Felix Greaves

Mortality from pandemic A/H1N1 2009 influenza in England: public health surveillance study

Objective To establish mortality from pandemic A/H1N1 2009 influenza up to 8 November 2009. Design Investigation of all reported deaths related to pandemic A/H1N1 in England. Setting Mandatory reporting systems established in acute hospitals and primary care. Participants Physicians responsible for the patient. Main outcome measures Numbers of deaths from influenza combined with mid-range estimates of numbers of cases of influenza to calculate age specific case fatality rates. Underlying conditions, time course of illness, and antiviral treatment. Results With the official mid-range estimate for incidence of pandemic A/H1N1, the overall estimated case fatality rate was 26 (range 11-66) per 100 000. It was lowest for children aged 5-14 (11 (range 3-36) per 100 000) and highest for those aged ≥65 (980 (range 300-3200) per 100 000). In the 138 people in whom the confirmed cause of death was pandemic A/H1N1, the median age was 39 (interquartile range 17-57). Two thirds of patients who died (92, 67%) would now be eligible for the first phase of vaccination in England. Fifty (36%) had no, or only mild, pre-existing illness. Most patients (108, 78%) had been prescribed antiviral drugs, but of these, 82 (76%) did not receive them within the first 48 hours of illness. Conclusions Viewed statistically, mortality in this pandemic compares favourably with 20th century influenza pandemics. A lower population impact than previous pandemics, however, is not a justification for public health inaction. Our data support the priority vaccination of high risk groups. We observed delayed antiviral use in most fatal cases, which suggests an opportunity to reduce deaths by making timely antiviral treatment available, although the lack of a control group limits the ability to extrapolate from this observation. Given that a substantial minority of deaths occur in previously healthy people, there is a case for extending the vaccination programme and for continuing to make early antiviral treatment widely available.

Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Summary Background In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. Methods We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. Findings Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0–5·8) from 75·9 years (75·9–76·0) to 81·3 years (80·9–81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3–43·6), whereas DALYs were reduced by 23·8% (20·9–27·1), and YLDs by 1·4% (0·1–2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7–41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1–12·7]) and tobacco (10·7% [9·4–12·0]). Interpretation Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. Funding Bill & Melinda Gates Foundation and Public Health England.

Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

Recent years have seen increasing interest in patient-centred care and calls to focus on improving the patient experience. At the same time, a growing number of patients are using the internet to describe their experiences of healthcare. We believe the increasing availability of patients’ accounts of their care on blogs, social networks, Twitter and hospital review sites presents an intriguing opportunity to advance the patient-centred care agenda and provide novel quality of care data. We describe this concept as a ‘cloud of patient experience’. In this commentary, we outline the ways in which the collection and aggregation of patients’ descriptions of their experiences on the internet could be used to detect poor clinical care. Over time, such an approach could also identify excellence and allow it to be built on. We suggest using the techniques of natural language processing and sentiment analysis to transform unstructured descriptions of patient experience on the internet into usable measures of healthcare performance. We consider the various sources of information that could be used, the limitations of the approach and discuss whether these new techniques could detect poor performance before conventional measures of healthcare quality.

Changes in health in England with analysis by English region and areas of deprivation: findings of the Global Burden of Disease Study 2013

Summary Background In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. Methods We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. Findings Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0–5·8) from 75·9 years (75·9–76·0) to 81·3 years (80·9–81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3–43·6), whereas DALYs were reduced by 23·8% (20·9–27·1), and YLDs by 1·4% (0·1–2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7–41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1–12·7]) and tobacco (10·7% [9·4–12·0]). Interpretation Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. Funding Bill & Melinda Gates Foundation and Public Health England.

‘Gamification’: Influencing health behaviours with games

Every month at Google Campus in London, dozens of software developers, clinicians, behavioural scientists and investors get together to discuss new strategies to influence health behaviours. The collective aim of these networking events is to develop digital ‘games with purpose’ that can improve health by integrating software design and game mechanics with public health theory and behavioural insights. Gamification is a purposely-broad umbrella term used to encompass the process of using ‘gaming’ elements to motivate and engage people in non-game contexts.1 Enhanced opportunities now exist to deliver behaviour change interventions through game platforms on new smartphone devices. Defying traditional stereotypes, people across demographic boundaries now play video games on a wide range of digital devices.2 Whilst such games continue to be primarily used for entertainment purposes, there is increasing interest in their potential to influence positive changes in health behaviours.3 This has been encouraged by the finding that rather than spending hours being sedentary and chasing intangible outcomes, players of active video games (e.g. Nintendo Wii Fit) are motivated to exert themselves to achieve activity goals through game mechanics.4,5 Whilst still in its infancy, we predict that gamification will become an increasingly familiar concept in healthcare as a consequence of two trends. The first builds on the consumers appetite for new smartphone devices that provide games designers with a wider audience to target and more attractive tools to use in designing interactive health interventions. The second factor is the enthusiasm and willingness of developers to incorporate the latest behavioural insights into electronic interventions.

Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

Background There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. Objective We attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care. Methods We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. Results There was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). Conclusions The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.

The need for a complex systems model of evidence for public health

This work was funded by a grant from The Health Foundation (London, UK) that supported HR, KG, and NS. HR was also supported by the UK National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Barts Health NHS Trust. LM is supported by the UK Medical Research Council ( MC_UU_12017/14 ) and the Chief Scientist Office ( SPHSU14 ). MW is funded in part by the UK NIHR as Director of its Public Health Research Programme.

