Identifying determinants of diabetes risk and outcomes for people with severe mental illness: a mixed-methods study

Definition of severe mental illness

Severe mental illness (SMI) describes a group of mental disorders that are persistent, cause serious functional impairment and substantially interfere with, or limit, major life activities. A key feature of SMI is the presence of psychosis, which is a loss of contact with external reality. Both the American Psychiatric Association1 and the World Health Organization2 (WHO) definitions of psychosis require the presence of hallucinations (i.e. perceptions occurring in the absence of corresponding external or somatic stimuli) and/or delusions (i.e. fixed false beliefs). These symptoms may be accompanied by cognitive impairment, disordered thinking and problems with motivation, energy and mood.

Although there is some debate about which conditions come under the SMI umbrella, there is consensus that the term includes schizophrenia, bipolar disorder and depression with psychotic symptoms. 3–5 Schizophrenia is characterised by diverse psychopathology including delusions, hallucinations and ‘negative symptoms’, such as impaired motivation, reduction in interest, limited spontaneous speech, social withdrawal and cognitive impairment. Psychotic symptoms tend to relapse and remit, although around 20% of people with schizophrenia may experience chronic unremitting residual symptoms. 6 Negative symptoms tend to be persistent and are associated with long-term effects on social functioning.

By contrast, bipolar disorder typically has an ‘episodic’ course, with recovery between relapses. It causes extreme mood swings ranging from mania or intense happiness, grandiosity, euphoria or irritability or decreased need for sleep, to low mood and depressive symptoms. Typically, a person with bipolar disorder cycles from one extreme to the other, while experiencing periods with few or no symptoms in between. Major depression is similarly a relapsing–remitting disorder, but it can have a chronic presentation. It is characterised by a persistent feeling of sadness or lack of interest in outside stimuli. When accompanied by psychotic symptoms, it is categorised as SMI.

Severe mental illness includes several other conditions, such as schizoaffective disorder and persistent delusional disorder, with symptoms that overlap with schizophrenia, bipolar disorder or severe depression. Apart from the presence of psychosis, these illnesses typically have a severe and enduring impact over the life course, affecting education, employment, relationships and wealth. 7–9

Global burden of disease due to severe mental illness

Although lifetime prevalence of SMI is < 5% (consistent across the world), these conditions contribute a large and increasing global burden of illness. The global age-standardised point prevalence of schizophrenia is estimated to be 0.28% [95% confidence interval (CI) 0.24% to 0.31%], but it contributes 13.4 (95% CI 9.9 to 16.7) million years lived with disability (YLD), equivalent to 1.7% of total YLD. 10 Similarly, the age-standardised prevalence rate of bipolar disorder is estimated to be 0.7% (95% CI 0.6% to 0.8%), contributing 1.3% of total YLD. 11

Treatment of severe mental illness

Treatment of SMI includes a combination of psychotropic medication, psychological therapies and psychosocial support.

In recognition of the high risk of comorbid physical conditions and the socioeconomic impacts of SMI, treatment guidelines also include provision of annual general practitioner (GP) health checks to monitor physical health; smoking cessation support; and supported employment, exercise and diet programmes. 12,13

The majority of people with SMI are likely to be prescribed antipsychotics, either typical (sometimes called ‘first generation’, developed in the 1950s) or atypical (sometimes called ‘second generation’, developed in the 1990s). 14 Although all antipsychotics have been associated with side effects such as drowsiness, tremors, muscle spasms and weight gain, atypical antipsychotics are thought to have fewer extrapyramidal (motor control) side effects, but some may be associated with greater metabolic side effects and weight gain. 14

Comorbid physical conditions and excess mortality and morbidity in severe mental illness

Burden-of-disease estimates do not account for the premature mortality and morbidity due to physical illnesses in SMI. People with SMI die, on average, 15–20 years earlier,15 with a death rate 3.7 times higher16 than the general population. Recent evidence indicates that this ‘mortality gap’ is widening for people with schizophrenia and bipolar disorder. 17,18 Suicide accounts for around 15% of the increased mortality, but the majority of premature deaths are due to physical disorders, including non-communicable diseases, such as diabetes. Rates of such disorders are increased in people with SMI, typically being two to three times higher. 4,19,20 It is estimated that as many as two out of three premature deaths among people with SMI are due to preventable physical illnesses. 15

Most of the risk factors for developing conditions such as diabetes are likely to be the same in people with SMI and the general population, but there are multiple additional reasons that could explain the high prevalence seen in SMI populations and the excess mortality from physical disorders. Mental and physical disorders have a complex bidirectional relationship, sharing individual and socioenvironmental risk factors (e.g. childhood adversity and social and economic disadvantage). 21,22 Medications for mental illness are often associated with adverse metabolic side effects, increasing the risk of cardiometabolic disorders. Sedentary behaviour is common because of the motivational deficits associated with these disorders, or the side effects of treatment such as tiredness and sedation. Additional health risk behaviours such as smoking, alcohol use and unhealthy diets are also elevated. Problems with cognition, energy and motivation also pose challenges for accessing and adhering to medical treatment for physical conditions. Moreover, people with SMI may be less likely to receive adequate treatment because of ‘diagnostic overshadowing’, whereby health-care services attribute reported physical symptoms to mental illness, and, consequently, fail to fully investigate and treat these symptoms. 23

Comorbid physical disorders such as diabetes can therefore both drive, and be a consequence of, the significant health and socioeconomic inequalities faced by people with SMI. A better understanding of the determinants, and improved prevention and treatment of physical illness, could help to tackle the inequalities for this group.

Prevention of diabetes

Clinical trials have shown that it is possible to prevent or delay the development of T2DM through lifestyle and/or pharmacological interventions. 26–30 Lifestyle interventions aim to reduce body weight and dietary fat intake, in particular saturated fat, while increasing dietary fibre and moderate physical activity, to ≈ 30 minutes a day. The UK has recently launched a National Diabetes Prevention Programme to support at-risk individuals to implement these lifestyle changes through referral to a behaviour change programme. 31

Management of diabetes

For most people, diabetes is a lifelong condition. People will manage diabetes themselves (self-management) for most of the time, with only a few hours per year spent in contact with health-care professionals. Consequently, people must develop the skills to manage their condition effectively. Structured self-management education programmes have been developed and are recommended by the National Institute for Health and Care Excellence (NICE) as an integral component of diabetes management. 32,33

Diabetes is managed through a combination of lifestyle changes and, when needed, treatment with antidiabetes drugs or insulin. The latest guidance highlights the importance of certain foods and dietary patterns. 32 In common with the general population, people with diabetes should be encouraged to eat a healthy diet. For individuals who are overweight, losing weight is important; loss of 10–15 kg of body weight may trigger remission. 32,34 Increased physical activity has profound benefits, including improved fitness, reduced insulin requirement, better glycaemic control, lower cardiovascular risk and greater life expectancy.

Prevention of diabetes in people with severe mental illness

No diabetes prevention studies have been undertaken in people taking antipsychotics, but lifestyle interventions have been used to prevent weight gain or manage obesity. Short-term studies with follow-ups of < 6 months report weight loss of around 3.1 kg over a period of 8–24 weeks,53 but the results of longer-term studies are less consistent. 54 Similarly, there have been no studies of pharmacological interventions, but the use of metformin leads to a modest 3.3-kg reduction in body weight over 3–6 months, in association with improved insulin sensitivity. 53,55

Screening and diagnosis of diabetes in people with severe mental illness

Although regular screening for diabetes is recommended for people with SMI,56–58 many people taking antipsychotics are not screened regularly; further work is needed to understand why this simple measure has not been embedded into routine clinical practice. 59,60 In 2011, targets for monitoring blood glucose were included in the national Quality and Outcomes Framework (QOF) to incentivise general practices to screen patients with SMI for diabetes. However, these targets were removed in 2014. 61

Management of diabetes in people with severe mental illness

Diabetes appears to have a greater impact on people with SMI than on the general population through an increased incidence of acute metabolic emergencies and diabetes complications. 62,63 Current NICE guidance12 suggests that diabetes in people with SMI should be managed in a similar way to diabetes in the general population. 64 However, the guidelines do not take into account the implications of antipsychotic use or the unique challenges people with SMI may face in managing their diabetes.

Additional challenges faced by people with severe mental illness

Although the principles of diabetes management are the same as for the rest of the population, people with SMI face additional challenges in achieving optimal diabetes outcomes. These include, but are not limited to:

• health-care systems that separate diabetes and mental health

• overshadowing when physical health problems are considered to be caused by the mental illness

• excessive weight gain caused by antipsychotics and effects on insulin secretion

• psychotic symptoms

• poor cognition that interferes with decisions about self-management

• lack of social support.

Given the burden of illness, and health and health-care inequalities for this population, improving diabetes care for people with SMI is a high priority for the NHS. 65 Little is known, however, about how SMI and other risk factors and challenges combine to generate high diabetes prevalence and poor diabetes outcomes, and how the quality and quantity of health-care services and interventions can influence these risk factors and outcomes in people with SMI.

Understanding this is a first step in developing health-care interventions to improve outcomes for people with diabetes and SMI. Better prevention and management of diabetes have the potential to significantly reduce the risk of diabetes complications, deliver large cost savings to the NHS and help reduce health inequalities (including life expectancy and morbidity) experienced by people with SMI.

In this report, we have focused on T2DM, as it is the most common type of diabetes in people with SMI. When the term ‘diabetes’ is used without specifying type, this refers to T2DM.

Clinical Practice Research Datalink

The Clinical Practice Research Datalink (CPRD) GOLD is the world’s largest computerised database of anonymised longitudinal medical records from primary care. The CPRD data set (originally the General Practice Research Database) was established in 1987. CPRD data include records of clinical events (medical diagnoses), referrals to specialists and secondary care settings, prescriptions issued in primary care, records of immunisations/vaccinations, diagnostic testing, lifestyle information (such as smoking and alcohol status), and all other types of care provided as part of routine general practice. Data have been collected on > 17 million patients from a network of 718 general practices throughout the UK, representing around 8% of the UK population registered with a GP. 95 A cross-sectional study of the regional distribution of clinical computer systems in primary care found that 9% (636/7526) of general practices in England used Vision^® (In Practice Systems Ltd, London, UK) software, from which the CPRD primarily draws its data. 95

Clinical information is captured in the CPRD database using ‘Read codes’, which are recorded by primary care practice staff as part of routine data entry. Read codes96 are a comprehensive hierarchical coding system of clinical terms used in primary care to classify diseases, history and symptoms, patient characteristics, procedures and tests. 97 The purpose of this coding system is to provide a standard language to enable accurate recording of patient information and more efficient retrieval of information for clinical or research purposes. 97

The CPRD data set benefits from being a large, retrospective and prospective longitudinal primary care record. Patient characteristics in the database are broadly representative of the general UK population in terms of age, sex and ethnicity. 98 Although other clinical computer reporting systems are used more commonly across the UK than the Vision system, patterns of area deprivation (based on the locations of general practices) have not been found to differ between these different systems. 95 Practices using the Vision software system are, however, geographically concentrated in the south and north-west of England – particularly around London and Manchester – with relatively few practices based in Yorkshire and the Humber, and the north-east. 95,98

Individual patient data in the CPRD can be linked electronically to external data sources, including Hospital Episode Statistics (HES) data for hospital admissions, Office for National Statistics (ONS) data for dates and causes of death, and the Index of Multiple Deprivation (IMD) to assess area deprivation in the locality. The CPRD provides a single extract of linked HES/ONS/IMD anonymised data.

