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1 Social inequalities in prevalence of diagnosed and undiagnosed diabetes and impaired glucose regulation in naïve participants in the Health Surveys for England series. Journal: BMJ Open Manuscript ID bmjopen-0-00 Article Type: Research Date Submitted by the Author: 0-Oct-0 Complete List of Authors: Moody, Alison; UCL, Epidemiology & Public Health Cowley, Giovanna; Barnet Hospital, Ng Fat, Linda; UCL, Epidemiology and Public Health Mindell, Jennifer; UCL, Epidemiology and Public Health <b>primary Subject Heading</b>: Diabetes and endocrinology Secondary Subject Heading: Epidemiology, Public health Keywords: General diabetes < DIABETES & ENDOCRINOLOGY, EPIDEMIOLOGY, PUBLIC HEALTH, SOCIAL MEDICINE, Inequalities, England -

2 Page of 0 BMJ Open TITLE: Social inequalities in prevalence of diagnosed and undiagnosed diabetes and impaired glucose regulation in naïve participants in the Health Surveys for England series. AUTHORS: Alison Moody, Giovanna Cowley, Linda NgFat, Jennifer S Mindell Alison Moody, Research Assistant, Research Department of Epidemiology & Public Health, UCL, London, UK a.moody@ucl.ac.uk Dr Giovanna Cowley, FY Doctor, Barnet Hospital, London, UK giovanna.cowley@nhs.net Dr Linda NgFat, Research Associate, Research Department of Epidemiology & Public Health, UCL, London, UK l.ngfat@ucl.ac.uk Dr Jennifer S Mindell, Reader in Public Health, Research Department of Epidemiology & Public Health, UCL, London, UK j.mindell@ucl.ac.uk Correspondence to: A Moody a.moody@ucl.ac.uk Health & Social Surveys Research Group, Research Department of Epidemiology & Public Health, UCL, - Torrington Place, London WCE BT Tel: 00-- Keywords: Diabetes mellitus; diagnosis; England; impaired glucose regulation; ethnicity; social inequalities Word count, excluding title page, abstract, references, figures and tables: words Number of tables: Number of figures: 0 Number of references: Number of supplementary files for online publication: file, containing tables Manuscript for BMJ Open Copyright statement The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence ( to the Publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to i) publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution and convert or allow conversion into any format including without limitation audio, iii) create any other derivative work(s) based in whole or part on the on the Contribution, iv) to exploit all subsidiary rights to exploit all subsidiary rights that currently exist or as may exist in the future in the Contribution, v) -

3 Page of the inclusion of electronic links from the Contribution to third party material where-ever it may be located; and, vi) licence any third party to do any or all of the above. All research articles will be made available on an Open Access basis (with authors being asked to pay an open access fee see The terms of such Open Access shall be governed by a Creative Commons licence details as to which Creative Commons licence will apply to the research article are set out in our worldwide licence referred to above. Competing interest statement All authors have completed the ICMJE uniform disclosure form at and declare: no support from any organisation for the submitted work; GC has no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; AM, LNF & JM are funded by the Health and Social Care Information Centre to work on the Health Survey for England; and no other relationships or activities that could appear to have influenced the submitted work. Funding The Health Survey for England (HSE) 00-0 was funded by the Health and Social Care Information Centre. AM, LNF & JM are funded to conduct the annual HSE but this specific study was not funded. The funders of the HSE took no part in the decisions to undertake or publish this study, nor in its conduct and writing this paper; the views expressed are those of the authors alone. Contributor statement AM had the original idea; all four authors prepared the analysis plan and interpreted the findings; AM & LNF conducted the analyses; AM & GC wrote the first draft of the manuscript; JM wrote the second draft; all four authors contributed to subsequent drafts and approved the final version. All authors had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. Transparency statement The lead author (the manuscript s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained. Research ethics approval HSE 00 was approved by Oxford B Research Ethics Committee (REC), ref. 0/H00/0; HSE 00 was approved by Oxford B REC, ref. 0/H00/; HSE 0 and 0 were approved by Oxford A REC, ref. 0/H00/; and HSE 0 was approved by Oxford A REC, ref. /sc/0. Provenance and peer review Not commissioned; externally peer reviewed. -

