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Objectives: To test the hypotheses that: (1) Prevalence of periodontal disease would be higher in people with a history of psychosis when compared to the general population and (2) Demographic, life‐style related factors and co‐morbid medical conditions would predict periodontal disease in people experiencing psychosis. Methods: The authors performed cross‐sectional analysis of baseline data from the UK Biobank study (2007–2010), identifying cases with psychosis using clinical diagnosis, antipsychotic medication, and self‐report. Demographic (age, gender, ethnicity, socioeconomic status), lifestyle‐related(BMI, blood pressure, smoking and alcohol intake, physical activity) and physical co‐morbidities (cancer, cardiovascular, respiratory, inflammatory disease and metabolic conditions) were included as potential risk factors for periodontal disease among people with a history of psychosis using logistic regression analyses. The analysis sample included 502,505 participants. Results: Risk of periodontal disease was higher in people with psychosis, regardless of how cases were identified. Patients with a clinical diagnosis had the highest proportion of periodontal disease compared to the general population (21.3% vs. 14.8%, prevalence ratio 1.40, 95% CI: 1.26–1.56). Older and female cases were more likely to experience periodontal disease. Lifestyle factors (smoking) and comorbidities (cardiovascular, cancer or respiratory disease) were associated with periodontal disease among people with a history of psychosis. Conclusions: The findings suggest that periodontal disease is more common in people with a history of psychosis, compared to the general population. Prevention and early diagnosis of periodontal disease should be a priority for oral health promotion programmes, which should also address modifiable risk factors like smoking which also contribute to co‐morbid systemic disease.

Objectives: To test the hypotheses that: (1) Prevalence of periodontal disease would be higher in people with a history of psychosis when compared to the general population and (2) Demographic, life‐style related factors and co‐morbid medical conditions would predict periodontal disease in people experiencing psychosis. Methods: The authors performed cross‐sectional analysis of baseline data from the UK Biobank study (2007–2010), identifying cases with psychosis using clinical diagnosis, antipsychotic medication, and self‐report. Demographic (age, gender, ethnicity, socioeconomic status), lifestyle‐related(BMI, blood pressure, smoking and alcohol intake, physical activity) and physical co‐morbidities (cancer, cardiovascular, respiratory, inflammatory disease and metabolic conditions) were included as potential risk factors for periodontal disease among people with a history of psychosis using logistic regression analyses. The analysis sample included 502,505 participants. Results: Risk of periodontal disease was higher in people with psychosis, regardless of how cases were identified. Patients with a clinical diagnosis had the highest proportion of periodontal disease compared to the general population (21.3% vs. 14.8%, prevalence ratio 1.40, 95% CI: 1.26–1.56). Older and female cases were more likely to experience periodontal disease. Lifestyle factors (smoking) and comorbidities (cardiovascular, cancer or respiratory disease) were associated with periodontal disease among people with a history of psychosis. Conclusions: The findings suggest that periodontal disease is more common in people with a history of psychosis, compared to the general population. Prevention and early diagnosis of periodontal disease should be a priority for oral health promotion programmes, which should also address modifiable risk factors like smoking which also contribute to co‐morbid systemic disease.

Background Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions. Aims To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions. Method This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively. Results There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year. Conclusions Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

Background Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions. Aims To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions. Method This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively. Results There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year. Conclusions Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

Background: People with severe mental illnesses (SMIs) have reduced life expectancy compared with the general population. Diabetes is a contributor to this disparity, with higher prevalence and poorer outcomes in people with SMI. Aim: To determine the impact of SMI on healthcare processes and outcomes for people with type 2 diabetes (T2DM). Design and setting: Retrospective, observational, matched, nested, case–control study conducted in England using patient records from the Clinical Practice Research Datalink, linked to Hospital Episode Statistics. Method: A range of healthcare processes (primary care consultations, physical health checks, and metabolic measurements) and outcomes (prevalence and hospitalisation for cardiovascular disease [CVD], and mortality risk) were compared for 2192 people with SMI and T2DM (cases) with 7773 people who had diabetes alone (controls). Sociodemographics, comorbidity, and medication prescription were covariates in regression models. Results: When compared with results for participants with T2DM only, SMI was associated with increased risk of all-cause mortality (hazard ratio [HR] 1.919, 95% confidence interval [CI] = 1.602 to 2.300) and CVD-specific mortality (HR 2.242, 95% CI = 1.547 to 3.250), higher primary care physician consultation rates (incidence rate ratio [IRR] 1.149, 95% CI = 1.111 to 1.188), more-frequent checks of blood pressure (IRR 1.024, 95% CI = 1.003 to 1.046) and cholesterol (IRR 1.038, 95% CI = 1.019 to 1.058), lower prevalence of angina (odds ratio 0.671, 95% CI = 0.450 to 1.001), more emergency admissions for angina (IRR 1.532, 95% CI = 1.069 to 2.195), and fewer elective admissions for ischaemic heart disease (IRR 0.682, 95% CI = 0.508 to 0.915). Conclusion: Monitoring of metabolic measurements was comparable for people with T2DM who did, and did not, have SMI. Increased mortality rates observed in people with SMI may be attributable to underdiagnosis of CVD and delays in treatment.

