The burden of diabetes-associated multiple long-term conditions on years of life spent and lost.

Diabetes mellitus is a central driver of multiple long-term conditions (MLTCs), but population-based studies have not clearly characterized the burden across the life course. We estimated the age of onset, years of life spent and loss associated with diabetes-related MLTCs among 46 million English adults. We found that morbidity patterns extend beyond classic diabetes complications and accelerate the onset of severe MLTCs by 20 years earlier in life in women and 15 years earlier in men. By the age of 50 years, one-third of those with diabetes have at least three conditions, spend >20 years with them and die 11 years earlier than the general population. Each additional condition at the age of 50 years is associated with four fewer years of life. Hypertension, depression, cancer and coronary heart disease contribute heavily to MLTCs in older age and create the greatest community-level burden on years spent (813 to 3,908 years per 1,000 individuals) and lost (900 to 1,417 years per 1,000 individuals). However, in younger adulthood, depression, severe mental illness, learning disabilities, alcohol dependence and asthma have larger roles, and when they occur, all except alcohol dependence were associated with long periods of life spent (11-14 years) and all except asthma associated with many years of life lost (11-15 years). These findings provide a baseline for population monitoring and underscore the need to prioritize effective prevention and management approaches.

Trends in hospitalization for cardio-renal disease and mortality in people with type 1 diabetes in England, 2009-2019.

AIM: To assess mortality and complication trends in people with type 1 diabetes during the 11 years before the SARS-CoV2 pandemic (2009-2019). MATERIALS AND METHODS: Sequential cohorts of people in England with type 1 diabetes aged ≥20 years from the National Diabetes Audit (2006/2007 to 2016/2017) were analysed. Discretized Poisson regression models, adjusted for age, sex, ethnicity, socioeconomic deprivation and duration of diabetes, were used to calculate mortality and hospitalization rates. RESULTS: Demographic characteristics changed little; average diabetes duration increased. All-cause mortality was unchanged. Cardiovascular and kidney disease mortality declined. Mortality from respiratory disease, diabetes and dementia increased in younger people (aged 20-74 years) as did mortality from liver disease and dementia in the elderly (aged ≥75 years). Younger Asian and Black people had lower all-cause mortality than those of White ethnicity; elderly Mixed, Asian and Black people had lower all-cause mortality. People from more deprived areas had higher all-cause mortality. The deprivation gradient for mortality was steeper at younger ages. In younger people, rates of hospitalization increased for myocardial infarction, stroke, heart failure and kidney disease but only for kidney disease in the elderly. Rates of a composite measure of cardiovascular hospitalizations increased in younger people (rate ratio [RR] 1.07, 95% confidence interval [CI] 1.03-1.11) but declined in the elderly (RR 0.91, 95% CI 0.86-0.95). CONCLUSION: Between 2009 and 2019, hospitalizations for cardiovascular disease increased at younger ages (20-74 years) and hospitalizations for kidney disease increased at all ages, but mortality from cardiovascular and kidney disease declined. All-cause mortality rates were unchanged.

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data. RESEARCH DESIGN AND METHODS: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden. RESULTS: Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset. CONCLUSIONS: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Trends in weight gain recorded in English primary care before and during the Coronavirus-19 pandemic: An observational cohort study using the OpenSAFELY platform.

BACKGROUND: Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. METHODS AND FINDINGS: With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before (δ-prepandemic) and during (δ-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m2/year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period (δ-change = δ-pandemic-δ-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain (δ-change ≥1.84 kg/m2/year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. CONCLUSIONS: We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

Indirect effects of the COVID-19 pandemic on diagnosing, monitoring, and prescribing in people with diabetes and strategies for diabetes service recovery internationally.

The COVID-19 pandemic has caused major disruptions in clinical services for people with chronic long-term conditions. In this narrative review, we assess the indirect impacts of the COVID-19 pandemic on diabetes services globally and the resulting adverse effects on rates of diagnosing, monitoring, and prescribing in people with type 2 diabetes. We summarise potential practical approaches that could address these issues and improve clinical services and outcomes for people living with diabetes during the recovery phase of the pandemic.

Prediabetes, participation in the English National Health Service Diabetes Prevention Programme, and associations with COVID-19-related mortality: A whole population study.