4 Page of 0 BMJ Open What this paper adds What is already known There are marked socio-economic inequalities in health and in risk factors for chronic noncommunicable diseases in the UK. Earlier diagnosis of diabetes and treatment of people with impaired glucose regulation (IGR) and undiagnosed diabetes is recommended by the National Institute for Health and Care Excellence (NICE) to reduce future consequence of diabetes. Little is known about the characteristics of people with IGR in England: a recent study showing an increase in prevalence of IGR to % of adults used low thresholds not generally accepted in the UK. What this study adds 00-0 Health Survey for England data demonstrate marked socio-economic inequalities in the age-standardised prevalence of IGR and undiagnosed and diagnosed diabetes, which vary by income, education, occupational social class, and area deprivation. Odds of IGR or undiagnosed diabetes rise dramatically with age but also vary by education and income as well as by ethnicity, body mass index, and waist circumference. Participants with diagnosed diabetes had better levels of blood pressure and cholesterol than people with undiagnosed diabetes, but worse self-reported general health. -

5 Page of ABSTRACT Objective We aimed to ascertain the extent of socio-economic inequalities in diagnosed and undiagnosed diabetes and impaired glucose regulation (IGR) in naïve random samples of the general population in England, as earlier diagnosis of diabetes and treatment of people with IGR can reduce adverse sequelae of diabetes. Design Five annual cross-sectional health examination surveys; data adjusted for complex survey design Setting Random selection of private homes across England, new sample annually 00-0 Participants Nationally-representative random samples of the general, free-living population: adult interviewed in, of, eligible addresses selected. Of, adults,, were interviewed,, saw a nurse, and, gave blood samples., adults had a valid glycated haemoglobin (HbAc) measurement and answered the diabetes questions. Main outcome measures Diagnosed diabetes, undiagnosed diabetes (HbAc mmol/mol), IGR (HbAc -mmol/mol) Results Overall, % of the population had IGR, % undiagnosed and % diagnosed diabetes. Agestandardised prevalence was highest among Asian (%(%CI -%), % (-%) and % (-%) respectively) and Black participants (%(-%), % (-%), and % (-0%) respectively). These were also higher among those with lower income, less education, lower occupational class, and greater deprivation. Education (odds ratio(or).(%ci.-.) for no qualifications vs degree or higher) and income(.(.-.) for lowest vs highest income quintile) remained significantly associated with IGR or undiagnosed diabetes on multivariate regression. The greatest odds of IGR or undiagnosed diabetes were with increasing age over y (e.g. OR.(.-0.) aged - vs -), but other significant associations were ethnic group (Asian(.(.0-.0)), Black (.(.-.)) or Other (.0(.0-.)) vs White); sex (OR.(.-.) for men vs women); BMI (.(.-.) for morbidly obese vs not overweight); and waist circumference (.00(.-.) for very high vs low). Conclusions Social inequalities in hyperglycaemia exist, additional to well-known demographic and anthropometric risk factors for diabetes and IGR. Abstract Word count: -

6 Page of 0 BMJ Open Strengths and limitations of this study This study uses a nationally representative sample of the free-living English population, with, participants, and non-response weighting. The combination of (self-reported) diagnosis and objectively measured glycated haemoglobin allowed identification of those with undiagnosed diabetes or impaired glucose regulation. Socio-economic as well as health-related co-variates were used to show a wide range of associations with diabetes and impaired glucose regulation. A weakness of the study is the single point of data collection, making direction of effect sometimes hard to ascertain. Despite the large sample, some sub-sample groups were small, and had correspondingly wider confidence intervals, e.g. undiagnosed diabetics (), Black (), Mixed () and Other ethnic groups (). Data-sharing statement This analysis was conducted on archived (anonymised) data, which are available from the UK data service: -

