Introduction: Through the INT ernational Conso R tium of P rimary Care B I g D ata Researchers ( INTRePID ), we compared the pandemic impact on the volume of primary care visits and uptake of virtual care in Australia, Canada, China, Norway, Singapore, South Korea, Sweden, the UK and the USA. Methods: Visit definitions were agreed on centrally, implemented locally across the various settings in INTRePID countries, and weekly visit counts were shared centrally for analysis. We evaluated the weekly rate of primary care physician visits during 2019 and 2020. Rate ratios (RRs) of total weekly visit volume and the proportion of weekly visits that were virtual in the pandemic period in 2020 compared with the same prepandemic period in 2019 were calculated. Results: In 2019 and 2020, there were 80 889 386 primary care physician visits across INTRePID. During the pandemic, average weekly visit volume dropped in China, Singapore, South Korea, and the USA but was stable overall in Australia (RR 0.98 (95% CI 0.92 to 1.05, p=0.59)), Canada (RR 0.96 (95% CI 0.89 to 1.03, p=0.24)), Norway (RR 1.01 (95% CI 0.88 to 1.17, p=0.85)), Sweden (RR 0.91 (95% CI 0.79 to 1.06, p=0.22)) and the UK (RR 0.86 (95% CI 0.72 to 1.03, p=0.11)). In countries that had negligible virtual care prepandemic, the proportion of visits that were virtual were highest in Canada (77.0%) and Australia (41.8%). In Norway (RR 8.23 (95% CI 5.30 to 12.78, p<0.001), the UK (RR 2.36 (95% CI 2.24 to 2.50, p<0.001)) and Sweden (RR 1.33 (95% CI 1.17 to 1.50, p<0.001)) where virtual visits existed prepandemic, it increased significantly during the pandemic. Conclusions: The drop in primary care in-person visits during the pandemic was a global phenomenon across INTRePID countries. In several countries, primary care shifted to virtual visits mitigating the drop in in-person visits.

There is emerging interest in multimorbidity in type 2 diabetes (T2D), which can be either concordant (T2D related) or discordant (unrelated), as a way of understanding the burden of disease in T2D. Current diabetes guidelines acknowledge the complex nature of multimorbidity, the management of which should be based on the patient’s individual clinical needs and comorbidities. However, although associations between multimorbidity, glycated haemoglobin (HbA1c), and mortality in people with T2D have been studied to some extent, significant gaps remain, particularly regarding different patterns of multimorbidity, including concordant and discordant conditions. This study explores associations between multimorbidity (total condition counts/concordant/discordant/different combinations of conditions), baseline HbA1c, and all-cause mortality in T2D. We studied two longitudinal cohorts of people with T2D using the UK Biobank (n = 20,569) and the Taiwan National Diabetes Care Management Program (NDCMP) (n = 59,657). The number of conditions in addition to T2D was used to quantify total multimorbidity, concordant, and discordant counts, and the effects of different combinations of conditions were also studied. Outcomes of interest were baseline HbA1c and all-cause mortality. For the UK Biobank and Taiwan NDCMP, mean (SD) ages were 60.2 (6.8) years and 60.8 (11.3) years; 7,579 (36.8%) and 31,339 (52.5%) were female; body mass index (BMI) medians (IQR) were 30.8 (27.7, 34.8) kg/m² and 25.6 (23.5, 28.7) kg/m²; and 2,197 (10.8%) and 9,423 (15.8) were current smokers, respectively. Increasing total and discordant multimorbidity counts were associated with lower HbA1c and increased mortality in both datasets. In Taiwan NDCMP, for those with four or more additional conditions compared with T2D only, the mean difference (95% CI) in HbA1c was −0.82% (−0.88, −0.76) p < 0.001. In UK Biobank, hazard ratios (HRs) (95% CI) for all-cause mortality in people with T2D and one, two, three, and four or more additional conditions compared with those without comorbidity were 1.20 (0.91–1.56) p < 0.001, 1.75 (1.35–2.27) p < 0.001, 2.17 (1.67–2.81) p < 0.001, and 3.14 (2.43–4.03) p < 0.001, respectively. Both concordant/discordant conditions were significantly associated with mortality; however, HRs were largest for concordant conditions. Those with four or more concordant conditions had >5 times the mortality (5.83 [4.28–7.93] p <0.001). HRs for NDCMP were similar to those from UK Biobank for all multimorbidity counts. For those with two conditions in addition to T2D, cardiovascular diseases featured in 18 of the top 20 combinations most highly associated with mortality in UK Biobank and 12 of the top combinations in the Taiwan NDCMP. In UK Biobank, a combination of coronary heart disease and heart failure in addition to T2D had the largest effect size on mortality, with a HR (95% CI) of 4.37 (3.59–5.32) p < 0.001, whereas in the Taiwan NDCMP, a combination of painful conditions and alcohol problems had the largest effect size on mortality, with an HR (95% CI) of 4.02 (3.08–5.23) p < 0.001. One limitation to note is that we were unable to model for changes in multimorbidity during our study period. Multimorbidity patterns associated with the highest mortality differed between UK Biobank (a population predominantly comprising people of European descent) and the Taiwan NDCMP, a predominantly ethnic Chinese population. Future research should explore the mechanisms underpinning the observed relationship between increasing multimorbidity count and reduced HbA1c alongside increased mortality in people with T2D and further examine the implications of different patterns of multimorbidity across different ethnic groups. Better understanding of these issues, especially effects of condition type, will enable more effective personalisation of care.

