People with Type Diabetes Mellitus (T1DM) self-reported views on their own condition management reveal links to potentially improved outcomes and potential areas for service improvement

BACKGROUND

The self-management of type 1 diabetes (T1DM) has moved forward in many areas over the last 40 years. Our study asked people with T1DM what is their experience of blood glucose (BG) monitoring day to day and how this influences decisions about insulin dosing.

METHODS

An on-line self-reported questionnaire containing 44 questions prepared after consultation with clinicians and patients was circulated to people with T1DM 116 responders provided completed responses. Fixed responses were allocated specific values (e.g. not confident = 0 fairly confident = 1). Multivariate regression analysis was carried out. Only those 5 factors with p-value <0.05 were retained.

RESULTS

59% of respondents were >50 years old and 66% had diabetes for >20 years, with 63% of patients reporting HbA1c results ≤8% or 64 mmol/mol. Findings included; 75% used only 1 m; 56% had used the same meter for ≥3 years; 10% had tried flash monitors; 47% were concerned about current BG level; 85% were concerned about long-term impact of higher BG. 72% of respondents keep BG level high to avoid hypoglycaemia; 25% used ≥7 mmol/L as pre-meal BG target to calculate dose; 65% were concerned they might be over/under-dosing; 83% did not discuss accuracy when choosing meter. However 85% were confident in their meter's performance. The factors that linked to LOWER HbA1c included LESS units of basal insulin (p < 0.001), HIGHER number of daily BG tests (p = 0.008), LOWER bedtime blood glucose (p = 0.009), HIGHER patient's concern over long-term impact of high BG (BG) (p < 0.009 but LOWER patient's concern over current BG values (p = 0.009). The final statistical model could explain 41% of the observed variation in HbA1c.

CONCLUSION

Many people still run their BG high to avoid hypoglycaemia. Concern about the longer-term consequences of suboptimal glycaemic control was associated with a lower HbA1c and is an area to explore in the future when considering how to help people with T1DM.

Links between the amount of antipsychotic medication prescribed per population at general practice level, local demographic factors and medication selection

BACKGROUND

Antipsychotic medications are the first-line pharmacological intervention for severe mental illnesses (SMI) such as schizophrenia and other psychoses, while also being used to relieve distress and treat neuropsychiatric symptoms in dementia. Our aim was to examine the factors relating to antipsychotic prescribing in general practices across England and how cost changes in recent years have impacted on antipsychotic prescribing.

METHODS

The study examined over time the prescribing volume and prices paid for antipsychotic medication by agent in primary care. Monthly prescribing in primary care was consolidated over 5 years (2013-2018) and DDD amount from WHO/ATC for each agent was used to convert the amount to total DDD/practice. The defined Daily Dose (DDD is the assumed average maintenance dose per day for a drug used for its main indication in adults.

RESULTS

We included 5750 general practices with practice population > 3000 and with > 30 people on their SMI register. In 2018/19 there were 10,360,865 prescriptions containing 136 million DDD with costs of £110 million at an average cost of £0.81/DDD issued in primary care. In 2017/18 there was a sharp increase in overall prices and they had not reduced to expected levels by the end of the 2018/19 evaluation year. There was a gradual increase in antipsychotic prescribing over 2013-2019 which was not perturbed by the increase in drug price in 2017/18. The strongest positive relation to increased prescribing of antipsychotics came from higher social disadvantage, higher population density (urban), and comorbidities e.g. chronic obstructive pulmonary disease (COPD). Higher % younger and % older populations, northerliness and non-white (Black and Minority Ethnic(BAME)) ethnicity were all independently associated with less antipsychotic prescribing. Higher DDD/general practice population was linked with higher proportion(%) injectable, higher %liquid, higher doses/prescription and higher %zuclopenthixol depot. Less DDD/population was linked with general practices using higher % risperidone and higher spending/dose of antipsychotic.