7 Page of INTRODUCTION Diabetes is a condition that can result in significant morbidity and mortality, () exacerbated by the latent period of several years during which diabetes is asymptomatic and may therefore remain undiagnosed. () During this time, people are at risk of the complications of diabetes but being undiagnosed, they do not receive treatment to reduce the risks of cardiovascular or other diabetesrelated diseases: at the time of diagnosis, up to 0% of people with diabetes already have microvascular and macrovascular complications. (,) Early glycaemic control can reduce complications, () so targeted testing for diabetes is advocated by Diabetes UK () and the American Diabetes Association (ADA). () In 0, the National Institute for Health and Care Excellence (NICE) published guidelines recommending targeted screening to identify undiagnosed diabetes in asymptomatic populations. () More recently, a further high risk category has been recognised: people with impaired glucose metabolism but with blood glucose levels below the diabetic range, referred to in this paper as having impaired glucose regulation (IGR). Different definitions of IGR exist but an International Expert Committee on the role of glycated haemoglobin (HbAc) in diagnosing diabetes suggested that people with HbAc of -mmol/mol (.0-.%) () should be included in this group. NICE adopted this recommendation in its 0 guidance. () Studies have shown an annual progression rate from IGR to diabetes of -0% (0,) and that treatment can prevent or delay progression to both diabetes (-) and its complications. (,) For these reasons, NICE recommends treating people with IGR, primarily with intensive lifestyle interventions but with metformin and orlistat where lifestyle changes fail or are inappropriate. () These guidelines have not yet been incorporated into the Quality and Outcomes Framework (QOF) for GPs. Socio-economic inequalities in health and risk factors are wellknown, () as is Tudor Hart s inverse care law, that those at highest need are least likely to receive healthcare. () Rising obesity levels, which are socially patterned, (0) have been paralleled by the increasing prevalence of type diabetes (,) ; information regarding trends in diagnosed diabetes is available from QOF and the National Diabetes Audit. (,) These databases provide information about patients so may not accurately reflect diabetes in the general population. In particular, they do not tell us about IGR or undiagnosed diabetes, nor do they provide information about social inequalities. Nationally representative information about those with undiagnosed diabetes and IGR is required to understand, manage and reduce the potential impact of diabetes on population health. A recent report used Health Survey for England (HSE) data to count and characterise people with non-diabetic hyperglycaemia (IGR) and assess the relative utility of four risk scores. We hypothesised that more deprived groups are likely to have higher prevalence of IGR and undiagnosed diabetes, both for absolute prevalence and as a proportion of all cases of diabetes. The objectives of this study were to: determine the prevalence, and trends in prevalence, of diagnosed and undiagnosed diabetes and IGR in nationally representative data from the Health Survey for England (HSE); examine socioeconomic and ethnic inequalities in IGR and undiagnosed and diagnosed diabetes; and examine the health status and biomarkers for CVD risk in people with these conditions. We also examined the summary performance characteristics of various current screening strategies. -

8 Page of 0 BMJ Open METHODS Participants The Health Survey for England is an annual, cross-sectional, general population survey of individuals living in private households in England, randomly selected by address. Data collection occurs throughout the year. The first stage is a health interview, including questions about diagnosed conditions, self-rated health, and measured height and weight. The second is a nurse visit, including further physical measurements, recording prescribed medication, and taking biological samples. All adults in selected households are eligible for interview; all interviewed participants are eligible for the nurse visit and biological sampling. Most years, around 000 adults have an interview and around 000 a nurse visit; all data collection occurs in the participants own home. For this analysis, survey data between 00 and 0 (during which time core questions and measurements remained comparable) were included, to ensure subsamples (particularly people with undiagnosed diabetes) were as large as possible. Research ethics approval was obtained from the relevant NHS NRES committees prior to starting each year s survey. Data Diabetes & IGR definitions Participants were grouped according to their glycated haemoglobin result and their answers to questions on diagnosed diabetes. Those who reported that their doctor had diagnosed them with diabetes (except only when pregnant), as well as those taking medication prescribed only for diabetes, were categorised as doctor-diagnosed diabetes. Those not diagnosed with diabetes were categorised as undiagnosed diabetes if glycated haemoglobin was mmol/mol (>.%), IGR if - mmol/mol (.0-.%), and normoglycaemic if <mmol/mol (<.0%). Participant characteristics Demographics and socio-economic position: Age was grouped from -y to + in 0y age groups. Ethnicity was self-assigned from a list of the 00 or 0 Census categories; categories used were White, mixed, Asian, Black and other. Equivalised household income, adjusted for the number of adults and children in the household, was divided into quintiles within each survey year. Highest educational qualification was categorised as degree or equivalent; A levels/nvq or other higher qualifications below degree level; other; and none. Occupational data were grouped into the fivecategory National Statistics-Socio-Economic Classification (NS-SEC). Area deprivation (the Index of Multiple Deprivation IMD 00 for HSE 00 and 00, and IMD 00 for HSE 0-0) was divided into survey year-specific quintiles. All data were self-reported except for area deprivation, which was assigned from the participant s postcode. Anthropometry: Body mass index (BMI) was calculated from height and weight measured by the trained interviewers. Waist circumference was measured by the trained nurses. BMI was first categorised as underweight (<.kg/m ), desirable (. to <kg/m ), overweight ( to <0kg/m ), obese I (0 to <kg/m ), obese II ( to <0kg/m ), and morbidly obese ( 0kg/m ). A second categorisation used World Health Organization (WHO) thresholds for Asian participants (<kg/m, to <kg/m, to <.kg/m,.kg/m ), reflecting their greater risk of diabetes -

