Evidence mapPaperPMID 35120182Full record

ArticlePloS one2022

Exploring HbA1c variation between Australian diabetes centres: The impact of centre-level and patient-level factors.

Matthew Quigley, Arul Earnest, Naomi Szwarcbard, Natalie Wischer, Sofianos Andrikopoulos, Sally Green, Sophia Zoungas

Abstract readMulticenter Study
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Matthew QuigleySchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0002-5871-8993
Arul EarnestSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Naomi SzwarcbardSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0002-6187-0726
Natalie WischerSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Sofianos AndrikopoulosSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Sally GreenSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Sophia ZoungasSchool Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0003-2672-0949

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing global diabetes incidence has profound implications for health systems and for people living with diabetes. Guidelines have established clinical targets but there may be variation in clinical outcomes including HbA1c, based on location and practice size. Investigating this variation may help identify factors amenable to systemic improvement interventions. The aims of this study were to identify centre-specific and patient-specific factors associated with variation in HbA1c levels and to determine how these associations contribute to variation in performance across diabetes centres.

methodsThis cross-sectional study analysed data for 5,872 people with type 1 (n = 1,729) or type 2 (n = 4,143) diabetes mellitus collected through the Australian National Diabetes Audit (ANDA). A linear mixed-effects model examined centre-level and patient-level factors associated with variation in HbA1c levels.

resultsMean age was: 43±17 years (type 1), 64±13 (type 2); median disease duration: 18 years (10,29) (type 1), 12 years (6,20) (type 2); female: 52% (type 1), 45% (type 2). For people with type 1 diabetes, volume of patients was associated with increases in HbA1c (p = 0.019). For people with type 2 diabetes, type of centre was associated with reduction in HbA1c (p <0.001), but location and patient volume were not. Associated patient-level factors associated with increases in HbA1c included past hyperglycaemic emergencies (type 1 and type 2, p<0.001) and Aboriginal and Torres Strait Islander status (type 2, p<0.001). Being a non-smoker was associated with reductions in HbA1c (type 1 and type 2, p<0.001).

conclusionsCentre-level and patient-level factors were associated with variation in HbA1c, but patient-level factors had greater impact. Interventions targeting patient-level factors conducted at a centre level including sick-day management, smoking cessation programs and culturally appropriate diabetes education for and Aboriginal and Torres Strait Islander peoples may be more important for improving glycaemic control than targeting factors related to the Centre itself.

Indexed as

AdultAgedAustraliaCross-Sectional StudiesDelivery of Health CareDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2FemaleGlycated HemoglobinHealth Services, IndigenousHumansLinear ModelsMaleMiddle AgedPractice Guidelines as TopicReproducibility of ResultsGlycated Hemoglobinhemoglobin A1c protein, human

Identifiers

PMID35120182
PMCPMC8815864

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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.