AIMS: To assess the effects of non-diabetic hyperglycaemia (NDH, also known as pre-diabetes), including the impact of the NHS Diabetes Prevention Programme (NHS DPP), on COVID-19-related mortality during the pandemic. METHODS: This study included all 61,438,225 individuals registered with General Practices in England and alive on 1st March 2020. We assessed COVID-19-related mortality in the 2,290,280(3.7 %) individuals with diagnosed NDH between March 2020 and February 2022 compared to those without diagnosed NDH or diabetes using Cox regression to adjust for demographic factors and cardiovascular comorbidities. Individuals with diagnosed NDH were further sub-categorised based on their contact with the NHS DPP (N = 376,590). Analyses were stratified by age (years) (<50, 50-69 and ≥ 70). RESULTS: There were 158,070 COVID-19 deaths; 17,280(11 %) for people with diagnosed NDH. The adjusted hazard ratio (HR) was 0.95(0.93-0.96),p < 0.001 for those with diagnosed NDH compared to those without diagnosed diabetes or NDH. By age (years), HRs were, 2.53(2.23-2.88),p < 0.001 for < 50, 1.29(1.24-1.35),p < 0.001 for 50-69 and 0.87(0.85-0.89),p < 0.001 for ≥ 70. NHS DPP attendance was associated with lower COVID-19 mortality with a dose-response relationship with engagement. CONCLUSIONS: Younger people with diagnosed NDH were at higher relative risk of COVID-19 mortality. Attendance at the NHS DPP was associated with significantly lower COVID-19-related mortality.

Early outcomes of referrals to the English National Health Service Digital Weight Management Programme.

OBJECTIVE: The study objective was to assess participant weight change for the English National Health Service (NHS) Digital Weight Management Programme, the first such digital intervention to achieve population coverage. METHODS: A service evaluation was used to assess intervention effectiveness for adults with obesity and a diagnosis of hypertension and/or diabetes, between April 2021 and March 2022, using prospectively collected, national service-level data in England. RESULTS: Of the 63,937 referrals made from general practices, within the time period, 31,861 (50%) chose to take up the 12-week Programme. There were 31,718 participants who had time to finish the Programme; of those, 14,268 completed the Programme (defined as attending ≥60%), a 45% completion rate. The mean weight change for those who had time to finish the Programme was -2.2 kg (95% CI: -2.25 to -2.16), for those who completed it was -3.9 kg (95% CI: -3.99 to -3.84), and for those who had time to finish the Programme but did not complete it was -0.74 kg (95% CI: -0.79 to -0.70). CONCLUSIONS: The NHS Digital Weight Management Programme is effective at achieving clinically meaningful weight loss. The outcomes compare favorably to web-based weight management interventions tested in randomized trials and those delivered as face-to-face interventions, and results suggest that the approach may, with increased participation, bring population-level benefits.

Can the introduction of a 12-lead ECG help reduce mortality in those presenting with foot ulceration to multidisciplinary diabetic foot clinics? An observational evaluation of a real-world implementation pilot in England.