9 Page of at lower BMI levels. We subdivided the obese categories into. to <.kg/m,. to <.kg/m, and.kg/m. Thirdly, Black participants were also categorised using those lower thresholds, as recommended by NICE in 0. Similarly, waist circumference was first categorised as low (<cm in men, <0cm in women), high (men -0cm, women 0-cm) and very high (men >0cm, women >cm). Secondly, an ethnic-group specific classification was used, where the threshold for low was <0cm in Asian and Black men but was unchanged for other men and for all women. Thirdly, sex-independent international thresholds were used that are more stringent for men: low <cm, high cm in both men and women. Finally, the NICE risk categorisation using both BMI and waist circumference was also used. Other health measures: Self-rated general health was based on the answer to a standard question about participants health in general. Responses of very good and good were combined, as were very bad and bad. Nurses measured blood pressure three times after a five minute rest, using an automated device, the Omron HEM 0, with one minute intervals between each measurement. Categories of blood pressure used information on medication prescribed for blood pressure control as well as the mean of the second and third measurements, limited to participants who had not eaten, smoked, drunk alcohol or exercised in the previous 0minutes. In 00 and 00, a spot urine was collected for laboratory measurement of albumin:creatinine ratio (ACR). Micro-albuminuria was defined as ACR. 0 mg/mmol in men and. 0 mg/mmol in women and macro-albuminuria as ACR >0 mg/mmol in either sex. In each year, blood samples were taken for serum total and HDL cholesterol as well as the EDTA sample for glycated haemoglobin. Urine and blood samples were posted to the Newcastle University Hospitals laboratory, Newcastle-upon-Tyne, for assay. All measurements used standard protocols which have been reported elsewhere, along with laboratory methods and quality assurance. 0, Analyses and statistics Weighting Data were weighted for probability of address selection (smaller regions were over-sampled ), dwelling unit selection (only one dwelling unit selected per address) and household selection (only one household selected per dwelling unit). In addition, the weights were calibrated for household non-response, and individual non-response to interview, nurse visit, or blood sample. The Health Survey for England is designed to be representative of people living in private households: nonresponse weighting is used to correct for differential response compared with known demographics from the census, and differential response to each stage of the survey. Since the analysis was limited to those with blood samples, the blood weights were used. Accounting for complex survey design The Health Survey for England uses a stratified clustered sample design, and should not be treated as a simple random sample. The strata variables supplied with each annual dataset are not necessarily identical, therefore government office region was used as an alternative. Clusters/primary sampling units were used as archived but recoded to avoid duplicated identifiers across years (e.g. cluster in 00 was a different location to cluster in 00). -

10 Page of 0 BMJ Open Analysis Data were analysed using IBM SPSS 0, with all significance tests taking account of the complex survey design (stratification and clustering) and weighting. Differences between groups are mentioned as being significant in this paper if p<0.0. Results are presented as crude rates (weighted for non-response, and accounting for complex survey design), and age- and sexstandardised. Logistic regression models were created, limited to participants without diagnosed diabetes. The two outcomes were a) IGR or undiagnosed diabetes, and b) undiagnosed diabetes. An outcome of IGR alone was not used as any testing for IGR in clinical practice would also aim to detect undiagnosed cases of diabetes. Explanatory variables considered were demographic (age group, sex, ethnic group), socio-economic factors and anthropometry. The main six-category obesity variable and three-category waist circumference were not collinear, with a correlation of 0., so both were included in the initial model. Missing data for the explanatory variables were included as separate categories, to enable inclusion of all participants. Income quintiles, educational qualifications, NS- SEC occupational category, and area deprivation quintiles were used to assess socio-economic inequalities. Of these, only income quintiles and educational qualifications were included in the final regression models, as these were the only variables which improved the fit of the model with the addition of each socio-economic variable. Income and educational qualifications had a correlation of 0.0. The current health status and biomarkers of cardiovascular risk of participants with IGR and undiagnosed or diagnosed diabetes were also compare with those of normoglycaemic participants. Screening metrics We also used our data to evaluate the effectiveness of screening using different categories for BMI and/or waist circumference, criteria for NHS Health Checks () (inviting people aged 0- or aged and above from certain ethnicities) and the Diabetes UK/Leicester risk score tool (but excluding family history of diabetes (,) ) as the criteria for screening to identify IGR and undiagnosed diabetes. We calculated the sensitivity, specificity, positive predictive value and negative predictive value for the various criteria in this general population sample. The STROBE checklist was used when writing this manuscript. The raw data are available to download from the UK Data Service. -

