Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database.

Background MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes. Methods Patients’ disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice. Results A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively. Conclusions The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning

Impact of multimorbidity count on all-cause mortality and glycaemic outcomes in people with type 2 diabetes: a systematic review protocol

Introduction: Type 2 diabetes (T2D) is a leading health priority worldwide. Multimorbidity (MM) is a term describing the co-occurrence of two or more chronic diseases or conditions. The majority of people living with T2D have MM. The relationship between MM and mortality and glycaemia in people with T2D is not clear. Methods and analysis: Medline, Embase, Cumulative Index of Nursing and Allied Health Complete, The Cochrane Library, and SCOPUS will be searched with a prespecified search strategy. The searches will be limited to quantitative empirical studies in English with no restriction on publication date. One reviewer will perform title screening and two review authors will independently screen the abstract and full texts using Covidence software, with disagreements adjudicated by a third reviewer. Data will be extracted using a using a Population, Exposure, Comparator and Outcomes framework. Two reviewers will independently extract data and undertake the risk of bias (quality) assessment. Disagreements will be resolved by consensus. A narrative synthesis of the results will be conducted and meta-analysis considered if appropriate. Quality appraisal will be undertaken using the Newcastle-Ottawa quality assessment scale and the quality of the cumulative evidence of the included studies will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. This protocol was prepared in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines to ensure the quality of our review. Ethics and dissemination: This review will synthesise the existing evidence about the impact of MM on mortality and glycaemic outcomes in people living with T2D and increase our understanding of this subject and will inform future practice and policy. Findings will be disseminated via conference presentations, social media and peer-reviewed publication

Stepping Up Telehealth: Using telehealth to support a new model of care for type 2 diabetes management in rural and regional primary care

Our proposal is to pilot the feasibility and acceptability of a telehealth intervention to enhance care in rural general practice for people with out-of-target Type 2 Diabetes (T2D). Our research program builds on the UK Medical Research Council framework in developing a model of care intervention that is well matched to the setting of General Practice and to the experiences and priorities of patients. We undertook an exploratory qualitative study, leading to the development of a practice-based intervention that we pilot tested for feasibility and acceptability before undertaking a larger pilot and a cluster RCT. We based our work on Normalisation Process Theory (NPT), a sociological theory of implementation, which describes how new practices become incorporated into routine clinical care as a result of individual and collective work. NPT suggested that our model of care intervention would need to be patient centred and include all members of the multidisciplinary diabetes team, including Endocrinologist, RN-CDE General Practitioners (GP), and generalist Practice Nurses (PNs). All of these groups are involved in the �work� of insulin initiation.The research reported in this paper is a project of the Australian Primary Health Care Research Institute which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research Evaluation and Development Strategy

General Practice Management of Type 2 Diabetes: 2016–18

[Extract] Diabetes is a national health priority. The Australian National Diabetes Strategy 2016–2020was released by the Australian Government in November 2013. The number of people with type 2 diabetes is growing, most likely the result of rising overweight and obesity rates, lifestyle and dietary changes, and an ageing population. Within 20 years, the number of people in Australia with type 2 diabetes may increase from an estimated 870,000 in 2014, to more than 2.5 million.1The most socially disadvantaged Australians are twice as likely to develop diabetes. If left undiagnosed or poorly managed, type 2 diabetes can lead to coronary artery disease (CAD), stroke, kidney failure, limb amputations and blindness. The early identification and optimal management of people with type 2 diabetes is therefore critical. General practice has the central role in type 2 diabetes management across the spectrum, from identifying those at risk right through to caring for patients at the end of life. These guidelines give up-to-date, evidence-based information tailored for general practice to support general practitioners (GPs) and their teams in providing high-quality management.1In the development of the 2016–18 edition of General practice management of type 2 diabetes, The Royal Australian College of General Practitioners (RACGP) has focused on factors relevant to current Australian clinical practice. The RACGP has used the skills and knowledge of your general practice peers who have an interest in diabetes management and are members of the RACGP Specific Interests Diabetes Network. This publication has been produced in accordance with the rules and processes outlined in the RACGP’s conflict of interest (COI) policy. The RACGP’s COI policy is available at www.racgp.org.au/support/policies/organisationalThis edition represents 19 years of a successful relationship between the RACGP and Diabetes Australia. We acknowledge the support of the RACGP Expert Committee – Quality Care, the Medical Education and Scientific Committee of Diabetes Australia, and RACGP staff in the development of these guideline

The impact of structured self-monitoring of blood glucose on clinical, behavioral, and psychosocial outcomes among adults with non-insulin-treated type 2 diabetes: a systematic review and meta-analysis

BackgroundSelf-monitoring of blood glucose (SMBG) is considered of little clinical benefit for adults with non-insulin-treated type 2 diabetes, but no comprehensive review of a structured approach to SMBG has been published to date.PurposeTo conduct a systematic review and meta-analysis of the impact of sSMBG on HbA1c, treatment modifications, behavioral and psychosocial outcomes, and; examine the moderating effects of sSMBG protocol characteristics on HbA1c.Data sourcesFour databases searched (November 2020; updated: February 2022).Study selectionInclusion criteria: non-randomized and randomized controlled trials (RCTs) and prospective observational studies; reporting effect of sSMBG on stated outcomes; among adults (≥18 years) with non-insulin-treated type 2 diabetes. Studies excluded if involving children or people with insulin-treated or other forms of diabetes.Data extraction and analysisOutcome data extracted, and risk of bias/quality assessed independently by two researchers. Meta-analysis was conducted for RCTs, and moderators explored (HbA1c only).Data synthesisFrom 2,078 abstracts, k=23 studies were included (N=5,372). Risk of bias was evident and study quality was low. Outcomes assessed included: HbA1c (k=23), treatment modification (k=16), psychosocial/behavioral outcomes (k=12). Meta-analysis revealed a significant mean difference favoring sSMBG in HbA1c (-0·29%, 95% CI: -0·46 to -0·11, k=13) and diabetes self-efficacy (0.17%, 95% CI: 0.01 to 0.33, k=2). Meta-analysis revealed no significant moderating effects by protocol characteristics.LimitationsFindings limited by heterogeneity in study designs, intervention characteristics, and psychosocial assessments.ConclusionA small positive effect of sSMBG on HbA1c and diabetes self-efficacy was observed. Narrative synthesis of sSMBG intervention characteristics may guide future implementation.PROSPERO registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020208857, identifier CRD42020208857