Associations between internet-based patient ratings and conventional surveys of patient experience in the English NHS: an observational study

Objective Unsolicited web-based comments by patients regarding their healthcare are increasing, but controversial. The relationship between such online patient reports and conventional measures of patient experience (obtained via survey) is not known. The authors examined hospital level associations between web-based patient ratings on the National Health Service (NHS) Choices website, introduced in England during 2008, and paper-based survey measures of patient experience. The authors also aimed to compare these two methods of measuring patient experience. Design The authors performed a cross-sectional observational study of all (n=146) acute general NHS hospital trusts in England using data from 9997 patient web-based ratings posted on the NHS Choices website during 2009/2010. Hospital trust level indicators of patient experience from a paper-based survey (five measures) were compared with web-based patient ratings using Spearmans rank correlation coefficient. The authors compared the strength of associations among clinical outcomes, patient experience survey results and NHS Choices ratings. Results Web-based ratings of patient experience were associated with ratings derived from a national paper-based patient survey (Spearman ρ=0.31–0.49, p<0.001 for all). Associations with clinical outcomes were at least as strong for online ratings as for traditional survey measures of patient experience. Conclusions Unsolicited web-based patient ratings of their care, though potentially prone to many biases, are correlated with survey measures of patient experience. They may be useful tools for patients when choosing healthcare providers and for clinicians to improve the quality of their services.

Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial

Summary Background Unnecessary antibiotic prescribing contributes to antimicrobial resistance. In this trial, we aimed to reduce unnecessary prescriptions of antibiotics by general practitioners (GPs) in England. Methods In this randomised, 2 × 2 factorial trial, publicly available databases were used to identify GP practices whose prescribing rate for antibiotics was in the top 20% for their National Health Service (NHS) Local Area Team. Eligible practices were randomly assigned (1:1) into two groups by computer-generated allocation sequence, stratified by NHS Local Area Team. Participants, but not investigators, were blinded to group assignment. On Sept 29, 2014, every GP in the feedback intervention group was sent a letter from Englands Chief Medical Officer and a leaflet on antibiotics for use with patients. The letter stated that the practice was prescribing antibiotics at a higher rate than 80% of practices in its NHS Local Area Team. GPs in the control group received no communication. The sample was re-randomised into two groups, and in December, 2014, GP practices were either sent patient-focused information that promoted reduced use of antibiotics or received no communication. The primary outcome measure was the rate of antibiotic items dispensed per 1000 weighted population, controlling for past prescribing. Analysis was by intention to treat. This trial is registered with the ISRCTN registry, number ISRCTN32349954, and has been completed. Findings Between Sept 8 and Sept 26, 2014, we recruited and assigned 1581 GP practices to feedback intervention (n=791) or control (n=790) groups. Letters were sent to 3227 GPs in the intervention group. Between October, 2014, and March, 2015, the rate of antibiotic items dispensed per 1000 population was 126·98 (95% CI 125·68–128·27) in the feedback intervention group and 131·25 (130·33–132·16) in the control group, a difference of 4·27 (3·3%; incidence rate ratio [IRR] 0·967 [95% CI 0·957–0·977]; p<0·0001), representing an estimated 73 406 fewer antibiotic items dispensed. In December, 2014, GP practices were re-assigned to patient-focused intervention (n=777) or control (n=804) groups. The patient-focused intervention did not significantly affect the primary outcome measure between December, 2014, and March, 2015 (antibiotic items dispensed per 1000 population: 135·00 [95% CI 133·77–136·22] in the patient-focused intervention group and 133·98 [133·06–134·90] in the control group; IRR for difference between groups 1·01, 95% CI 1·00–1·02; p=0·105). Interpretation Social norm feedback from a high-profile messenger can substantially reduce antibiotic prescribing at low cost and at national scale; this outcome makes it a worthwhile addition to antimicrobial stewardship programmes. Funding Public Health England.

Patients’ Ratings of Family Physician Practices on the Internet: Usage and Associations With Conventional Measures of Quality in the English National Health Service

Background Patients are increasingly rating their family physicians on the Internet in the same way as they might rate a hotel on TripAdvisor or a seller on eBay, despite physicians’ concerns about this process. Objective This study aims to examine the usage of NHS Choices, a government website that encourages patients to rate the quality of family practices in England, and associations between web-based patient ratings and conventional measures of patient experience and clinical quality in primary care. Methods We obtained all (16,952) ratings of family practices posted on NHS Choices between October 2009 and December 2010. We examined associations between patient ratings and family practice and population characteristics. Associations between ratings and survey measures of patient experience and clinical outcomes were examined. Results 61% of the 8089 family practices in England were rated, and 69% of ratings would recommend their family practice. Practices serving younger, less deprived, and more densely populated areas were more likely to be rated. There were moderate associations with survey measures of patient experience (Spearman ρ 0.37−0.48, P<.001 for all 5 variables), but only weak associations with measures of clinical process and outcome (Spearman ρ less than ±0.18, P<.001 for 6 of 7 variables). Conclusion The frequency of patients rating their family physicians on the Internet is variable in England, but the ratings are generally positive and are moderately associated with other measures of patient experience and weakly associated with clinical quality. Although potentially flawed, patient ratings on the Internet may provide an opportunity for organizational learning and, as it becomes more common, another lens to look at the quality of primary care.