Hospital Episode Statistics

The HES database contains patient records for inpatient admissions, outpatient appointments and accident and emergency (A&E) attendances at NHS hospitals in England. 99 Admissions recorded in HES include those for physical and mental health problems where the patient was admitted to an acute hospital (but admissions recorded do not include specialist mental health facility admissions). HES Admitted Patient Care data contain information on patients’ demographics, admission sources and methods, discharge methods and destination, and primary and secondary diagnoses, as well as procedures conducted during the stay.

Diagnoses from hospital admissions are classified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), codes. Procedures are classified using the Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes.

Office for National Statistics mortality data

The ONS mortality data include information from a person’s death certificate, such as cause of death, and date and place of death, for all registered deaths in England and Wales. 100 These data are the most complete source of information on deaths based on information from medical practitioners and/or coroners.

Index of Multiple Deprivation data

The English IMDs provide a relative measure of deprivation at the lower-layer super output area (LSOA) level. There are around 35,000 LSOAs in England, each containing an average of 672 households. 101 The IMD rank used for the linkage is from 2010, and is a combined index of seven domains: income, employment, health and disability, education, crime, barriers to housing and services, and living environment deprivation. 102

Deviations from the study protocol

The protocols for the overall study and for the quantitative study (study reference: 17_161R) have been published and are publicly available. 103,104

There were some changes to the study protocol for the quantitative analyses.

First, we planned to stratify all analyses by ethnicity. However, the small numbers in minority ethnic categories precluded doing this for some analyses.

Second, for objective 2 (see Chapter 5, Objective 2: factors associated with variation in diabetes and mental health outcomes in people with severe mental illness and diabetes), the methods were changed from repeated measures to patient-level analysis, as the former did not add value.

Third, for objective 7 (see Chapter 5, Objective 7: identifying health-care interventions associated with better outcomes for people with diabetes and severe mental illness), following discussions with clinical experts at a research team workshop, we had planned to look at several physical health checks including blood pressure, cholesterol, HbA_1c, body mass index (BMI), retinopathy screening, diabetes education and influenza vaccination. However, we were unable to explore in detail all health-care interventions that we originally planned as a result of inconsistencies in the recording of relevant activities in patient records. We therefore prioritised investigating the most common physical health checks, including blood pressure, cholesterol, HbA_1c level and BMI, for which recording was more complete.

Fourth, we identified that a significant proportion of the study population had diabetes diagnosed before the onset of SMI. Therefore, we needed to explore any potential order of diagnosis effects; these investigations were added to the planned analyses.

Fifth, for the final integration of study findings, we did not develop a logic model as proposed in the protocol, because of the tentative findings about how diabetes care provision may be associated with outcomes in people with SMI and diabetes.

There were no deviations from the study protocol for the scoping of the literature or the qualitative study.

Study organisation and governance

The study research team, comprising all co-applicants, was responsible for day-to-day management of the project, and held bimonthly project management meetings. A Study Steering Committee (SSC) with an independent chairperson and service user, and clinical, academic and commissioning representation was established, meeting every 6 months. The study was sponsored by Bradford District Care NHS Foundation Trust, which was also represented on the SSC.

Overview of patient and public involvement

This study has been supported by DIAbetes and Mental illness: improving Outcomes aND Services (DIAMONDS) VOICE, a PPI group that has provided ‘expert by experience’ input to the study, with the aim of ensuring that the research remains relevant to people with SMI and that the outcomes of the research have the potential to have a meaningful impact on care and services.

Established in 2015, DIAMONDS VOICE comprises service users living with SMI and diabetes, and family members who provide support. The group is facilitated by a PPI co-ordinator and has 12 members who have been involved throughout the study, and a further two who have joined more recently. Three of these are family members or carers of people with diabetes and SMI. The group has met quarterly to discuss study progress, to offer advice on challenges faced by the research team and to undertake activities to support and inform the study. In addition, individual members of the group have contributed beyond the regular meetings, attending the co-design workshops and helping to raise the profile of the study through their networks and at local and regional dissemination events.

John Radford is a member of DIAMONDS VOICE and also a co-investigator on this study. He has participated in study research team meetings and SSC meetings, as well as co-design workshops (see Chapter 7, Co-design workshops).

Patient and public involvement activities

DIAMONDS VOICE has been engaged in project-related activities, starting from the grant application stages and continuing throughout the study duration (described below). The group has led the write-up of this section of the report.

Ongoing patient and public involvement

DIAMONDS VOICE will continue to be involved after this study ends, for example through advising on key events for dissemination, giving feedback on materials used in dissemination of research to the public to ensure that they are effective and appropriate, and advising about future research priorities.

Impact of patient and public involvement on the research

Patient and public involvement has had a clear impact on the design, management and dissemination of this study. Importantly, the DIAMONDS VOICE group influenced the choice of mixed methods through their recommendation to explore the lived experience of SMI and diabetes in addition to conducting analyses of longitudinal patient records.

The study has benefited from PPI representation at study research team and SSC levels of governance, which has ensured that the research has continued to address questions that are meaningful to people living with SMI and diabetes. For example, although sleep difficulties were not identified as an important variable in the scoping of the literature, DIAMONDS VOICE representatives highlighted the significance of the relationship between sleep and the day-to-day management of SMI and diabetes. As a result, and in recognition that CPRD recording of sleep difficulties would be incomplete, a decision was taken to explore this issue through the qualitative interviews.

Input from DIAMONDS VOICE has also had an impact at the operational level of the study. Group members reviewed all patient- and public-facing project documentation, indicating ways to improve clarity and readability. They also advised on ways to make promotional materials engaging and appealing and have been instrumental in promoting the study across their mental health networks and expanding the stakeholder network. Topic guides and recruitment strategies were refined in accordance with advice from the group.

Reflections on the experience of participating in patient and public involvement

Researcher and member feedback on PPI involvement in this and other studies is collected annually by the PPI co-ordinator (see Appendix 1, Reflections on the experience of participating in patient and public involvement).

Ethics approval

A data-use agreement for CPRD records and linked HES and ONS mortality data was granted by the International Scientific Advisory Committee (reference: 17_161R).

Data sets

Three data sets were extracted from the CPRD for the quantitative analyses; the relationships between these data sets are shown in Figure 2. Data set A contains anonymised longitudinal health records of a cohort of 32,759 people with SMI. Records were extracted from the CPRD GOLD population cohort if they:

• had at least one clinical or referral event with a SMI diagnostic code in their medical history (see Report Supplementary Material 2, Table S1, for the code list)

• were actively registered with one of the contributing general practices in England in the study period from 1 April 2000 to 31 March 2016

• were eligible for the linkages required (HES, IMD and ONS death)

• were aged ≥ 18 years when SMI was diagnosed

• had continuous health data up to research standard [up to standard (UTS)] in the study period. 98

FIGURE 2.

Venn diagram illustrating the relationship between CPRD data sets.

Data set B is a subset of data set A and contains records of 2761 people with SMI who also had T2DM. T2DM was defined as the presence of at least one clinical diagnostic code in a patient’s clinical or referral records (see Report Supplementary Material 2, Tables S2 and S3). We excluded people who had a record of type 1 diabetes mellitus after their diagnosis of T2DM.

Data set C comprises a cohort of 9573 control patients with a diagnosis of T2DM and no record of SMI who were matched to people in data set B (cases). These controls, with a diagnosis of T2DM and no record of SMI, were matched to cases based on age, sex and practice in a ratio of 4 : 1.

Variables

Using the longlist identified, as described in Chapter 4, Results, we constructed a range of candidate variables associated with the development of T2DM and with physical and mental health outcomes based on whether or not (1) the risk factor could be measured using the information in the CPRD and its linked data sets, (2) the data had been collected in the CPRD and its linked data sets and the quality of data recording was systematic enough for analysis and (3) it was likely to have an impact on the outcomes of interest.

Table 3 summarises the data sources and construction methods for the candidate risk factors that were used to measure patients’ baseline characteristics.

It should be noted that patients’ follow-up periods varied for each objective, depending on the patient samples and research questions. We provide a detailed definition of follow-up period for each objective in the sections that follow.

Table 4 summarises the outcome variables under examination, along with their associated data sources, analysis methods and related objective(s).

Methods

To improve data quality, we developed and applied four common inclusion criteria to the extracted data set before carrying out analyses. These criteria were designed to account for the nature of SMI and diabetes, as well as for the features and limitations of this longitudinal data set:

• First, we identified the diagnosis dates of SMI and diabetes. Diagnosis date is commonly identified as the date when the earliest diagnostic code was recorded in primary care. Because the first contact for (and diagnosis of) SMI can be in secondary care, we identified the diagnosis date for SMI as the earliest of the date of first GP diagnosis or the date of the first hospital admission for the condition. We also applied this method to the identification of diabetes diagnosis, although this condition is more commonly diagnosed in a primary care setting. This increased the number of people with SMI (data set A) to 32,781 and people with both SMI and T2DM (data set B) to 3448. Our matched controls (data set C), therefore, decreased in number to 9551 people.

• Second, we excluded patients whose diagnosis of SMI and diabetes was recorded in the 90 days following registration with their current practice. As patients’ primary care records could be transferred between practices, we used this step to exclude those patients whose earliest primary care records of SMI and diabetes were likely to be updated from medical history because of changes of practices, rather than a new diagnosis.

• Third, we excluded patients who were diagnosed with SMI and diabetes before the age of 18 years, using our modified diagnosis dates.

• Fourth, we identified the patients with diagnosis codes of both type 1 diabetes and T2DM and removed people with potential type 1. For the patients with codes of both types, we followed an existing identification algorithm (from study NIHR 14_168R113), which categorised patients as potentially having type 1 diabetes if (1) their first diagnostic code of diabetes was recorded before 18 years of age or (2) there was a diagnostic code of type 1 recorded before 18 years of age or (3) they had been treated with insulin only.

Most analyses were conducted on patient-level data sets that typically had two levels of hierarchy: (1) patient-level observations nested within (2) practices. We accounted for the data structure by applying multilevel mixed-effects models with practice-level random intercepts.

Although a rich medical information source, these routine primary care data have been collected for administrative purposes, and the data quality depends on various factors such as the accurate and complete utilisation of the computerised recording system, standardised coding and the introduction of financial incentive schemes (such as the QOF) that reward general practices for performing incentivised Read-coded activities. We therefore identified an UTS follow-up period for each patient, which included comprehensive medical records for research. The start of the UTS period was defined as the later date between patient current registration and practice UTS dates; the end of this period was identified as the earliest among patient death, transfer-out and practice last collection dates.