Background: People with severe mental illnesses (SMIs) have reduced life expectancy compared with the general population. Diabetes is a contributor to this disparity, with higher prevalence and poorer outcomes in people with SMI. Aim: To determine the impact of SMI on healthcare processes and outcomes for people with type 2 diabetes (T2DM). Design and setting: Retrospective, observational, matched, nested, case–control study conducted in England using patient records from the Clinical Practice Research Datalink, linked to Hospital Episode Statistics. Method: A range of healthcare processes (primary care consultations, physical health checks, and metabolic measurements) and outcomes (prevalence and hospitalisation for cardiovascular disease [CVD], and mortality risk) were compared for 2192 people with SMI and T2DM (cases) with 7773 people who had diabetes alone (controls). Sociodemographics, comorbidity, and medication prescription were covariates in regression models. Results: When compared with results for participants with T2DM only, SMI was associated with increased risk of all-cause mortality (hazard ratio [HR] 1.919, 95% confidence interval [CI] = 1.602 to 2.300) and CVD-specific mortality (HR 2.242, 95% CI = 1.547 to 3.250), higher primary care physician consultation rates (incidence rate ratio [IRR] 1.149, 95% CI = 1.111 to 1.188), more-frequent checks of blood pressure (IRR 1.024, 95% CI = 1.003 to 1.046) and cholesterol (IRR 1.038, 95% CI = 1.019 to 1.058), lower prevalence of angina (odds ratio 0.671, 95% CI = 0.450 to 1.001), more emergency admissions for angina (IRR 1.532, 95% CI = 1.069 to 2.195), and fewer elective admissions for ischaemic heart disease (IRR 0.682, 95% CI = 0.508 to 0.915). Conclusion: Monitoring of metabolic measurements was comparable for people with T2DM who did, and did not, have SMI. Increased mortality rates observed in people with SMI may be attributable to underdiagnosis of CVD and delays in treatment.

Background: Gal d 1 (ovomucoid) is the dominant allergen in the chicken egg white. Hypoallergenic variants of this allergen can be used in immunotherapy as an egg allergy treatment approach. We hypothesised that disruption of two of the nine cysteine-cysteine bridges by site-directed mutagenesis will allow the production of a hypoallergenic variant of the protein; Methods: Two cysteine residues at C192 and C210 in domain III of the protein were mutated to alanine using site-directed mutagenesis, to disrupt two separate cysteine-cysteine bridges. The mutated and non-mutated proteins were expressed in Escherichia coli (E. coli) by induction with isopropyl β-d-1-thiogalactopyranoside (IPTG). The expressed proteins were analysed using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting to confirm expression. Immunoglobulin E (IgE) reactivity of the two proteins was analysed, by immunoblotting, against a pool of egg-allergic patients’ sera. A pool of non-allergic patients’ sera was also used in a separate blot as a negative control; Results: Mutant Gal d 1 showed diminished IgE reactivity in the immunoblot by showing lighter bands when compared to the non-mutated version, although there was more of the mutant protein immobilised on the membrane when compared to the wild-type protein. The non-allergic negative control showed no bands, indicating an absence of non-specific binding of secondary antibody to the proteins; Conclusion: Disruption of two cysteine bridges in domain III of Gal d 1 reduces IgE reactivity. Following downstream laboratory and clinical testing, this mutant protein can be used in immunotherapy to induce tolerance to Gal d 1 and in egg allergy diagnosis.