CONCLUSIONS

The levels of antipsychotic prescribing at general practice level are driven by social factors/comorbidities. We found a link between depot prescriptions with higher antipsychotic DDD and risperidone prescriptions with lower antipsychotic DDD. It is important that all prescribers are aware of these drivers / links.

The treatment rate of erectile dysfunction (ED) in younger men with type 2 diabetes is up to four times higher than the equivalent non-diabetes population

INTRODUCTION

Erectile dysfunction (ED) is common in older age and in diabetes mellitus (DM). Phosphodiesterase type 5-inhibitors (PDE5-is) are the first-line for ED. We investigated how the type of diabetes and age of males affect the PDE5-i use in the primary care setting.

METHODS

From 2018 to 2019, the general practice level quantity of all PDE5-i agents was taken from the general practice (GP) Prescribing Dataset in England. The variation in outcomes across practices was examined across one year, and for the same practice against the previous year.

RESULTS

We included 5761 larger practices supporting 25.8 million men of whom 4.2 million ≥65 years old. Of these, 1.4 million had T2DM, with 0.8 million of these >65. About 137 000 people had T1DM. About 28.8 million tablets of PDE5-i were prescribed within the 12 months (2018-2019) period in 3.7 million prescriptions (7.7 tablets/prescription), at total costs of £15.8 million (£0.55/tablet). The NHS ED limit of one tablet/user/wk suggests that 540 000 males are being prescribed a PDE5-i at a cost of £29/y each. With approximately 30 000 GPs practising, this is equivalent to one GP providing 2.5 prescriptions/wk to overall 18 males. There was a 3x variation between the highest decile of practices (2.6 tablets/male/y) and lowest decile (0.96 tablets/male/y). The statistical model captured 14% of this variation and showed that T1DM males were the largest users, while men age <65 with T2DM were being prescribed four times as much as non-DM. Those T2DM >65 were prescribed 80% of the non-DM amount.

CONCLUSION

There is a wide variation in the use of PDE5-is. With only 14% variance capture, other factors including wide variation in patient awareness, prescribing rules of local health providers, and recognition of the importance of male sexual health by GP prescribers might have a significant impact.

Influences on the use of antidepressants in primary care: All England general practice-level analysis of demographic, practice-level and prescriber factors

INTRODUCTION

General practice (GP) antidepressants (ADs) prescribing in England has almost doubled in the past decade: how does location, GP characteristics, and prescribing selection influence antidepressant prescribing rate (ADPR) and growth.

METHODS

Stepwise multivariate regression analysis was applied to national public relevant data for each general practice to establish associations between these factors and ADPR. The regression coefficient was applied to the actual change in the number of different ADs and costs/dose to extrapolate the impact of these on growth.

RESULTS

In 2017-2018, 2.1 billion doses of antidepressant were prescribed into a population of 52 million people in 6,146 larger practices. In the model, location demographics accounted for 62% of the variation in ADPR: including practice size and health raised this to 71%, and local prescribing behaviour to 80%. Practices using more different drugs and lower-cost/dose had higher ADPR. Extrapolation showed that 40% of growth in ADPR could be attributed to the historic changes in these factors.

CONCLUSIONS

While practice location factors do impact on AD prescription rates, local long-term physical health condition prevalence and prescribing behaviours are almost as important. We hope that our findings can provide insights that are helpful to local clinical behaviour and medicines management.

COVID-19: Generate and apply local modelled transmission and morbidity effects to provide an estimate of the variation in overall relative healthcare resource impact at general practice granularity

INTRODUCTION

Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the name given to the 2019 novel coronavirus. COVID-19 is the name given to the disease associated with the virus. SARS-CoV-2 is a new strain of coronavirus not been previously identified in humans.

METHODS

Two key factors, case incidence and case morbidity, were analysed for England. When taken together they give an estimate of relative demand on healthcare utilisation. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, %age >16, at fulltime work/education, %age over 60, %BME ethnicity, social deprivation as IMD2019, location as latitude/longitude, and patient engagement as %self-confident in their own long-term condition management. Average case morbidity was calculated. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice.