We took a step-by-step forward approach to build the models from the most basic form (including only exposure variables) to expanded models with a wide range of adjustments. This approach was adopted to detect the potential confounding effect one variable might have on another, such as the association between SMI status and health outcomes attributed to older age. We adjusted for patient characteristics and time in all the final models as reported in this report (see Variables and statistical methods and Report Supplementary Material 3). Other candidate explanatory variables were included in the final models if they had significant association (p < 0.1) with the outcome of interest or their inclusion had improved the goodness of fit of models. We used c-statistics to compare logistic models and the Akaike information criterion and Bayesian information criterion (BIC) for negative binomial and Cox proportional hazards models. In the results tables (see Tables 6, 9, 13, 16, 22 and 23, and Report Supplementary Materials 4–12), we report a core model including only risk factors with good data quality and an extended model that also included covariates with less complete data, such as lifestyle and biometric measures.

Objective

The objective was to identify which sociodemographic, illness, family history and lifestyle factors are associated with the development of diabetes in people with SMI.

Study population

We used data set A (see Figure 2) containing records of 32,781 adults with SMI and applied the four common inclusion criteria, as outlined in Methods. From the remaining sample, people were included in the analysis if they (1) were diagnosed with SMI in their UTS continuous data period, (2) either had no record of T2DM in their medical history or had T2DM diagnosed only after SMI and (3) had a follow-up period of at least 1 day.

The follow-up period of eligible patients started from the later date between SMI diagnosis and UTS period start plus 15 months, to ensure that at least 15 months’ UTS continuous data were available for extracting baseline patient characteristics. The end of the follow-up period was identified as the end of the UTS continuous data period or the end of the study period, whichever was earlier. The final sample eligible for analysis included 14,838 people with SMI and without T2DM at baseline.

Variables and statistical methods

Descriptions of the outcome and candidate explanatory variables used in analyses for this objective, and their associated data sources and analysis methods, can be found in Tables 3 and 4. Further details on the statistical model specification have been provided in Report Supplementary Material 3.

For the status of diabetes, we adjusted for the length of follow-up (in years) and financial years to account for the effect of time in the multilevel logistic regressions. Robust standard errors for correlations by practices were specified in these regressions.

Results

Summary of findings

Type 2 diabetes prevalence was 10.2% in the total sample of 29,281 people with SMI and 9.1% in the UTS-restricted sample (n = 15,984). The key predictors for developing T2DM in people with SMI included older age, ethnicity (Asian and black), socioeconomic deprivation, physical comorbidities (hypertension and cardiovascular disease) and antipsychotics. Potential risk factors may also include being overweight or obese, and glucose dysregulation.

Objective

The objective was to identify which sociodemographic, illness, family history and lifestyle factors are associated with variations in diabetes and mental health outcomes among people with SMI and diabetes.

Study population

We used data set B containing records of 3448 patients with a clinical diagnosis of both SMI and T2DM, identified from either primary care records or hospital admissions (see Figure 2 for details of relationships between data sets). After applying the common eligibility criteria regarding registration, diagnosis age and diabetes type, there were 2984 patients in the sample.

The start of the follow-up period was identified as the latest date of the recorded SMI diagnosis, T2DM diagnosis or the start of UTS data period plus 15 months, so that patients were followed up after the second diagnosis and there was a 15-month window to obtain their baseline characteristics. The end of follow-up was identified as the end of the UTS data period or the end of the study period date, whichever was earlier. A total of 2754 patients with a follow-up length of at least 1 day were included for analysis.

Variables and statistical methods

Candidate risk factors are summarised in Table 3. The physical and mental health outcomes under examination included:

• glycaemic and cardiovascular control (HbA_1c, blood pressure and cholesterol levels)

• diabetes complications (hyperglycaemia, hypoglycaemia, microvascular complications and macrovascular complications)

• hospital admissions for macrovascular complications

• mental health outcomes (SMI relapses and depression and anxiety)

• mortality.

Details on the construction and statistical methods of these outcome variables are provided in Table 4, and further details on the methodology can be found in Report Supplementary Material 1.

Results

Descriptive statistics

Patient baseline characteristics are summarised in Table 7 and health outcomes are summarised in Figures 3 and 4, and Table 8. These descriptive statistics are reported for the whole eligible sample, as well as by diagnosis order.

TABLE 7 - Objective 2: patient baseline characteristics for people with SMI and T2DM for the total sample and stratified by diagnosis order

SD, standard deviation.

HbA_1c of 7.5% = 58 mmol/mol.

Characteristic	People with SMI and T2DM
	Total	SMI before T2DM	SMI after T2DM (including same day)
Patients, n (%)	2754 (100)	2019 (73.3)	735 (26.7)
Diagnosis age (years), mean (SD)
SMI	47.60 (17.76)	41.88 (15.01)	63.30 (15.05)
T2DM	55.54 (13.75)	54.98 (13.48)	57.06 (14.37)
SMI type, n (%)
Schizophrenia	1469 (53.3)	1120 (55.5)	349 (47.5)
Schizoaffective disorder	150 (5.5)	130 (6.4)	20 (2.7)
Bipolar disorder	862 (31.3)	619 (30.7)	243 (33.1)
Depression and psychosis	228 (8.3)	126 (6.2)	102 (13.9)
Other affective disorder	34 (1.2)	18 (0.9)	16 (2.2)
Mixed	10 (0.4)	5 (0.3)	5 (0.7)
Missing	1 (0.0)	1 (0.1)	0 (0)
Age at follow-up start (years), mean (SD)	58.19 (14.26)	56.08 (13.45)	63.99 (14.81)
Duration (years), mean (SD)
SMI	10.70 (12.05)	14.33 (12.08)	0.72 (2.63)
T2DM	2.70 (5.05)	1.12 (3.12)	7.02 (6.60)
Sex, n (%)
Male	1301 (47.2)	993 (49.2)	308 (41.9)
Female	1453 (52.8)	1026 (50.8)	427 (58.1)
Ethnicity, n (%)
White	2259 (82.0)	1650 (81.7)	609 (82.9)
Asian	197 (7.2)	140 (6.9)	57 (7.8)
Black	150 (5.5)	116 (5.8)	34 (4.6)
Mixed	26 (0.9)	18 (0.9)	8 (1.1)
Other	51 (1.9)	42 (2.1)	9 (1.2)
Unknown	71 (2.6)	53 (2.6)	18 (2.5)
Deprivation (IMD 2010), n (%)
1st quintile (least deprived)	344 (12.5)	235 (11.6)	109 (14.8)
2nd quintile	438 (15.9)	317 (15.7)	121 (16.5)
3rd quintile	525 (19.1)	382 (18.9)	143 (19.5)
4th quintile	690 (25.1)	514 (25.5)	176 (24.0)
5th quintile	754 (27.4)	570 (28.2)	184 (25.0)
Missing	3 (0.1)	1 (0.1)	2 (0.3)
Follow-up length (years)
Mean (SD)	4.84 (4.09)	5.16 (4.21)	3.96 (3.62)
Median (minimum, maximum)	3.78 (0.003, 23.80)	4.14 (0.003, 23.80)	2.84 (0.005, 18.78)
Comorbidities
Cardiovascular disease, n (%)	396 (14.4)	223 (11.1)	173 (23.5)
Hypertension, n (%)	978 (35.5)	629 (31.2)	349 (47.5)
Learning disability, n (%)	33 (1.2)	22 (1.1)	11 (1.5)
Dementia, n (%)	67 (2.4)	33 (1.6)	34 (4.6)
Charlson Index score, mean (SD)	0.55 (0.80)	0.48 (0.70)	0.79 (1.00)
Medications, n (%)
Antidepressants	1455 (52.8)	992 (49.1)	463 (63.0)
Antipsychotics
Typical	553 (20.1)	455 (22.5)	98 (13.3)
Atypical	1293 (47.0)	1049 (52.0)	244 (33.2)
Antihypertensive	1347 (48.9)	877 (43.4)	470 (64.0)
Antidiabetics	1098 (39.9)	561 (27.8)	537 (73.1)
Lipid-lowering drugs	1114 (40.5)	668 (33.1)	446 (60.7)
Statins	1083 (39.3)	647 (32.1)	436 (59.3)
Lifestyle factors, n (%)
Smoking
Non-smoker	770 (28.0)	512 (25.4)	258 (35.1)
Ex-smoker	508 (18.5)	349 (17.3)	159 (21.6)
Current smoker	860 (31.2)	699 (34.6)	161 (21.9)
Missing	616 (22.4)	459 (22.7)	157 (21.4)
Drinking
Non-drinker	566 (20.6)	420 (20.8)	146 (19.9)
Ex-drinker	187 (6.8)	137 (6.8)	50 (6.8)
Current drinker	847 (30.8)	648 (32.1)	199 (27.1)
Missing	1154 (41.9)	814 (40.3)	340 (46.3)
Substance use	55 (2.0)	41 (2.0)	14 (1.9)
Biometric measures
BMI (kg/m2), mean (SD)	32.40 (7.17)	33.24 (7.31)	30.41 (6.39)
< 20, n (%)	29 (1.1)	12 (0.6)	17 (2.3)
20–24, n (%)	238 (8.6)	138 (6.8)	100 (13.6)
25–29, n (%)	574 (20.8)	375 (18.6)	199 (27.1)
30–40, n (%)	946 (34.4)	707 (35.0)	239 (32.5)
> 40, n (%)	289 (10.5)	233 (11.5)	56 (7.6)
Missing, n (%)	678 (24.6)	554 (27.4)	124 (16.9)
HbA1c (%),a mean (SD)	7.67 (1.94)	7.77 (2.00)	7.49 (1.83)
≤ 7.5, n (%)	1094 (39.7)	681 (33.7)	413 (56.2)
> 7.5, n (%)	678 (24.6)	443 (21.9)	235 (32.0)
Missing, n (%)	982 (35.7)	895 (44.3)	87 (11.8)
Cholesterol (mmol/l), mean (SD)	5.13 (1.41)	5.32 (1.44)	4.66 (1.19)
≤ 5, n (%)	1130 (41.0)	725 (35.9)	405 (55.1)
> 5, n (%)	1044 (37.9)	828 (41.0)	216 (29.4)
Missing	580 (21.1)	466 (23.1)	114 (15.5)
Diastolic blood pressure (mmHg), mean (SD)	80.09 (10.61)	81.37 (10.74)	76.80 (9.54)
≤ 80, n (%)	1427 (51.8)	929 (46.0)	498 (67.8)
> 80, n (%)	1013 (36.8)	830 (41.1)	183 (24.9)
Missing	314 (11.4)	260 (12.9)	54 (7.4)
Systolic blood pressure (mmHg), mean (SD)	134.44 (17.76)	134.47 (17.72)	134.36 (17.90)
≤ 140, n (%)	1748 (63.5)	1262 (62.5)	486 (66.1)
> 140, n (%)	692 (25.1)	497 (24.6)	195 (26.5)
Missing, n (%)	314 (11.4)	260 (12.9)	54 (7.4)
Family history of diabetes, n (%)	430 (15.6)	316 (15.7)	114 (15.5)

FIGURE 3.

