Research

Diabetes complications in people with alcohol use disorder and type 2 diabetes

Sarah Cook, Sonia Saxena, Rohini Mathur, Thomas Beaney, Shamini Gnani, Ana Luisa Neves, Arti Maini, Ravi Parekh, Kate Walters, David Osborn and Jennifer K Quint
BJGP Open 3 June 2025; BJGPO.2024.0133. DOI: https://doi.org/10.3399/BJGPO.2024.0133

Abstract

Background People living with alcohol use disorder (AUD) who develop type 2 diabetes (T2DM) may be at higher risk of diabetes complications.

Aim Our aim was to compare diabetes monitoring and incidence of diabetes complications between people with and without AUD prior to T2DM diagnosis attending primary care in England.

Design & setting We used the Clinical Practice Research Datalink Aurum linked with Hospital Episode Statistics and Office for National Statistics mortality data. The target population was people with incident T2DM diagnosed between 2004 and 2019.

Method We defined AUD from codes indicating i) clinical diagnosis; ii) alcohol withdrawal; or iii) chronic alcohol-related harm. Outcomes were end-stage renal disease (ESRD), lower limb amputation, myocardial infarction (MI), stroke, cardiovascular disease (CVD) mortality, and all-cause mortality. We compared yearly HbA1c, creatinine, and cholesterol monitoring activities for the first 5 years post T2DM diagnosis.

Results The study population was 543 509 people, of whom 15 237 (2.8%) had a code for AUD. Adjusting for measured confounders, people with AUD had higher rates of ESRD ( incidence rate ratio [IRR] 1.95, 95% confidence intervals [CI] = 1.71 to 2.23), lower limb amputation (IRR 1.78, 95% CI = 1.50 to 2.21), stroke (IRR 1.36, 95% CI = 1.25 to 1.47), CVD mortality (IRR 1.74, 95% CI = 1.63 to 1.86), and all-cause mortality (IRR 2.10, 95% CI = 2.04 to 2.17) but not MI (IRR 0.91, 95% CI = 0.82 to 1.00) compared with people without AUD. Laboratory diabetes monitoring was high in people with (83.5–91.1%) and without (83.7–92.4%) AUD.

Conclusion People with AUD had nearly double the rates of most of the diabetes complications investigated compared with people without AUD.

How this fits in

People living with alcohol use disorder (AUD) who develop type 2 diabetes (T2DM) may experience more challenges with disease management than people without AUD and be at higher risk of diabetes complications. Two previous studies, both using electronic health record data from the US, have investigated the relationship between AUD and diabetes complications with contrasting results. To our knowledge, this relationship has not been investigated in other countries with different health services to the US. In a primary care population in England we found that people with AUD at the time of T2DM diagnosis were more likely to experience diabetes complications, with 40% higher rates of stroke and nearly double the rates of end-stage renal disease, lower limb amputation, cardiovascular disease mortality, and all-cause mortality. T2DM laboratory blood test monitoring in primary care did not explain this.

Introduction

AUD is a serious public health concern affecting approximately 8.6% of adult men and 1.7% of adult women worldwide.1 People with AUD have more than two times higher all-cause mortality compared with the general population,2 and are five times more likely to die from diabetes.3

People living with AUD who develop a chronic disease may experience more challenges with disease management than people without AUD. For T2DM, where management is complex and requires a high level of interaction with health services, this may translate to worse health outcomes. If not well managed, T2DM can lead to a range of complications, the consequences of which can be very severe, including amputation, end-stage renal disease (ESRD), cardiovascular disease (CVD), and mortality.4,5

Two previous studies using electronic health record (EHR) data from the US have investigated the relationship between AUD and diabetes complications with contrasting results. A matched cohort study including 8120 people with T2DM and hypertension from Ohio found that people who also had AUD had 27% higher odds of diabetic neuropathy, 62% higher odds of myocardial infarction (MI), and 54% higher odds of all-cause mortality, but no higher odds of stroke or diabetic renal disease.6 In contrast, a cohort study of 106 174 people in Massachusetts, who were beneficiaries of Medicare or Medicaid, found that people with AUD had 53% higher odds of lower limb amputation and 26% higher odds of cerebrovascular disease, but lower odds of eye complications and diabetic neuropathy, and no difference in odds of nephropathy, diabetes-related hospitalisation, or ischaemic heart disease.7 To our knowledge, the relationship between AUD and diabetes complications has not been investigated in countries with different health services to the US. If differential access to health services increases the risk of some complications, these differences could be expected to be smaller in countries that have a national health service, such as in the UK, where health care is free at the point of delivery.