AIMS/HYPOTHESIS: The risk of dying within 2 years of presentation with diabetic foot ulceration is over six times the risk of amputation, with CVD the major contributor. Using an observational evaluation of a real-world implementation pilot, we aimed to assess whether for those presenting with diabetic foot ulceration in England, introducing a 12-lead ECG into routine care followed by appropriate clinical action was associated with reduced mortality. METHODS: Between July 2014 and December 2017, ten multidisciplinary diabetic foot services in England participated in a pilot project introducing 12-lead ECGs for new attendees with foot ulceration. Inception coincided with launch of the National Diabetes Footcare Audit (NDFA), whereby all diabetic footcare services in England were invited to enter data on new attendees with foot ulceration. Poisson regression models assessed the mortality RR at 2 and 5 years following first assessment of those receiving care in a participating pilot unit vs those receiving care in any other unit in England, adjusting for age, sex, ethnicity, deprivation, type and duration of diabetes, ulcer severity, and morbidity in the year prior to first assessment. RESULTS: Of the 3110 people recorded in the NDFA at a participating unit during the pilot, 33% (1015) were recorded as having received an ECG. A further 25,195 people recorded in the NDFA had attended another English footcare service. Unadjusted mortality in the pilot units was 16.3% (165) at 2 years and 37.4% (380) at 5 years for those who received an ECG, and 20.5% (430) and 45.2% (950), respectively, for those who did not receive an ECG. For people included in the NDFA at other units, unadjusted mortality was 20.1% (5075) and 42.6% (10,745), respectively. In the fully adjusted model, mortality was not significantly lower for those attending participating units at 2 (RR 0.93 [95% CI 0.85, 1.01]) or 5 years (RR 0.95 [95% CI 0.90, 1.01]). At participating units, mortality in those who received an ECG vs those who did not was lower at 5 years (RR 0.86 [95% CI 0.76, 0.97]), but not at 2 years (RR 0.87 [95% CI 0.72, 1.04]). Comparing just those that received an ECG with attendees at all other centres in England, mortality was lower at 5 years (RR 0.87 [95% CI 0.78, 0.96]), but not at 2 years (RR 0.86 [95% CI 0.74, 1.01]). CONCLUSIONS/INTERPRETATION: The evaluation confirms the high mortality seen in those presenting with diabetic foot ulceration. Overall mortality at the participating units was not significantly reduced at 2 or 5 years, with confidence intervals just crossing parity. Implementation of the 12-lead ECG into the routine care pathway proved challenging for clinical teams-overall a third of attendees had one, although some units delivered the intervention to over 60% of attendees-and the evaluation was therefore underpowered. Nonetheless, the signals of potential mortality benefit among those who had an ECG suggest that units in a position to operationalise implementation may wish to consider this. DATA AVAILABILITY: Data from the National Diabetes Audit can be requested through the National Health Service Digital Data Access Request Service process at: https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services/data-set-catalogue/national-diabetes-audit-nda.

Non-pharmacological interventions to improve cardiovascular risk factors in people with diabetic foot disease: A systematic review and meta-analysis.

Cardiovascular disease (CVD) risk in those with diabetic foot disease is very high. Non-pharmacological interventions may improve this risk, though no previous evidence synthesis has been completed. This systematic review aimed to investigate the impact of non-pharmacological interventions on CVD risk factors in diabetic ulcer disease. Multiple databases and trials registers were searched from inception to December 6th 2023. We included reports of randomised controlled trials investigating the impact of non-pharmacological interventions on cardiovascular risk in those with type 1 or type 2 diabetes and current or previous diabetic foot disease. Twenty studies were included. Extracted data included: study design and setting; participant sociodemographic factors; and change in cardiovascular risk factors. Data were synthesised using random effects meta-analyses and narrative syntheses. Interventions included nutritional supplementation, collaborative care, hyperbaric oxygen therapy, patient education, nurse-led intervention, self-management, family support, relaxation and exercise, over a median duration of 12 weeks. Significant post-intervention changes were observed in fasting plasma glucose, serum insulin levels, insulin sensitivity and resistance, glycated haemoglobin, triglycerides, total cholesterol, low-density lipoprotein-cholesterol and C-reactive protein. No effects were detected in very low- or high-density lipoprotein-cholesterol or body mass index. Non-pharmacological interventions show promise in improving CVD risk in diabetic foot disease.

Weight trends amongst adults with diabetes or hypertension during the COVID-19 pandemic: an observational study using OpenSAFELY.

BACKGROUND: COVID-19 pandemic restrictions may have influenced behaviours related to weight. AIMS: To describe patterns of weight change amongst adults living in England with Type 2 Diabetes (T2D) and/or hypertension during the COVID-19 pandemic. Design and Setting With the approval of NHS England, we conducted an observational cohort study using the routinely collected health data of approximately 40% of adults living in England, accessed through the OpenSAFELY service inside TPP. METHOD: We investigated clinical and sociodemographic characteristics associated with rapid weight gain (>0·5kg/m2/year) using multivariable logistic regression. RESULTS: We extracted data on adults with T2D (n=1,231,455, 44% female, 76% white British) or hypertension (n=3,558,405, 50% female, 84% white British). Adults with T2D lost weight overall (median δ = -0.1kg/m2/year [IQR: -0.7, 0.4]), however, rapid weight gain was common (20.7%) and associated with sex (male vs female: aOR 0.78[95%CI 0.77, 0.79]); age, older age reduced odds (e.g. 60-69-year-olds vs 18-29-year-olds: aOR 0.66[0.61, 0.71]); deprivation, (least-deprived-IMD vs most-deprived-IMD: aOR 0.87[0.85, 0.89]); white ethnicity (Black vs White: aOR 0.95[0.92, 0.98]); mental health conditions (e.g. depression: aOR 1.13 [1.12, 1.15]); and diabetes treatment (non-insulin treatment vs no pharmacological treatment: aOR 0.68[0.67, 0.69]). Adults with hypertension maintained stable weight overall (median δ = 0.0kg/m2/year [ -0.6, 0.5]), however, rapid weight gain was common (24.7%) and associated with similar characteristics as in T2D. CONCLUSION: Amongst adults living in England with T2D and/or hypertension, rapid pandemic weight gain was more common amongst females, younger adults, those living in more deprived areas, and those with mental health condition.