Average cholesterol and HbA_1c, 2000/1–2015/16. (a) Average cholesterol (mmol/l) and 95% CI; and (b) average HbA_1c (%) and 95% CI.

FIGURE 4.

Average blood pressure (mmHg), 2000/1–2015/16. (a) Systolic blood pressure and 95% CIs; and (b) diastolic blood pressure and 95% CIs.

TABLE 8 - Objective 2: physical and mental health outcomes for people with SMI and T2DM, by diagnosis order

SD, standard deviation.

Health outcome	People with SMI and T2DM
	Total	SMI before T2DM	SMI after T2DM (including same day)
Diabetes complications: primary care diagnosis, n (%)
Hyperglycaemia	89 (3.2)	67 (3.3)	22 (3.0)
Hypoglycaemia	115 (4.2)	75 (3.7)	40 (5.4)
Macrovascular complications (combined)	187 (6.8)	123 (6.1)	64 (8.7)
Myocardial infarction	74 (2.7)	44 (2.2)	30 (4.1)
Peripheral vascular disease	55 (2.0)	43 (2.1)	12 (1.6)
Stroke	73 (2.7)	47 (2.3)	26 (3.5)
Microvascular complications (combined)
Baseline	229 (8.3)	81 (4.0)	148 (20.1)
New complications at follow-up	461 (16.7)	365 (18.1)	96 (13.1)
Nephropathy
Baseline	28 (1.0)	12 (0.6)	16 (2.2)
New nephropathy at follow-up	62 (2.3)	45 (2.2)	17 (2.3)
Neuropathy
Baseline	72 (2.6)	26 (1.3)	46 (6.3)
New neuropathy at follow-up	82 (3.0)	62 (3.1)	20 (2.7)
Retinopathy
Baseline	156 (5.7)	48 (2.4)	108 (14.7)
New retinopathy at follow-up	385 (14.0)	303 (15.0)	82 (11.2)
Diabetes complications: admissions, mean (SD)
Macrovascular complications (combined)	0.14 (0.58)	0.13 (0.55)	0.17 (0.67)
Ischaemic heart disease	0.09 (0.50)	0.09 (0.46)	0.12 (0.58)
Peripheral vascular disease	0.01 (0.17)	0.01 (0.19)	0.01 (0.10)
Cerebrovascular disease	0.04 (0.22)	0.03 (0.21)	0.05 (0.26)
Mental health outcomes, n (%)
SMI relapses	782 (28.4)	559 (27.7)	223 (30.3)
Depression and anxiety	580 (21.1)	413 (20.5)	167 (22.7)
All-cause mortality, n (%)	458 (16.6)	296 (14.7)	162 (2.0)

Table 7 shows that 73.3% (2019/2754) of eligible patients had SMI diagnosed before T2DM, and 26.7% had T2DM diagnosed first (including same-day diagnosis). Patient characteristics differed between the two groups. Patients who had SMI before T2DM were diagnosed at an earlier age for both conditions: average diagnosis age was 41.9 years for SMI and 55.0 years for T2DM. In comparison, patients who had T2DM first were diagnosed with T2DM at around 57.1 years of age and with SMI at around 63.3 years of age. Patients who had SMI before T2DM were also more likely to be male and to live in the most deprived neighbourhoods.

Patients who had T2DM before SMI were more likely to have depression and psychosis as the type of SMI. The prevalence in this group was more than double the prevalence in people diagnosed with SMI first. These patients also had a higher prevalence of comorbidities and were more likely to be prescribed medications other than antipsychotics.

There were fewer missing data for baseline lifestyle factors and biometric measures in patients who had T2DM diagnosed first than in people diagnosed with SMI first. The latter were more likely to smoke cigarettes (although there were a large number of missing data in both groups, limiting definitive conclusions). People diagnosed with SMI first also had higher levels of HbA_1c, cholesterol and blood pressure at baseline, and were more likely to be obese than people diagnosed with T2DM first.

For physical health outcomes, we examined the variations in glycaemic and cardiovascular control by plotting time trends against financial years (see Figures 3 and 4). There was a general trend for improved control among patients in both groups between 2000 and 2016. On average, people who had T2DM before SMI had better baseline control, and continued to have similar, or better, control for cholesterol and blood pressure in their follow-up periods. However, the average levels of HbA_1c in this group were consistently above the NICE-recommended threshold of 58 mmol/mol (7.5%) in this period, whereas the average levels of HbA_1c among people diagnosed with SMI first dropped (and remained) below this threshold from around 2004/5.

The apparent improvements in cholesterol, HbA_1c and blood pressure might reflect improvements in care and control over time, but they might also reflect changes in the composition of the study population. Recorded prevalence of diabetes increased throughout the period and practices were financially incentivised for case-finding from 2004 onwards. As case-finding improved, less severe cases at earlier stages of the condition may have constituted an increasing proportion of the study population over time.

As summarised in Table 8, people who had SMI before T2DM had, in general, better physical and mental health outcomes. Patients in this group were less likely to develop macrovascular complications and had fewer hospital admissions for these complications in the follow-up period. These patients also had lower rates of SMI relapses and depression and anxiety, and had a lower mortality rate. The SMI before T2DM group had a lower baseline prevalence of microvascular complications, but were more likely to develop these conditions during the follow-up period than the other group. Those diagnosed with diabetes before SMI will have had diabetes for 5 or 6 years before entering the data set for this analysis; therefore, they would have had the ‘opportunity’ to develop complications in the pre-baseline period. As a result, they would be expected to have a higher baseline prevalence of microvascular complications (and fewer new microvascular complications during follow-up) than those diagnosed with SMI first.

Summary of findings

The descriptive statistics showed that people diagnosed with SMI before T2DM differed from those diagnosed with T2DM first in characteristics including diagnosis age, types of SMI, socioeconomic status, comorbidities, medication and lifestyle, as well as baseline diabetes and cardiovascular controls.

After adjustment using regression analyses, we found that older age, socioeconomic deprivation and the presence of multimorbidity were the common risk predictors for physical health outcomes. The duration of T2DM and the use of medications were also associated with poorer status of some of these physical health outcomes. All-cause mortality risk increased with older age, male sex, white ethnicity and socioeconomic deprivation. The risk of poorer mental health outcomes, however, was primarily predicted by younger age, type of SMI and comorbidities.

Objective

The objective was to compare health-care interventions and physical and mental health outcomes in people with SMI and diabetes with those of people with SMI without diabetes.

Study population

We used data set A, containing records of 32,781 people with a clinical diagnosis of SMI (see Figure 2). After applying the common inclusion criteria regarding diagnosis date and age, practice registration, and type, 29,281 patients remained in the sample.

The start of follow-up was calculated as the later date of SMI diagnosis or the start of UTS data plus 15 months, so that each patient was followed up from (or after) the diagnosis of SMI, with a 15-month window for collecting baseline characteristics. The end of follow-up was identified as either the end of UTS data or the end of the study period, whichever occurred first. A total of 26,977 patients with a follow-up length of at least 1 day were included in the analyses.

Variables and statistical methods

The exposure variable for this objective was the status of T2DM. The other candidate explanatory variables are summarised in Table 3. The outcome variables under investigation were as follows (see Table 4 for further details):

• glycaemic and cardiovascular control (HbA_1c, blood pressure and cholesterol levels)

• diabetes complications (hyperglycaemia, hypoglycaemia, microvascular complications and macrovascular complications)

• hospital admissions for macrovascular complications

• mental health outcomes (SMI relapses)

• all-cause mortality

• health checks (glucose, cholesterol, blood pressure and BMI).

For this objective, we used SMI relapse as the mental health outcome. Markers of depression and anxiety were not examined as the clinical Read codes for these overlapped with the diagnostic codes of depression and psychosis (a type of SMI).

Variation in receiving health-care interventions was explored using levels of health checks. The QOF61 rewards primary care providers for performing regular health checks and monitoring the physical health status for people with a wide range of long-term conditions, including SMI. We therefore selected four QOF-incentivised physical health checks (glucose, cholesterol, blood pressure and BMI) to examine their provision and association with T2DM status. For further details on the methodology, see Report Supplementary Material 3.

Results

Descriptive statistics

The baseline characteristics for eligible patients are summarised in Table 11. Of the 26,977 people with SMI, 2755 (10.2%) also had a diagnosis of T2DM. Patients with T2DM were diagnosed with SMI at an older age; the mean diagnosis age was 40.7 years for people without T2DM and 47.6 years for people with T2DM. People with T2DM were more likely to be female, to be of Asian or black ethnicity and to live in socioeconomically deprived neighbourhoods than people without T2DM. Furthermore, there was a higher probability of having a family history of diabetes in people with T2DM.

TABLE 11 - Objective 3: baseline characteristics for people with SMI, with and without T2DM

SD, standard deviation.

HbA_1c of 7.5% = 58 mmol/mol.