There is some evidence that AUD may impact levels of diabetes monitoring. A US EHR study found lower levels of some diabetes monitoring activities (low-density lipoprotein cholesterol and eye screening) among people with diabetes and AUD, although HbA1c monitoring levels in this study were the same.8 We hypothesised that differential uptake of diabetes monitoring among people with AUD may contribute to differences in the risk of developing diabetes complications.

The aims of this study were to compare the incidence of T2DM complications between people with and without prior AUD in primary care in England and to investigate whether diabetes monitoring mitigated any differences in complication rates between those with and without AUD.

Method

Setting and data sources

We used data from the Clinical Practice Research Datalink (CPRD) Aurum.9,10 The CPRD Aurum contains EHR data collected during routine health care from a sample of primary care practices in the UK which use EMIS Web general practice patient management software. This includes information about demographic characteristics, diagnoses and symptoms, medication prescriptions, and laboratory tests. The CPRD Aurum population is representative of the UK population in terms of age, gender, geographic location, and deprivation.9 The May 2022 version of the database used for this study covered 19.83% of the UK population.10

CPRD Aurum data were linked with Hospital Episode Statistics Admitted Patient Care (HES APC),11 Index of Multiple Deprivation (IMD),12 and Office for National Statistics (ONS) mortality data.13

HES contains data on patients admitted to NHS hospitals in England. As eligibility for linkage with HES data was a requirement for this study, we restricted our study population to people resident in England rather than the whole of the UK.

Participants

The target population was people with incident T2DM between 1 January 2004 and 1 January 2020. We defined date of T2DM incidence as the first date for a T2DM diagnosis or a T2DM-related code from a broader code list of diabetes SNOMED CT codes (https://github.com/NHLI-Respiratory-Epi/diabetes_alcohol_use_disorder/blob/main/diabetes_broad_list_vs3.xls). Exclusion criteria are shown in Figure 1.

Figure 1.
Figure 1. Selection of people with incident type 2 diabetes (T2DM) between 2004 and 2019

IMD = Index of Multiple Deprivation.

Codelists used for deriving the T2DM cohort and all other variables used for these analyses are available on Github (https://github.com/NHLI-Respiratory-Epi/diabetes_alcohol_use_disorder).

Study variables

We adapted the International Classification of Diseases 10 (ICD-10) definition of AUD to capture diagnoses of alcohol dependence syndrome and harmful alcohol use as defined in Chapter F10.1–F10.9 Mental and Behavioural disorders due to Alcohol.14,15 In line with this definition, AUD was defined as presence of a SNOMED-CT code in the primary care record which indicated i) clinical diagnosis; or ii) alcohol withdrawal; or iii) chronic alcohol-related harm to health (physical or mental); or any ICD-10 code from F10.1–F10.9 from the HES APC records prior to T2DM diagnosis.

High levels of blood glucose over time can lead to damage to the small blood vessels (microvascular complications) and to the larger blood vessels (macrovascular complications). We included a range of outcomes including both microvascular and macrovascular complications which were likely to be well recorded in EHR data.

Diabetes complications included as outcomes were:

  1. ESRD as indicated by initiation of chronic dialysis or renal transplantation defined either in primary or secondary care.

  2. Amputations of the lower limb (leg, foot, or toe). Amputation was defined from HES APC data using the Office of Population Censuses and Surveys' Classification of Surgical Operations and Procedures, 4th revision (OPCS-4) codes (X9, X10, and X11)16

  3. First episode MI defined as the first SNOMED-CT code in primary care or first ICD-10 code as the primary diagnosis for hospital admission post T2DM diagnosis

  4. First episode stroke defined as the first SNOMED-CT code in primary care or first ICD-10 code as the primary diagnosis for hospital admission post T2DM diagnosis

  5. CVD mortality defined using death date and underlying cause of death recorded by ONS

  6. All-cause mortality defined using death date from ONS death records.

Confounding factors included in the model were:

  • Socio-demographic factors (age at T2DM diagnosis, current age, gender, region, ethnicity, and quintiles of area-level socioeconomic status measured by IMD)

  • Calendar time (year of T2DM diagnosis and calendar period)

  • Smoking status

  • Body mass index

  • Physical health conditions defined as number of comorbidities using the Charlson Comorbidity Index,17 excluding diabetes

  • Mental health condition (depression, anxiety, and severe mental illness).