Gathering data for decisions: best practice use of primary care electronic records for research

In Australia, there is limited use of primary health care data for research and for data linkage between health care settings. This puts Australia behind many developed countries. In addition, without use of primary health care data for research, knowledge about patients' journeys through the health care system is limited. There is growing momentum to establish "big data" repositories of primary care clinical data to enable data linkage, primary care and population health research, and quality assurance activities. However, little research has been conducted on the general public's and practitioners' concerns about secondary use of electronic health records in Australia. International studies have identified barriers to use of general practice patient records for research. These include legal, technical, ethical, social and resource-related issues. Examples include concerns about privacy protection, data security, data custodians and the motives for collecting data, as well as a lack of incentives for general practitioners to share data. Addressing barriers may help define good practices for appropriate use of health data for research. Any model for general practice data sharing for research should be underpinned by transparency and a strong legal, ethical, governance and data security framework. Mechanisms to collect electronic medical records in ethical, secure and privacy-controlled ways are available. Before the potential benefits of health-related data research can be realised, Australians should be well informed of the risks and benefits so that the necessary social licence can be generated to support such endeavours.Rachel Canaway, Douglas IR Boyle, Jo‐Anne E Manski‐Nankervis, Jessica Bell, Jane S Hocking, Ken Clarke, Malcolm Clark, Jane M Gunn, Jon D Emer

Associations between multimorbidity and glycaemia (HbA1c) in people with type 2 diabetes: cross-sectional study in Australian general practice

Objectives: To 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). Design: Cross-sectional study. Setting: Australian general practice. Participants: 69 718 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 (%). Results: Mean (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. Conclusions: Multimorbidity 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

Multimorbidity, mortality, and HbA1c in type 2 diabetes: a cohort study with UK and Taiwanese cohorts

Background: 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. Methods and findings: 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. Conclusions: 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

Multimorbidity, glycaemic variability and time in target range in people with type 2 diabetes: a baseline analysis of the GP-OSMOTIC trial

Aims: To explore associations between multimorbidity condition counts (total; concordant (diabetes-related); discordant (unrelated to diabetes)) and glycaemia (HbA1c; glycaemic variability (GV); time in range (TIR)) using data from a randomised controlled trial examining effectiveness of continuous glucose monitoring (CGM) in people with type 2 diabetes (T2D). Methods: Cross-sectional study: 279 people with T2D using baseline data from the General Practice Optimising Structured MOnitoring To Improve Clinical outcomes (GP-OSMOTIC) trial from 25 general practices in Australia. Number of long-term conditions (LTCs) in addition to T2D used to quantify total/concordant/discordant multimorbidity counts. GV (measured by coefficient of variation (CV)) and TIR derived from CGM data. Multivariable linear regression models used to examine associations between multimorbidity counts, HbA1c (%), GV and TIR. Results: Mean (SD) age of participants 60.4 (9.9) years; 40.9% female. Multimorbidity was present in 89.2% of participants. Most prevalent comorbid LTCs: hypertension (57.4%), painful conditions (29.8%), coronary heart disease (22.6%) and depression (19.0%). No evidence of associations between multimorbidity counts, HbA1c, GV and TIR. Conclusions: While multimorbidity was common in this T2D cohort, it was not associated with HbA1c, CV or TIR. Future studies should explore factors other than glycaemia that contribute to the increased mortality observed in those with multimorbidity and T2D

Chronic disease IMPACT (chronic disease early detection and improved management in primary care project): an Australian stepped wedge cluster randomised trial

Background: Interrelated chronic vascular diseases (chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD)) are common with high morbidity and mortality. This study aimed to assess if an electronic-technology-based quality improvement intervention in primary care could improve detection and management of people with and at risk of these diseases. Methods: Stepped-wedge trial with practices randomised to commence intervention in one of five 16-week periods. Intervention included (1) electronic-technology tool extracting data from general practice electronic medical records and generating graphs and lists for audit; (2) education regarding chronic disease and the electronic-technology tool; (3) assistance with quality improvement audit plan development, benchmarking, monitoring and support. De-identified data analysis using R 3.5.1 conducted using Bayesian generalised linear mixed model with practice and time-specific random intercepts. Results: At baseline, eight included practices had 37,946 active patients (attending practice ≥3 times within 2 years) aged ≥18 years. Intervention was associated with increased OR (95% CI) for: kidney health checks (estimated glomerular filtration rate, urine albumin:creatinine ratio (uACR) and blood pressure) in those at risk 1.34 (1.26–1.42); coded diagnosis of CKD 1.18 (1.09–1.27); T2D diagnostic testing (fasting glucose or HbA1c) in those at risk 1.15 (1.08–1.23); uACR in patients with T2D 1.78 (1.56–2.05). Documented eye checks within recommended frequency in patients with T2D decreased 0.85 (0.77–0.96). There were no significant changes in other assessed variables. Conclusions: This electronic-technology-based intervention in primary care has potential to help translate guidelines into practice but requires further refining to achieve widespread improvements across the interrelated chronic vascular diseases