Risk of COVID-19 hospital admission and COVID-19 mortality during the first COVID-19 wave with a special emphasis on ethnic minorities: an observational study of a single, deprived, multiethnic UK health economy

© 2021 The Authors. Published by BMJ. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.1136/bmjopen-2020-046556Objectives The objective of this study was to describe variations in COVID-19 outcomes in relation to local risks within a well-defined but diverse single-city area. Design Observational study of COVID-19 outcomes using quality-assured integrated data from a single UK hospital contextualised to its feeder population and associated factors (comorbidities, ethnicity, age, deprivation). Setting/participants Single-city hospital with a feeder population of 228 632 adults in Wolverhampton. Main outcome measures Hospital admissions (defined as COVID-19 admissions (CA) or non-COVID-19 admissions (NCA)) and mortality (defined as COVID-19 deaths or non-COVID-19 deaths). Results Of the 5558 patients admitted, 686 died (556 in hospital); 930 were CA, of which 270 were hospital COVID-19 deaths, 47 non-COVID-19 deaths and 36 deaths after discharge; of the 4628 NCA, there were 239 in-hospital deaths (2 COVID-19) and 94 deaths after discharge. Of the 223 074 adults not admitted, 407 died. Age, gender, multimorbidity and black ethnicity (OR 2.1 (95% CI 1.5 to 3.2), p<0.001, compared with white ethnicity, absolute excess risk of <1/1000) were associated with CA and mortality. The South Asian cohort had lower CA and NCA, lower mortality compared with the white group (CA, 0.5 (0.3 to 0.8), p<0.01; NCA, 0.4 (0.3 to 0.6), p<0.001) and community deaths (0.5 (0.3 to 0.7), p<0.001). Despite many common risk factors for CA and NCA, ethnic groups had different admission rates and within-group differing association of risk factors. Deprivation impacted only the white ethnicity, in the oldest age bracket and in a lesser (not most) deprived quintile. Conclusions Wolverhampton’s results, reflecting high ethnic diversity and deprivation, are similar to other studies of black ethnicity, age and comorbidity risk in COVID-19 but strikingly different in South Asians and for deprivation. Sequentially considering population and then hospital-based NCA and CA outcomes, we present a complete single health economy picture. Risk factors may differ within ethnic groups; our data may be more representative of communities with high Black, Asian and minority ethnic populations, highlighting the need for locally focused public health strategies. We emphasise the need for a more comprehensible and nuanced conveyance of risk

Observational cross-sectional study of the association of poor broadband provision with demographic and health outcomes: the Wolverhampton Digital ENablement (WODEN) programme

ObjectivesThe association between impaired digital provision, access and health outcomes has not been systematically studied. The Wolverhampton Digital ENablement programme (WODEN) is a multiagency collaborative approach to determine and address digital factors that may impact on health and social care in a single deprived multiethnic health economy. The objective of this study is to determine the association between measurable broadband provision and demographic and health outcomes in a defined population.DesignAn observational cross-sectional whole local population-level study with cohorts defined according to broadband provision.Setting/participantsData for all residents of the City of Wolverhampton, totalling 269 785 residents.Primary outcomesPoor broadband provision is associated with variation in demographics and with increased comorbidity and urgent care needs.ResultsBroadband provision was measured using the Broadband Infrastructure Index (BII) in 158 City localities housing a total of 269 785 residents. Lower broadband provision as determined by BII was associated with younger age (p&lt;0.001), white ethnic status (p&lt;0.001), lesser deprivation as measured by Index of Multiple Deprivation (p&lt;0.001), a higher number of health comorbidities (p&lt;0.001) and more non-elective urgent events over 12 months (p&lt;0.001).ConclusionLocal municipal and health authorities are advised to consider the variations in broadband provision within their locality and determine equal distribution both on a geographical basis but also against demographic, health and social data to determine equitable distribution as a platform for equitable access to digital resources for their residents.</jats:sec

GP assessment of unmet need in a complex multimorbid population using a data-driven and clinical triage system: a prospective cohort study

