A Randomised Control Trial to Explore the Impact and Efficacy of the Healum Collaborative Care Planning Software and App on Condition Management in the Type 2 Diabetes Mellitus Population in NHS Primary Care.

INTRODUCTION: Effective and scalable solutions to support management of Type 2 Diabetes (T2D) at a distance are a priority for health systems worldwide. The use of personalised care planning has been shown to be effective at improving the health outcomes and the experience of care amongst people with T2D and other long-term health conditions. Here we describe a specific example of such an intervention. METHODS: The sample comprised 197 participants with T2D randomised to either the active intervention group with digital health planning (App + usual care), with 115 participants, or the control group (usual care), with 82 participants. We analysed data in relation to changes in body mass index (BMI) and glycated haemoglobin (HbA1c) over a 6-month follow-up period. We also analysed responses to questionnaires sent out and held interviews with participants that were in the active treatment group and therefore had a care plan created and access to an app. RESULTS: The active treatment group had significant reductions in HbA1c (p < 0.01) and BMI (p < 0.037) vs the control group (no significant change). The average percentage change in HbA1c for the treatment group over 6 months was - 7.4% (± SE 1.4%), compared with 1.8% (± SE 2.1%) for the control group. The average percentage change in BMI for the treatment group was - 0.7% (± SE 0.4%) and it was - 0.2% (± SE 0.5%) for the control group. A higher percentage of the active treatment group reduced their HbA1c and BMI than the control group. For HbA1c, 72.4% of the active treatment group reduced their HbA1c, compared to 41.5% of the control group. For BMI, 52.7% of the active treatment group experienced a reduction, compared to 42.9% for the control group. Self-measured quality of life (QoL) improved for patients in the active treatment group, shown by an increase in their pre-trial to post-trial EQ-5D-5L rating by an average of 0.0464 (± SE 0.0625), compared to a decrease of 0.0086 (± SE 0.0530) for the control group. The average EQ VAS score also increased pre- to post-trial for the active treatment group, on average by 8.2%, whereas it decreased by an  average of - 2.8% for the control group. CONCLUSION: These findings point to how the provision of personalised plans of care, support and education linked to a mobile app, can result in HbA1c and BMI reduction for many individuals with T2D. The use of a patient management app as well as a personalised care plan also led to an improvement in patient self-rated QoL and engagement.

Assessment of the effect of the COVID-19 pandemic on UK HbA1c testing: implications for diabetes management and diagnosis.

AIMS: The COVID-19 pandemic, and the focus on mitigating its effects, has disrupted diabetes healthcare services worldwide. We aimed to quantify the effect of the pandemic on diabetes diagnosis/management, using glycated haemoglobin (HbA1c) as surrogate, across six UK centres. METHODS: Using routinely collected laboratory data, we estimated the number of missed HbA1c tests for 'diagnostic'/'screening'/'management' purposes during the COVID-19 impact period (CIP; 23 March 2020 to 30 September 2020). We examined potential impact in terms of: (1) diabetes control in people with diabetes and (2) detection of new diabetes and prediabetes cases. RESULTS: In April 2020, HbA1c test numbers fell by ~80%. Overall, across six centres, 369 871 tests were missed during the 6.28 months of the CIP, equivalent to >6.6 million tests nationwide. We identified 79 131 missed 'monitoring' tests in people with diabetes. In those 28 564 people with suboptimal control, this delayed monitoring was associated with a 2-3 mmol/mol HbA1c increase. Overall, 149 455 'screening' and 141 285 'diagnostic' tests were also missed. Across the UK, our findings equate to 1.41 million missed/delayed diabetes monitoring tests (including 0.51 million in people with suboptimal control), 2.67 million screening tests in high-risk groups (0.48 million within the prediabetes range) and 2.52 million tests for diagnosis (0.21 million in the pre-diabetes range; ~70 000 in the diabetes range). CONCLUSIONS: Our findings illustrate the widespread collateral impact of implementing measures to mitigate the impact of COVID-19 in people with, or being investigated for, diabetes. For people with diabetes, missed tests will result in further deterioration in diabetes control, especially in those whose HbA1c levels are already high.