11 Page 0 of RESULTS Participants, eligible households (occupied private residences) were identified, of which at least one adult was interviewed in, (%) co-operating households. All, adults (aged +) in cooperating households were eligible for interview, nurse visit and blood sample:, (%) were interviewed,, (%) saw a nurse, and, (%) gave a blood sample. Non-response were due to non-contacts, refusals, or inability to give informed consent (due to mental capacity or language difficulties) at the individual interview or nurse stage. Blood samples were not available because of refusal, unsuccessful attempts to take blood, or medical reasons for ineligibility (including pregnancy, anticoagulants or recent fits). The analytic sample comprised, adults who had a valid glycated haemoglobin measurement and answered the diabetes questions. The mean age was.y (standard error 0.); % were male; % were in households headed by managerial/professional or intermediate occupations. % had missing occupation data, % missing income, % missing BMI, % missing waist circumference and <% missing ethnicity., participants were normoglycaemic, 00 had IGR, had undiagnosed diabetes and diagnosed diabetes. Restricting the sample to those without diagnosed diabetes yielded,0 for Tables and. Social inequalities in diabetes and IGR There were marked socio-economic gradients in prevalence of IGR and undiagnosed and diagnosed diabetes. For area deprivation, inequalities in IGR (Table ) were masked in the crude rates (Table S) due to confounding by age. Inequalities in IGR by income and education were attenuated by age/sex-standardisation but remained marked, with rates of 0%(%CI -%) in the lowest and %(-%) in the highest income quintile and 0% (-%) for those with A level/nvq or higher educational qualifications and % (-%) for those with no qualifications (Table ). The occupational classification showed higher IGR rates in participants in semi-routine occupations or lower supervisory/technical occupations than in other groups

12 Page of 0 BMJ Open Table Age- and sex-standardised Prevalence of IGR, undiagnosed, and diagnosed diabetes 00-0, by socio-demographic indicators, with % confidence intervals. Normoglycaemia IGR Undiagnosed diabetes Diagnosed All N Prevalence % of all diabetes diabetes %(CI) %(CI) %(CI) a diabetes %(CI) %(CI) All (-) (-). (.-.) (-) (-) (-) Sex p=0. p<0.0 p=0. p<0.0 p<0.0 Men (0-) (0-). (.-.) (0-) (-) (-) 0 Women (-) (-). (.-.) (0-) (-) (-) 0 Age group p<0.0 p<0.0 p=0. p<0.0 p< (-) (-) (0.0-0.) (-) (0-) (0-) - 0. (-) (-) (0.-0.) (-) (-) (-) - (0-) (-). (0.-.) (-) (-) (-) - (0-) (-). (.-.) (-0) (-) (-) 00 - (-) (-). (.-.) (-0) (-) (0-) - (-) (0-). (.-.0) (-) (-) (-) + (-) (-). (.-.0) 0 (-) (-) 0 (-) HSE year p=0. p=0.0 p=0.0 p=0. p= (-) (0-). (.-.) (-) (-) (-) 0 00 (0-) (-). (0.-.) (-) (-) (-) 0 0 (-) (-). (.-.) (0-) (-) (-) 0 0 (-) 0 (-). (.-.) (-) (-) (-) 0 (-) (0-). (.-.) 0 (-) (-) (-) Occupation p<0.0 p=0.0 p=0. p<0.0 p<0.0 Managerial/ professional (-) 0 (-). (.0-.) (-) (-) (-) Intermediate (-) 0 (-). (0.-.) (-) (-) (-) 00 Small employers/ own account (0-) (-). (.-.) (-) (-) (-) workers Lower supervisory/ (-) (-). (.-.) (-) (-) (-) technical Semi-routine (-0) (-).0 (.-.) (-) (-) (-0) Equivalised household income p<0.0 p<0.0 p=0.0 p<0.0 p<0.0 Highest quintile (-) 0 (-).0 (0.-.) (-) (-) (-) nd highest (-) (-). (.0-.) (0-) (-) (-) rd (-) (0-). (.0-.) 0 (-) (-) (-) nd lowest (-) (-).0 (.-.) (-) (-) (-) Lowest quintile (-) (-). (.-.) 0 (0-0) (-) (-) Highest qualification p<0.0 p<0.0 p=0.0 p<0.0 p<0.0 Degree or above (-) 0 (-) 0. (0.-.) (-) (-) (-) NVQ/A level to below degree (-) 0 (-). (.-.) (-) (-) (-) Other (-) (0-). (.-.) (-) (-) (-) 0 None (-) (-). (.-.) (-) (-) 0 (-) Quintiles of area deprivation p<0.0 p<0.0 p=0. p<0.0 p<0.0 Least deprived (-) 0 (-).0 (0.-.) 0 (-) (-) (-) 0 nd lowest (-) 0 (-). (.-.) (-) (-) (-) Middle (0-) (0-). (.-.) (-) (-) (-) 0 nd highest (-) (-). (.-.) 0 (-) (-) (-0) Most deprived (-) (-). (.-.) (-) (-0) (0-) Ethnic specific BMI category b p<0.0 p<0.0 p=0. p<0.0 p<

