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We lead multidisciplinary applied research and training to rethink the way health care is delivered in general practice and across the community.
Exploring the levels of variation, inequality and use of physical activity intervention referrals in England primary care from 2017-2020: a retrospective cohort study
OBJECTIVES: In this study, we explore the use of physical activity intervention referrals in primary care in England and compare their use with the rate of cardiovascular disease (CVD) risk factors in England from 2017 to 2020. We also explore variation and inequalities in referrals to these interventions in England across the study period. DESIGN: Retrospective cohort study. SETTING: England primary care via the Royal College of General Practitioners Research Surveillance Centre. PARTICIPANTS: The Royal College of General Practitioners Research Surveillance Centre, a sentinel network across England covering a population of over 15 000 000 registered patients, was used for data analyses covering the 2017-2020 financial years and including patients with long-term conditions indicating CVD risk factors. OUTCOME MEASURES: An existing ontology of primary care codes was used to capture physical activity interventions and a new ontology was designed to cover long-term conditions indicating CVD risk factors. Single factor analysis of variance, paired samples t-test and two-tailed, one proportion z-tests were used to determine the significance of our findings. RESULTS: We observed statistically significant variation in physical activity intervention referrals for people with CVD risk factors from different ethnic groups and age groups across different regions of England as well as a marked decrease during the COVID-19 pandemic. Interestingly, a significant difference was not seen for different socioeconomic groups or sexes. Across all attributes and time periods (with the exception of the 18-39 group, 2017-2019), we observed a statistically significant underuse of physical activity intervention referrals. CONCLUSIONS: Our findings identified statistically significant variation and underuse of physical activity referrals in primary care in England for individuals at risk of CVD for different population subgroups, especially different ethnicities and age groups, across different regions of England and across time, with the COVID-19 pandemic exerting a significant negative impact on referral rates.
A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study
Background: Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes. Methods: We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemoglobin (HbA1c), for initiating dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, sodium–glucose co-transporter-2 inhibitors, sulfonylureas, and thiazolidinediones. The model used nine routinely available clinical features of people with type 2 diabetes at drug initiation as predictive factors (age, duration of diabetes, sex, and baseline HbA1c, BMI, estimated glomerular filtration rate, HDL cholesterol, total cholesterol, and alanine aminotransferase). The model was developed and validated with observational data from England (Clinical Practice Research Datalink [CPRD] Aurum), in people with type 2 diabetes aged 18–79 years initiating one of the five drug classes between Jan 1, 2004, and Oct 14, 2020, with holdback validation according to geographical region and calendar period. The model was further validated in individual-level data from three published randomised drug trials in type 2 diabetes (TriMaster three-drug crossover trial and two parallel-arm trials [NCT00622284 and NCT01167881]). For validation in CPRD, we assessed differences in observed glycaemic effectiveness between matched (1:1) concordant and discordant groups receiving therapy that was either concordant or discordant with model-predicted optimal therapy, with optimal therapy defined as the drug class with the highest predicted glycaemic effectiveness (ie, lowest predicted 12-month HbA1c). Further validation involved pairwise drug class comparisons in all datasets. We also evaluated associations with long-term outcomes in model-concordant and model-discordant groups in CPRD, assessing 5-year risks of glycaemic failure (confirmed HbA1c ≥69 mmol/mol), all-cause mortality, major adverse cardiovascular events or heart failure (MACE-HF) outcomes, renal progression, and microvascular complications using Cox proportional hazards regression adjusting for relevant demographic and clinical covariates. Findings: The five-drug class model was developed from 100 107 drug initiations in CPRD. In the overall CPRD cohort (combined development and validation cohorts), 32 305 (15·2%) of 212 166 drug initiations were of the model-predicted optimal therapy. In model-concordant groups, mean observed 12-month HbA1c benefit was 5·3 mmol/mol (95% CI 4·9–5·7) in the CPRD geographical validation cohort (n=24 746 drug initiations, n=12 373 matched pairs) and 5·0 mmol/mol (4·3–5·6) in the CPRD temporal validation cohort (n=9682 drug initiations, n=4841 matched pairs) compared with matched model-discordant groups. Predicted HbA1c differences were well calibrated with observed HbA1c differences in the three clinical trials in pairwise drug class comparisons, and in pairwise comparisons of the five drug classes in CPRD. 5-year risk of glycaemic failure was lower in model-concordant versus model-discordant groups in CPRD (adjusted hazard ratio [aHR] 0·62 [95% CI 0·59–0·64]). For long-term non-glycaemic outcomes, model-concordant versus model-discordant groups had a similar 5-year risk of all-cause mortality (aHR 0·95 [0·83–1·09]) and lower risks of MACE-HF outcomes (aHR 0·85 [0·76–0·95]), renal progression (aHR 0·71 [0·64–0·79]), and microvascular complications (aHR 0·86 [0·78–0·96]). Interpretation: We have developed a five-drug class model that uses routine clinical data to identify optimal glucose-lowering therapies for people with type 2 diabetes. Individuals on model-predicted optimal therapy had lower 12-month HbA1c, were less likely to need additional glucose-lowering therapy, and had a lower risk of diabetes complications than individuals on non-optimal therapy. With setting-specific optimisation, the use of routinely collected parameters means that the model is easy to introduce to clinical care in most countries worldwide. Funding: UK Medical Research Council.