RESULTS

The case incidence regression is a dynamic situation but it currently shows that Urban, %Working, and age >60 were the strongest determinants of case incidence. The local population comorbidity remains unchanged. The range of relative healthcare impact was wide with 80% of practices falling at 20%-250% of the national median. Once practice population numbers were included we found that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID-19 healthcare resources. The model provides useful information about the relative impact of Covid-19 on healthcare workload at GP practice granularity in all parts of England.

CONCLUSION

Covid-19 is impacting on the utilisation of health/social care resources across the world. This model provides a way of predicting relative local levels of disease burden based on defined criteria, thereby providing a method for targeting limited care resources to optimise national/regional/local responses to the COVID-19 outbreak.

A phased approach to unlocking during the COVID-19 pandemic-Lessons from trend analysis

BACKGROUND

The COVID-19 pandemic has led to radical political control of social behaviour. The purpose of this paper is to explore data trends from the pandemic regarding infection rates/policy impact, and draw learning points for informing the unlocking process.

METHODS

The daily published cases in England in each of 149 Upper Tier Local Authority (UTLA) areas were converted to Average Daily Infection Rate (ADIR), an R-value - the number of further people infected by one infected person during their infectious phase with Rate of Change of Infection Rate (RCIR) also calculated. Stepwise regression was carried out to see what local factors could be linked to differences in local infection rates FINDINGS: By the 19th April 2020 the infection R has fallen from 2.8 on 23rd March before the lockdown and has stabilised at about 0.8, sufficient for suppression. However there remain significant variations between England regions. Regression analysis across UTLAs found that the only factor relating to reduction in ADIR was the historic number of confirmed number infection/000 population, There is however wide variation between Upper Tier Local Authorities (UTLA) areas. Extrapolation of these results showed that unreported community infection may be 150 times higher than reported cases, providing evidence that by the end of the second week in April, 26.8% of the population may already have had the disease and so have increased immunityExtrapolation of these results showed that unreported community infection may be 150 times higher than reported cases, providing evidence that by the end of the second week in April, 26.8% of the population may already have had the disease and so have increased immunity.

INTERPRETATION

Analysis of current case data using infectious ratio has provided novel insight into the current national state and can be used to make better-informed decisions about future management of restricted social behaviour and movement.

Cost of hospital treatment of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) compared to the non-diabetes population: a detailed economic evaluation

OBJECTIVES

Other than age, diabetes is the largest contributor to overall healthcare costs and reduced life expectancy in Europe. This paper aims to more exactly quantify the net impact of diabetes on different aspects of healthcare provision in hospitals in England, building on previous work that looked at the determinants of outcome in type 1 diabetes (T1DM) and type 2 diabetes (T2DM).

SETTING

NHS Digital Hospital Episode Statistics (HES) in England was combined with the National Diabetes Audit (NDA) to provide the total number in practice of people with T1DM/T2DM.

OUTCOME MEASURES

We compared differences between T1DM/T2DM and non-diabetes individuals in relation to hospital activity and associated cost.

RESULTS

The study captured 90% of hospital activity and £36 billion/year of hospital spend. The NDA Register showed that out of a total reported population of 58 million, 2.9 million (6.5%) had T2DM and 240 000 (0.6%) had T1DM. Bed-day analysis showed 17% of beds are occupied by T2DM and 3% by T1DM. The overall cost of hospital care for people with diabetes is £5.5 billion/year. Once the normally expected costs including the older age of T2DM hospital attenders are allowed for this fell to £3.0 billion/year or 8% of the total captured secondary care costs. This equates to £560/non-diabetes person compared with £3280/person with T1DM and £1686/person with T2DM. For people with diabetes, the net excess impact on non-elective/emergency work is £1.2 billion with additional estimated diabetes-related accident & emergency attendances at 440 000 costing the NHS £70 million/year. T1DM individuals required five times more secondary care support than non-diabetes individuals. T2DM individuals, even allowing for the age, require twice as much support as non-diabetes individuals.