The Effect of the COVID-19 Pandemic on HbA1c Testing: Prioritization of High-Risk Cases and Impact of Social Deprivation.

INTRODUCTION: Studies show that the COVID-19 pandemic disproportionately affected people with diabetes and those from disadvantaged backgrounds. During the first 6 months of the UK lockdown, > 6.6 M glycated haemoglobin (HbA1c) tests were missed. We now report variability in the recovery of HbA1c testing, and its association with diabetes control and demographic characteristics. METHODS: In a service evaluation, we examined HbA1c testing across ten UK sites (representing 9.9% of England's population) from January 2019 to December 2021. We compared monthly requests from April 2020 to those in the equivalent 2019 months. We examined effects of (i) HbA1c level, (ii) between-practice variability, and (iii) practice demographics. RESULTS: In April 2020, monthly requests dropped to 7.9-18.1% of 2019 volumes. By July 2020, testing had recovered to 61.7-86.9% of 2019 levels. During April-June 2020, we observed a 5.1-fold variation in the reduction of HbA1c testing between general practices (12.4-63.8% of 2019 levels). There was evidence of limited prioritization of testing for patients with HbA1c > 86 mmol/mol during April-June 2020 (4.6% of total tests vs. 2.6% during 2019). Testing in areas with the highest social disadvantage was lower during the first lockdown (April-June 2020; trend test p < 0.001) and two subsequent periods (July-September and October-December 2020; both p < 0.001). By February 2021, testing in the highest deprivation group had a cumulative fall in testing of 34.9% of 2019 levels versus 24.6% in those in the lowest group. CONCLUSION: Our findings highlight that the pandemic response had a major impact on diabetes monitoring and screening. Despite limited test prioritization in the > 86 mmol/mol group, this failed to acknowledge that those in the 59-86 mmol/mol group require consistent monitoring to achieve the best outcomes. Our findings provide additional evidence that those from poorer backgrounds were disproportionately disadvantaged. Healthcare services should redress this health inequality.

COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?

INTRODUCTION: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus (COVID-19), its successive variants and the associated pandemic caused. We have previously shown that for people living with type 2 diabetes (T2DM), the risk of being admitted to hospital or dying following a COVID-19 infection progressively decreased through the first months of 2021. In this subsequent analysis we have examined how the UK COVID-19 vaccination programme impacted differentially on COVID-19 outcomes in people with T1DM or T2DM compared to appropriate controls. METHODS: T1DM and T2DM affected individuals were compared with their matched controls on 3:1 ratio basis. A 28-day hospital admission or mortality was used as the binary outcome variable with diabetes status and vaccination for COVID-19 as the main exposure variables. RESULTS: A higher proportion of T1DM individuals vs their controls was found to be vaccinated at the point of their first recorded positive COVID-19 test when compared to T2DM individuals vs their controls. Regarding the 28-day hospital admission rate, there was a greater and increasing protective effect of subsequent vaccination dosage (one, two or three) in mitigating the effects of COVID-19 infection versus no vaccination in T1DM than in T2DM individuals when compared with matched controls. Similar effects were observed in T2DM for death. Across both diabetes and non-diabetes individuals, those at greater socio-economic disadvantage were more likely to test positive for COVID-19 in the early phase of the pandemic. For T2DM individuals socio-economic disadvantage was associated with a greater likelihood of hospital admission and death, independent of vaccination status. Age and male sex were also independently associated with 28-day hospital admission in T2DM and to 28-day mortality, independent of vaccination status. African ethnicity was also an additional factor for hospital admission in people with T2DM. CONCLUSION: A beneficial effect of COVID-19 vaccination was seen in mitigating the harmful effects of COVID-19 infection; this was manifest in reduced hospital admission rate in T1DM individuals with a lesser effect in T2DM when compared with matched controls, regarding both hospital admission and mortality. Socio-economic disadvantage influenced likelihood of COVID-19 confirmed infection and the likelihood of hospital admission/death independent of the number of vaccinations given in T2DM.

Variability in Test Interval Is Linked to Glycated Haemoglobin (HbA1c) Trajectory over Time.

