Evidence map›Paper›PMID 29220399›Full record

ArticlePloS one2017

"What they see is what you get": Prescribing antibiotics for respiratory tract infections in primary care: Do high prescribers diagnose differently? An analysis of German routine data.

Susann Hueber, Thomas Kuehlein, Roman Gerlach, Martin Tauscher, Angela Schedlbauer

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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

5 authors.

Susann HueberUniversitätsklinikum Erlangen, Institute of General Practice, Erlangen, Germany.ORCID http://orcid.org/0000-0001-8214-4048
Thomas KuehleinUniversitätsklinikum Erlangen, Institute of General Practice, Erlangen, Germany.
Roman GerlachKassenärztliche Vereinigung Bayern, München, Germany.
Martin TauscherKassenärztliche Vereinigung Bayern, München, Germany.
Angela SchedlbauerUniversitätsklinikum Erlangen, Institute of General Practice, Erlangen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCharacteristics of high and low prescribers of antibiotics in German primary care were analysed using population data. We aimed to evaluate differences in prescribing rates and factors being associated with high prescribing, and whether high prescribers made the diagnosis of perceived bacterial infections more often.

methodsRoutine data were provided by the Bavarian Association of Statutory Health Insurance Physicians. Routine data are delivered by primary care practices on a quarterly basis. We analysed data from 2011 and 2012. Patients older than 15 years with respiratory tract infections consulting a primary care physician were selected (6.647 primary care practices). Patient and physician characteristics associated with high prescribing were identified using stepwise logistic regression.

resultsMean prescribing rate of antibiotics was 24.9%. Prescribing rate for high prescribers was 43.5% compared to 8.5% for low prescribers. High prescribers made the diagnosis of perceived bacterial infections more often (Mhigh = 64.5%, Mlow = 45.2%). In the adjusted regression model, perceived bacterial infections were strongly associated with high prescribing (OR = 13.9, 95% CI [10.2, 18.8]). Treating patients with comorbidities was associated with lower prescribing of antibiotics (OR = 0.6, 95% CI [0.4, 0.8]). High prescribers had a higher practice volume, a higher degree of prescribing dominance, and were situated more often in deprived areas and in rural settings.

interpretationCompared to findings of studies in other European countries, prescribing rates were low. There was a considerable difference between prescribing rates of high and low prescribers. Diagnostic labelling was the best predictor for high prescribing. Current guidelines recommend considering antibiotic treatment for patients with co-morbidities. In our study, treating a large number of high-risk patients was not associated with high prescribing.

Indexed as

Drug PrescriptionsPractice Patterns, Physicians'Anti-Bacterial AgentsFemaleGermanyHumansMaleRespiratory Tract InfectionsAnti-Bacterial Agents

Identifiers

PMID29220399
PMCPMC5722345

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.