CONCLUSIONS

This analysis shows that additional cost of provision of hospital services due to their diabetes comorbidities is £3 billion above that for non-diabetes, and that within this, T1DM has three times as much cost impact as T2DM. We suggest that supporting patients in diabetes management may significantly reduce hospital activity.

Antidepressant Prescribing in England: Patterns and Costs

Objective

The number of prescriptions for antidepressants (ADs) in England and Wales has almost doubled in the past decade. The objective of this article is to describe the current prescribing rates of different antidepressants by general practice (GP) practice.

Methods

We collated the prescribing behavior in each GP practice in the year April 1, 2017, to March 31, 2018. The monthly GP practice prescribing data reports for medication prescribing for each British National Formulary code and practice, as well as the prescriptions, quantity, and costs were examined in relation to prescribing practice.

Results

The data showed that 2.1 billion doses of antidepressant were prescribed to a total population of 52 million people. That equates to 11% of individuals taking ≥ 1 antidepressants on any day. Selective serotonin reuptake inhibitors (SSRIs) were the most prescribed class of ADs, with sertraline the most prescribed SSRI. The other most prescribed ADs were citalopram, fluoxetine, and mirtazapine. Some older agents, such as trimipramine and doxepin, are prescribed at a very high tariff.

Conclusions

Broadly, the findings are in keeping with National Institute for Health and Care Excellence guidance in that the bulk of prescriptions were for SSRIs. Regular audit of patient treatment at a general practice level will ensure appropriate targeted use of licensed medications as supported by the evidence base.

General practice (GP) level analysis shows that patients' own perceptions of support within primary care as reported in the GP patient survey (GPPS) are as important as medication and services in improving glycaemic control

BACKGROUND

The way that GP practices organize their services impacts as much on glycaemia in type 2 diabetes as does prescribing.

AIM

Our aim was to evaluate the link between patients' own perception of support within primary care and the % patients at each GP practice at target glycaemic control (TGC) and at high glycaemic risk (HGR).

DESIGN AND SETTING

Utilisation of National Diabetes Audit (NDA) available data combined with the General practitioner patient survey (GPPS).

METHOD

The NDA 2016_17 published data on numbers of type 2 patients, levels of local diabetes services and the target glycaemic control (TGC) % and high glycaemic risk (HGR) % achieved. The GPPS 2017 published % "No" responses from long term condition (LTC) patients to the question "In the last 6 months, had you enough support from local services or organisations to help manage LTCs?". Multivariate regression was used on the set of indicators capturing patients' demographics and services provided.

RESULTS

6498 practices were included (with more than 2.5 million T2DM patients) and median values with band limits that included 95% practices for % "No" response to the question above was 12% (2%-30%), for TGC 67% (54%-78%) and for HGR 6% (2%-13%). The model accounted for 25% TGC variance and 26% HGR variance. The standardised β values shown as (TGC/HGR) (+=more people; -=less people) for older age (+0.24/-0.25), sulphonylurea use (-0.21/+0.14), greater social disadvantage (-0.09/+0.21), GPPS Support %No (-0.08/+0.12), %Completion 8 checks (+0.09/-0.12) and metformin use (+0.11/-0.05).

CONCLUSION

The relation between the person with diabetes and clinician in primary care is shown to be quantitatively potentially as important in influencing glycaemic outcome as the services provided and medication prescribed. We suggest that all of us in who work in the health care system can bear this in mind in our everyday work.

Analysis of English general practice level data linking medication levels, service activity and demography to levels of glycaemic control being achieved in type 2 diabetes to improve clinical practice and patient outcomes

OBJECTIVE

Evaluate relative clinical effectiveness of treatment options for type 2 diabetes mellitus (T2DM) using a statistical model of real-world evidence within UK general practitioner practices (GPP), to quantify the opportunities for diabetes care performance improvement.

