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Diabetes Distilled: Mortality risk with COVID-19 in Scotland

People in Scotland with type 1 diabetes were 2.4 times and people with type 2 diabetes 1.4 times more likely to die or require critical care unit admission with COVID-19 as those without diabetes during the first wave of COVID-19, according to this cohort study. Almost 90% of those with diabetes who died or required critical care were over 60 years of age, while less than 3% were aged under 50 years. In those with diabetes compared to those without, older age, male sex and longer diabetes duration were associated with increased risk of requiring critical care or dying from COVID-19, as were living in a care home or in a deprived area, having a previously identified COVID-19 risk condition, retinopathy, chronic kidney disease, worse glycaemic control, history of smoking, or a hospital admission for diabetic ketoacidosis or hypoglycaemia in the last 5 years. Absolute risk of these serious consequences of COVID-19 was small, developing in only 3 per 1000 people with diabetes, although higher than the rate of 1 per 1000 in those without diabetes. This study, amongst others, has informed the development of a “Shiny app” to estimate an individual’s vulnerability to severe COVID-19 complications. Given that people with diabetes are a group with varying risk levels, such models may assist us in identifying those at highest risk of serious consequences from COVID-19, and this might in turn help inform their decision-making about how to protect themselves

Of the nearly 5.5 million people in Scotland, 5.8% (close to 320 000) have diabetes, and the current study shows that 0.3% of this cohort required critical care or died from COVID-19 during the study period from March to July 2020 (the first wave of the pandemic). This was compared to 0.1% of those without diabetes. The authors highlight a strong association between serious adverse outcomes and the number of previous hospitalisations for hypoglycaemia, diabetic ketoacidosis or other reasons in the preceding 5 years, as well as increasing HbA1c, but no linear relationship with BMI. NSAIDs, PPIs and anticoagulants were associated with increased risk, as were sulfonylureas and insulin. Risk increased with more diabetes drug classes used in the last 3 years, as well as the number of other drugs used over the same time period. Being on antihypertensive medication was associated with lower risk; however, the benefits of the individual drug classes will need further study. No association between ethnicity and risk was seen in this study, and this was attributed to Scotland only having a very small non-white population with diabetes.

Around one quarter of the people admitted to hospital with COVID-19 in the UK have diabetes. Previously in Diabetes Distilled, we reported data regarding mortality from COVID-19 amongst the 4.8% of the population in England diagnosed with diabetes. One third of in-hospital deaths from COVID-19 during a 10-week period earlier in the pandemic were in those with diabetes and, as in the present study, the risks were greater in those with type 1 than type 2 diabetes (Barron et al, 2020). A separate cohort study looking at deaths in hospital or the community due to COVID-19 also demonstrated that those with poor pre-illness glycaemic control, poor renal function and higher BMI were at greater mortality risk (Holman et al, 2020).

Although some of the risk factors identified in both the NHS England and Scottish studies are non-modifiable, some, such as glycaemic control and BMI, are amenable to lifestyle changes and optimising drug therapy, and so should prompt action.

The risk of serious outcomes from COVID-19 varies amongst people with diabetes, from some who are at little more risk than the general population to others who are at very high risk. This study, amongst others, has informed the development of COVID-age, a “Shiny app” to estimate an individual’s vulnerability to severe COVID-19 complications. The app converts the absolute risk score produced by a (not yet fully validated) prediction model to the age at which the same absolute risk was seen in a person without diabetes at the same stage of the pandemic. Other COVID-19 risk calculators include QCOVID (Clift et al, 2020), which predicts the risk of catching and dying from (or being admitted to hospital with) COVID-19 in the general population, and the 4C mortality score (Knight et al, 2020), which is used on admission to hospital to assess mortality risk from presumed or confirmed COVID-19.

All models will need reviewing and updating as the disease and its management change. It is important to understand that these are not triage tools for assessing the acute care needs of symptomatic individuals (Sperrin and McMillan, 2020), and currently QCOVID is ONLY to be used for academic research, peer review and validation purposes, and it must NOT be used with data or information relating to any individual. All the models stress the importance of using clinical judgement and not relying on risk scores; nonetheless, the COVID-age app may be useful in helping people understand their risk and how that should influence their behaviour.

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Barron E, Bakhai C, Kar P et al (2020) Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study. Lancet Diabetes Endocrinol 8: 813–22
 
Clift AK, Coupland CAC, Keogh RH et al (2020) Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ 371: m3731
 
Holman N, Knighton P, Kar P et al (2020) Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study. Lancet Diabetes Endocrinol 8: 823–33
 
Knight SR, Ho A, Pius R et al; ISARIC4C investigators (2020) Risk stratification of patients admitted to hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 370: m3339
 
Sperrin M, McMillan B. Prediction models for covid-19 outcomes. BMJ 371: m3777

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