Characteristic	People with SMI
	Total	Without T2DM	With T2DM
Patients, n (%)	26,977 (100)	24,222 (89.8)	2755 (10.2)
Age at diagnosis (years), mean (SD)
SMI	41.41 (18.06)	40.71 (17.97)	47.60 (17.75)
T2DM			55.55 (13.77)
SMI type, n (%)
Schizophrenia	14,166 (52.5)	12,696 (52.4)	1470 (53.4)
Schizoaffective disorder	1213 (4.5)	1063 (4.4)	150 (5.4)
Bipolar disorder	9264 (34.3)	8402 (34.7)	862 (31.3)
Depression and psychosis	1837 (6.8)	1609 (6.6)	228 (8.3)
Other affective disorder	424 (1.6)	390 (1.6)	34 (1.2)
Mixed	72 (0.3)	62 (0.3)	10 (0.4)
Missing	1 (0.0)	0 (0)	1 (0.0)
Age at follow-up start (years), mean (SD)	46.71 (17.77)	45.76 (17.79)	55.02 (15.19)
Duration of SMI (years), mean (SD)	5.38 (9.33)	5.14 (9.08)	7.54 (11.10)
Sex, n (%)
Male	13,349 (49.5)	12,048 (49.7)	1301 (47.2)
Female	13,628 (50.5)	12,174 (50.3)	1454 (52.8)
Ethnicity, n (%)
White	22,790 (84.5)	20,531 (84.8)	2259 (82.0)
Asian	982 (3.6)	785 (3.2)	197 (7.2)
Black	1094 (4.1)	943 (3.9)	151 (5.5)
Mixed	320 (1.2)	294 (1.2)	26 (0.9)
Other	386 (1.4)	335 (1.4)	51 (1.9)
Unknown	1405 (5.2)	1334 (5.5)	71 (2.6)
Deprivation (IMD 2010), n (%)
1st quintile (least deprived)	3912 (14.5)	3568 (14.7)	344 (12.5)
2nd quintile	4709 (17.5)	4271 (17.6)	438 (15.9)
3rd quintile	5249 (19.5)	4724 (19.5)	525 (19.1)
4th quintile	6333 (23.5)	5643 (23.3)	690 (25.1)
5th quintile (most deprived)	6733 (25.0)	5978 (24.7)	755 (27.4)
Missing	41 (0.2)	38 (0.2)	3 (0.1)
Follow-up length (years)
Mean (SD)	6.14 (5.35)	5.93 (5.25)	8.02 (5.83)
Median (minimum, maximum)	4.58 (0.003, 26.21)	4.33 (0.003, 26.21)	7.10 (0.003, 25.91)
Family history of diabetes, n (%)	2446 (9.1)	2015 (8.3)	431 (15.6)
Comorbidities
Cardiovascular disease, n (%)	1550 (5.8)	1212 (5.0)	338 (12.3)
Hypertension, n (%)	3199 (11.9)	2423 (10.0)	776 (28.2)
Dementia, n (%)	424 (1.6)	369 (1.5)	55 (2.0)
Learning disability, n (%)	252 (0.9)	229 (1.0)	23 (0.8)
Charlson Index score, mean (SD)	0.33 (0.63)	0.31 (0.61)	0.45 (0.76)
Medications, n (%)
Antidepressants	13,046 (48.4)	11,603 (47.9)	1443 (52.4)
Antipsychotics
Typical	4637 (17.2)	3970 (16.4)	667 (24.2)
Atypical	9088 (33.7)	8143 (33.6)	945 (34.3)
Antihypertensive	4775 (17.7)	3660 (15.1)	1115 (40.5)
Antidiabetics	1055 (3.9)	86 (0.4)	969 (35.2)
Lipid-lowering drugs	2391 (8.9)	1564 (6.5)	827 (30.0)
Statins	2304 (8.5)	1502 (6.2)	802 (29.1)
Lifestyle factors, n (%)
Smoking
Non-smoker	4898 (18.2)	4259 (17.6)	639 (23.2)
Ex-smoker	2555 (9.5)	2211 (9.1)	344 (12.5)
Current smoker	7748 (28.7)	7031 (29.0)	717 (26.0)
Missing	11,776 (43.7)	10,721 (44.3)	1055 (38.3)
Drinking alcohol
Non-drinker	2733 (10.1)	2321 (9.6)	412 (15.0)
Ex-drinker	938 (3.5)	803 (3.3)	135 (4.9)
Current drinker	6862 (25.4)	6182 (25.5)	680 (24.7)
Missing	16,444 (61.0)	14,916 (61.6)	1528 (55.5)
Substance use	2551 (9.5)	2354 (9.7)	197 (7.2)
Biometric measures
BMI (kg/m2), mean (SD)	27.59 (6.36)	26.98 (6.05)	31.36 (6.91)
< 20, n (%)	969 (3.6)	932 (3.9)	37 (1.3)
20–24, n (%)	3662 (13.6)	3434 (14.2)	228 (8.3)
25–29, n (%)	3852 (14.3)	3336 (13.8)	516 (18.7)
30–40, n (%)	3098 (11.5)	2372 (9.8)	726 (26.4)
> 40, n (%)	530 (2.0)	351 (1.5)	179 (6.5)
Missing, n (%)	14,866 (55.1)	13,797 (57.0)	1069 (38.8)
HbA1c (%),a mean (SD)	6.71 (1.85)	5.78 (1.30)	7.47 (1.88)
≤ 7.5	1629 (6.0)	894 (3.7)	735 (26.7)
> 7.5	448 (1.7)	35 (0.1)	413 (15.0)
Missing	24,900 (92.3)	23,293 (96.2)	1607 (58.3)
Cholesterol (mmol/l), mean (SD)	5.16 (1.20)	5.23 (1.15)	4.92 (1.33)
≤ 5, n (%)	3160 (11.7)	2372 (9.8)	788 (28.6)
> 5, n (%)	3283 (12.2)	2681 (11.1)	602 (21.9)
Missing, n (%)	20,534 (76.1)	19,169 (79.1)	1365 (49.6)
Diastolic blood pressure (mmHg), mean (SD)	77.68 (10.34)	77.41 (10.30)	79.50 (10.40)
≤ 80, n (%)	10,760 (39.9)	9494 (39.2)	1266 (46.0)
> 80, n (%)	5216 (19.3)	4458 (18.4)	758 (27.5)
Missing, n (%)	11,001 (40.8)	10,270 (42.4)	731 (26.5)
Systolic blood pressure (mmHg), mean (SD)	127.82 (18.07)	126.95 (17.88)	133.79 (18.28)
≤ 140, n (%)	12,996 (48.2)	11,521 (47.6)	1475 (53.5)
> 140, n (%)	2980 (11.1)	2431 (10.0)	549 (19.9)
Missing, n (%)	11,001 (40.8)	10,270 (42.4)	731 (26.5)

The baseline prevalence of cardiovascular disease and hypertension was substantially higher in people with both SMI and T2DM than in people without T2DM, consistent with the patterns of baseline prescriptions; the proportions of people receiving antihypertensive and lipid-lowering drugs were much higher in those with T2DM than in those without T2DM. People with both diagnoses were also more likely to receive other medications, including antidepressants, antipsychotics and antidiabetes medications.

People with T2DM, on average, had a higher baseline BMI and poorer glycaemic and blood pressure controls. The average cholesterol level, however, was lower in people with T2DM than in people without T2DM. In general, there were large numbers of missing data for lifestyle factors and biometric measures in people without T2DM, and the patterns of missing values for these variables differed between the two groups. Therefore, these comparisons should be interpreted with caution.

The patterns for the health outcomes of diabetes and cardiovascular control are shown in Figures 5 and 6. These figures show a common trend: that diabetes and cardiovascular control measures improved from 2000/1 through to 2015/16 for both people with and people without T2DM. The gap between these two groups, however, widened for average cholesterol and HbA_1c levels.

FIGURE 5.

Objective 3: average cholesterol and HbA_1c, 2000/1–2015/16 for people with SMI with T2DM and for people with SMI without T2DM. (a) Average cholesterol (mmol/l) and 95% CIs; and (b) average HbA_1c (%) and 95% CIs.

FIGURE 6.

Objective 3: average blood pressure (mmHg) and 95% CI, 2000/1–2015/16 for people with SMI with T2DM and for people with SMI without T2DM. (a) Systolic blood pressure and 95% CIs; and (b) diastolic blood pressure and 95% CIs.

People with T2DM who had a lower cholesterol level at baseline continued to have better cholesterol control in the follow-up period than people without T2DM. The average level dropped below 5 mmol/l, the NICE-recommended threshold, after 2003/4 for people with T2DM, whereas the average level for people without T2DM remained above this threshold in this period.

The average level of HbA_1c for people with T2DM was consistently higher than the average level for people without T2DM. There was a decreasing trend in people without T2DM, a pattern that might suggest that an increased number of T2DM patients had been identified among people with SMI through regular physical health checks, as incentivised under QOF.

For blood pressure, the gap between people with and people without T2DM remained constant over time. Systolic blood pressure was higher in people with T2DM throughout this period, a pattern consistent with the baseline measures of these two groups. Although diastolic blood pressure was higher in people with T2DM at baseline, results suggested better control for these patients during follow-up.

Descriptive statistics for the other health outcomes and physical health checks are summarised in Table 12. There was a general pattern that people with T2DM had higher crude risks for poorer physical and mental health outcomes, and received more frequent physical health checks, than people without T2DM.

TABLE 12 - Objective 3: descriptive statistics for physical and mental health outcomes, and physical health checks for people with SMI with T2DM and for people with SMI without T2DM

SD, standard deviation.

Descriptive statistic	People with SMI
	Total	Without T2DM	With T2DM
Diabetes complications: primary care diagnosis, n (%)
Hyperglycaemia	147 (0.5)	23 (0.1)	124 (4.5)
Hypoglycaemia	138 (0.5)	18 (0.1)	120 (4.4)
Macrovascular complications (combined)	942 (3.5)	717 (3.0)	225 (8.2)
Myocardial infarction	378 (1.4)	292 (1.2)	86 (3.1)
Peripheral vascular disease	192 (0.7)	123 (0.5)	69 (2.5)
Stroke	414 (1.5)	323 (1.3)	91 (3.3)
Microvascular complications (combined)
Baseline	332 (1.2)	122 (0.5)	210 (7.6)
Follow-up	690 (2.6)	210 (0.9)	480 (17.4)
Nephropathy
Baseline	53 (0.2)	31 (0.1)	22 (0.8)
Follow-up	171 (0.6)	103 (0.4)	68 (2.5)
Neuropathy
Baseline	149 (0.6)	82 (0.3)	67 (2.4)
Follow-up	192 (0.7)	105 (0.4)	87 (3.2)
Retinopathy
Baseline	158 (0.6)	11 (0.1)	147 (5.3)
Follow-up	398 (1.5)	4 (0.0)	394 (14.3)
Diabetes complications: admissions, mean (SD)
Macrovascular complications (combined)	0.07 (0.40)	0.05 (0.34)	0.18 (0.76)
Ischaemic heart disease	0.04 (0.35)	0.03 (0.28)	0.12 (0.68)
Peripheral vascular disease	0.00 (0.05)	0 (0)	0.01 (0.17)
Cerebrovascular disease	0.02 (0.18)	0.02 (0.18)	0.04 (0.24)
Mental health outcomes, n (%)
SMI relapses	10,665 (39.5)	9458 (39.1)	1207 (43.8)
All-cause mortality, n (%)	3038 (11.3)	2580 (10.7)	458 (16.6)
Number of health checks per year, mean (SD)
Glucose	0.50 (0.81)	0.44 (0.72)	1.04 (1.27)
Cholesterol	0.46 (0.65)	0.38 (0.60)	1.10 (0.74)
Blood pressure	1.26 (2.85)	1.14 (2.91)	2.37 (2.02)
BMI	0.77 (2.52)	0.69 (2.60)	1.53 (1.41)

Summary of findings

A key finding is that, after adjustment, having diabetes increases the probability of receiving physical health checks and is a significant risk factor for both poorer physical and mental health outcomes.

The physical and mental health outcomes examined responded differently to variations in patient characteristics. For instance, advancing age was associated with increased risk of poorer physical health outcomes, but with reduced risk of mental health outcomes (SMI relapses); female sex was associated with a lower risk of diabetes complications and mortality, but a higher risk of SMI relapses. Type of SMI was a significant predictor for poorer mental health status, but had less influence on physical health outcomes, and antidepressant uptake reduced the risk of SMI relapses, but increased the risk of macrovascular complications.

The analyses also showed that receipt of the four physical health checks under investigation was affected by a wide range of factors, including older age, female sex, ethnicity, type of SMI, the presence of comorbidities and the use of various medications.

Objective

The objective was to compare health-care interventions and physical and mental health outcomes in people with diabetes and SMI with those of people with diabetes without SMI.

Study population

There was a cohort of 12,999 patients in the CPRD data who had a clinical diagnosis of diabetes. This cohort included 3448 patients in data set B who had been identified as ‘cases’ for having both SMI and T2DM. A total of 9551 patients in data set C (see Figure 2) had been identified as matched ‘controls’ based on age, sex and general practice. For this objective, we used data sets B and C so that patients with both SMI and T2DM (cases) could be compared with non-SMI patients with T2DM (controls) of the same age band, sex and general practice.