We investigated three indicators of yearly diabetes monitoring in the first 5 years post T2DM diagnosis: HbA1c, serum creatinine, and total cholesterol. These were considered present if there was a code recorded, regardless of whether measurement values were recorded or valid.

Statistical analysis

Separate Poisson regression models were fitted for each outcome, adjusting for a) age, gender, calendar time, age and year of T2DM diagnosis; and b) all measured confounders. People with the outcome prior to T2DM diagnosis were excluded for that specific analysis. In sensitivity analysis, models were fitted using the AUD variable split by whether coding suggested people were currently drinking or not at the time of T2DM diagnosis. The ’not currently drinking‘ group was defined from 1) a SNOMED-CT code in primary care for non-drinking more recent than their most recent code for AUD; or 2) most recent AUD SNOMED-CT code in primary care indicated remission; or 3) no AUD codes in primary or secondary care in the past 5 years.

Descriptive analysis was conducted comparing the proportion of people each year with codes for i) HbA1c, ii) serum creatinine, and iii) total cholesterol by whether people had a code for AUD prior to T2DM diabetes diagnosis for the first 5 years post T2DM diagnosis using χ2 tests. The denominator for each year was restricted to people in follow-up at that time.

Variables indicating whether each laboratory test was measured in the first year post T2DM diagnosis were added to the fully adjusted Poisson regression models to see if this explained any of the relationship between AUD and rate of diabetes complications (were mediators of the relationship).

Complete case analysis was used for regression modelling. Analyses were conducted using Stata (version 17).

Analyses were conducted using Stata (version 17).

Results

There were 543 509 people with incident T2DM identified between 2004 and 2019. Of these, 15 237 (2.8%) had a previous code for AUD. Follow-up time was 3 145 295 person years for people without codes for AUD and 71 466 person years for people with codes for AUD. Median follow-up time was 5.1 years (interquartile range [IQR] 2.2–9.1 years).

The characteristics of the study population at time of T2DM diagnosis are shown in Table 1. People with a code for AUD prior to T2DM diagnosis were younger at T2DM diagnosis, more likely to be male, to currently smoke, and to have a higher number of co-existing physical and mental health conditions (Table 1).

View this table:
Table 1. Characteristics of people with and without a code for alcohol use disorder (AUD) at time of type 2 diabetes (T2DM) diagnosis

The rates of diabetes complications in people with and without AUD are shown in Table 2.

View this table:
Table 2. Rates of diabetes complications following type 2 diabetes diagnosis in people with and without codes for alcohol use disorder

Adjusting for measured confounders (model 2), people with AUD had higher rates of all outcomes except MI (IRR 0.91, 95% CI = 0.82 to 1.00): ESRD (IRR 1.95, 95% CI = 1.71 to 2.23), lower limb amputation (IRR 1.78, 95% CI = 1.50 to 2.21), stroke (IRR 1.36, 95% CI = 1.25 to 1.47), CVD mortality (IRR 1.74, 95% CI = 1.63 to 1.86), and all-cause mortality (IRR 2.10, 95% CI = 2.04 to 2.17) compared with people without AUD (Table 3).

View this table:
Table 3. Incidence rate ratio for diabetes complications in people with alcohol use disorder (AUD) codes compared with people without AUD codes

After adjusting for all measured confounders (model 2), incidence rate ratios for ESRD were higher in the AUD group likely to be currently drinking (IRR 2.23, 95% CI = 1.92 to 2.60) than in the AUD group who were likely to be not currently drinking (IRR 1.37, 95% CI = 1.06 to 1.79) (P = 0.001) (Table 4). The same was observed for all-cause mortality (IRR currently drinking compared with people without AUD 2.33, 95% CI = 2.25 to 2.42; IRR not currently drinking 1.62, 95% CI = 1.45 to 1.81). The association with MI was different in the two AUD groups, with no association with MI in people with AUD with codes suggesting they were not currently drinking compared with no AUD (IRR 1.05, 95% CI = 0.90 to 1.22), while rates of MI were lower in the AUD group who were currently drinking compared with no AUD (IRR 0.83, 95% CI = 0.74 to 0.94).There was no statistical evidence for differences between the AUD groups for lower limb amputation, stroke, and CVD mortality (Table 4).

View this table:
Table 4. Rates and incidence rate ratios (95% CI) for diabetes complications by whether people with alcohol use disorder (AUD) had codes indicating current drinking at time of type 2 diabetes (T2DM) diagnosis

There were very small differences between people with codes for AUD and people without codes for AUD for yearly diabetes monitoring. These differences increased slightly over the first 5 years following T2DM diagnosis, however, recording of all three laboratory tests was consistently high in everyone (Table 5). Among people without codes for AUD, approximately 90% had at least one HbA1c code each year (approximately 86–87% in people with codes for AUD), approximately 87% had serum creatinine (approximately 85% with AUD codes), and approximately 88% (85% with AUD codes) had total cholesterol codes (Table 5).