Background: Improving care for people with long-term conditions is central to NHS policy. It has been suggested that the Quality and Outcomes Framework (QOF), a pay-for-performance scheme in primary care that rewards practices for delivering effective interventions in the management of longterm conditions, does not encourage high quality care for this group of patients. Aim: To examine the evidence that the QOF has improved quality of care of patients with long-term conditions. Design Systematic review Method We used electronic databases to search for peer-reviewed empirical quantitative research on the QOF published since the scheme’s introduction in 2004. We searched for studies examining the effect of QOF on a broad range of processes and outcomes of care, including coordination and integration of care, holistic and personalised care, self-care, patient experience, physiological and biochemical outcomes, health service utilisation, and mortality. We carried out a narrative synthesis because the studies were heterogeneous. Results: The QOF was associated with a modest slowing of the increase in emergency admission rates, a modest slowing of the increase in consultation rates in severe mental illness, and modest improvements in certain aspects of the care of diabetes. The nature of the evidence means that we cannot be sure that any of these associations is causal. No clear effect on mortality has been demonstrated. We found no evidence to suggest that QOF influences, positively or negatively, integration or coordination of care, holistic or personalised care, self-care, or patients’ experience, quality of life or satisfaction. Conclusion: The NHS should consider more broadly what constitutes high quality primary care for people with long-term conditions, and consider other ways of motivating primary care to deliver it.

Background The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example. Methods We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients. Results We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework. Conclusion We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.

Background The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example. Methods We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients. Results We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework. Conclusion We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.

This project aimed to develop novel therapeutics and methods to tackle egg allergy. In this study, recombinant variants of egg allergens were produced that may be used in future immunotherapy. Genetic engineering was also used to develop new strategies to produce non-allergenic chicken eggs.

OBJECTIVES: Little is known about service utilisation by patients with severe mental illness (SMI) in UK primary care. We examined their consultation rate patterns and whether they were impacted by the introduction of the Quality and Outcomes Framework (QOF), in 2004. DESIGN: Retrospective cohort study using individual patient data collected from 2000 to 2012. SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a large UK primary care database. PARTICIPANTS: SMI cases (346 551) matched to 5 individuals without SMI (1 732 755) on age, gender and general practice. OUTCOME MEASURES: Consultation rates were calculated for both groups, across 3 types: face-to-face (primary outcome), telephone and other (not only consultations but including administrative tasks). Poisson regression analyses were used to identify predictors of consultation rates and calculate adjusted consultation rates. Interrupted time-series analysis was used to quantify the effect of the QOF. RESULTS: Over the study period, face-to-face consultations in primary care remained relatively stable in the matched control group (between 4.5 and 4.9 per annum) but increased for people with SMI (8.8-10.9). Women and older patients consulted more frequently in the SMI and the matched control groups, across all 3 consultation types. Following the introduction of the QOF, there was an increase in the annual trend of face-to-face consultation for people with SMI (average increase of 0.19 consultations per patient per year, 95% CI 0.02 to 0.36), which was not observed for the control group (estimates across groups statistically different, p=0.022). CONCLUSIONS: The introduction of the QOF was associated with increases in the frequency of monitoring and in the average number of reported comorbidities for patients with SMI. This suggests that the QOF scheme successfully incentivised practices to improve their monitoring of the mental and physical health of this group of patients.

OBJECTIVES: Little is known about the prevalence of comorbidity rates in people with severe mental illness (SMI) in UK primary care. We calculated the prevalence of SMI by UK country, English region and deprivation quintile, antipsychotic and antidepressant medication prescription rates for people with SMI, and prevalence rates of common comorbidities in people with SMI compared with people without SMI. DESIGN: Retrospective cohort study from 2000 to 2012. SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a UK primary care database. PARTICIPANTS: Each identified case (346 551) was matched for age, sex and general practice with 5 randomly selected control cases (1 732 755) with no diagnosis of SMI in each yearly time point. OUTCOME MEASURES: Prevalence rates were calculated for 16 conditions. RESULTS: SMI rates were highest in Scotland and in more deprived areas. Rates increased in England, Wales and Northern Ireland over time, with the largest increase in Northern Ireland (0.48% in 2000/2001 to 0.69% in 2011/2012). Annual prevalence rates of all conditions were higher in people with SMI compared with those without SMI. The discrepancy between the prevalence of those with and without SMI increased over time for most conditions. A greater increase in the mean number of additional conditions was observed in the SMI population over the study period (0.6 in 2000/2001 to 1.0 in 2011/2012) compared with those without SMI (0.5 in 2000/2001 to 0.6 in 2011/2012). For both groups, most conditions were more prevalent in more deprived areas, whereas for the SMI group conditions such as hypothyroidism, chronic kidney disease and cancer were more prevalent in more affluent areas. CONCLUSIONS: Our findings highlight the health inequalities faced by people with SMI. The provision of appropriate timely health prevention, promotion and monitoring activities to reduce these health inequalities are needed, especially in deprived areas.