Patients with unmet healthcare needs are more likely to access unscheduled care. Identifying these patients through data driven and clinical risk stratification for active case management in primary care can help address patient need and reduce demand on acute services. Determine how a proactive digital healthcare system can be used to undertake comprehensive needs analysis of patients at risk of unplanned admission and mortality. Prospective cohort study of 6 general practices in a deprived UK city. To identify those with unmet needs our population underwent digitally driven risk stratification into Escalated and Non-escalated groups using seven risk factors. The Escalated group underwent further stratification using GP clinical assessment into Concern and No Concern groupings. The Concern group underwent Unmet Needs Analysis (UNA). From 24,746, 515 (2.1%) were triaged into the Concern group and 164 (0.6%) underwent UNA. These patients were more likely to be older (t=4.69, <0.001), female (X =4.46, <0.05), have a PARR score ≥80 (X =4.31, <0.05), be a nursing home resident (X =6.75, <0.01) or on an end-of-life register (X =14.55, <0.001). Following UNA 143(87.2%) patients had further review planned or were referred for further input. The majority of patients had 4 domains of need. In those who GPs would not be surprised if they died within the next few months n=69 (42.1%) were not on an EOL register. This study showed how an integrated, patient centred, digital care system working with GPs can highlight and implement resources to address the escalating care needs of complex individuals. [Abstract copyright: Copyright © 2023, The Authors.

A prediction algorithm to improve the accuracy of the Gold Standard Framework Surprise Question end-of-life prognostic categories in an acute hospital admission cohort-controlled study. The Proactive Risk-Based and Data-Driven Assessment of Patients at the End of Life (PRADA)

Objective To determine the accuracy of a clinical data algorithm allocated end-of-life prognosis amongst hospital inpatients.Method The model allocated a predicted Gold Standard Framework end-of-life prognosis to all acute medical patients admitted over a 2-year period. Mortality was determined at 1 year.Results Of 18,838 patients, end-of-life prognosis was unknown in 67.9%. A binary logistic regression model calculated 1-year mortality probability (X2=6650.2, p or < 1 year respectively), with subsidiary classification of “No” to Green (months), Amber (weeks) or Red (days). This digitally driven prognosis allocation (100% vs baseline 32.1%) yielded cohorts of GSFSQ-Yes 15,264 (81%), GSFSQ-No Green 1,771 (9.4%) and GSFSQ-No Amber or Red 1,803 (9.6%).There were 5,043 (26.8%) deaths at 1 year. In Cox’s survival, model allocated cohorts were discrete for mortality (GSFSQ-Yes 16.4% v GSFSQ-No 71.0% (p<0.001). For the GSFSQ-No classification, the mortality Odds Ratio was 12.4 (11.4 – 13.5) (p<0.001) vs GSFSQ-Yes (c-statistic of 0.71 (0.70 – 0.73), p<0.001; accuracy, positive and negative predictive values of 81.2%, 83.6%, 83.6% respectively. If this tool had been utilised at the time of admission, the potential to reduce subsequent hospital admissions, death-in-hospital, and bed days was all p<0.001.Conclusions The defined model successfully allocated end-of-life prognosis in cohorts of hospitalised patients with strong performance metrics for prospective 1 year mortality, yielding the potential to provide anticipatory care and improve outcomes

Digital health and inpatient palliative care: a cohort-controlled study

Objectives End of life has unacceptable levels of hospital admission and death. We aimed to determine the association of a novel digital specific system (Proactive Risk-Based and Data-Driven Assessment of Patients at the End of Life, PRADA) to modify such events.Methods A cohort-controlled study of those discharged alive, who died within 90 days of discharge, comparing PRADA (n=114) with standard care (n=3730).Results At 90 days, the PRADA group were more likely to die (78.9% vs 46.2%, p<0.001), had a shorter time to death (58±90 vs 178±186 days, p<0.001) but readmission (20.2% vs 37.9%, p<0.001) or death in hospital (4.4% vs 28.9%, p<0.001) was lower with reduced risk for a combined 90-day outcome of postdischarge non-elective admission or hospital death (OR 0.45, 95% CI 0.27–0.74, p<0.001). Tightening criteria with 1:1 matching (n=83 vs 83) showed persistent significant findings in PRADA contact with markedly reduced adverse events (OR 0.15, 95% CI 0.02–0.96, p<0.05).Conclusions Being seen in hospital by a specialist palliative care team using the PRADA tool was associated with significantly improved postdischarge outcomes pertaining to those destined to die after discharge