The effectiveness of digital delivery versus group-based face-to-face delivery of the English National Health Service Type 2 Diabetes Prevention Programme: a non-inferiority retrospective cohort comparison study.

BACKGROUND: Face-to-face group-based diabetes prevention programmes have been shown to be effective in many settings. Digital delivery may suit some patients, but research comparing the effectiveness of digital with face-to-face delivery is scarce. The aim was to assess if digital delivery of the English National Health Service Diabetes Prevention Programme (NHS DPP) is non-inferior to group-based face-to-face delivery in terms of weight change, and evaluate factors associated with differential change. METHODS: The study included those recruited to the NHS DPP in 2017-2018. Individual-level data from a face-to-face cohort was compared to two cohorts on a digital pilot who (i) were offered no choice of delivery mode, or (ii) chose digital over face-to-face. Changes in weight at 6 and 12 months were analysed using mixed effects linear regression, having matched participants from the digital pilot to similar participants from face-to-face. RESULTS: Weight change on the digital pilot was non-inferior to face-to-face at both time points: it was similar in the comparison of those with no choice (difference in weight change: -0.284 kg [95% CI: -0.712, 0.144] at 6 months) and greater in digital when participants were offered a choice (-1.165 kg [95% CI: -1.841, -0.489]). Interactions between delivery mode and sex, ethnicity, age and deprivation were observed. CONCLUSIONS: Digital delivery of the NHS DPP achieved weight loss at least as good as face-to-face. Patients who were offered a choice and opted for digital experienced better weight loss, compared to patients offered face-to-face only.

Diabetes and Multiple Long-term Conditions: A Review of Our Current Global Health Challenge.

Use of effective treatments and management programs is leading to longer survival of people with diabetes. This, in combination with obesity, is thus contributing to a rise in people living with more than one condition, known as multiple long-term conditions (MLTC or multimorbidity). MLTC is defined as the presence of two or more long-term conditions, with possible combinations of physical, infectious, or mental health conditions, where no one condition is considered as the index. These include a range of conditions such as cardiovascular diseases, cancer, chronic kidney disease, arthritis, depression, dementia, and severe mental health illnesses. MLTC has major implications for the individual such as poor quality of life, worse health outcomes, fragmented care, polypharmacy, poor treatment adherence, mortality, and a significant impact on health care services. MLTC is a challenge, where interventions for prevention and management are lacking a robust evidence base. The key research directions for diabetes and MLTC from a global perspective include system delivery and care coordination, lifestyle interventions and therapeutic interventions.

Prevalence of multiple long-term conditions (multimorbidity) in England: a whole population study of over 60 million people.

