital data and the other by the fact that hospital data does not inform us about non-medical aspects that may have a relevant importance on improper hospitalization. To explore these hidden aspects, it would be advisable to integrate hospital data with an audit involving all stakeholders. With all the limitations of the case, the results of our study give a satisfactory picture with respect to avoidable hospitalizations in the age of 0-17 in the province of Trento. An analysis of the criteria for using tonsillectomy would allow a control of most cases. More generally, a homogenization of the organization of primary care and of the hospital-territory relationship could be useful.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".