ArticleBMJ open2020
Associations between multimorbidity and glycaemia (HbA1c) in people with type 2 diabetes: cross-sectional study in Australian general practice.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Global prevalence of multimorbidity among people living with type 2 diabetes: a systematic review and meta-analysis.BMC public health · 2025Pooled it
- Homebound status and glycemic control among older adults: results from a national survey.GeroScience · 2026Article
- Multimorbidity and the Number of Conditions Increase With the Duration of Type 1 Diabetes and Associate With Increased Mortality.Journal of diabetes · 2026Observational
- Prevalence and factors associated with comorbidities in Iranian patients with type 2 diabetes: A national study.PloS one · 2026Article
- Modifiable and Non-Modifiable Risk Factors and Vascular Damage Progression in Type 2 Diabetes: A Primary Care Analysis.Journal of clinical medicine · 2025Article
- Multimorbidity in elderly patients with or without T2DM: A real-world cross-sectional analysis based on primary care and hospitalisation data.Journal of global health · 2024Article
- Management of older adults with diabetes mellitus: Perspective from geriatric medicine.Journal of diabetes investigation · 2024Review
- Determinants of health-related quality of life of patients with type 2 diabetes and multimorbidity: a cross-sectional study.Hormones (Athens, Greece) · 2024Article
- Network of doctors for multimorbidity and diabetes - the NOMAD intervention: protocol for feasibility trial of multidisciplinary team conferences for people with diabetes and multimorbidity.Pilot and feasibility studies · 2024Article
- Multimorbidity, Frailty and Diabetes in Older People-Identifying Interrelationships and Outcomes.Journal of personalized medicine · 2022Review
- Diabetes Mellitus Diagnosis and Screening in Australian General Practice: A National Study.Journal of diabetes research · 2022Article
- Older People With Type 2 Diabetes-Individualising Management With a Specialised Community Team (OPTIMISE): Perspectives of Participants on Care.Clinical diabetes : a publication of the American Diabetes Association · 2021Article
- Multimorbidity and achievement of treatment goals among patients with type 2 diabetes: a primary care, real-world study.BMC health services research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo explore the prevalence of multimorbidity as well as individual and combinations of long-term conditions (LTCs) in people with type 2 diabetes (T2D) attending Australian general practice, using electronic health record (EHR) data. We also examine the association between multimorbidity condition count (total/concordant(T2D related)/discordant(unrelated)) and glycaemia (glycated haemoglobin, HbA1c).
designCross-sectional study.
settingAustralian general practice.
participants69 718 people with T2D with a general practice encounter between 2013 and 2015 captured in the MedicineInsight database (EHR Data from 557 general practices and >3.8 million Australian patients). PRIMARY AND SECONDARY OUTCOME MEASURES: Prevalence of multimorbidity, individual and combinations of LTCs. Multivariable linear regression models used to examine associations between multimorbidity counts and HbA1c (%).
resultsMean (SD) age 66.42 (12.70) years, 46.1% female and mean (SD) HbA1c 7.1 (1.4)%. More than 90% of participants with T2D were living with multimorbidity. Discordant conditions were more prevalent (83.4%) than concordant conditions (69.9 %). The three most prevalent discordant conditions were: painful conditions (55.4%), dyspepsia (31.6%) and depression (22.8%). The three most prevalent concordant conditions were hypertension (61.4%), coronary heart disease (17.1%) and chronic kidney disease (8.5%). The three most common combinations of conditions were: painful conditions and hypertension (38.8%), painful conditions and dyspepsia (23.1%) and hypertension and dyspepsia (22.7%). We found no associations between any multimorbidity counts (total, concordant and discordant) or combinations and HbA1c.
conclusionsMultimorbidity was common in our cohort of people with T2D attending Australian general practice, but was not associated with glycaemia. Although we did not explore mortality in this study, our results suggest that the increased mortality in those with multimorbidity and T2D observed in other studies may not be linked to glycaemia. Interestingly, discordant conditions were more prevalent than concordant conditions with painful conditions being the second most common comorbidity. Better understanding of the implications of different patterns of multimorbidity in people with T2D will allow more effective tailored care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.