Aims: We previously showed that the glycated haemoglobin (HbA1c) testing frequency links to diabetes control. Here, we examine the effect of variability in test interval, adjusted for the frequency, on change in HbA1c (ΔHbA1c). Materials & Methods. HbA1c results were collected on 83,872 people with HbA1c results at baseline and 5 years (±3 months) later and ≥6 tests during this period. We calculated the standard deviation (SD) of test interval for each individual and examined the link between deciles of SD of the test interval and ΔHbA1c level, stratified by baseline HbA1c. Results: In general, less variability in testing frequency (more consistent monitoring) was associated with better diabetes control. This was most evident with moderately raised baseline HbA1c levels (7.0-9.0% (54-75 mmol/mol)). For example, in those with a starting HbA1c of 7.0-7.5% (54-58 mmol/mol), the lowest SD decile was associated with little change in HbA1c over 5 years, while for those with the highest decile, HbA1c rose by 0.4-0.6% (4-6 mmol/mol; p < 0.0001). Multivariate analysis showed that the association was independent of the age/sex/hospital site. Subanalysis suggested that the effect was most pronounced in those aged <65 years with baseline HbA1c of 7.0-7.5% (54-58 mmol/mol). We observed a 6.7-fold variation in the proportion of people in the top-three SD deciles across general practices. Conclusions: These findings indicate that the consistency of testing interval, not the just number of tests/year, is important in maintaining diabetes control, especially in those with moderately raised HbA1c levels. Systems to improve regularity of HbA1c testing are therefore needed, especially given the impact of COVID-19 on diabetes monitoring.

Remote Healthcare Program in Mexico in the Context of the COVID-19 Pandemic.

OBJECTIVES: Given the challenge of limiting the transmission of coronavirus disease 2019 while simultaneously continuing to provide medical care for other chronic and degenerative diseases and monitor therapy, a remote medical advice and guidance program was created to help individuals receive health services at their homes. METHODS: A descriptive, cross-sectional, and observational study was performed from April 13, 2020 to April 19, 2021 among Petroleos Mexicanos (PEMEX) health services beneficiaries. Mobile text messages were used to assign patients throughout Mexico to a doctor, who coordinated a video call with the nursing service for advice and remote care. RESULTS: In total, 17,472 calls were registered, with an average duration of 10.28 minutes. Almost half (49.4%) of the people who requested attention through the remote program were workers, 31.5% were workers' relatives, and 19.1% were retired workers. The average age of the program users was 50.66 ± 18.02 years. A total of 144 doctors from 28 different specialties participated. In this program, 7,163 calls were made because of respiratory symptoms, 1,514 for therapeutic monitoring (prescriptions), and 8,183 because of other diseases, including mental health. CONCLUSIONS: Remote healthcare programs have shown the potential to support the health system. This program is the first to deploy real-time video calls with a health team utilizing institutional electronic clinical records in Mexico. The implementation of this pandemic management program impacted the number of consultations given remotely, resulting in effective triage.

Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors.