Patients with T2DM were included if (1) they satisfied the four common inclusion criteria regarding diagnosis date and age, practice registration and type of diabetes; (2) they had a follow-up length of at least 1 day, in which the start of follow-up was identified as the later date of T2DM diagnosis or the start of UTS data plus 15 months, and the end of follow-up was defined as the earlier of the end of UTS data or the end of study period date; and (3) they were nested within a matched case–control cluster. A total of 9965 patients remained in the final sample for analyses.

Variables and statistical methods

The exposure variable was SMI status. The candidate explanatory variables are listed in Table 3, with detailed descriptions. The outcome variables under examination were as described in Objective 3: comparing health-care interventions and health outcomes in people with severe mental illness and diabetes with those in people with severe mental illness without diabetes, Variables and statistical methods.

The description and analysis methods for physical and mental health outcomes are provided in Table 4. The outcome, mental health status, was identified by the markers of depression and anxiety only, because we were unable to include SMI relapse owing to its perfect correlation with SMI status. The four physical health checks were chosen using the QOF indicators for people with diabetes. We therefore examined the level of health checks for HbA_1c, cholesterol, blood pressure and BMI.

The patient-level observations for this objective were nested within matched case–control clusters, an additional data hierarchy to the data used in previous objectives. We adopted statistical methods that examine variations within case–control clusters, ‘within’ estimators, to compare cases only with controls of the same age, sex and registered with the same practice. For further details on the methodology, see Report Supplementary Material 3.

Results

Descriptive statistics

The comparison of baseline characteristics between cases and controls, and by diagnosis order, are summarised in Table 14. In our sample of 9965 patients, 2192 (22%) cases were matched to 7773 (78%) controls. Just under 90% of cases had been matched to at least three controls. Cases had similar distribution in age and sex to controls. However, they differed for a range of other characteristics, and by diagnosis orders.

TABLE 14 - Objective 4: baseline characteristics for people with T2DM with SMI and people with T2DM without SMI, stratified by diagnosis order

SD, standard deviation.

HbA_1c of 7.5% = 58 mmol/mol.

Note

Total, N = 9965 patients [cases, n = 2192 (22.0%); controls, n = 7773 (78.0%)].

Characteristic	SMI before T2DM		SMI after T2DM (including same day)
	Cases (T2DM + SMI)	Controls (T2DM)	Cases (T2DM + SMI)	Controls (T2DM)
Patients (n)	1666	5883	526	1890
Number of controls, n (%)
4 controls	1200 (72.0)		399 (75.9)
3 controls	255 (15.3)		68 (12.9)
2 controls	107 (6.4)		31 (5.9)
1 control	104 (6.2)		28 (5.3)
Diagnosis age (years), mean (SD)
SMI	43.02 (15.09)		63.70 (14.69)
T2DM	56.36 (12.86)	56.84 (12.47)	58.22 (14.11)	62.12 (13.36)
SMI type, n (%)
Schizophrenia	913 (54.8)		248 (47.2)
Schizoaffective disorder	96 (5.8)		17 (3.2)
Bipolar disorder	528 (31.7)		173 (32.9)
Depression and psychosis	110 (6.6)		74 (14.1)
Other affective disorder	15 (0.9)		11 (2.1)
Mixed	4 (0.2)		3 (0.6)
Age at follow-up start (years), mean (SD)	56.86 (12.81)	57.55 (12.32)	60.25 (13.73)	63.08 (13.10)
Duration of T2DM (years), mean (SD)	0.52 (2.13)	0.74 (2.66)	2.09 (4.65)	0.99 (3.19)
Follow-up duration (years), mean (SD)	5.39 (4.22)	6.14 (4.39)	8.02 (4.57)	6.53 (4.52)
Family history of diabetes, n (%)	245 (14.7)	1123 (19.1)	79 (15.0)	319 (16.9)
Sex, n (%)
Male	822 (49.3)	2861 (48.6)	229 (43.5)	846 (44.8)
Female	844 (50.7)	3022 (51.4)	297 (56.5)	1044 (55.2)
Ethnicity, n (%)
White	1376 (82.6)	4657 (79.2)	450 (85.6)	1612 (85.3)
Asian	108 (6.5)	413 (7.0)	31 (5.9)	86 (4.6)
Black	86 (5.2)	213 (3.6)	20 (3.8)	44 (2.3)
Mixed	16 (1.0)	39 (0.7)	5 (1.0)	9 (0.5)
Other	35 (2.1)	118 (2.0)	6 (1.1)	29 (1.5)
Not stated/unknown	45 (2.7)	443 (7.5)	14 (2.7)	110 (5.8)
Deprivation (IMD 2010), n (%)
1st quintile (least deprived)	195 (11.7)	900 (15.3)	84 (16.0)	311 (16.5)
2nd quintile	271 (16.3)	1101 (18.7)	87 (16.5)	401 (21.2)
3rd quintile	310 (18.6)	1217 (20.7)	105 (20.0)	352 (18.6)
4th quintile	423 (25.4)	1340 (22.8)	119 (22.6)	405 (21.4)
5th quintile (most deprived)	466 (28.0)	1319 (22.4)	129 (24.5)	420 (22.2)
Missing	1 (0.1)	6 (0.1)	2 (0.4)	1 (0.1)
Comorbidities
Cardiovascular disease, n (%)	193 (11.6)	922 (15.7)	92 (17.5)	384 (20.3)
Hypertension, n (%)	521 (31.3)	2655 (45.1)	213 (40.5)	929 (49.2)
Dementia, n (%)	25 (1.5)	19 (0.3)	7 (1.3)	13 (0.7)
Learning disability, n (%)	18 (1.1)	17 (0.3)	1 (0.2)	4 (0.2)
Charlson Index score, mean (SD)	0.48 (0.71)	0.51 (0.77)	0.55 (0.78)	0.62 (0.85)
Medications, n (%)
Antidepressants	817 (49.0)	1156 (19.7)	245 (46.6)	367 (19.4)
Antipsychotics
Typical	361 (21.7)	63 (1.1)	73 (13.9)	27 (1.4)
Atypical	870 (52.2)	41 (0.7)	87 (16.5)	11 (0.6)
Antidiabetics	353 (21.2)	1218 (20.7)	168 (31.9)	425 (22.5)
Antihypertensive	723 (43.4)	3200 (54.4)	277 (52.7)	1149 (60.8)
Lipid-lowering drugs	517 (31.0)	1981 (33.7)	167 (31.8)	696 (36.8)
Statins	501 (30.1)	1921 (32.7)	164 (31.2)	679 (35.9)
Lifestyle factors, n (%)
Smoking
Non-smoker	410 (24.6)	1691 (28.7)	134 (25.5)	540 (28.6)
Ex-smoker	299 (18.0)	1248 (21.2)	91 (17.3)	411 (21.8)
Current smoker	550 (33.0)	924 (15.7)	100 (19.0)	299 (15.8)
Missing	407 (24.4)	2020 (34.3)	201 (38.2)	640 (33.9)
Drinking
Non-drinker	325 (19.5)	565 (9.6)	64 (12.2)	197 (10.4)
Ex-drinker	112 (6.7)	111 (1.9)	16 (3.0)	30 (1.6)
Current drinker	519 (31.2)	1549 (26.3)	137 (26.1)	511 (27.0)
Missing	710 (42.6)	3658 (62.2)	309 (58.8)	1152 (61.0)
Substance use	31 (1.9)	28 (0.5)	8 (1.5)	7 (0.4)
Biometric measures
BMI (kg/m2), mean (SD)	33.28 (7.08)	32.77 (6.92)	31.89 (6.57)	31.91 (6.95)
< 20, n (%)	8 (0.5)	35 (0.6)	4 (0.8)	15 (0.8)
20–24, n (%)	102 (6.1)	344 (5.9)	43 (8.2)	128 (6.8)
25–29, n (%)	303 (18.2)	983 (16.7)	98 (18.6)	368 (19.5)
30–40, n (%)	582 (34.9)	1770 (30.1)	151 (28.7)	501 (26.5)
> 40, n (%)	181 (10.9)	511 (8.7)	40 (7.6)	139 (7.4)
Missing, n (%)	490 (29.4)	2240 (38.1)	190 (36.1)	739 (39.1)
HbA1c (%),a mean (SD)	7.85 (2.03)	7.92 (1.96)	7.71 (1.84)	7.82 (1.89)
≤ 7.5, n (%)	506 (30.4)	1758 (29.9)	166 (31.6)	568 (30.1)
> 7.5, n (%)	350 (21.0)	1335 (22.7)	110 (20.9)	393 (20.8)
Missing, n (%)	810 (48.6)	2790 (47.4)	250 (47.5)	929 (49.2)
Cholesterol (mmol/l), mean (SD)	5.39 (1.42)	5.30 (1.29)	5.24 (1.42)	5.22 (1.25)
≤ 5, n (%)	563 (33.8)	2071 (35.2)	159 (30.2)	684 (36.2)
> 5, n (%)	703 (42.2)	2457 (41.8)	189 (35.9)	756 (40.0)
Missing, n (%)	400 (24.0)	1355 (23.0)	178 (33.8)	450 (23.8)
Diastolic blood pressure (mmHg), mean (SD)	81.58 (10.59)	82.20 (10.70)	81.07 (11.16)	81.36 (10.71)
≤ 80, n (%)	761 (45.7)	2484 (42.2)	250 (47.5)	896 (47.4)
> 80, n (%)	690 (41.4)	2453 (41.7)	184 (35.0)	742 (39.3)
Missing, n (%)	215 (12.9)	946 (16.1)	92 (17.5)	252 (13.3)
Systolic blood pressure (mmHg), mean (SD)	134.79 (17.48)	139.59 (18.11)	139.27 (19.91)	141.75 (18.41)
≤ 140, n (%)	1026 (61.6)	2973 (50.5)	254 (48.3)	901 (47.7)
> 140, n (%)	425 (25.5)	1964 (33.4)	180 (34.2)	737 (39.0)
Missing, n (%)	215 (12.9)	946 (16.1)	92 (17.5)	252 (13.3)

In general, people with SMI were more likely to come from the most deprived neighbourhoods than people without SMI. This socioeconomic disadvantage was further increased if SMI was diagnosed before T2DM. We observed a similar pattern in the baseline prevalence of dementia and learning disability, which were more common in people with SMI than in people without SMI; people with SMI diagnosed before T2DM were most likely to be affected.

Recorded physical health status, including cardiovascular disease, hypertension and Charlson Index diseases, showed opposite patterns. People with SMI were less likely to have a recorded diagnosis of these physical comorbidities than people without SMI; prevalence of these diseases was lower in people with SMI diagnosed before T2DM than in people who had T2DM first. As expected, larger proportions of people with SMI had been prescribed antidepressants and antipsychotics than people without SMI. The prescription rates of antihypertensive and lipid-lowering drugs were lower in people with SMI than in controls, a pattern consistent with the lower prevalence of recorded hypertension and cardiovascular disease in people with SMI.

Factors including BMI, smoking status and alcohol use were better recorded for people who had SMI before T2DM than for the rest of the sample. Although results should be interpreted with caution, we found that people with SMI had a higher BMI and were more likely to have substance misuse at baseline. Furthermore, people with SMI tended to have better diabetes and blood pressure controls, but a higher cholesterol level, at baseline than people without SMI.