13 Page of Not overweight (-0) (-) 0. (0.-0.) (-) (-) (-) overweight (-) 0 (-). (0.-.) (-) (-) (-) Obese (-) (-). (.-.) (-) (-) (0-) Obese (-) (-). (.-.) (-) (-) (-0) 00 Morbidly obese (-) 0 (-).0 (.-.) (-) 0 (-) (0-) Ethnic specific waist c p<0.0 p<0.0 p=0. p<0.0 p<0.0 Low 0 (-0) (-) 0. (0.-0.) (-) (-) (-) 0 High (-) 0 (-).0 (0.-.) (-0) (-) (-) Very high (-) (-). (.-.) (-) (-) (0-) Ethnic group p<0.0 p<0.0 p=0.0 p<0.0 p<0.0 White (-) 0 (0-). (.-.) (0-) (-) (-) Mixed (-) 0 (-). (0.-.) (-) 0 (-) Asian (-0) (-). (.-.) (-) (-0) Black (-) (-). (0.-.) (-0) (-) Other (0-) (-).0 (0.-.) (-) (-) All non-white (-) (-). (.0-.) (-0) (0-) (-) 0 a To show variation in the prevalence of undiagnosed diabetes, this is presented to one d.p. b BMI: Body mass index (kg/m ); Black and Asian participants: Overweight: BMI to <.kg/m ; Obese I: BMI. to <. kg/m ; Obese II: BMI. to <. kg/m ; Morbidly obese: BMI. kg/m ; Other participants: Overweight: BMI to <0 kg/m ; Obese I: BMI 0 to < kg/m ; Obese II: BMI to <0 kg/m ; Morbidly obese: BMI 0 kg/m. c Black and Asian men: Low <0cm, High 0-0cm, Very high >0cm; All other men Low <cm; High -0cm; Very high >0cm; Women (regardless of ethnicity): Low <0cm, High 0-cm, Very high >cm. Prevalence of doctor-diagnosed diabetes across the years was %, undiagnosed diabetes was %, and a further % had IGR. In general, the characteristics of people with diagnosed diabetes, undiagnosed diabetes and IGR followed similar patterns: each increased with age, overweight, lower socio-economic position, and were higher among minority ethnic groups than the white population (after age-standardisation). There were some differences, for example, while both diagnosed and undiagnosed diabetes were more common among men, age-standardised prevalence of IGR was not different between the sexes (Table ). Predictors of IGR or undiagnosed diabetes Among people without diagnosed diabetes, age was the biggest risk factor, with those aged and over having almost 0 times higher odds of IGR/undiagnosed diabetes (Table ). The other important associations, which each had an independent effect, were ethnicity and both general and abdominal obesity. Unlike the prevalence figures, men had a % higher odds of IGR than women. Restricting the outcome to undiagnosed diabetes, similar results were seen: although the sex difference, effects of BMI and waist circumference and associations with lower education were substantially greater, the odds for Black people was similar to that for White participants. However, income was no longer significant. -

14 Page of 0 BMJ Open Table. Factors associated with IGR or undiagnosed diabetes, or undiagnosed diabetes IGR or undiagnosed diabetes Undiagnosed diabetes OR % CI p OR % CI p Female <0.00 <0.00 Male..-. < < <0.00 < < < < < < < < <0.00 White <0.00 <0.00 Mixed Asian < <0.00 Black..-. < Other Degree or higher < A-level/NVQ to below degree Other No qualifications..-. < Highest income quintile nd highest Middle nd lowest Lowest quintile Missing

15 Page of Ethnic-specific BMI categories a <0.00 <0.00 Not overweight Overweight Obese I..-. < <0.00 Obese II.0.-. < <0.00 Morbidly obese..-. < <0.00 Not measured <0.00 Ethnic-specific waist circumference categories < Low waist 0.00 High waist..-. < Very high waist < <0.00 Not measured..-.0 < <0.00 a BMI: Body mass index (kg/m ); Black and Asian participants: Overweight: BMI to <.kg/m ; Obese I: BMI. to <. kg/m ; Obese II: BMI. to <. kg/m ; Morbidly obese: BMI. kg/m ; Other participants: Overweight: BMI to <0 kg/m ; Obese I: BMI 0 to < kg/m ; Obese II: BMI to <0 kg/m ; Morbidly obese: BMI 0 kg/m. b Black and Asian men: Low <0cm, High 0-0cm, Very high >0cm; All other men Low <cm; High -0cm; Very high >0cm; Women (regardless of ethnicity): Low <0cm, High 0-cm, Very high >cm. -

