Developing new approaches to measuring NHS outputs and productivity

The Centre for Health Economics and National Institute of Economic and Social Research have recently completed a project funded by the Department of Health to improve measurement of the productivity of the NHS. The researchers have suggested better ways of measuring both outputs and inputs to improve estimates of productivity growth. Past estimates of NHS output growth have not taken account of changes in quality. The CHE/NIESR team conclude that the routine collection of health outcome data on patients is vital to measure NHS quality. They also propose making better use of existing data to quality adjust output indices to capture improvements in hospital survival rates and reductions in waiting times. With these limited adjustments the team estimate that annual NHS output growth averaged 3.79% between 1998/99 and 2003/04.The research team has also developed improved ways of measuring NHS inputs, particularly by drawing on better information about how many people are employed in the NHS and by recognising that staff are becoming increasingly better qualified. There have been substantial increases in staffing levels, pharmaceutical use and investment in equipment and buildings since 1998/99. The net effect of this growth in both outputs and inputs is that, according to the research team’s estimates, NHS productivity declined by about 1.59% a year since 1998/99. This is not out of line with estimates of growth rates in other UK and US service sectors, including insurance and business services. Nor is it surprising that recent years have seen negative growth in the NHS. There are at least two reasons. First, there has been an unprecedented increase in NHS expenditure. The NHS has had to employ more staff to meet the requirements of the European Working Time Directive and hospital consultants and general practitioners, in particular, have benefited from new pay awards.Second, the NHS collects very little information about what actually happens to patients as a result of their contact with the health service. Until there is routine collection of health outcomes data, measurement of the quality of NHS output will remain partial and productivity growth is likely to be underestimated.

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated crosssectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards

Developing new approaches to measuring NHS outputs and productivity

In March 2004 the Department of Health commissioned a research team from the Centre for Health Economics at the University of York and the National Institute for Economic and Social Research to develop new approaches to measuring NHS outputs and productivity. The research objectives were development of: 1)A comprehensive measure of NHS outputs and productivity. 2) Methods to facilitate regular in-year analysis of NHS productivity. 3) Output measures capable of measuring efficiency and productivity at subnational levels.

Profiling the time-course changes in neuromuscular function and muscle damage over two consecutive tournament stages in elite rugby sevens players

Objectives: Many International Rugby Board (IRB) sevens competitions require that two tournament stages are played over consecutive weekends, but the impact this has on player physical performance and recovery is lacking. We examined the influence of two consecutive tournaments on neuromuscular function (NMF) and muscle damage in rugby sevens players. Design: Ten elite international rugby sevens players completed this observational study over 2 tournaments, separated by 5 days, during the IRB sevens series. Methods: On the morning of day 1 and 2, of both tournament 1 (T1) and 2 (T2), players performed countermovement jumps (CMJ; jump height [JH]) and capillary blood samples (creatine kinase [CK]) were collected. After the last match of each day, further capillary samples were collected. Additional, CMJ were performed 12 and 60. h post-T1. Results: Player JH decreased from day 1 to day 2 during T1 (mean ± SD; -6.0 ± 5.4%; P= 0.016), was reduced at 12 (-26.1 ± 5.0%; P< 0.001) and 60. h post-T1 (-7.1 ± 4.8%; P= 0.003) and remained lower, at am day 1 of T2 (-8.0 ± 6.0%; P= 0.007), when compared with day 1 of T1. Player JH was lower on day 1 and 2 of T2, compared with T1 (P< 0.05). CK concentrations were greater than baseline at all time points during each tournament (P< 0.001); no between tournament differences in CK responses existed (P= 0.302). Conclusions: A single sevens tournament reduces NMF such that players are not fully recovered by the start of the second competition stage, however CK returns to baseline in-between and shows the same pattern across two consecutive tournaments

Longleaf pine carbon data

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Longleaf pine biomass data

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Longleaf pine carbon data

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SACRO:Semi-Automated Checking of Research Outputs

This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy. To tackle this bottleneck, we proposed to:Produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in Quality Assurance.Design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types such as AI.Work with a range of different types of TRE in different sectors and organisations to ensure wide applicability.Work with public and patients to explore what is needed for public trust, e.g., that any automation is acting as "an extra pair of eyes": supporting not supplanting TRE staff.Supported by funding from DARE UK (Data and Analytics Research Environments UK), we met these aims through production of documentation, open-source code repositories, and a 'Consensus' statement embodying principles organisations should uphold when deploying any sort of automated disclosure control.Looking forward, we are now ready for extensive user testing and refinement of the resources produced. Following a series of presentations to national and international audiences, a range of different organisations arein the process of trialling the SACRO toolkits. We are delighted that DARE UK has awarded funding to support a Community of Interest group (CoI). This will address ongoing support and the user-led creation of 'soft' resources (such as user guides, 'help desks', and mentoring schemes) to remove blocks to adoption: both for TREs, and crucially for researchers.There are two other areas where we are now ready to make significant advances: applying SACRO to allow principles-based OSDC for 'conceptual data spaces (e.g. via data pooling or federated analytics) and expanding the scope of risk assessment of AI/Machine Learning models to more complex models and types of data. This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK