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

Improving event prediction using general practitioner clinical judgement in a digital risk stratification model: a pilot study

BACKGROUND

Numerous tools based on electronic health record (EHR) data that predict risk of unscheduled care and mortality exist. These are often criticised due to lack of external validation, potential for low predictive ability and the use of thresholds that can lead to large numbers being escalated for assessment that would not have an adverse outcome leading to unsuccessful active case management. Evidence supports the importance of clinical judgement in risk prediction particularly when ruling out disease. The aim of this pilot study was to explore performance analysis of a digitally driven risk stratification model combined with GP clinical judgement to identify patients with escalating urgent care and mortality events.

METHODS

Clinically risk stratified cohort study of 6 GP practices in a deprived, multi-ethnic UK city. Initial digital driven risk stratification into Escalated and Non-escalated groups used 7 risk factors. The Escalated group underwent stratification using GP global clinical judgement (GCJ) into Concern and No concern groupings.

RESULTS

3968 out of 31,392 patients were data stratified into the Escalated group and further categorised into No concern (n = 3450 (10.9%)) or Concern (n = 518 (1.7%)) by GPs. The 30-day combined event rate (unscheduled care or death) per 1,000 was 19.0 in the whole population, 67.8 in the Escalated group and 168.0 in the Concern group (p < 0.001). The de-escalation effect of GP assessment into No Concern versus Concern was strongly negatively predictive (OR 0.25 (95%CI 0.19-0.33; p < 0.001)). The whole population ROC for the global approach (Non-escalated, GP No Concern, GP Concern) was 0.614 (0.592-0.637), p < 0.001, and the increase in the ROC area under the curve for 30-day events was all focused here (+ 0.4% (0.3-0.6%, p < 0.001), translating into a specific ROC c-statistic for GP GCJ of 0.603 ((0.565-0.642), p < 0.001).

CONCLUSIONS

The digital only component of the model performed well but adding GP clinical judgement significantly improved risk prediction, particularly by adding negative predictive value.

Application of information from external data to correct for collider bias in a Covid-19 hospitalised cohort

BACKGROUND

Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes.

METHODS

The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 - a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19.

RESULTS

Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity - differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis.

CONCLUSIONS

In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.

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

BACKGROUND

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.

AIM

To determine how a proactive digital healthcare system can be used to undertake comprehensive needs analysis of patients at risk of unplanned admission and mortality.

DESIGN & SETTING

Prospective cohort study of six general practices in a deprived UK city.

METHOD

To identify those with unmet needs, the study's 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 groups. The Concern group underwent Unmet Needs Analysis (UNA).

RESULTS

From 24 746 patients, 516 (2.1%) were triaged into the Concern group and 164 (0.7%) underwent UNA. These patients were more likely to be older (t = 4.69, P<0.001), female (X2 = 4.46, P<0.05), have a Patients At Risk of Re-hospitalisation (PARR) score ≥80 (X2 = 4.31, P<0.05), be a nursing home resident (X2 = 6.75, P<0.01), or on an end-of-life (EOL) register (X2 = 14.55, P<0.001). Following UNA, 143 (87.2%) patients had further review planned or were referred for further input. The majority of patients had four 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.

CONCLUSION

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.

Revisiting ethnic discrepancies in COVID-19 hospitalized cohorts: a correction for collider bias

OBJECTIVES

Studies from the first waves of the coronavirus disease 2019 (COVID-19) pandemic suggest that individuals from minority ethnicities are at an increased risk of worse outcomes. Concerns exist that this relationship is potentially driven by bias from analyzing hospitalized patients only. We investigate this relationship and the possible presence of bias.

STUDY DESIGN AND SETTING

Using data from South London hospitals across two COVID-19 waves (February 2020 - May 2021), the relationship between ethnicity and COVID-19 outcomes was examined using regression models. Three iterations of each model were completed: 1) an unadjusted analysis, 2) adjusting for covariates (medical history and deprivation), and 3) adjusting for covariates and bias induced by conditioning on hospitalization.

RESULTS

Among 3,133 patients, those who were Asian had a two-fold increased risk of death during the hospital stay that was consistent across the two COVID-19 waves and was not affected by correcting for conditioning on hospitalization. However, wave-specific effects demonstrate significant differences between ethnic groups until bias from using a hospitalized cohort was corrected for.

CONCLUSION

Worsened COVID-19 outcomes in minority ethnicities may be minimized by correcting for bias induced by conditioning on hospitalization. Consideration of this bias should be a key component of study design.

Clinical outcomes following COVID-19 infection in ethnic minority groups in the UK: a systematic review and meta-analysis

OBJECTIVES

This study evaluated the differences in clinical outcomes of COVID-19 infection between ethnic minorities and the White ethnic group in the UK.

STUDY DESIGN

Systematic review and meta-analysis.

METHODS

This study included adult residents in the UK with confirmed COVID-19 infection. The outcomes evaluated in this study were mortality, intensive care unit (ICU) admission and invasive mechanical ventilation (IMV). Outcome data were compared between individuals from ethnic minority groups and individuals from a White ethnic background. MEDLINE, Embase, Cochrane, medRxiv and PROSPERO were searched for articles published between May 2020 and April 2021. The risk of bias was evaluated using the Newcastle-Ottawa Scale checklist.

PROSPERO ID

CRD42021248117.