OBJECTIVES: Little is known about the prevalence of comorbidity rates in people with severe mental illness (SMI) in UK primary care. We calculated the prevalence of SMI by UK country, English region and deprivation quintile, antipsychotic and antidepressant medication prescription rates for people with SMI, and prevalence rates of common comorbidities in people with SMI compared with people without SMI. DESIGN: Retrospective cohort study from 2000 to 2012. SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a UK primary care database. PARTICIPANTS: Each identified case (346 551) was matched for age, sex and general practice with 5 randomly selected control cases (1 732 755) with no diagnosis of SMI in each yearly time point. OUTCOME MEASURES: Prevalence rates were calculated for 16 conditions. RESULTS: SMI rates were highest in Scotland and in more deprived areas. Rates increased in England, Wales and Northern Ireland over time, with the largest increase in Northern Ireland (0.48% in 2000/2001 to 0.69% in 2011/2012). Annual prevalence rates of all conditions were higher in people with SMI compared with those without SMI. The discrepancy between the prevalence of those with and without SMI increased over time for most conditions. A greater increase in the mean number of additional conditions was observed in the SMI population over the study period (0.6 in 2000/2001 to 1.0 in 2011/2012) compared with those without SMI (0.5 in 2000/2001 to 0.6 in 2011/2012). For both groups, most conditions were more prevalent in more deprived areas, whereas for the SMI group conditions such as hypothyroidism, chronic kidney disease and cancer were more prevalent in more affluent areas. CONCLUSIONS: Our findings highlight the health inequalities faced by people with SMI. The provision of appropriate timely health prevention, promotion and monitoring activities to reduce these health inequalities are needed, especially in deprived areas.

OBJECTIVES: Little is known about service utilisation by patients with severe mental illness (SMI) in UK primary care. We examined their consultation rate patterns and whether they were impacted by the introduction of the Quality and Outcomes Framework (QOF), in 2004. DESIGN: Retrospective cohort study using individual patient data collected from 2000 to 2012. SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a large UK primary care database. PARTICIPANTS: SMI cases (346 551) matched to 5 individuals without SMI (1 732 755) on age, gender and general practice. OUTCOME MEASURES: Consultation rates were calculated for both groups, across 3 types: face-to-face (primary outcome), telephone and other (not only consultations but including administrative tasks). Poisson regression analyses were used to identify predictors of consultation rates and calculate adjusted consultation rates. Interrupted time-series analysis was used to quantify the effect of the QOF. RESULTS: Over the study period, face-to-face consultations in primary care remained relatively stable in the matched control group (between 4.5 and 4.9 per annum) but increased for people with SMI (8.8-10.9). Women and older patients consulted more frequently in the SMI and the matched control groups, across all 3 consultation types. Following the introduction of the QOF, there was an increase in the annual trend of face-to-face consultation for people with SMI (average increase of 0.19 consultations per patient per year, 95% CI 0.02 to 0.36), which was not observed for the control group (estimates across groups statistically different, p=0.022). CONCLUSIONS: The introduction of the QOF was associated with increases in the frequency of monitoring and in the average number of reported comorbidities for patients with SMI. This suggests that the QOF scheme successfully incentivised practices to improve their monitoring of the mental and physical health of this group of patients.

RNA interference (RNAi) is a powerful, sequence specific, and long-lasting method of gene knockdown, and can be elicited by the expression of short-hairpin RNA (shRNA) molecules driven via polymerase III type 3 promoters from a DNA vector or transgene. To further develop RNAi as a tool in zebrafish, we have characterized the zebrafish U6 and H1 snRNA promoters and compared the efficiency of each of the promoters to express an shRNA and silence a reporter gene, relative to previously characterized U6 promoters from pufferfish, chicken, and mouse. Our results show that the zebrafish polymerase III promoters were capable of effective gene silencing in the zebrafish ZF4 cell line, but were ineffective in mammalian Vero cells. In contrast, mouse and chicken promoters were active in Vero but not ZF4 cells, highlighting the importance of homologous promoters to achieve effective silencing.

