Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020.

BACKGROUND AND AIM: The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. DESIGN: This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. METHODS: The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. RESULTS: Age-standardised admission rates were higher in Asian (RR 1.40 [1.38-1.41] in 2019) and Black (RR 1.37 [1.37-1.38]) and lower in Mixed groups (RR 0.91 [0.90-0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and 'other' ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. CONCLUSIONS: This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level.

Resource requirements for reintroducing elective surgery during the COVID-19 pandemic: modelling study.

BACKGROUND: The COVID-19 response required the cancellation of all but the most urgent surgical procedures. The number of cancelled surgical procedures owing to Covid-19, and the reintroduction of surgical acivirt, was modelled. METHODS: This was a modelling study using Hospital Episode Statistics data (2014-2019). Surgical procedures were grouped into four urgency classes. Expected numbers of surgical procedures performed between 1 March 2020 and 28 February 2021 were modelled. Procedure deficit was estimated using conservative assumptions and the gradual reintroduction of elective surgery from the 1 June 2020. Costs were calculated using NHS reference costs and are reported as millions or billions of euros. Estimates are reported with 95 per cent confidence intervals. RESULTS: A total of 4 547 534 (95 per cent c.i. 3 318 195 to 6 250 771) patients with a pooled mean age of 53.5 years were expected to undergo surgery between 1 March 2020 and 28 February 2021. By 31 May 2020, 749 247 (513 564 to 1 077 448) surgical procedures had been cancelled. Assuming that elective surgery is reintroduced gradually, 2 328 193 (1 483 834 - 3 450 043) patients will be awaiting surgery by 28 February 2021. The cost of delayed procedures is €5.3 (3.1 to 8.0) billion. Safe delivery of surgery during the pandemic will require substantial extra resources costing €526.8 (449.3 to 633.9) million. CONCLUSION: As a consequence of the Covid-19 pandemic, provision of elective surgery will be delayed and associated with increased healthcare costs.

Development of a Clinical Prediction Model for In-hospital Mortality from the South African Cohort of the African Surgical Outcomes Study.

BACKGROUND: Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data. METHODS: A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model. RESULTS: Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827-0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort. CONCLUSION: The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.

Age of patients undergoing surgery.

BACKGROUND: Advancing age is independently associated with poor postoperative outcomes. The ageing of the general population is a major concern for healthcare providers. Trends in age were studied among patients undergoing surgery in the National Health Service in England. METHODS: Time trend ecological analysis was undertaken of Hospital Episode Statistics and Office for National Statistics data for England from 1999 to 2015. The proportion of patients undergoing surgery in different age groupings, their pooled mean age, and change in age profile over time were calculated. Growth in the surgical population was estimated, with associated costs, to the year 2030 by use of linear regression modelling. RESULTS: Some 68 205 695 surgical patient episodes (31 220 341 men, 45·8 per cent) were identified. The mean duration of hospital stay was 5·3 days. The surgical population was older than the general population of England; this gap increased over time (1999: 47·5 versus 38·3 years; 2015: 54·2 versus 39·7 years). The number of people aged 75 years or more undergoing surgery increased from 544 998 (14·9 per cent of that age group) in 1999 to 1 012 517 (22·9 per cent) in 2015. By 2030, it is estimated that one-fifth of the 75 years and older age category will undergo surgery each year (1·49 (95 per cent c.i. 1·43 to 1·55) million people), at a cost of €3·2 (3·1 to 3·5) billion. CONCLUSION: The population having surgery in England is ageing at a faster rate than the general population. Healthcare policies must adapt to ensure that provision of surgical treatments remains safe and sustainable.

Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics.

BACKGROUND: Despite evidence of high activity, the number of surgical procedures performed in UK hospitals, their cost and subsequent mortality remain unclear. METHODS: Time-trend ecological study using hospital episode data from England, Scotland, Wales and Northern Ireland. The primary outcome was the number of in-hospital procedures, grouped using three increasingly specific categories of surgery. Secondary outcomes were all-cause mortality, length of hospital stay and healthcare costs according to standard National Health Service tariffs. RESULTS: Between April 1, 2009 and March 31, 2014, 39 631 801 surgical patient episodes were recorded. There was an annual average of 7 926 360 procedures (inclusive category), 5 104 165 procedures (intermediate category) and 1 526 421 procedures (restrictive category). This equates to 12 537, 8073 and 2414 procedures per 100 000 population per year, respectively. On average there were 85 181 deaths (1.1%) within 30 days of a procedure each year, rising to 178 040 deaths (2.3%) after 90 days. Approximately 62.8% of all procedures were day cases. Median length of stay for in-patient procedures was 1.7 (1.3-2.0) days. The total cost of surgery over the 5 yr period was £54.6 billion ($104.4 billion), representing an average annual cost of £10.9 billion (inclusive), £9.5 billion (intermediate) and £5.6 billion (restrictive). For each category, the number of procedures increased each year, while mortality decreased. One-third of all mortalities in national death registers occurred within 90 days of a procedure (inclusive category). CONCLUSIONS: The number of surgical procedures in the UK varies widely according to definition. The number of procedures is slowly increasing whilst the number of deaths is decreasing.

Point prevalence of surgical checklist use in Europe: relationship with hospital mortality.

BACKGROUND: The prevalence of use of the World Health Organization surgical checklist is unknown. The clinical effectiveness of this intervention in improving postoperative outcomes is debated. METHODS: We undertook a retrospective analysis of data describing surgical checklist use from a 7 day cohort study of surgical outcomes in 28 European nations (European Surgical Outcomes Study, EuSOS). The analysis included hospitals recruiting >10 patients and excluding outlier hospitals above the 95th centile for mortality. Multivariate logistic regression and three-level hierarchical generalized mixed models were constructed to explore the relationship between surgical checklist use and hospital mortality. Findings are presented as crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: A total of 45 591 patients from 426 hospitals were included in the analysis. A surgical checklist was used in 67.5% patients, with marked variation across countries (0-99.6% of patients). Surgical checklist exposure was associated with lower crude hospital mortality (OR 0.84, CI 0.75-0.94; P=0.002). This effect remained after adjustment for baseline risk factors in a multivariate model (adjusted OR 0.81, CI 0.70-0.94; P<0.005) and strengthened after adjusting for variations within countries and hospitals in a three-level generalized mixed model (adjusted OR 0.71, CI 0.58-0.85; P<0.001). CONCLUSIONS: The use of surgical checklists varies across European nations. Reported use of a checklist was associated with lower mortality. This observation may represent a protective effect of the surgical checklist itself, or alternatively, may be an indirect indicator of the quality of perioperative care. CLINICAL TRIAL REGISTRATION: The European Surgical Outcomes Study is registered with ClinicalTrials.gov, number NCT01203605.