METHOD

From the National Diabetes Audit in 2015-2016 and 2016-2017, GPP target glycaemic control (TGC-%HbA1c ≤58 mmol/mol) and higher glycaemic risk (HGR -%HbA1c results >86 mmol/mol) outcomes were linked using multivariate linear regression to prescribing, demographics and practice service indicators. This was carried out both cross-sectionally (XS) (within year) and longitudinally (Lo) (across years) on 35 indicators. Standardised β coefficients were used to show relative level of impact of each factor. Improvement opportunity was calculated as impact on TGC & HGR numbers.

RESULTS

Values from 6525 GPP with 2.7 million T2DM individuals were included. The cross-sectional model accounted for up to 28% TGC variance and 35% HGR variance, and the longitudinal model accounted for up to 9% TGC and 17% HGR variance. Practice service indicators including % achieving routine checks/blood pressure/cholesterol control targets were positively correlated, while demographic indicators including % younger age/social deprivation/white ethnicity were negatively correlated. The β values for selected molecules are shown as (increased TGC; decreased HGR), canagliflozin (XS 0.07;0.145/Lo 0.04;0.07), metformin (XS 0.12;0.04/Lo -;-), sitagliptin (XS 0.06;0.02/Lo 0.10;0.06), empagliflozin (XS-;0.07/Lo 0.09;0.07), dapagliflozin (XS -;0.04/Lo -;0.4), sulphonylurea (XS -0.18;-0.12/Lo-;-) and insulin (XS-0.14;0.02/ Lo-0.09;-). Moving all GPP prescribing and interventions to the equivalent of the top performing decile of GPP could result in total patients in TGC increasing from 1.90 million to 2.14 million, and total HGR falling from 191 000 to 123 000.

CONCLUSIONS

GPP using more legacy therapies such as sulphonylurea/insulin demonstrate poorer outcomes, while those applying holistic patient management/use of newer molecules demonstrate improved glycaemic outcomes. If all GPP moved service levels/prescribing to those of the top decile, both TGC/HGR could be substantially improved.

Real-world practice level data analysis confirms link between variability within Blood Glucose Monitoring Strip (BGMS) and glycosylated haemoglobin (HbA1c) in Type 1 Diabetes

AIMS/HYPOTHESIS

Our aim was to quantify the impact of Blood Glucose Monitoring Strips variability (BGMSV) at GP practice level on the variability of reported glycated haemoglobin (HbA1cV) levels.

METHODS

Overall GP Practice BGMSV and HbA1cV were calculated from the quantity of main types of BGMS being prescribed combined with the published accuracy, as % results within ±% bands from reference value for the selected strip type. The regression coefficient between the BGMSV and HbA1cV was calculated. To allow for the aggregation of estimated three tests/day over 13 weeks (ie, 300 samples) of actual Blood Glucose (BG) values up to the HbA1c, we multiplied HbA1cV coefficient by √300 to estimate an empirical value for impact of BGMSV on BGV.

RESULTS

Four thousand five hundred and twenty-four practice years with 159 700 T1DM patient years where accuracy data were available for more than 80% of strips prescribed were included, with overall BGMSV 6.5% and HbA1c mean of 66.9 mmol/mol (8.3%) with variability of 13 mmol/mol equal to 19% of the mean. At a GP practice level, BGMSV and HbA1cV as % of mean HbA1c (in other words, the spread of HbA1c) were closely related with a regression coefficient of 0.176, P < 0.001. Thus, greater variability in the BGMS at a GP practice level resulted in a greater spread of HbA1C readings in T1DM patients. Applying this factor for BGMS to the national ISO accepted standard where 95% results must be ≤±15% from reference, revealed that for BG, 95% results would be ≤±45% from the reference value. Thus, the variation in BG is three times that of the BGMS. For a patient with BG target @10 mmol/L using the worst performing ISO standard strips, on 1/20 occasions (average 1/week) actual blood glucose value could be >±4.5 mmol/L from target, compared with the best performing BGMS with BG >±2.2 mmol/L from reference on 1/20 occasions.

CONCLUSION

Use of more variable/less accurate BGMS is associated both theoretically and in practice with a larger variability in measured BG and HbA1c, with implications for patient confidence in their day-to-day monitoring experience.