16 Page of 0 BMJ Open Health status of those with IGR, undiagnosed, or diagnosed diabetes. Normoglycaemic people were generally the healthiest. Those with IGR had higher rates than normoglycaemic participants of hypertension, high total cholesterol, low HDL cholesterol, high total:hdl ratio, and albuminuria (Table ). Many of these differences remained after adjusting for the different age/sex profile of the groups. The proportion with untreated hypertension was highest among those with undiagnosed diabetes; overall hypertension rates were higher among those with diagnosed or undiagnosed diabetes, and lower among normoglycaemic and IGR participants. Those with undiagnosed diabetes were also more likely to have poor levels of HDL cholesterol, and a higher total:hdl cholesterol ratio. Those with diagnosed diabetes were least likely to have high total cholesterol. Self-rated health showed a marked gradient, with the age-standardised proportion reporting bad or very bad health being low among normoglycaemic and IGR participants and highest in those with diagnosed diabetes. Screening instruments for IGR and/or undiagnosed diabetes Among those without diagnosed diabetes, a number of possible screening criteria, using information that should be readily available to individuals or their GPs, were applied. Table S shows the screening characteristics in this general population sample, along with the proportion of the population so identified for screening. Despite the HSE data not including family history of diabetes, the best measure examined was based on the Diabetes UK/Leicester risk score, finding % of people with IGR or undiagnosed diabetes with a specificity of % if using moderate risk (score +). -

17 Page of Table Health conditions by glycaemia category Normoglycaemia (%) Unadjusted rates IGR Undiagnosed (%) diabetes (%) Diagnosed diabetes (%) Normoglycaemia (%) Age- and sex-standardised rates IGR Undiagnosed (%) diabetes (%) Diagnosed diabetes (%) Blood pressure / Hypertension <0/0, no medication 0 <0/0 but not <0/0, no medication /0, no medication <0/0, on medication <0/0 but not <0/0, on medication 0/0, on medication 0 Total cholesterol <mmol/l to <mmol/l 0 mmol/l HDL cholesterol mmol/l <mmol/l Total: HDL cholesterol ratio < 0 0 < 0 0 No albuminuria 0 0 Micro-albuminuria a 0 0 Macro-albuminuria a 0 0 Self-reported general health Very good/ good 0 Fair Very bad/ bad a Micro-albuminuria: albumin:creatinine ratio (ACR). 0 mg/mmol in men,. 0 mg/mmol in women; macro-albuminuria: ACR >0 mg/mmol in either sex. -

18 Page of 0 BMJ Open DISCUSSION Our study provides up-to-date information on prevalence and trends in prevalence of diabetes, undiagnosed diabetes and IGR in the general population and social inequalities in these. These survey data show the average prevalence of diagnosed diabetes in adults aged + in England in 00-0 was %, the same as the prevalence reported by QOF over the 0/0 period among those aged +. () As expected, diabetes was more prevalent with increasing age and obesity as well as amongst males, people of Black and Asian ethnicities and those in lower socio-economic positions. Around a quarter of people with diabetes are still undiagnosed. Consistent with previous work, (-0) HSE participants with IGR have health characteristics between those with normoglycaemia and with diabetes, supporting the idea of a continuum of disease. (,) We also showed that low socioeconomic status, known to be associated with diabetes, (,) is also associated with IGR; education was the best marker. The prevalence of people with IGR is lower than expected and appears to be static. There is a graded association of cardiovascular characteristics across the continuum of impaired glucose metabolism; a diagnosis of diabetes appears to have beneficial impacts on risk factor modification. A number of different screening criteria can each identify the majority of those with impaired glucose metabolism but support the use of a validated risk assessment tool as the most sensitive screening method. The main strengths of this study are the analysis of data from nationally representative random samples of the free-living population, rather than limiting analysis to those in the healthcare system with diagnoses or tests of blood glucose levels. Non-response weighting, recommended as the best approach to enhancing representativeness, () was used, making these data as generalizable as possible for England. Being a general health examination survey, the richness of data about each participant enables wider examination than a diabetes-focussed study, hence the breadth of information on socio-economic position and the range of variables that could be examined. However, all surveys are limited in the extent of the questionnaire; we had no information on family history of diabetes, one component of the Diabetes UK/Leicester risk score tool. The nature of the study results in other limitations, particularly in how the categories of impaired glucose metabolism were defined. First, to be included in the diagnosed diabetes group, participants needed to selfreport a diagnosis or be on diabetic medication. For this reason, a proportion of diagnosed diabetics may have been missed. The effect of this is likely to be small; furthermore, if people do not report that they have diabetes and are not treating their condition, it may be appropriate to consider them as undiagnosed diabetics. Another problem is the use of a single HbAc test to categorise participants. In practice, symptoms or a confirmatory test would often be used to diagnose diabetes in people with an HbAc of mmol/mol. Applications of such strict definitions might alter the number of patients in each category but any such alteration and its impact are likely to be small art a population level. As discussed, impaired glucose metabolism is a continuum and, although categories are defined for practical reasons, all those with raised HbAc levels are at risk and could benefit from identification and risk reduction. Although the direction of association cannot be determined in cross-sectional surveys, these are the only ethical way to monitor trends in IGR and undiagnosed diabetes; cohort studies should always release such results to their participants, thus naïve participants are required for these analyses. -