View this table:
Table 5. Diabetes laboratory monitoring in the first 5 years following type 2 diabetes diagnosis by alcohol use disorder status

Further adjustment for laboratory tests (Model 3) in the first year following T2DM diagnosis did not explain the association between AUD and any of the diabetes complications (Table 3).

Discussion

Summary

In a primary care population in England, people with AUD at the time of T2DM diagnosis were more likely to experience diabetes complications, with 40% higher rates of stroke and nearly double the rates of ESRD, lower limb amputation, CVD mortality, and all-cause mortality. Diabetes monitoring in primary care was consistently high in people with and without codes for AUD (>83%), and diabetes monitoring in the year post T2DM did not contribute to explaining differences in higher rates of diabetes complications observed. People with AUD with codes suggesting they were not drinking at the time of their T2DM diagnosis had lower rates of ESRD and all-cause mortality.

Strengths and limitations

CPRD Aurum is a large nationally representative dataset, and we were able to include over 500 000 people with incident T2DM and 15 237 people with AUD, ensuring the study was well powered. However, it is worth also considering that with a very large sample size, small differences between groups may be statistically significant but not clinically relevant in practice. While we found statistical evidence for a difference in diabetes blood monitoring between those with and without AUD, these differences were very small in practice. EHR data are not collected for research purposes and are reliant on what is coded by healthcare practitioners. This is influenced both by coding practices and health service use. Here, we focused on harder end points in order to minimise measurement error. However, that meant we did not consider the full range of diabetes complications, for example diabetic retinopathy, where coding may be related to eye screening practices, or other important indicators of diabetes monitoring such as foot screening. We also did not distinguish between codes for laboratory measurements and whether valid test results were present, therefore there may be some cases where a blood test was requested but not conducted. Our definition of AUD was specific and we have not included people who may be at high risk but did not meet diagnostic criteria for AUD. Conversely, we only included people who were registered with a primary care practice (therefore people experiencing homelessness are much less likely to be included) who had their AUD detected and coded in their healthcare records. These people may be more engaged with health services generally so their levels of diabetes monitoring may be higher than in the whole target population. The categorisation of currently and not currently drinking was pragmatic based on the available data and we cannot say we definitely captured people’s drinking status at T2DM diagnosis, nor have we been able to capture drinking trajectories which change over time.

It was a strength that we were able to adjust for a wide range of confounders, however, residual confounding due to inaccurate or missing data on confounders is still possible. For example, only area-level and not individual-level data on socioeconomic status were available.

Comparison with existing literature

Our study findings are similar to Leung et al.7 who found among a cohort study of 106 175 people with T2DM registered with Medicare or Medicaid in Massachusetts in 2004 that people with AUD had 53% higher odds of lower limb amputation and 26% higher odds of cerebrovascular disease in the following year, but no difference in odds of developing ischaemic heart disease. While the effect sizes in our study were higher, these are not directly comparable as Leung et al. reported odds ratios rather than rate ratios and a 1 year follow-up (between 2004 and 2005).

Findings from our study and Leung et al.7 contrast with Winhusen et al.6 who found in a matched cohort study of 8120 people with T2DM and hypertension that people with AUD had 62% higher odds of MI and 54% higher odds of all-cause mortality but no association with stroke. The reasons for the differences between these studies are not clear but could be due to the difference in study design (matched versus unmatched), follow-up time, and underlying risk in the study populations. Of note, Winhusen et al.6 included as their target population people with hypertension and T2DM rather than all people with T2DM.

Implications for research and practice

Our findings have highlighted a substantially increased rate of diabetes complications in people with AUD who develop T2DM. While we hypothesised that lower levels of diabetes monitoring in people with AUD may contribute to a higher risk of diabetes complications, our findings did not support this, at least in terms of blood tests. However, we did not assess quality of interactions between patients and healthcare practitioners, or outcomes in terms of communicating findings to patients and actions taken to improve management if needed. This needs further investigation, especially as our findings suggest that most people with AUD and T2DM attend routine blood tests, representing a potential missed opportunity for intervention.