Sodium-glucose co-transporter-2 inhibitors, the latest residents on the block: Impact on glycaemic control at a general practice level in England

AIMS

To determine, using published general practice-level data, how differences in Type 2 diabetes mellitus (T2DM) prescribing patterns relate to glycaemic target achievement levels.

METHODS

Multiple linear regression modelling was used to link practice characteristics and defined daily dose (DDD) of different classes of medication in 2015/2016 and changes between that year and the year 2014/2015 in medication to proportion of patients achieving target glycaemic control (glycated haemoglobin A1c [HbA1c] ≤58 mmol/mol [7.5%]) and proportion of patients at high glycaemic risk (HbA1c >86 mmol/mol [10.0%]) for practices in the National Diabetes Audit with >100 people with T2DM on their register.

RESULTS

Overall, HbA1c outcomes were not different between the years studied. Although, in percentage terms, most practices increased their use of sodium-glucose co-transporter-2 (SGLT2) inhibitors (96%), dipeptidyl peptidase-4 (DPP-4) inhibitors (76%) and glucagon-like peptide 1 (GLP-1) analogues (53%), there was wide variation in the use of older and newer therapies. For example, 12% of practices used >200% of the national average for some newer agents. In cross-sectional analysis, greater prescribing of metformin and analogue insulin were associated with a higher proportion of patients achieving HbA1c ≤58 mmol/mol; the use of SGLT2 inhibitors and metformin was associated with a reduced proportion of patients with HbA1c >86 mol/mol; otherwise associations for sulphonylureas, GLP-1 analogues, SGLT2 inhibitors and DPP-4 inhibitors were neutral or negative. In year-on-year analysis there was ongoing deterioration in glycaemic control, which was offset to some extent by increased use of SGLT2 inhibitors and GLP-1 analogues, which were associated with a greater proportion of patients achieving HbA1c levels ≤58 mmol/mol and a smaller proportion of patients with HbA1c levels >86 mmol/mol. SGLT2 inhibitor prescribing was associated with significantly greater improvements than those found for GLP-1 analogues.

CONCLUSION

Greater use of newer agents was associated with improvement in glycaemic outcomes but was not sufficient to compensate for the prevailing decline. This may reflect wide variability in the prescribing of newer agents. We found that SGLT inhibitors may be superior to other oral agents in relation to HbA1c outcome. Serious consideration should be given to their use.

The pattern of prescribing of glucose modulating agents for type 2 diabetes in general practices in England 2016/17

BACKGROUND

In the financial year 2016/17 there were 52.0 million items prescribed for diabetes at a total net ingredient cost of £983.7 million - up from 28.9 million prescription items and £572.4 million in 2006/07. Anti-diabetes drugs (British National Formulary section 6.1.2) make up 45.1 per cent of the total £983.7 million net ingredient cost of drugs used in diabetes and account for 72.0 per cent of prescription items for all diabetes prescribing.

METHODS

We examined the way that agents licensed to treat type 2 diabetes were used across GP practices in England in the year 2016/2017. Analysis was at a GP practice level not at the level of patient data.

RESULTS

Annual prescribing costs / patient / medication type for monotherapy varied considerable from £11/year for gliclazide and glimepiride to £885/year for Liraglutide. The use of SGLT-2i agents grew strongly at 70% per annum to around 100,000 DDD with prescriptions seen in 95% of GP practices. Liraglutide expenditure (11% of total) was high for a relatively small number of patients (1.3% of Defined Daily Doses), with still significant spend on exenatide. Liraglutide use significantly exceeded that of other glucagon-like peptide-1 (GLP-1) agonists.

CONCLUSIONS

Our work demonstrates the significant cost of medication to modulate tissue glucose levels in type 2 diabetes and the dominance of some non-generic preparations in terms of number of prescriptions and overall spend. There are some older sulphonylureas in use, which should not generally be prescribed. Regular audit of patient treatment at a general practice level will ensure appropriate targeted use of licensed medications and of their cost effectiveness.