INTRODUCTION: Research is ongoing to increase our understanding of how much a previous diagnosis of type 2 diabetes mellitus (T2DM) affects someone's risk of becoming seriously unwell following a COVID-19 infection. In this study we set out to determine the relative likelihood of death following COVID-19 infection in people with T2DM when compared to those without T2DM. This was conducted as an urban population study and based in the UK. METHODS: Analysis of electronic health record data was performed relating to people living in the Greater Manchester conurbation (population 2.82 million) who had a recorded diagnosis of T2DM and subsequent COVID-19 confirmed infection. Each individual with T2DM (n = 13,807) was matched with three COVID-19-infected non-diabetes controls (n = 39,583). Data were extracted from the Greater Manchester Care Record (GMCR) database for the period 1 January 2020 to 30 June 2021. Social disadvantage was assessed through Townsend scores. Death rates were compared in people with T2DM to their respective non-diabetes controls; potential predictive factors influencing the relative likelihood of admission were ascertained using univariable and multivariable logistic regression. RESULTS: For individuals with T2DM, their mortality rate after a COVID-19 positive test was 7.7% vs 6.0% in matched controls; the relative risk (RR) of death was 1.28. From univariate analysis performed within the group of individuals with T2DM, the likelihood of death following a COVID-19 recorded infection was lower in people taking metformin, a sodium-glucose cotransporter 2 inhibitor (SGLT2i) or a glucagon-like peptide 1 (GLP-1) agonist. Estimated glomerular filtration rate (eGFR) and hypertension were associated with increased mortality and had odds ratios of 0.96 (95% confidence interval 0.96-0.97) and 1.92 (95% confidence interval 1.68-2.20), respectively. Likelihood of death following a COVID-19 infection was also higher in those people with a diagnosis of chronic obstructive pulmonary disease (COPD) or severe enduring mental illness but not with asthma, and in people taking aspirin/clopidogrel/insulin. Smoking in people with T2DM significantly increased mortality rate (odds ratio of 1.46; 95% confidence interval 1.29-1.65). In a combined analysis of patients with T2DM and controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of death (accounting for 26% of variance) were T2DM, age, male gender and social deprivation (higher Townsend score). CONCLUSION: Following confirmed infection with COVID-19 a number of factors are associated with mortality in individuals with T2DM. Prescription of metformin, SGLT2is or GLP-1 agonists and non-smoking status appeared to be associated with a reduced the risk of death for people with T2DM. Age, male sex and social disadvantage are associated with an increased risk of death.

The Risk Factors Potentially Influencing Hospital Admission in People with Diabetes, Following SARS-CoV-2 Infection: A Population-Level Analysis.

INTRODUCTION: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus and the associated global pandemic (Covid-19). People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus. METHODS: This population-based study included people living in the Greater Manchester conurbation who had a recorded diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and subsequent Covid-19 infection. Each individual with T1DM (n = 862) or T2DM (n = 13,225) was matched with three Covid-19-infected non-diabetes controls. RESULTS: For individuals with T1DM, hospital admission rate in the first 28 days after a positive Covid-19 test was 10% vs 4.7% in age/gender-matched controls [relative risk (RR) 2.1]. For individuals with T2DM, hospital admission rate after a positive Covid-19 test was 16.3% vs 11.6% in age/gender-matched controls (RR 1.4). The average Townsend score was higher in T2DM (1.8) vs matched controls (0.4), with a higher proportion of people with T2DM observed in the top two quintiles of greatest disadvantage (p < 0.001). For Covid-19-infected individuals with T1DM, factors influencing admission likelihood included age, body mass index (BMI), hypertension, HbA1c, low HDL-cholesterol, lower estimated glomerular filtration rate (eGFR), chronic obstructive pulmonary disease (COPD) and being of African/mixed ethnicity. In Covid-19-infected individuals with T2DM, factors related to a higher admission rate included age, Townsend index, comorbidity with COPD/asthma and severe mental illness (SMI), lower eGFR. Metformin prescription lowered the likelihood. For multivariate analysis in combined individuals with T2DM/controls, factors relating to higher likelihood of admission were having T2DM/age/male gender/diagnosed COPD/diagnosed hypertension/social deprivation (higher Townsend index) and non-white ethnicity (all groups). CONCLUSION: In a UK population we have confirmed a significantly higher likelihood of admission in people with diabetes following Covid-19 infection. A number of factors mediate that increased likelihood of hospital admission. For T2DM, the majority of factors related to increased admission rate are common to the general population but more prevalent in T2DM. There was a protective effect of metformin in people with T2DM.

The change in glycaemic control immediately after COVID-19 vaccination in people with type 1 diabetes.