Effects of aspirin and omega-3 fatty acids on age-related macular degeneration in ASCEND-Eye: a randomised placebo-controlled trial in a population with diabetes
Purpose Aspirin and omega-3 fatty acids (FAs) are potential disease modifiers of age-related macular degeneration (AMD), but previous studies have produced inconsistent findings. Randomised evidence for the efficacy and safety of aspirin and omega-3 FAs on AMD is presented in this study. Design ASCEND-Eye is a substudy of eye effects in the 2×2 factorial design ASCEND (A Study of Cardiovascular Events iN Diabetes) double-blind, randomised, placebo-controlled trial for the primary prevention of cardiovascular events. Reports of AMD diagnoses were sourced from 6 monthly ASCEND follow-up questionnaires and a Visual Function Questionnaire. Participants 15 480 UK adults at least 40 years of age with diabetes but no evident cardiovascular disease. Interventions 100 mg aspirin daily versus placebo and, separately, 1 g omega-3 FAs daily versus placebo. Main outcome measure The first post-randomisation reports of AMD. Results During 7.4 years of follow-up, 122 (1.6%) participants randomised to aspirin were reported as having AMD, compared with 138 (1.8%) randomised to placebo (rate ratio 0.88; 95% CI 0.69 to 1.12; p=0.31). AMD occurred in 130 (1.7%) participants randomised to omega-3 FAs, compared with 130 (1.7%) randomised to placebo (rate ratio 0.99; 95% CI 0.78 to 1.27; p=0.99). Conclusion No clinically-meaningful effects of aspirin or omega-3 FAs on AMD were found. Although the study had very limited statistical power to detect clinically relevant effects, these data overcome some methodological limitations of previous observational studies, providing randomised evidence of both treatments on AMD, which could contribute to future meta-analyses.
Short Message Service (SMS) Text Messages in Health Care
Short messaging service (SMS) messages or text messages have the potential for high reach at low cost. Using SMS presents an opportunity to deliver health-related information, reminders, and support that provides self-management directly to individuals in a way other mediums cannot. SMS messages are already successfully used as appointment and medication refill reminders. Further research utilizing extant behavioral science knowledge to develop message content could present an opportunity to use SMS to improve broader self-management of diabetes. Machine learning and artificial intelligence could be used to tailor content and identify which populations would derive the most benefit in which contexts.
Prevalence and patterns of testing for anaemia in primary care in England.