Improving type 2 diabetes mellitus glycaemic outcomes is possible without spending more on medication: Lessons from the UK National Diabetes Audit

AIMS

To determine the factors at general practice level that relate to glycaemic control outcomes in people with type 2 diabetes (T2DM).

METHODS

Data were accessed from 4050 general practices (50% of total) covering 1.6 million patients with T2DM in the UK National Diabetes Audit 2013 to 2014 and 2014 to 2015. This audit reported characteristics, services and outcomes in the T2DM population, including percentage of patients who had total glycaemic control (TGC), defined as glycated haemoglobin (HbA1c) ≤7.5% (58 mmol/mol), and the percentage who were at higher glycaemic risk (HGR), defined as HbA1c >10% (86 mmol/mol); the respective figures were 67.2% and 6.2%. The medication data were examined in terms of annual defined daily doses (DDDs). Multivariate linear regression analysis was used to identify associations between DDD and patient and practice characteristics.

RESULTS

Over the period 2012/2013 to 2015/2016, patient numbers grew 4% annually and annual medication expenditure by 8%, but glycaemic control outcomes did not improve. The main findings were that practices with better outcomes: had a higher percentage of patients aged >65 years; provided more effective diabetes services (including case identification, care checks, patient education, percentage of patients with blood pressure and cholesterol under control and more patients with type 1 diabetes achieving target HbA1c levels); spent less overall on prescribing per patient with T2DM; and on average, prescribed fewer sulphonylureas, less insulin (for patients with T2DM), fewer glucagon-like peptide-1 agonists, more metformin, more dipeptidyl peptidase-4 inhibitors, and more blood glucose monitoring strips. Ethnicity and social disadvantage and levels of thiazolidinedione (glitazone) prescribing had no significant impact on outcomes. Sodium-glucose co-transporter-2 inhibitor use was too low for an effect to be observed in the period examined.

CONCLUSIONS

If all practices brought their service and medication to the level of the top decile practices, they could achieve 74.7% compared with the median of 67.3% of patients achieving TGC, showing an increase of 213 000 in patients achieving TGC, while reducing the number at HGR to 3.8% compared with 6.1%, benefiting 62 000 patients. This could have a major impact on the overall consequent healthcare costs of managing diabetes complications with their attendant mortality risks.

Route to improving Type 1 diabetes mellitus glycaemic outcomes: real-world evidence taken from the National Diabetes Audit

AIM

To use general practice-level data for England, available through the National Diabetes Audit, and primary care prescribing data to identify prescription treatment factors associated with variations in achieved glucose control (HbA_1c ).

METHODS

General practice-level National Diabetes Audit data on Type 1 diabetes, including details of population characteristics, services, proportion of people achieving target glycaemic control [HbA_1c ≤58 mmol/mol (7.5%)] and proportion of people at high glycaemic risk [HbA_1c >86 mmol/ml (10%)], were linked to 2013-2016 primary care diabetes prescribing data on insulin types and blood glucose monitoring for all people with diabetes.

RESULTS

A wide variation was found between the 10^th percentile and the 90^th percentile of general practices in both target glycaemic control (15.6% to 44.8%, respectively) and high glycaemic risk (4.8% to 28.6%, respectively). Our analysis suggests that, given the extrapolated total of 280 000 people with Type 1 diabetes in the UK, there may be the potential to increase the number of those within target glycaemic control from 80 000 to 101 000; 53% of this increase (11 000 people) would result from service improvements and 47% (10 000 people) from medication and technology changes. The same improvements would also provide the opportunity to reduce the number of people at high glycaemic risk from 42 000 to 26 500. A key factor associated with practice-level target HbA_1c achievement would be greater use of insulin pumps for up to an additional 56 000 people.

CONCLUSION

If the HbA_1c achievement rates in service provision, medication and use of technology currently seen in practices in the 90^th percentile were to be matched with regard to HbA_1c achievement rates in all general practices, glycaemic control might be improved for 36 500 people, with all the attendant health benefits.