The health outcomes of diabetes and cardiovascular control were compared between people with and people without SMI (see Figures 7 and 8), and were analysed separately for people diagnosed with SMI first and diagnosed with T2DM first. These analyses showed a common decreasing trend in the average levels of cholesterol and blood pressure during the study period. The mean values of HbA_1c remained stable and around the 7.5% (58 mmol/mol) threshold for both groups. People with SMI had lower HbA_1c and blood pressure levels than people without SMI, a pattern consistent with the comparison of baseline levels. The cholesterol levels of people with SMI, particularly those with SMI diagnosed before T2DM, remained above the cholesterol levels of controls.

FIGURE 7.

Objective 4: average cholesterol and HbA_1c, 2000/1–2015/16, for people with T2DM and SMI and people with T2DM without SMI. Average cholesterol (mmol/l) and 95% CIs for (a) SMI diagnosed before T2DM; (b) SMI diagnosed after T2DM (including same day); average HbA_1c (%) and 95% CIs for (c) SMI diagnosed before T2DM; and (d) SMI diagnosed after T2DM (including same day).

FIGURE 8.

Objective 4: average blood pressure, 2000/1–2015/16, for people with T2DM and SMI and people with T2DM without SMI. Systolic blood pressure (mmHg) and 95% CIs for (a) SMI diagnosed before T2DM; (b) SMI diagnosed after T2DM (including same day); diastolic blood pressure (mmHg) and 95% CIs for (c) SMI diagnosed before T2DM; and (d) SMI diagnosed after T2DM (including same day).

Descriptive statistics for the other outcome variables are provided in Table 15 and show that the pattern of diabetes complications differed between people with SMI and people without SMI, and that complications were also affected by diagnosis order. In general, the crude risks of both recorded macro- and microvascular complications were lower in people with SMI than in people without SMI, if SMI was diagnosed before T2DM. These crude risks were higher in people with SMI than in controls if T2DM was diagnosed first. Depression and anxiety were also more likely to be diagnosed in people with SMI than in people without SMI, and the crude mortality rate was higher in the SMI population. For the four physical health checks, we found that people with SMI tended to have more frequent checks than controls if SMI was diagnosed first, and less frequent checks if T2DM was diagnosed first.

TABLE 15 - Objective 4: descriptive statistics for diabetes complications, mental health outcomes, mortality and health checks for people with T2DM with SMI and people with T2DM without SMI

SD, standard deviation.

Note

Total, N = 9965 [cases, n = 2192 (22.0%); controls, n = 7773 (78.0%)].

Descriptive statistic	SMI diagnosed before T2DM		SMI diagnosed after T2DM (including same day)
	Cases (T2DM + SMI)	Controls (T2DM)	Cases (T2DM + SMI)	Controls (T2DM)
Patients (n)	1666	5883	526	1890
Diabetes complications: primary care diagnosis, n (%)
Hyperglycaemia	52 (3.1)	155 (2.6)	31 (5.9)	49 (2.6)
Hypoglycaemia	60 (3.6)	253 (4.3)	41 (7.8)	85 (4.5)
Macrovascular complications (combined)	102 (6.1)	481 (8.2)	82 (15.6)	203 (10.7)
Myocardial infarction	37 (2.2)	196 (3.3)	33 (6.3)	78 (4.1)
Peripheral vascular disease	36 (2.2)	183 (3.1)	22 (4.2)	64 (3.4)
Stroke	38 (2.3)	142 (2.4)	34 (6.5)	79 (4.2)
Microvascular complications (combined)
Baseline	45 (2.7)	253 (4.3)	39 (7.4)	69 (3.7)
Follow-up	310 (18.6)	1416 (24.1)	141 (26.8)	514 (27.2)
Nephropathy
Baseline	6 (0.4)	32 (0.5)	4 (0.8)	8 (0.4)
Follow-up	35 (2.1)	121 (2.1)	24 (4.6)	55 (2.9)
Neuropathy
Baseline	17 (1.0)	88 (1.5)	13 (2.5)	24 (1.3)
Follow-up	54 (3.2)	251 (4.3)	35 (6.7)	103 (5.5)
Retinopathy
Baseline	25 (1.5)	161 (2.7)	28 (5.3)	42 (2.2)
Follow-up	257 (15.4)	1227 (20.9)	112 (21.3)	433 (22.9)
Diabetes complications: admissions, mean (SD)
Macrovascular complications (combined)	0.13 (0.54)	0.20 (1.12)	0.32 (0.99)	0.23 (0.74)
Ischaemic heart disease	0.09 (0.45)	0.15 (1.07)	0.23 (0.90)	0.17 (0.66)
Peripheral vascular disease	0.01 (0.20)	0.01 (0.16)	0.02 (0.17)	0.01 (0.11)
Cerebrovascular disease	0.03 (0.19)	0.03 (0.24)	0.08 (0.33)	0.05 (0.26)
Emergency	0.09 (0.41)	0.10 (0.45)	0.21 (0.71)	0.14 (0.52)
Elective	0.03 (0.21)	0.08 (0.93)	0.07 (0.35)	0.07 (0.37)
Mental health outcomes, n (%)
Depression and anxiety	343 (20.6)	993 (16.9)	232 (44.1)	297 (15.7)
All-cause mortality, n (%)	248 (14.9)	672 (11.4)	116 (22.1)	348 (18.4)
Number of health checks per year, mean (SD)
HbA1c	1.94 (1.35)	1.90 (1.07)	1.73 (0.70)	1.94 (1.08)
Cholesterol	1.40 (1.07)	1.34 (0.79)	1.31 (0.58)	1.38 (0.91)
Blood pressure	2.94 (2.70)	2.96 (2.21)	2.92 (1.69)	3.25 (8.60)
BMI	2.16 (2.11)	1.91 (1.74)	1.80 (1.30)	2.07 (8.57)

Summary of findings

For this objective, we investigated the impact of SMI status on physical and mental health outcomes, as well as the utilisation of health-care interventions. We also explored whether or not health inequalities among people with SMI were associated with the order of diagnosis of SMI and diabetes.

People with SMI had tighter diabetes and blood pressure controls and better chances of receiving physical health checks than people without SMI. Despite this, for health outcomes, we found significant associations between SMI and increased risk for all-cause mortality and depression.

The results also suggested that elective admissions for macrovascular complications were significantly lower for people with SMI, whereas the rate of emergency admissions was elevated for this group (although the difference was not statistically significant). Further investigations indicated that most of these elective admissions had chronic ischaemic heart disease as the main diagnosis, and most emergency patients had been admitted for angina. These admission patterns might suggest a service gap; people with SMI are less likely to be referred by their GPs to specialist care for their cardiovascular disease and, when their symptoms deteriorate, they are more likely to be admitted as emergencies than people without SMI.

The analyses of the primary care data also supported this suggestion. Although the rate for recorded macrovascular complications was similar in people with and people without SMI, people with SMI were less likely to have a primary care record of chronic heart conditions, such as angina and chronic heart disease. This under-reporting/diagnosis pattern for people with SMI, which has been mentioned in the literature,124 could help explain the inequalities in specialist and hospital care experienced by people with SMI.

Objective

The objective was to provide a comparison of, and cost estimation for, diabetes health-care provision for people with and people without SMI.

Study population

We used the patient sample identified for objective 4 (see Objective 4: comparing the health outcomes of people with diabetes and severe mental illness with the health outcomes of people with diabetes without severe mental illness, Study population) to compare health-care use and associated costs between people with SMI and T2DM (cases) and their matched controls (non-SMI, T2DM). These patients were included for the analyses of this objective if they (1) had a length of follow-up of at least 1 year and (2) belonged to a case–control cluster. The start and end of follow-up were identified using the same method as for objective 4.

Variables and statistical methods

The exposure variable in this objective was the status of SMI. The candidate explanatory variables are summarised in Table 3.

The outcome variables were health-care use and the associated costs, including:

• primary care service use (consultations, prescriptions and diagnostic tests)

• hospital admissions for physical conditions (number of admissions and length of stay)

• hospital admissions for mental health problems

• associated costs for this service use.

The data construction and analysis methods are provided in Table 4. Mental health-related hospital admissions were identified using Healthcare Resource Group (HRG) codes. 125 Primary care service use and hospital admissions were costed using a bottom-up approach. A&E attendance and the use of community mental health services were not included in the analyses, as linkages to these data sources were not available in the CPRD data set. For an overview of all sources of health-care utilisation data and unit costs (both primary and secondary care), see Report Supplementary Material 13. Health-care use and associated costs were aggregated on an annual basis.

For the consultation costs, we followed the approach adopted in Ride et al. 126 and multiplied the duration of consultations by the costs of 1 minute of staff time, which were extracted from Unit Costs of Health and Social Care 2018. 127 Multiple visits to the same staff on the same day were considered as duplicates, whereas visits to different staff in a day were counted as different visits. For details on the categorising of consultations, see Report Supplementary Material 14.

The costs of prescriptions were calculated as the number of prescriptions multiplied by unit costs in 2018, as extracted from the prescription cost analysis for 2018. 128 Prescription records were aggregated at British National Formulary subparagraph level; higher hierarchy levels (paragraph, section or chapter) levels were used if subparagraph codes were unavailable.

For diagnostic tests, we followed Ride et al. 126 and grouped the test records into the categories as set out in the NHS reference costs. Costs were then estimated using the levels of tests and the unit costs extracted from the NHS Reference Costs 2017/18. 129 For details of this approach, see Report Supplementary Material 15.

The use and cost of hospital care were calculated for admissions to general hospitals only (including non-specialist mental health providers), but not admission to specialist mental health facilities. Hospital activities, such as diagnoses and procedures, were firstly grouped into HRGs using the costing grouper that corresponds to the NHS Reference Costs 2017/18. The generated HRG codes were then used to link the inpatient episodes to the national average costs from NHS Reference Costs 2017/18. 129 The calculated costs were therefore expressed in 2018 prices. To compare people with SMI with people without SMI, we separated hospital admissions and associated costs by mental health- and physical health-related admissions using HRG codes.

We conducted generalised linear regression models to explore the impact of SMI status and other explanatory variables on health-care resource use and health-care costs, and to take into account the non-negative, highly skewed and leptokurtic characteristics of cost data. The choice of distributional family and link function were informed by the Park test130 and the Pregibon link test. 131

Results

Regression analysis results for adjusted health-care resource utilisation and costs based on baseline characteristics

To take sample heterogeneity into account, we conducted a series of generalised linear regressions to adjust resource use and cost results by people’s baseline characteristics. These results can be found in the ‘adjusted’ section in Table 18. Baseline characteristics that were controlled for included age at diabetes diagnosis, sex, ethnicity, deprivation, comorbidities, medication use, time since diabetes diagnosis and financial year of diabetes diagnosis. Generalised linear regression with gamma distribution and log-link function was chosen, based on the Park and Pregibon tests, because of the non-normality of the data. After adjustment, the differences in resource use and costs between people with and people without SMI remained significant, except for differences in numbers of prescription-related and test-related consultations.

