The impact of COVID-19 on mental health service utilisation in England.

The COVID-19 pandemic has had a significant impact on population mental health and the need for mental health services in many countries, while also disrupting critical mental health services and capacity, as a response to the pandemic. Mental health providers were asked to reconfigure wards to accommodate patients with COVID-19, thereby reducing capacity to provide mental health services. This is likely to have widened the existing mismatch between demand and supply of mental health care in the English NHS. We quantify the impact of these rapid service reconfigurations on activity levels for mental health providers in England during the first thirteen months (March 2020-March 2021) of the COVID-19 pandemic. We use monthly mental health service utilisation data for a large subset of mental health providers in England from January 1, 2015 to March 31, 2021. We use multivariate regression to estimate the difference between observed and expected utilisation from the start of the pandemic in March 2020. Expected utilisation levels (i.e. the counterfactual) are estimated from trends in utilisation observed during the pre-pandemic period January 1, 2015 to February 31, 2020. We measure utilisation as the monthly number of inpatient admissions, discharges, net admissions (admissions less discharges), length of stay, bed days, number of occupied beds, patients with outpatient appointments, and total outpatient appointments. We also calculate the accumulated difference in utilisation from the start of the pandemic period. There was a sharp reduction in total inpatient admissions and net admissions at the beginning of the pandemic, followed by a return to pre-pandemic levels from September 2020. Shorter inpatient stays are observed over the whole period and bed days and occupied bed counts had not recovered to pre-pandemic levels by March 2021. There is also evidence of greater use of outpatient appointments, potentially as a substitute for inpatient care.

Healthcare Costs for People with Serious Mental Illness in England: An Analysis of Costs Across Primary Care, Hospital Care, and Specialist Mental Healthcare.

BACKGROUND: Serious mental illness (SMI) is a set of disabling conditions associated with poor outcomes and high healthcare utilisation. However, little is known about patterns of utilisation and costs across sectors for people with SMI. OBJECTIVE: The aim was to develop a costing methodology and estimate annual healthcare costs for people with SMI in England across primary and secondary care settings. METHODS: A retrospective observational cohort study was conducted using linked administrative records from primary care, emergency departments, inpatient admissions, and community mental health services, covering financial years 2011/12-2013/14. Costs were calculated using bottom-up costing and are expressed in 2013/14 British pounds (GBP). Determinants of annual costs by sector were estimated using generalised linear models. RESULTS: Mean annual total healthcare costs for 13,846 adults with SMI were £4989 (median £1208), comprising 19% from primary care (£938, median £531), 34% from general hospital care (£1717, median £0), and 47% from inpatient and community-based specialist mental health services (£2334, median £0). Mean annual costs related specifically to mental health, as distinct from physical health, were £2576 (median £290). Key predictors of total cost included physical comorbidities, ethnicity, neighbourhood deprivation, SMI diagnostic subgroup, and age. Some associations varied across care context; for example, older age was associated with higher primary care and hospital costs, but lower mental healthcare costs. CONCLUSIONS: Annual healthcare costs for people with SMI vary significantly across clinical and socioeconomic characteristics and healthcare sectors. This analysis informs policy and research, including estimation of health budgets for particular patient profiles, and economic evaluation of health services and policies.

Can productivity growth measures identify best performing hospitals? Evidence from the English National Health Service.

Health-care systems around the world face limited financial resources, and England is no exception. The ability of the health-care system in England to operate within its financial resources depends in part on continually increasing its productivity. One means of achieving this is to identify and disseminate throughout the system the most efficient processes. We examine the annual productivity growth achieved by 151 hospitals over five financial years, using the same methods developed to measure productivity of the National Health Service as a whole. We consider whether there are hospitals that consistently achieve higher than average productivity growth. These could act as examples of good practice for others to follow and provide a means of increasing system performance. We find that the productivity growth of some hospitals over the whole period exhibits better than average performance, but there is little or no evidence of consistency in the performance of these hospitals over adjacent years. Even the best performers exhibit periods of very poor performance and vice versa. We therefore conclude that accepted methods of measuring productivity growth for the health system as a whole do not appear suitable for identifying good performance at the hospital level.

Hospital Trusts productivity in the English NHS: Uncovering possible drivers of productivity variations.

BACKGROUND: Health care systems in OECD countries are increasingly facing economic challenges and funding pressures. These normally demand interventions (political, financial and organisational) aimed at improving the efficiency of the health system as a whole and its single components. In 2009, the English NHS Chief Executive, Sir David Nicholson, warned that a potential funding gap of £20 billion should be met by extensive efficiency savings by March 2015. Our study investigates possible drivers of differential Trust performance (productivity) for the financial years 2010/11-2012/13. METHODS: Following accounting practice, we define Productivity as the ratio of Outputs over Inputs. We analyse variation in both Total Factor and Labour Productivity using ordinary least squares regressions. We explicitly included in our analysis factors of differential performance highlighted in the Nicholson challenge as the sources were the efficiency savings should come from. Explanatory variables include efficiency in resource use measures, Trust and patient characteristics, and quality of care. RESULTS: We find that larger Trusts and Foundation Trusts are associated with lower productivity, as are those treating a greater proportion of both older and/or younger patients. Surprisingly treating more patients in their last year of life is associated with higher Labour Productivity.