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.

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.

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.

"Stay at Home, Protect the National Health Service, Save Lives": A cost benefit analysis of the lockdown in the United Kingdom.

INTRODUCTION: The COVID-19 pandemic has transformed lives across the world. In the UK, a public health driven policy of population "lockdown" has had enormous personal and economic impact. METHODS: We compare UK response and outcomes with European countries of similar income and healthcare resources. We calibrate estimates of the economic costs as different % loss in Gross Domestic Product (GDP) against possible benefits of avoiding life years lost, for different scenarios where current COVID-19 mortality and comorbidity rates were used to calculate the loss in life expectancy and adjusted for their levels of poor health and quality of life. We then apply a quality-adjusted life years (QALY) value of £30,000 (maximum under national guidelines). RESULTS: There was a rapid spread of cases and significant variation both in severity and timing of both implementation and subsequent reductions in social restrictions. There was less variation in the trajectory of mortality rates and excess deaths, which have fallen across all countries during May/June 2020. The average age at death and life expectancy loss for non-COVID-19 was 79.1 and 11.4 years, respectively, while COVID-19 were 80.4 and 10.1 years; including adjustments for life-shortening comorbidities and quality of life plausibly reduces this to around 5 QALY lost for each COVID-19 death. The lowest estimate for lockdown costs incurred was 40% higher than highest benefits from avoiding the worst mortality case scenario at full life expectancy tariff and in more realistic estimations they were over 5 times higher. Future scenarios showed in the best case a QALY value of £220k (7xNICE guideline) and in the worst-case £3.7m (125xNICE guideline) was needed to justify the continuation of lockdown. CONCLUSION: This suggests that the costs of continuing severe restrictions are so great relative to likely benefits in lives saved that a rapid easing in restrictions is now warranted.

Personal protective equipment (PPE) and infection among healthcare workers - What is the evidence?

BACKGROUND: The worldwide outbreak of coronavirus disease-19 (COVID-19) has already put healthcare workers (HCWs) at a high risk of infection. The question of how to give HCWs the best protection against infection is a priority. METHODS: We searched systematic reviews and original studies in Medline (via Ovid) and Chinese Wan Fang digital database from inception to May, 2020, using terms 'coronavirus', 'health personnel', and 'personal protective equipment' to find evidence about the use of full-body PPEs and other PPEs by HCW exposed highly infectious diseases. RESULTS: Covering more of the body could provide better protection for HCWs. Of importance, it is not just the provision of PPE but the skills in donning and doffing of PPE that are important, this being a key time for potential transmission of pathogen to the HCW and in due time from them to others. In relation to face masks, the evidence indicates that a higher-level specification of face masks and respirators (such as N95) seems to be essential to protect HCWs from coronavirus infection. In community setting, the use of masks in the case of well individuals could be beneficial. Evidence specifically around PPE and protection from the COVID-19 virus is limited. CONCLUSION: Covering more of the body, and a higher-level specification of masks and respirators could provide better protection for HCWs. Community mask usecould be beneficial. High quality studies still need to examine the protection of PPE against COVID-19.