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.
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).
Between 7 March and 8 May 2020, there were 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.
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.
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.
Approximately 13% of the total UK workforce is employed in the health and care sector. Despite substantial workforce planning efforts, the effectiveness of this planning has been criticised. ...Education, training, and workforce plans have typically considered each health-care profession in isolation and have not adequately responded to changing health and care needs. The results are persistent vacancies, poor morale, and low retention. Areas of particular concern highlighted in this Health Policy paper include primary care, mental health, nursing, clinical and non-clinical support, and social care. Responses to workforce shortfalls have included a high reliance on foreign and temporary staff, small-scale changes in skill mix, and enhanced recruitment drives. Impending challenges for the UK health and care workforce include growing multimorbidity, an increasing shortfall in the supply of unpaid carers, and the relative decline of the attractiveness of the National Health Service (NHS) as an employer internationally. We argue that to secure a sustainable and fit-for-purpose health and care workforce, integrated workforce approaches need to be developed alongside reforms to education and training that reflect changes in roles and skill mix, as well as the trend towards multidisciplinary working. Enhancing career development opportunities, promoting staff wellbeing, and tackling discrimination in the NHS are all needed to improve recruitment, retention, and morale of staff. An urgent priority is to offer sufficient aftercare and support to staff who have been exposed to high-risk situations and traumatic experiences during the COVID-19 pandemic. In response to growing calls to recognise and reward health and care staff, growth in pay must at least keep pace with projected rises in average earnings, which in turn will require linking future NHS funding allocations to rises in pay. Through illustrative projections, we show that, to sustain annual growth in the workforce at approximately 2·4%, increases in NHS expenditure of 4% annually in real terms will be required. Above all, a radical long-term strategic vision is needed to ensure that the future NHS workforce is fit for purpose.
Since 2008, the Improving Access to Psychological Therapies (IAPT) programme has disseminated evidence-based interventions for depression and anxiety problems. In order to maintain quality standards, ...government policy in England sets the expectation that 50% of treated patients should meet recovery criteria according to validated patient-reported outcome measures. Using national IAPT data, we found evidence suggesting that the prevalence of mental health problems is greater in poorer areas and that these areas had lower average recovery rates. After adjusting benchmarks for local index of multiple deprivation, we found significant differences between unadjusted (72.5%) and adjusted (43.1%) proportions of underperforming clinical commissioning group areas.
In principle, questionnaire data on public views about hypothetical trade‐offs between improving total health and reducing health inequality can provide useful normative health inequality aversion ...parameter benchmarks for policymakers faced with real trade‐offs of this kind. However, trade‐off questions can be hard to understand, and one standard type of question finds that a high proportion of respondents—sometimes a majority—appear to give exclusive priority to reducing health inequality. We developed and tested two e‐learning interventions designed to help respondents understand this question more completely. The interventions were a video animation, exposing respondents to rival points of view, and a spreadsheet‐based questionnaire that provided feedback on implied trade‐offs. We found large effects of both interventions in reducing the proportion of respondents giving exclusive priority to reducing health inequality, though the median responses still implied a high degree of health inequality aversion and—unlike the video—the spreadsheet‐based intervention introduced a substantial new minority of non‐egalitarian responses. E‐learning may introduce as well as avoid biases but merits further research and may be useful in other questionnaire studies involving trade‐offs between conflicting values.
There is a prevailing popular belief that expenditure on management by health‐care providers is wasteful, diverts resources from patient care, and distracts medical and nursing staff from getting on ...with their jobs. There is little existing evidence to support either this narrative or counter‐claims. We explore the relationship between management and public sector hospital performance using a fixed effects empirical econometric specification on a panel data set consisting of all 129 non‐specialist acute National Health Service (NHS) hospitals in England for the financial years 2012/13 to 2018/19. Measures of managerial input and quality of management practice are constructed from NHS Electronic Staff Records and NHS Staff Survey data. Hospital accounts and Hospital Episode Statistics data are used to construct five measures of financial performance and of timely and high‐quality care. We find no evidence of association either between quantity of management and management quality or directly between quantity of management and any of our measures of hospital performance. However, there is some evidence that higher‐quality management is associated with better performance. NHS managers have limited discretion in performing their managerial functions, being tightly circumscribed by official guidance, targets, and other factors outside their control. Given these constraints, our findings are unsurprising.
Abstract This articles serves as a guide to using cost-effectiveness analysis (CEA) to address health equity concerns. We first introduce the "equity impact plane," a tool for considering trade-offs ...between improving total health—the objective underpinning conventional CEA—and equity objectives, such as reducing social inequality in health or prioritizing the severely ill. Improving total health may clash with reducing social inequality in health, for example, when effective delivery of services to disadvantaged communities requires additional costs. Who gains and who loses from a cost-increasing health program depends on differences among people in terms of health risks, uptake, quality, adherence, capacity to benefit, and—crucially—who bears the opportunity costs of diverting scarce resources from other uses. We describe two main ways of using CEA to address health equity concerns: 1) equity impact analysis, which quantifies the distribution of costs and effects by equity-relevant variables, such as socioeconomic status, location, ethnicity, sex, and severity of illness; and 2) equity trade-off analysis, which quantifies trade-offs between improving total health and other equity objectives. One way to analyze equity trade-offs is to count the cost of fairer but less cost-effective options in terms of health forgone. Another method is to explore how much concern for equity is required to choose fairer but less cost-effective options using equity weights or parameters. We hope this article will help the health technology assessment community navigate the practical options now available for conducting equity-informative CEA that gives policymakers a better understanding of equity impacts and trade-offs.
Abstract Objective To model the social distribution of quality-adjusted life expectancy (QALE) in England by combining survey data on health-related quality of life with administrative data on ...mortality. Methods Health Survey for England data sets for 2010, 2011, and 2012 were pooled (n = 35,062) and used to model health-related quality of life as a function of sex, age, and socioeconomic status (SES). Office for National Statistics mortality rates were used to construct life tables for age-sex-SES groups. These quality-of-life and length-of-life estimates were then combined to predict QALE as a function of these characteristics. Missing data were imputed, and Monte-Carlo simulation was used to estimate standard errors. Sensitivity analysis was conducted to explore alternative regression models and measures of SES. Results Socioeconomic inequality in QALE at birth was estimated at 11.87 quality-adjusted life-years (QALYs), with a sex difference of 1 QALY. When the socioeconomic-sex subgroups are ranked by QALE, a differential of 10.97 QALYs is found between the most and least healthy quintile groups. This differential can be broken down into a life expectancy difference of 7.28 years and a quality-of-life adjustment of 3.69 years. Conclusions The methods proposed in this article refine simple binary quality-adjustment measures such as the widely used disability-free life expectancy, providing a more accurate picture of overall health inequality in society than has hitherto been available. The predictions also lend themselves well to the task of evaluating the health inequality impact of interventions in the context of cost-effectiveness analysis.
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