Introduction
Total population mortality rates have been falling and life expectancy increasing for more than 30 years. Diabetes remains a significant risk factor for premature death. Here we used the ...Oxford Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) practices to determine diabetes-related vs non-diabetes-related mortality rates.
Methods
RCGP RSC data were provided on annual patient numbers and deaths, at practice level, for those with and without diabetes across four age groups (< 50, 50–64, 65–79, ≥ 80 years) over 15 years. Investment in diabetes control, as measured by the cost of primary care medication, was also taken from GP prescribing data.
Results
We included 527 general practices. Over the period 2004–2019, there was no significant change in life years lost, which varied between 4.6 and 5.1 years over this period. The proportion of all diabetes deaths by age band was significantly higher in the 65–79 years age group for men and women with diabetes than for their non-diabetic counterparts. For the year 2019, 26.6% of deaths were of people with diabetes. Of this 26.6%, 18.5% would be expected from age group and non-diabetes status, while the other 8.1% would not have been expected—pro rata to nation, this approximates to approximately 40,000 excess deaths in people with diabetes vs the general population.
Conclusion
There remains a wide variation in mortality rate of people with diabetes between general practices in UK. The mortality rate and life years lost for people with diabetes vs non-diabetes individuals have remained stable in recent years, while mortality rates for the general population have fallen. Investment in diabetes management at a local and national level is enabling us to hold the ground regarding the life-shortening consequences of having diabetes as increasing numbers of people develop T2DM at a younger age.
•The way that multi-morbidity is addressed in primary care has an impact on outcome.•We are seeing clustering of physical/mental health conditions in a single patient.•We used independent data sets, ...GP practice patient survey and National Diabetes Audit.•We showed a critical relation between the person with diabetes & clinician perception.•This is as important in influencing glycaemia as the services provided/medication.
The way that GP practices organize their services impacts as much on glycaemia in type 2 diabetes as does prescribing.
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).
Utilisation of National Diabetes Audit (NDA) available data combined with the General practitioner patient survey (GPPS).
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.
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).
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.
Having type 1 diabetes reduces the life expectancy of adults in the United Kingdom by as much as 13 years.1 Despite incontrovertible evidence that good care reduces the risk of complications such as ...blindness, renal failure, and premature cardiovascular disease and death,2 as well as complications of treatment such as severe hypoglycaemia,3 fewer than 30% of UK adults with type 1 diabetes achieve current national treatment targets for glucose control.4 The challenges of managing type 1 diabetes do not lessen after the age of 18 years.
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.
The NHS Diabetic Eye Screening Programme aims to reduce the risk of sight loss among people with diabetes in England by enabling prompt diagnosis of sight-threatening retinopathy. However, the rate ...of screening uptake between practices can vary from 55% to 95%. Existing research focuses on the impact of patient demographics but little is known about GP practice-related factors that can make a difference.
To identify factors contributing to high or low patient uptake of retinopathy screening.
Qualitative case-based study; nine purposively selected GP practices (deprived/affluent; high/low screening uptake) in three retinopathy screening programme areas.
Semi-structured interviews were conducted with patients, primary care professionals, and screeners. A comparative case-based analysis was carried out to identify factors related to high or low screening uptake.
Eight possible factors that influenced uptake were identified. Five modifiable factors related to service and staff interactions: communication with screening services; contacting patients; integration of screening with other care; focus on the newly diagnosed; and perception of non-attenders. Three factors were non-modifiable challenges related to practice location: level of deprivation; diversity of ethnicities and languages; and transport and access. All practices adopted strategies to improve uptake, but the presence of two or more major barriers made it very hard for practices to achieve higher uptake levels.
A range of service-level opportunities to improve screening attendance were identified that are available to practices and screening teams. More research is needed into the complex interfaces of care that make up retinopathy screening.
Aims
To examine the factors that relate to antipsychotic prescribing in general practices across England and how these relate to cost changes in recent years.
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.
Since 2014 many antipsychotic agents have moved to generic provision. In 2017_18 supplies of certain generic agents were affected by substantial price increases.
Method
The study examined over time the prescribing volume and prices paid for antipsychotic medication by agent in primary care and considered if price change affected agent selection by prescribers.
The NHS in England/Wales publishes each month the prescribing in general practice by BNF code. This was aggregated for the year 2018_19 using Defined Daily doses (DDD) as published by the World Health Organisation Annual Therapeutic Classification (WHO/ATC) and analysed by delivery method and dose level. Cost of each agent year-on-year was determined.
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.
Result
Description
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. We included 5,750 GP Practices with practice population >3000 and with >30 people on their SMI register.
Effect of price
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.
Regression
Demographic factors
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 (BME)) ethnicity were all independently associated with less antipsychotic prescribing.
Prescribing Factors
Higher DDD/general practice population was linked with higher %injectable, higher %liquid, higher doses/prescription and higher %zuclopenthixol. Less DDD/population was linked with general practices using higher %risperidone and higher spending/dose of antipsychotic.
Conclusion
Higher levels of antipsychotic prescribing are driven by social factors/comorbidities. The link with depot medication prescriptions, alludes to the way that antipsychotics can induce receptor supersensitivity with consequent dose escalation.
Background:
The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being ...used to increase test frequency with less focus on accuracy.
Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact.
Method:
BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).
The Parkes error grid model with its associated category of risk including “alter clinical decision” and “affect clinical outcomes” was used, with the profile of frequency of expected results fitted into each BGM accuracy category.
Results:
Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.
However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the “alter clinical action” category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%.
Conclusion:
There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.
Background
Sitagliptin was launched into the UK Market in 2007, as the first member of a new class of oral glucose lowering medications, the Dipetidyl Peptidase 4 inhibitors.
Aim
To review the ...efficacy and safety of sitagliptin and discuss its place in therapy.
Method
Expert review using published reviews and papers published on sitagliptin, information on guideline recommendations for sitagliptin and DPP4 inhibitors, and discussion of the author's use of the agent.
Results
Evidence from a Cochrane review and meta-analysis of 14 trials or study arms suggests that sitagliptin lowers HBA1c by 0.7% in sitagliptin versus placebo trials. Evidence from a pooled safety database of 3415 people taking sitagliptin, and the Cochrane review show that the drug is well tolerated, causes no hypoglycaemia and is weight neutral. No specific signals of concern for the safety of sitagliptin have so far arisen in the pooled database. Guidelines recommend its use in triple therapy with metformin and sulphonylurea in dual therapy with metformin or sulphonylurea or thiazolidinedione in certain circumstances.
Conclusion
Sitagliptin from this initial data appears to be a safe, weight neutral and effective anti-diabetic agent.
Summary
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.
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