OBJECTIVES: To determine the prevalence of multiple long-term conditions (MLTC) at whole English population level, stratifying by age, sex, socioeconomic status and ethnicity. DESIGN: A whole population study. SETTING: Individuals registered with a general practice in England and alive on 31 March 2020. PARTICIPANTS: 60,004,883 individuals. MAIN OUTCOME MEASURES: MLTC prevalence, defined as two or more of 35 conditions derived from a number of national patient-level datasets. Multivariable logistic regression was used to assess the independent associations of age, sex, ethnicity and deprivation decile with odds of MLTC. RESULTS: The overall prevalence of MLTC was 14.8% (8,878,231), varying from 0.9% (125,159) in those aged 0-19 years to 68.2% (1,905,979) in those aged 80 years and over. In multivariable regression analyses, compared with the 50-59 reference group, the odds ratio was 0.04 (95% confidence interval (CI): 0.04-0.04; p < 0.001) for those aged 0-19 years and 10.21 (10.18-10.24; p < 0.001) for those aged 80 years and over. Odds were higher for men compared with women, 1.02 (1.02-1.02; p < 0.001), for the most deprived decile compared with the least deprived, 2.26 (2.25-2.27; p < 0.001), and for Asian ethnicity compared with those of white ethnicity, 1.05 (1.04-1.05; p < 0.001). Odds were lower for black, mixed and other ethnicities (0.94 (0.94-0.95) p < 0.001, 0.87 (0.87-0.88) p < 0.001 and 0.57 (0.56-0.57) p < 0.001, respectively). MLTC for persons aged 0-19 years were dominated by asthma, autism and epilepsy, for persons aged 20-49 years by depression and asthma, for persons aged 50-59 years by hypertension and depression and for those aged 60 years and older, by cardiometabolic factors and osteoarthritis. There were large numbers of combinations of conditions in each age group ranging from 5936 in those aged 0-19 years to 205,534 in those aged 80 years and over. CONCLUSIONS: While this study provides useful insight into the burden across the English population to assist health service delivery planning, the heterogeneity of MLTC presents challenges for delivery optimisation.

Association between the month of starting a weight management program and weight change in people at high risk of type 2 diabetes: A prospective cohort study.

OBJECTIVE: Weight loss is one of the most common New Year's resolutions, but it is unclear whether attempting to lose weight in January is more successful than attempting it at other times of the year. METHODS: In this prospective cohort study from the English National Health Service (NHS) Diabetes Prevention Program, adults with nondiabetic hyperglycemia were enrolled in a structured behavioral weight management program. Repeated measures models assessed the mean difference between baseline and follow-up weight adjusting for monthly variation in weight among those with ≥1 weight measurement. RESULTS: Among 85,514 participants with a mean baseline BMI of 30.3 kg/m2 (range: 13.4 to 84.2), mean weight change at the end of the program after an average 7.9 (SD: 4.5) sessions over 6.4 (SD: 5.6) months was -2.00 kg (95% CI: -2.02 to -1.97 kg) or -2.33% (95% CI: -2.35% to -2.32%). Compared with participants starting in January, participants starting in other months lost less weight, ranging between 0.28 kg (95% CI: 0.10 to 0.45 kg) less weight in those starting in March and 0.71 kg (95% CI: 0.55 to 0.87 kg) less weight in those starting in November. April and May were the only exceptions, in which the estimates followed the same direction but were not statistically significant. Higher session attendance mediated the effects, with participants starting in January attending, on average, 0.2 to 0.7 more sessions than those starting in other months. CONCLUSIONS: People starting a weight management program in January lost 12% to 30% more weight than people starting it at other times of the year.

Comparison of weight change between face-to-face and digital delivery of the English National Health service diabetes prevention programme: An exploratory non-inferiority study with imputation of plausible weight outcomes.

Worldwide evidence suggests face-to-face diabetes prevention programmes are effective in preventing and delaying the onset of type 2 diabetes by encouraging behaviour change towards weight loss, healthy eating, and increased exercise. There is an absence of evidence on whether digital delivery is as effective as face-to-face. During 2017-18 patients in England were offered the National Health Service Diabetes Prevention Programme as group-based face-to-face delivery, digital delivery ('digital-only') or a choice between digital and face-to-face ('digital-choice'). The contemporaneous delivery allowed for a robust non-inferiority study, comparing face-to-face with digital only and digital choice cohorts. Changes in weight at 6 months were missing for around half of participants. Here we take a novel approach, estimating the average effect in all 65,741 individuals who enrolled in the programme, by making a range of plausible assumptions about weight change in individuals who did not provide outcome data. The benefit of this approach is that it includes everyone who enrolled in the programme, not restricted to those who completed. We analysed the data using multiple linear regression models. Under all scenarios explored, enrolment in the digital diabetes prevention programme was associated with clinically significant reductions in weight which were at least equivalent to weight loss in the face-to-face programme. Digital services can be just as effective as face-to-face in delivering a population-based approach to the prevention of type 2 diabetes. Imputation of plausible outcomes is a feasible methodological approach, suitable for analysis of routine data in settings where outcomes are missing for non-attenders.