We found incidence rate ratios for ESRD and all-cause mortality were lower in people with AUD with codes suggesting they were not currently drinking, compared with those with codes suggesting current drinking. Interventions to help people reduce their drinking have been shown to decrease all-cause mortality.18 Diagnosis of diabetes can be a strong motivator for behaviour change and offers an opportunity for timely intervention.19 Our findings underline the importance of a holistic integrated care approach which includes consideration of the wider context, including alcohol use, for people following diagnosis of T2DM. Here we have identified a substantially higher risk of diabetes complications in people with AUD following T2DM diagnosis, but further work investigating mediators and effect modifiers is needed to better understand mechanisms by which AUD influences outcomes in T2DM. Complementary qualitative research would be valuable to understand how diabetes is managed in people with AUD and what the barriers and supporting factors are in helping people manage both conditions simultaneously. This would help in designing effective targeted interventions and developing resources to improve outcomes for people with AUD and T2DM.

Notes

Funding

SC was funded by a National Institute for Health and Care Research (NIHR) Three Research Schools Mental Health Fellowship for this work (MH055). SS is funded by the NIHR Senior Investigator Award, NIHR School for Public Health Research (SPHR) (grant number: NIHR 204000), NIHR Northwest London Applied Research Collaboration, and Imperial NIHR Biomedical Research Centre. The NIHR SPHR is a partnership between the Universities of Bristol, Cambridge, and Sheffield; Imperial; University College London; the London School of Hygiene and Tropical Medicine; LiLaC — a collaboration between the Universities of Liverpool and Lancaster; and Fuse — the Centre for Translational Research in Public Health, a collaboration between Newcastle, Durham, Northumbria, Sunderland, and Teesside Universities. RM is supported by Barts Charity (MGU0504). TB is supported by a grant from the Wellcome Trust. SG is funded by the NIHR SPHR and NIHR Northwest London Applied Research Collaboration. ALN is funded by the NIHR Northwest London Applied Research Collaboration and NIHR Northwest London Patient Safety Research Collaboration. Infrastructure for this research was supported by the NIHR Imperial Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethical approval

CPRD has NHS Health Research Authority (HRA) Research Ethics Committee (REC) approval to allow the collection and release of anonymised primary care data for observational research (NHS HRA REC reference number: 05/MRE04/87). Each year CPRD obtains Section 251 regulatory support through the HRA Confidentiality Advisory Group (CAG), to enable patient identifiers, without accompanying clinical data, to flow from CPRD contributing GP practices in England to NHS Digital, for the purposes of data linkage (CAG reference number: 21/CAG/0008). The protocol for this research was approved by CPRD’s Research Data Governance (RDG) Process (protocol number: 22_002317) and the approved protocol is available on request. Linked pseudonymised data were provided for this study by CPRD. Data are linked by NHS Digital, the statutory trusted third party for linking data, using identifiable data held only by NHS Digital. Select general practices consent to this process at a practice level, with individual patients having the right to opt out.

Provenance

Freely submitted; externally peer reviewed.

Data

Data are available on request from the CPRD. Their provision requires the purchase of a licence, and this licence does not permit the authors to make them publicly available to all. This work used data from the version collected in May 2022 and have clearly specified the data selected within each Method section. To allow identical data to be obtained by others, via the purchase of a licence, the code lists will be provided on request. Licences are available from the CPRD (http://www.cprd.com): The Clinical Practice Research Datalink Group, The Medicines and Healthcare products Regulatory Agency, 10 South Colonnade, Canary Wharf, London E14 4PU.

Public and patient involvement

The study research question and outcomes were discussed with five people with lived experience of alcohol use disorder and diabetes prior to the analysis stage. The findings from the study were shared with and discussed with 23 people with lived experience of alcohol use disorder and diabetes (personal experience or carers/relatives) prior to submitting this manuscript for publication.

Acknowledgements

We would like to thank Melinda King for support with facilitating public involvement work informing this publication and all the public contributors who took part in shaping this research.

Competing interests

The authors declare that no competing interests exist.

  • Received June 3, 2024.
  • Revision received October 30, 2024.
  • Accepted November 27, 2024.

This article is Open Access: CC BY license (https://creativecommons.org/licenses/by/4.0/)

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Diabetes complications in people with alcohol use disorder and type 2 diabetes
Sarah Cook, Sonia Saxena, Rohini Mathur, Thomas Beaney, Shamini Gnani, Ana Luisa Neves, Arti Maini, Ravi Parekh, Kate Walters, David Osborn, Jennifer K Quint
BJGP Open 3 June 2025; BJGPO.2024.0133. DOI: 10.3399/BJGPO.2024.0133
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British Journal of General Practice