Final Report to the Department of Health

Final Report to the Department of Health

Final Report to the Department of Health

Final Report to the Department of Health

An inverse care law persists in almost all low-income and middle-income countries, whereby socially disadvantaged people receive less, and lower-quality, health care despite having greater need. By contrast, a disproportionate care law persists in high-income countries, whereby socially disadvantaged people receive more health care, but of worse quality and insufficient quantity to meet their additional needs. Both laws are caused not only by financial barriers and fragmented health insurance systems but also by social inequalities in care seeking and co-investment as well as the costs and benefits of health care. Investing in more integrated universal health coverage and stronger primary care, delivered in proportion to need, can improve population health and reduce health inequality. However, trade-offs sometimes exist between health policy objectives. Health-care technologies, policies, and resourcing should be subjected to distributional analysis of their equity impacts, to ensure the objective of reducing health inequalities is kept in sight.

Background Deaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19-associated deaths. Methods Weekly mortality data for 1 January 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: (i) all-cause deaths and (ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (week starting 7 March, to week ending 8 May). Findings Between7Marchand8May2020,therewere 47 243 (95% CI: 46 671 to 47 815) excess deaths in England and Wales, of which 9948 (95% CI: 9376 to 10 520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100 000 (95% CI: 49 to 50) in the South West to 102 per 100 000 (95% CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100 000 (95% CI: −1 to 0) in Wales (ie, mortality rates were no higher than expected) to 26 per 100 000 (95% CI: 25 to 26) in the West Midlands. Interpretation The COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response.

Background Provision of universal coverage is essential for achieving equity in healthcare, but inequalities still exist in universal healthcare systems. Between 2004/2005 and 2011/2012, the National Health Service (NHS) in England, which has provided universal coverage since 1948, made sustained efforts to reduce health inequalities by strengthening primary care. We provide the first comprehensive assessment of trends in socioeconomic inequalities of primary care access, quality and outcomes during this period.Methods Whole-population small area longitudinal study based on 32 482 neighbourhoods of approximately 1500 people in England from 2004/2005 to 2011/2012. We measured slope indices of inequality in four indicators: (1) patients per family doctor, (2) primary care quality, (3) preventable emergency hospital admissions and (4) mortality from conditions considered amenable to healthcare.Results Between 2004/2005 and 2011/2012, there were larger absolute improvements on all indicators in more-deprived neighbourhoods. The modelled gap between the most-deprived and least-deprived neighbourhoods in England decreased by: 193 patients per family doctor (95% CI 173 to 213), 3.29 percentage points of primary care quality (3.13 to 3.45), 0.42 preventable hospitalisations per 1000 people (0.29 to 0.55) and 0.23 amenable deaths per 1000 people (0.15 to 0.31). By 2011/2012, inequalities in primary care supply and quality were almost eliminated, but socioeconomic inequality was still associated with 158 396 preventable hospitalisations and 37 983 deaths amenable to healthcare.Conclusions Between 2004/2005 and 2011/2012, the NHS succeeded in substantially reducing socioeconomic inequalities in primary care access and quality, but made only modest reductions in healthcare outcome inequalities.

Background There are substantial socioeconomic inequalities in both life expectancy and healthcare use in England. In this study, we describe how these two sets of inequalities interact by estimating the social gradient in hospital costs across the life course.Methods Hospital episode statistics, population and index of multiple deprivation data were combined at lower-layer super output area level to estimate inpatient hospital costs for 2011/2012 by age, sex and deprivation quintile. Survival curves were estimated for each of the deprivation groups and used to estimate expected annual costs and cumulative lifetime costs.Results A steep social gradient was observed in overall inpatient hospital admissions, with rates ranging from 31 298/100 000 population in the most affluent fifth of areas to 43 385 in the most deprived fifth. This gradient was steeper for emergency than for elective admissions. The total cost associated with this inequality in 2011/2012 was £4.8 billion. A social gradient was also observed in the modelled lifetime costs where the lower life expectancy was not sufficient to outweigh the higher average costs in the more deprived populations. Lifetime costs for women were 14% greater than for men, due to higher costs in the reproductive years and greater life expectancy.Conclusions Socioeconomic inequalities result in increased morbidity and decreased life expectancy. Interventions to reduce inequality and improve health in more deprived neighbourhoods have the potential to save money for health systems not only within years but across peoples’ entire lifetimes, despite increased costs due to longer life expectancies.