AIMS: Evidence suggests that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels after COVID-19 vaccination. We aimed to assess this objectively. METHODS: We examined the interstitial glucose profile of 97 consecutive adults (age ≥ 18 years) with T1DM using the FreeStyle Libre® flash glucose monitor in the periods immediately before and after their first COVID-19 vaccination. The primary outcome measure was percentage (%) interstitial glucose readings within the target range 3.9-10 mmol/L for 7 days prior to the vaccination and the 7 days after the vaccination. Data are mean ± standard error. RESULTS: There was a significant decrease in the % interstitial glucose on target (3.9-10.0) for the 7 days following vaccination (mean 52.2% ± 2.0%) versus pre-COVID-19 vaccination (mean 55.0% ± 2.0%) (p = 0.030). 58% of individuals with T1DM showed a reduction in the 'time in target range' in the week after vaccination. 30% showed a decrease of time within the target range of over 10%, and 10% showed a decrease in time within target range of over 20%. The change in interstitial glucose proportion on target in the week following vaccination was most pronounced for people taking metformin/dapagliflozin + basal bolus insulin (change -7.6%) and for people with HbA1c below the median (change -5.7%). CONCLUSION: In T1DM, we have shown that initial COVID-19 vaccination can cause temporary perturbation of interstitial glucose, with this effect more pronounced in people talking oral hypoglycaemic medication plus insulin, and when HbA1c is lower.

Disruption Caused by the COVID-19 Pandemic on HbA1c Testing and Its Implications for Diabetes (DM) Management/Diagnosis

We explored the impact of the COVID-19 pandemic on DM management/diagnosis. We extracted routine HbA1c data from laboratory systems at 5 UK hospitals from October2017-September2020 (representing 3.3million people;~4.8% of the UK population). From these data(3million tests), we calculated monthly missed monitoring/diagnostic tests from 23 March-30September 2020. We found that HbA1c tests dropped by 82-88% in April and had not reached expected volumes by September(Figure 1). During the 6-month period, in people with DM/at risk of DM, 206,422 monitoring tests were missed. Of these, 23,466 (11.4%) had previous HbA1c values ≥59mmol/mol. The testing delay in this group would, on average, result in a mean increase in HbA1c of 5.7 mmol/mol above that expected if monitoring was according to NICE guidance. There were also an estimated 81,245 missed diagnostic tests. Of these, ~6,105(7.5%) would be expected to be in the pre-DM range(42-47 mmol/mol) and ~3,633(4.5%) within DM range (≥48 mmol/mol), with ~1,333 of these having HbA1c values of ≥76 mmol/mol. Extrapolating to the UK population, this equates to missed monitoring tests in 489,000 people with sub-optimally-controlled DM, ~127,000 missed pre-DM and 76,000 missed DM diagnoses. Our findings illustrate the widespread collateral impact of implementing measures to mitigate COVID-19 impact in people with, or being investigated for DM.

Analysis of continuous glucose tracking data in people with type 1 diabetes after COVID-19 vaccination reveals unexpected link between immune and metabolic response, augmented by adjunctive oral medication.

INTRODUCTION: The COVID-19 vaccination programme is under way worldwide. Anecdotal evidence is increasing that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels post-vaccination which normally settles within 2-3 days. We report an analysis of BG profiles of 20 individuals before/after vaccination. METHODS: We examined the BG profile of 20 consecutive adults (18 years of age or more) with T1DM using the FreeStyle Libre flash glucose monitor in the period immediately before and after COVID-19 vaccination. The primary outcome measure was percentage (%) BG readings in the designated target range 3.9-10 mmmol/L as reported on the LibreView portal for 7 days prior to the vaccination (week -1) and the 7 days after the vaccination (week +1). RESULTS: There was a significant decrease in the %BG on target following the COVID-vaccination for the 7 days following vaccination (mean 45.2% ± SE 4.2%) vs pre-COVID-19 vaccination (mean 52.6% ± SE 4.5%). This was mirrored by an increase in the proportion of readings in other BG categories 10.1%-13.9%/≥14%. There was no significant change in BG variability in the 7days post-COVID-19 vaccination. This change in BG proportion on target in the week following vaccination was most pronounced for people taking Metformin/Dapagliflozin+basal-bolus insulin (-23%) vs no oral hypoglycaemic agents (-4%), and median age <53 vs ≥53 years (greater reduction in %BG in target for older individuals (-18% vs -9%)). CONCLUSION: In T1DM, we have shown that COVID-19 vaccination can cause temporary perturbation of BG, with this effect more pronounced in patients talking oral hypoglycaemic medication plus insulin, and in older individuals. This may also have consequences for patients with T2DM who are currently not supported by flash glucose monitoring.

How fast should social restrictions be eased in England as COVID-19 vaccinations are rolled out?