Objectives General practitioners (GPs) are often the first health professionals to assess patients with osteoarthritis (OA). Despite clinical guideline recommendations for non-surgical intervention as first-line therapies, the most frequent referral from a GP for a person with knee OA is to an orthopaedic surgeon. The aim of our study was to explore patient factors that impact on the decision to progress to total knee replacement (TKR), including the experience of patients in general practice, their perceptions of their condition, and their access and use of community-based allied health interventions. Design Qualitative investigation using semi-structured interviews. The Candidacy framework was selected as a lens to examine the factors driving healthcare access. Data were analysed using a thematic analysis approach. Codes identified in the data were mapped to the seven Candidacy domains. Themes corresponding to each domain were described. Setting A public hospital in Melbourne, Australia. Participants 27 patients with knee OA who were on a waiting list to undergo TKR. Results Ten themes described factors influencing access and use of non-surgical interventions and decision-making for undergoing TKR: (1) History of knee problems, change in symptoms; (2) Physical and psychosocial functioning (Identification of Candidacy); (3) GP and social networks as information sources, access to care (Navigation); (4) Referral pathways (Permeability of services); (5) Communication of impact (Appearances at health services); (6) GP-Surgeon as the predominant referral pathway (Adjudications); (7) Physical activity as painful; (8) Beliefs about effectiveness of non-surgical interventions (Offers and resistance); (9) Familiarity with local system; and (10) Availability (Operating conditions and local production of Candidacy). Conclusions Using the Candidacy framework to analyse patients’ experiences when deciding to progress to TKR highlighted missed opportunities in general practice to orient patients to first try non-surgical interventions. Patients with knee OA also require improved support to navigate allied health services.

Objectives This study examined the effect of using patient-reported outcome measures (PROMs) routinely to assess and address depressive symptoms and diabetes distress among adults with type 2 diabetes. Design A systematic review of published peer-reviewed studies. Data sources Medline, Embase, CINAHL Complete, PsycINFO, The Cochrane Library and Cochrane Central Register of Controlled Trials were searched. Eligibility criteria Studies including adults with type 2 diabetes, published in English, from the inception of the databases to 24 February 2022 inclusive; and where the intervention included completion of a PROM of depressive symptoms and/or diabetes distress, with feedback of the responses to a healthcare professional. Data extraction and synthesis Using Covidence software, screening and risk of bias assessment were conducted by two reviewers independently with any disagreements resolved by a third reviewer. Results The search identified 4512 citations, of which 163 full-text citations were assessed for eligibility, and nine studies met the inclusion criteria. Five studies involved assessment of depressive symptoms only, two studies assessed diabetes distress only, and two studies assessed both. All studies had an associated cointervention. When depressive symptoms were assessed (n=7), a statistically significant between-group difference in depressive symptoms was observed in five studies; with a clinically significant (>0.5%) between-group difference in HbA1c in two studies. When diabetes distress was assessed (n=4), one study demonstrated statistically significant difference in depressive symptoms and diabetes distress; with a clinically significant between-group difference in HbA1c observed in two studies. Conclusion Studies are sparse in which PROMs are used to assess and address depressive symptoms or diabetes distress during routine clinical care of adults with type 2 diabetes. Further research is warranted to understand how to integrate PROMs into clinical care efficiently and determine appropriate interventions to manage identified problem areas. PROSPERO registration number CRD42020200246.

The majority of care for people with type 2 diabetes occurs in general practice, however when insulin initiation is required it often does not occur in this setting or in a timely manner and this may have implications for the development of complications. Increased insulin initiation in general practice is an important goal given the increasing prevalence of type 2 diabetes and a relative shortage of specialists. Coordination between primary and secondary care, and between medical and nursing personnel, may be important in achieving this. Relational coordination theory identifies key concepts that underpin effective interprofessional work: communication which is problem solving, timely, accurate and frequent and relationships between professional roles which are characterized by shared goals, shared knowledge and mutual respect. This study explores roles and relationships between health professionals involved in insulin initiation in order to gain an understanding of factors which may impact on this task being carried out in the general practice setting.

Background: The use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA). Methods: Variables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014–15 Australian National Health Survey (NHS). Results: There were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour. Conclusions: In this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.