Evidence map›Paper›PMID 39316397›Full record

ArticleJAMA network open2024

Validation of Administrative Data and Timing of Point Prevalence Surveys for Antibiotic Monitoring.

Riccardo Boracchini, Giulia Brigadoi, Elisa Barbieri, Cecilia Liberati, Sara Rossin, Francesca Tesser, Lorenzo Chiusaroli, Giulia Camilla Demarin, Linda Maestri, Francesca Tirelli and 5 more

Abstract readValidation Study
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Riccardo BoracchiniLaboratory of Healthcare Research and Pharmacoepidemiology, Division of Biostatistics, Epidemiology, and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Giulia BrigadoiDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.
Elisa BarbieriDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.
Cecilia LiberatiDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.
Sara RossinPediatric Emergency Department, University of Padua, Padua, Italy.
Francesca TesserDepartment of Women's and Children's Health, University of Padua, Padua, Italy.
Lorenzo ChiusaroliDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.
Giulia Camilla DemarinDepartment of Women's and Children's Health, University of Padua, Padua, Italy.
Linda MaestriDepartment of Women's and Children's Health, University of Padua, Padua, Italy.
Francesca TirelliPediatric Emergency Department, University of Padua, Padua, Italy.
Carlo GiaquintoDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.
Liviana Da DaltPediatric Emergency Department, University of Padua, Padua, Italy.
Silvia BressanPediatric Emergency Department, University of Padua, Padua, Italy.
Anna CantaruttiLaboratory of Healthcare Research and Pharmacoepidemiology, Division of Biostatistics, Epidemiology, and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Daniele DonàDivision of Pediatric Infectious Diseases, Department of Women's and Children's Health, University of Padua, Padua, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Point prevalence surveys (PPSs) are used globally to collect data on antibiotic prescriptions. However, the optimal frequency for data collection to ensure comprehensive understanding of antibiotic use and to target and monitor stewardship interventions remains unknown. Objective: To identify the optimal frequency for collecting data on antibiotic use among the pediatric population through PPSs leveraging administrative data. Design, Setting, and Participants: This prognostic study used a cross-sectional validation approach and was conducted in pediatric outpatient and inpatient settings in the Veneto region of Italy. Antibiotics were classified according to the World Health Organization Access, Watch and Reserve criteria. Prescribing rates of access antibiotics were analyzed for pediatric inpatients with records dated between October 1, 2014, and December 31, 2022, and outpatients with records dated between January 1, 2010, and December 31, 2022. The study included children younger than 15 years with an antibiotic prescription who were admitted to the pediatric acute care unit or evaluated by a primary care pediatrician. Data analysis was performed from October 2023 to January 2024. Main Outcomes and Measures: An algorithm was developed to identify optimal time frames for conducting PPSs. This approach sought to minimize the discrepancy between quarterly and yearly PPS results, aiming to accurately estimate annual antibiotic prescribing rates in both inpatient and outpatient settings (primary outcome). External validity of the optimal PPS time frames derived from outpatient data when applied to the inpatient setting was also investigated. Validation involved assessing the effectiveness of administrative data in identifying strategic PPS periods for capturing inpatient antibiotic use patterns (secondary outcome). Results: This analysis included 106 309 children: 3124 were inpatients (1773 males [56.8%]) and 103 185 were outpatients (53 651 males [52.0%]). A total of 5099 and 474 867 antibiotic prescriptions from inpatients and outpatients were analyzed, respectively. Outpatients tended to be older than inpatients, with a median age of 3.2 (IQR, 1.3-6.3) years vs 2.6 (IQR, 0.6-6.6) years, respectively, and with a lower burden of clinical comorbidities (≥1 comorbidity: 6618 [6.4%] vs 1141 [36.5%], respectively). The algorithm successfully identified distinct time frames within the calendar year from inpatient and outpatient records optimized for PPS data collection. Rates obtained from the quarterly PPS during these identified periods exhibited greater agreement with annual antibiotic prescribing rates (inpatient: r = 0.17, P < .001; and outpatient: r = 0.42, P < .001) than those derived from the yearly PPS (inpatient: r = 0.04, P = .58; and outpatient: r = 0.05, P = .34), with a Δ reduction of up to 89.8% (where Δ represents the percentage point change in antibiotic prescribing rates). Furthermore, the optimal PPS time frames gleaned from the outpatient data demonstrated robust applicability to the inpatient setting, yielding comparable results in both scenarios. Conclusions and Relevance: This study evaluated the potential of administrative data in determining the optimal timing of PPS implementation. The quarterly PPS balanced precision and sustainability, especially when implemented during strategically selected periods across different seasons. Further studies are needed to validate the algorithm used in this study, especially in post-COVID-19 pandemic years and different settings.

Indexed as

Anti-Bacterial AgentsAntimicrobial StewardshipAdolescentChildChild, PreschoolCross-Sectional StudiesDrug PrescriptionsFemaleHumansInfantItalyMalePractice Patterns, Physicians'PrevalenceAnti-Bacterial Agents

Identifiers

PMID39316397
PMCPMC11423167

What Socratic holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.