INTRODUCTION: Vaccination against the COVID-19 virus began in December 2020 in the UK and into Spring 2021 has been running at 5% population/week. High levels of social restrictions were implemented for the third time in January 2021 to control the second wave and resulting increases in hospitalisations and deaths. Easing those restrictions must balance multiple challenging priorities, weighing the risk of more deaths and hospitalisations against damage done to mental health, incomes and standards of living, education and provision of non-Covid-19 healthcare. METHODS: Weekly and monthly officially published data for 2020/21 were used to estimate the influence of seasonality and social restrictions on the spread of COVID-19 by age group, on the economy and on healthcare services. These factors were combined with the estimated impact of vaccinations and immunity from past infections into a model that retrospectively reflected the actual numbers of reported deaths closely both in 2020 and early 2021. The model was applied prospectively to the next 6 months to evaluate the impact of different speeds of easing social restrictions. RESULTS: The results show vaccinations as significantly reducing the number of hospitalisations and deaths. The central estimate is that relative to rapid easing, the avoided loss of 57 000 life-years from a strategy of relatively slow easing over the next several  months comes at a cost in terms of GDP reduction of around £0.4 million/life-year loss avoided. This is over 10 times higher than the usual limit the NHS uses for spending against Quality Adjusted Life Years (QALYs) saved. Alternative assumptions for key factors affecting the spread of the virus give significantly different trade-offs between costs and benefits of different speeds of easing. Disruption of non-Covid-19 Healthcare provision also increases in times of higher levels of social restrictions. CONCLUSION: In most cases, the results favour a somewhat faster easing of restrictions in England than current policy implies.

Modelling the impact of the mandatory use of face coverings on public transport and in retail outlets in the UK on COVID-19-related infections, hospital admissions and mortality.

INTRODUCTION: The rapid spread of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2/)(COVID-19) virus resulted in governments around the world instigating a range of measures, including mandating the wearing of face coverings on public transport/in retail outlets. METHODS: We developed a sequential assessment of the risk reduction provided by face coverings using a step-by-step approach. The UK Office of National Statistics (ONS) Population Survey data were utilised to determine the baseline total number of community-derived infections. These were linked to reported hospital admissions/hospital deaths to create case admission risk ratio and admission-related fatality rate. We evaluated published evidence to establish an infection risk reduction for face coverings. We calculated an Infection Risk Score (IRS) for a number of common activities and related it to the effectiveness of reducing infection and its consequences, with a face covering, and evaluated their effect when applied to different infection rates over 3 months from July 24, 2020, when face coverings were made compulsory in England on public transport/retail outlets. RESULTS: We show that only 7.3% of all community-based infection risk is associated with public transport/retail outlets. In the week of July 24, The reported weekly community infection rate was 29 400 new cases at the start (July 24). The rate of growth in hospital admissions and deaths for England was around -15%/week, suggesting the infection rate, R, in the most vulnerable populations was just above 0.8. In this situation, average infections over the evaluated 13 week follow-up period, would be 9517/week with face covering of 40% effectiveness, thus, reducing average infections by 844/week, hospital admissions by 8/week and deaths by 0.6/week; a fall of 9% over the period total. If, however, the R-value rises to 1.0, then, average community infections would stay at 29 400/week and mandatory face coverings could reduce average weekly infections by 3930, hospital admissions by 36 and deaths by 2.9/week; a 13% reduction. If the R-value rose and stayed at 1.2, then, expected average community-derived hospital admissions would be 975/week and 40% effective face coverings would reduce this by 167/week and reduce possible expected hospital deaths from 80/week to 66/week. These reductions should be seen in the context that there was an average of 102 000/week all-cause hospital emergency admissions in England in June and 8900 total reported deaths in the week ending August 7, 2020. CONCLUSION: We have illustrated that the policy on mandatory use of face coverings in retail outlets/on public transport may have been very well followed, but may be of limited value in reducing hospital admissions and deaths, at least at the time that it was introduced, unless infections begin to rise faster than currently seen. The impact appears small compared with all other sources of risk, thereby raising questions regarding the effectiveness of the policy.