Background Despite epidemiological data on anaemia being available on a global scale, its prevalence in the United Kingdom is not well described. Aim To investigate anaemia prevalence and testing patterns for haemoglobin and other blood parameters. Design and Setting A population-based cohort study using data drawn from the Clinical Practice Research Datalink Aurum database in 2019. Method We extracted demographic data for each person who was registered at their current practice during 2019, including linked data on Index of Multiple Deprivation. We calculated anaemia prevalence in 2019 based on World Health Organization specified age and gender thresholds for haemoglobin. We classified anaemia based on mean corpuscular volume and ferritin. We followed up people with anaemia for up to one year to investigate longitudinal testing patterns for haemoglobin. Results The cohort contained 14 million people. Anaemia prevalence in 2019 was 4.1% (5.1 % females and 3.1% males). Prevalence was higher in people aged >65 years, Black and Asian ethnicities, and people living in areas with higher social deprivation. Only half of people with anaemia and a mean corpuscular volume of ≤100 fL had an accompanying ferritin value recorded. About half of people with anaemia had a follow-up haemoglobin test within one-year, most of which still indicated anaemia. Conclusion Anaemia is prevalent in the UK with large disparities between levels of demographic variables. Investigation and follow-up of anaemia is suboptimal in many patients. Health interventions aimed at improving anaemia investigation and treatment are needed, particularly in these at-risk groups.
Patients′ and clinicians′ experiences of remote consultation? A narrative synthesis
Aims: To identify, evaluate and summarize evidence of patient and clinician experiences of being involved in video or telephone consultations as a replacement for in-person consultations. Design: Narrative synthesis. Data sources: Medline; EMBASE; EMCARE; CINAHL and BNI. Searching took place from January 2021 to April 2021. Papers included were published between 2013 and 2020. Review Methods: Papers were appraised by two independent reviewers for methodological quality. Data extraction was conducted according to the standardized tool from Joanna Briggs Institute. Results: Seven qualitative studies were included, from five countries and from the perspective of patients, relatives, administrators, nurses, physiotherapists and physicians. We developed two main themes: Pragmatic Concerns and Therapeutic Concerns. Each theme contained two categories: Pragmatic Concerns: (a) the convenience of non-face to face consultations; (b) using technology and equipment in a consultation; Therapeutic Concerns (c) building therapeutic relationships; and (d) embracing benefits and addressing challenges. Conclusion: This narrative synthesis presents the existing evidence on clinician and patient experience of participating in non-face to face consultations. Experiences are varied but largely focus on communication and forming relationships, using the technology successfully and the ability for patients to self-manage with support from clinicians who are not in-person. More high-quality studies are required to explore the experiences of patients and clinicians accessing remote consultations as a result of global implementation post-SARS-CoV-2 pandemic to identify any learning and education opportunities. Impact: Health care staff can provide high-quality care through video or telephone appointments as well as face to face appointments. This review has, however, identified that the evidence is limited and weak in this area and recommends there is research further to inform practice and influence future care.
Are Time Series Foundation Models Ready for Vital Sign Forecasting in Healthcare?
The rise of foundation models, particularly large language models like ChatGPT, has revolutionized natural language processing and demonstrated remarkable generalization across numerous healthcare applications. Building on this success, foundation models for time series forecasting have emerged, offering new opportunities by leveraging pretraining on large-scale datasets. However, existing time series foundation models are pretrained with minimal clinical data, and their potentials for continuously recorded clinical time series, such as vital signs, remain largely under-explored. This motivates our endeavor to integrate time series foundation models with vital sign data to address critical clinical challenges, particularly in predicting patient deterioration. Through an extensive evaluation of various settings and configurations of these models, alongside comparisons with conventional forecasting models, we highlight the significant opportunities for improvement in developing clinically useful time series forecasting models. In a word, the “ChatGPT” moment for time series foundation models, in the typical clinical domain, is yet to come.
Mechanism-Based Middle-Range Theories: Using Realist Syntheses to Reconcile Specificity to Context and Generalizability
Realist synthesis is a recognized methodological approach to evidence synthesis to inform evidence-based health policy and practice. The implicit assumption behind research synthesis is that the evidence it generates should be generalizable––drawing broad inferences from specific observations. While this understanding is generally shared among social scientists, tensions exist between having generalizable evidence and how this evidence can be useful in specific contexts. This paper considers the role of mechanism-based middle-range theories obtained from realist synthesis in bridging specificity to context and generalizability. Retroductive theorizing in realist synthesis helps to identify ideas about mechanisms related to the phenomenon embedded in the social and organizational contexts that could, in principle, have a much broader application. Also, because mechanism-based middle-range theories are linked to contextual features, they capture contextual nuances to enhance evidence implementation. We conclude that middle-range mechanisms provide an opportunity to achieve generalizability and contextualization in implementation science.