19 Page of The HSE gives unique information about undiagnosed populations and social inequalities in health, not available in routine data; until recently, prevalence estimates of undiagnosed diabetes and IGR were based on models. () Unlike the recent Public Health England report, () we found marked socioeconomic inequalities in IGR. They examined only deprivation quintiles but failed to adjust for confounding by age; we also examined three individual-level markers of socio-economic position and found similar health inequalities, with education and income remaining significant in a multivariate regression model with IGR or undiagnosed diabetes as the outcome. We found the average prevalence of undiagnosed diabetes in 00-0 was.%. Approximately % of people with diabetes remain undiagnosed, a higher proportion than the AHPO Diabetes Prevalence Model estimates. () This proportion has fluctuated over the five years but the prevalence of both undiagnosed diabetes and IGR have not changed appreciably over this time, possibly mirroring the stabilising prevalence of obesity in adults in England. 0 It is too soon to assess the impact of 0 NICE guidance and the introduction of targeted screening for earlier diagnosis. The average IGR prevalence was %, with no discernible trend over our years of data. This contrasts starkly with another recent study using HSE data that suggested an increase in IGR prevalence from.% in 00 to.% in 0. () This is explained by the differences between definitions of IGR. We used an HbAc range of -mmol/mol, as recommended by an international expert panel, NICE and Diabetes UK, (,) whereas Mainous et al. used a range of - mmol/mol (.%-.%) as recommended by ADA, () finding similar 0 prevalence in the UK as in the USA using the same definition. () The differences between the results suggest that there is an increasing prevalence of people with an HbAc of -mmol/mol but not with an HBAc of - mmol/mol. There are two potential explanations for this. First, there may be a lag period and the impact of the increasing prevalence of those with lower range IGR is yet to be felt. The second explanation is that people with lower range IGR do not necessarily develop increasingly deranged glucose metabolism, meaning a change in prevalence of this group would not necessarily be reflected in groups with higher HbAc levels. If this is the case, it would provide significant support for the use of an HbAc of mmol/mol as the lower threshold for IGR as per current UK recommendations. We showed a continued increase in the prevalence of diabetes over the five years, consistent with information from other databases. (,) This is unsurprising in the context of rising levels of obesity but provides concerning insight into the potential impact of this serious and costly condition on population health in the future. A large proportion of people with diabetes are still undiagnosed, so do not benefit from the potential advantages that a diagnosis carries. Modelling studies have suggested a benefit in using screening to detect early diabetes. (,) In contrast, recent data from the ADDITION trial () does not show a reduction in mortality with screening for diabetes. In terms of biomarkers, our data support the assumption that a diagnosis can be beneficial, (0) particularly for lipid and blood pressure modification: people who are diagnosed will benefit from QOF and guideline driven risk factor modification, resulting in a stalling or reduction of cardiovascular risk factor progression despite advancing disease. However, self-reported health was worst in this group. There is a clear case for identifying IGR as cost-effective interventions exist to prevent or delay diabetes (,,,) for people in this category. Our lower prevalence of IGR implies that interventions to manage this population may be more feasible and less costly was anticipated based on an earlier study. IGR and undiagnosed diabetes are more prevalent in more deprived groups, even after -

20 Page of 0 BMJ Open adjusting for demographic factors and obesity. Undiagnosed diabetes as a proportion of all cases is also higher in these groups. It is recognised that individual agentic interventions may widen inequalities, as disadvantaged groups are less likely to participate. () Thus only if GPs manage to target deprived groups successfully for detection and support with lifestyle modification, this programme could reduce social inequalities in health. Similarly, focusing screening and advice by occupational health departments on employees in lower supervisory and technical and semi-routine occupations could also reduce health inequalities and employee morbidity. Future health examination surveys are needed to monitor continuing trends in changes in national prevalence and social inequalities of IGR and undiagnosed diabetes, as CCGs pilot and roll out detection and intervention programmes. Acknowledgments We thank our colleagues at UCL and NatCen Social Research, the interviewers and nurses, and the participants in the Health Survey for England series. -

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