Figure 9 demonstrates the 10-year trend of annual health-care cost per person for people with diabetes and SMI and people with diabetes but no SMI. As shown, people with SMI incurred significantly higher annual health-care costs across this 10-year horizon. Cases and controls followed a similar trend: higher costs were incurred in the first year after diagnosis, followed by a cost reduction in the second year; thereafter, the annual costs slowly increased year on year. This trend was more prominent in people with SMI and diabetes than in those with diabetes alone.

FIGURE 9.

Objective 6: trend for annual health-care cost per person with SMI and T2DM (cases) and matched person with T2DM but no SMI (controls).

Table 19 shows the impact of SMI on total costs among people with diabetes. SMI increased the total costs by 82.6% (exp^0.602 – 1), increased the primary care costs by 70.9% and increased secondary care costs by 110.9%.

TABLE 19 - Objective 6: generalised linear regression results showing the impact of SMI on costs among people with T2DM

SD, standard deviation.

Adjusted risk factors	Total (n = 6383)			Primary care (n = 6383)			Secondary care (n = 6383)
	Coefficient	p-value	95% CI	Coefficient	p-value	95% CI	Coefficient	p-value	95% CI
Intercept	7.848	< 0.0001	7.450 to 8.247	6.281	< 0.0001	6.090 to 6.474	7.619	< 0.0001	7.004 to 8.234
Age (years) at T2DM diagnosis	0.013	< 0.0001	0.010 to 0.017	–0.001	0.357	–0.003 to 0.001	0.020	< 0.0001	0.015 to 0.026
Sex
Male	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Female	0.043	0.0325	–0.042 to 0.127	0.178	< 0.0001	0.137 to 0.219	0.027	0.678	–0.102 to 0.156
Case
With SMI (case)	0.602	< 0.0001	0.502 to 0.702	0.253	< 0.0001	0.204 to 0.301	0.746	< 0.0001	0.594 to 0.899
Without SMI (control)	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Ethnicity
White	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Non-white (Asian, black, other, mixed)	0.151	0.031	0.014 to 0.289	0.062	0.064	–0.004 to 0.128	0.180	0.094	–0.031 to 0.390
Unknown		< 0.0001	–1.524 to –1.172	–0.425	< 0.0001	–0.511 to –0.339	–2.544	< 0.0001	–2.823 to –2.265
Deprivation (IMD 2010)
1st quintile (least deprived)	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
2nd quintile	0.175	0.017	0.032 to 0.319	0.107	0.003	0.037 to 0.177	0.135	0.228	–0.084 to 0.354
3rd quintile	0.143	0.050	0.000 to 0.286	0.164	< 0.0001	0.095 to 0.234	0.105	0.344	–0.113 to 0.324
4th quintile	0.157	0.025	0.020 to 0.295	0.141	< 0.0001	0.073 to 0.208	0.101	0.350	–0.111 to 0.312
5th quintile (most deprived)	0.136	0.054	–0.002 to 0.275	0.155	< 0.0001	0.088 to 0.223	0.059	0.589	–0.155 to 0.273
Missing	0.233	0.736	–1.121 to 1.588	0.462	0.170	–0.199 to 1.123	0.009	0.993	–2.052 to 2.070
Comorbidities
Hypertension	0.126	0.007	0.034 to 0.218	0.126	< 0.0001	0.082 to 0.170	0.132	0.066	–0.008 to 0.272
Charlson Index diseases, mean (SD)	0.298	< 0.0001	0.238 to 0.357	0.218	< 0.0001	0.189 to 0.246	0.342	< 0.0001	0.251 to 0.433
Medications within 15-month window
Antidepressants	0.260	< 0.0001	0.161 to 0.359	0.423	< 0.0001	0.374 to 0.472	0.202	0.008	0.052 to 0.352
Antidiabetes	0.152	0.017	0.027 to 0.278	0.144	< 0.0001	0.084 to 0.205	0.142	0.143	–0.048 to 0.333
T2DM duration	0.062	< 0.0001	0.045 to 0.079	0.158	< 0.0001	0.149 to 0.166	0.024	0.074	–0.002 to 0.050
Financial year at T2DM diagnosis
1998–9	0.270	0.303	–0.244 to 0.785	–0.168	0.188	–0.419 to 0.082	0.384	0.335	–0.397 to 1.165
1999–2000	0.352	0.108	–0.077 to 0.781	0.161	0.133	–0.049 to 0.370	0.382	0.249	–0.268 to 1.031
2000–1	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
2001–2	0.121	0.427	–0.177 to 0.418	0.194	0.009	0.048 to 0.339	0.042	0.854	–0.409 to 0.493
2002–3	0.094	0.499	–0.179 to 0.368	0.124	0.067	–0.009 to 0.257	–0.004	0.984	–0.421 to 0.412
2003–4	0.034	0.805	–0.234 to 0.301	0.159	0.017	0.029 to 0.290	–0.087	0.677	–0.494 to 0.320
2004–5	0.019	0.891	–0.252 to 0.289	0.215	0.001	0.084 to 0.347	–0.071	0.736	–0.483 to 0.341
2005–6	–0.044	0.755	–0.318 to 0.231	0.283	< 0.0001	0.149 to 0.416	–0.255	0.232	–0.674 to 0.164
2006–7	–0.150	0.262	–0.413 to 0.112	0.300	< 0.0001	0.172 to 0.427	–0.401	0.051	–0.804 to 0.002
2007–8	–0.101	0.481	–0.382 to 0.180	0.329	< 0.0001	0.194 to 0.465	–0.356	0.106	–0.787 to 0.076
2008–9	–0.349	0.015	–0.631 to –0.068	0.316	< 0.0001	0.179 to 0.453	–0.646	0.003	–1.075 to –0.217
2009–10	–0.351	0.015	–0.634 to –0.067	0.302	< 0.0001	0.165 to 0.440	–0.634	0.004	–1.067 to –0.201
2010–11	–0.442	0.003	–0.734 to –0.150	0.250	< 0.0001	0.110 to 0.391	–0.807	< 0.0001	–1.257 to –0.357
2011–12	–0.434	0.005	–0.735 to –0.134	0.229	0.002	0.086 to 0.373	–0.775	0.001	–1.238 to –0.312
2012–13	–0.928	< 0.0001	–1.231 to –0.625	0.163	0.029	0.016 to 0.310	–1.460	< 0.0001	–1.927 to –0.993
2013–14	–1.143	< 0.0001	–1.466 to –0.821	–0.029	0.710	–0.185 to 0.126	–1.506	< 0.0001	–2.001 to –1.012
2014–15	–1.503	< 0.0001	–1.854 to –1.151	–0.180	0.037	–0.350 to –0.011	–2.152	< 0.0001	–2.693 to –1.610

Summary of findings

People with SMI and diabetes had higher costs than people with diabetes alone. We found that the main cost driver was secondary care, which accounted for 83.3% of overall health-care costs for those with diabetes and SMI and for 72.5% of overall health-care costs for people with diabetes alone. On average, people with diabetes and SMI use more primary care services every year than people with diabetes alone. The same trend applied to hospitalisation: people with diabetes and SMI tended to have more hospital admissions every year and to stay in hospital longer than those with diabetes only. This difference was regardless of whether or not the admissions were mental health related.

Objective

The objective was to identify which health-care interventions may be associated with better health outcomes for people with SMI and diabetes.

Study population

For objective 4, we combined data sets B and C (see Figure 2) and used comparisons between people with SMI (cases) and people without SMI (controls) to estimate the association between SMI status and the provision of physical health checks. For objective 7, we conducted further investigations to explore the association between SMI and health checks in the presence of macrovascular complications.

The development of macrovascular complications as a physical health outcome was defined as the presence of myocardial infarction, peripheral vascular disease or stroke during patients’ follow-up periods. Although under constant review, the QOF has been incentivising primary care providers to perform physical health checks as ongoing management for patients on the register for these conditions. For instance, regular checks of blood pressure and cholesterol levels were included as QOF indicators for patients with coronary heart disease (CHD) and stroke before 2011/12. These indicators were also incentivised by the QOF for patients with CHD, stroke and peripheral arterial disease in 2012/13 and 2013/14. Since 2014/15, however, only checks on blood pressure remain incentivised for patients with CHD, stroke or peripheral arterial disease, and regular checks for cholesterol levels are no longer required. 111 We hypothesised that physical health checks for blood pressure and cholesterol could influence people’s health outcomes, and would also be affected by the presence of macrovascular complications.

For this objective, we therefore applied restrictions on the patient sample used in objective 4 (see Objective 4: comparing the health outcomes of people with diabetes and severe mental illness with the health outcomes of people with diabetes without severe mental illness, Study population) and compared only cases and controls with the same status of macrovascular complications (as a health outcome), so that the impact of these complications on health checks was equal within case–control clusters. A total of 8724 patients remained in this restricted sample after controls with a macrovascular complication status different from that of cases were excluded.

Variables and statistical methods

The exposure and explanatory variables and statistical methods for this objective were primarily based on the core and extended models, as reported in objective 4 (see Objective 4: comparing the health outcomes of people with diabetes and severe mental illness with the health outcomes of people with diabetes without severe mental illness).

We explored the suitability of the CPRD data set for assessing individual interventions in terms of quality and consistency of recording by practices. Recording was not sufficiently comprehensive to allow detailed measurement of frequent and often complex interventions such as medication history and comprehensive care pathways. For referrals, we were not able to confirm attendance as we did not have linkage to outpatient data. However, we were able to ascertain whether or not lower frequency interventions such as health checks, diabetes education and influenza vaccination had been performed and we used these as the basis of our analysis.

For the impact of SMI on receipt of health checks, we re-estimated the core and extended models of blood pressure, cholesterol, HbA_1c and BMI, as reported in objective 4. An interaction term between the status of SMI and macrovascular complications was added to these models to explore whether or not the impact of SMI could be modified by the status of complications.

For the impact of health checks on health outcomes, we separately estimated the association between the level of each health check, measured as the mean number per year, and markers of depression and anxiety using the core model reported in objective 4 (see Objective 4: comparing the health outcomes of people with diabetes and severe mental illness with the health outcomes of people with diabetes without severe mental illness). We then added an interaction term between the level of health checks and SMI status to explore whether or not the association between health checks and depression could be modified by this.

We did not investigate the impact of health checks on physical health outcomes because of the confounding relationship between the two. We expected that the level of health checks would potentially be affected by the other physical health outcomes for reasons similar to why these health checks would be affected by macrovascular complications status. For instance, patients with poorer diabetes and cardiovascular control could potentially receive more frequent physical checks; death in the follow-up period would reduce health checks received, but deteriorated health status prior to death would increase physical health checks. Owing to this two-way causality, the estimated association between health checks and physical health outcomes would be biased using these patient-level data.

After initial investigation, we were unable to proceed with investigating the impact of retinopathy screening, diabetes education and influenza vaccination on health outcomes because of limitations in the overall data quality and the under-recording issue in primary care data for people with SMI. A descriptive summary of the provision of these health-care interventions can be found in Appendix 4, Table 32.

Results