RESULTS

Fourteen studies (767,177 participants) were included in the current review. In the adjusted analysis, the pooled odds ratio (OR) for mortality following COVID-19 infection was higher for Black (OR 1.83, 95% confidence interval [CI]: 1.21-2.76, number of studies [k] = 6), Asian (OR 1.16, 95% CI: 0.85-1.57, k = 6) and Mixed and Other (MO) ethnic groups (OR 1.12, 95% CI: 1.04-1.20, k = 5) compared with the White ethnic group. The adjusted and unadjusted ORs of ICU admission for many of the ethnic minority groups were more than double the OR values for the White ethnic group (Black ethnic group = OR 2.32, 95% CI: 1.73-3.11, k = 5; Asian ethnic group = OR 2.34, 95% CI: 1.89-2.90, k = 5; and MO ethnic group = OR 2.26, 95% CI: 1.64-3.11, k = 4). In the adjusted analysis for IMV, the ORs were similarly significantly raised in ethnic minority groups compared with the White ethnic group (Black ethic group = OR 2.03, 95% CI: 1.80-2.29, k = 3; Asian ethnic group = OR 1.84, 95% CI: 1.20-2.80, k = 3; and MO ethnic group = OR 2.09, 95% CI: 1.35-3.22, k = 3).

CONCLUSION

This review found that in the UK, Black, Asian and MO ethnic groups experienced increased COVID-19-related disease severity and mortality compared with the White ethnic group majority.

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.

Risk of Covid-19 Severe Outcomes and Mortality in Migrants and Ethnic Minorities Compared to the General Population in the European WHO Region: a Systematic Review

The Covid-19 pandemic has had a major impact on migrants and ethnic minorities (MEMs). Socio-economic factors and legal, administrative and language barriers are among the reasons for this increased susceptibility. The aim of the study is to investigate the impact of Covid-19 on MEMs compared to the general population in terms of serious outcomes. We conducted a systematic review collecting studies on the impact of Covid-19 on MEMs compared to the general population in the WHO European Region regarding hospitalisation, intensive care unit (ICU) admission and mortality, published between 01/01/2020 and 19/03/2021. Nine researchers were involved in selection, study quality assessment and data extraction. Of the 82 studies included, 15 of the 16 regarding hospitalisation for Covid-19 reported an increased risk for MEMs compared to the white and/or native population and 22 out of the 28 studies focusing on the ICU admission rates found an increased risk for MEMs. Among the 65 studies on mortality, 43 report a higher risk for MEMs. An increased risk of adverse outcomes was reported for MEMs. Social determinants of health are among the main factors involved in the genesis of health inequalities: a disadvantaged socio-economic status, a framework of structural racism and asymmetric access to healthcare are linked to increased susceptibility to the consequences of Covid-19. These findings underline the need for policymakers to consider the socio-economic barriers when designing prevention plans.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12134-023-01007-x.

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

OBJECTIVES

The 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.

DESIGN

An observational cross-sectional whole local population-level study with cohorts defined according to broadband provision.

SETTING/PARTICIPANTS

Data for all residents of the City of Wolverhampton, totalling 269 785 residents.

PRIMARY OUTCOMES

Poor broadband provision is associated with variation in demographics and with increased comorbidity and urgent care needs.

RESULTS

Broadband 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<0.001), white ethnic status (p<0.001), lesser deprivation as measured by Index of Multiple Deprivation (p<0.001), a higher number of health comorbidities (p<0.001) and more non-elective urgent events over 12 months (p<0.001).

CONCLUSION

Local 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.

The mental health experiences of ethnic minorities in the UK during the Coronavirus pandemic: A qualitative exploration

Background

Worldwide, the Coronavirus pandemic has had a major impact on people's health, lives, and livelihoods. However, this impact has not been felt equally across various population groups. People from ethnic minority backgrounds in the UK have been more adversely affected by the pandemic, especially in terms of their physical health. Their mental health, on the other hand, has received less attention. This study aimed to explore the mental health experiences of UK adults from ethnic minorities during the Coronavirus pandemic. This work forms part of our wider long-term UK population study "Mental Health in the Pandemic."

Methods

We conducted an exploratory qualitative study with people from ethnic minority communities across the UK. A series of in-depth interviews were conducted with 15 women, 14 men and 1 non-binary person from ethnic minority backgrounds, aged between 18 and 65 years old (mean age = 40). We utilized purposefully selected maximum variation sampling in order to capture as wide a variety of views, perceptions and experiences as possible. Inclusion criteria: adults (18+) from ethnic minorities across the UK; able to provide full consent to participate; able to participate in a video- or phone-call interview. All interviews took place via MS Teams or Zoom. The gathered data were transcribed verbatim and underwent thematic analysis following Braun and Clarke carried out using NVivo 12 software.

Results

The qualitative data analysis yielded seven overarching themes: (1) pandemic-specific mental health and wellbeing experiences; (2) issues relating to the media; (3) coping mechanisms; (4) worries around and attitudes toward vaccination; (5) suggestions for support in moving forward; (6) best and worst experiences during pandemic and lockdowns; (7) biggest areas of change in personal life. Generally, participants' mental health experiences varied with some not being affected by the pandemic in a way related to their ethnicity, some sharing positive experiences and coping strategies (exercising more, spending more time with family, community cohesion), and some expressing negative experiences (eating or drinking more, feeling more isolated, or even racism and abuse, especially toward Asian communities). Concerns were raised around trust issues in relation to the media, the inadequate representation of ethnic minorities, and the spread of fake news especially on social media. Attitudes toward vaccinations varied too, with some people more willing to have the vaccine than others.

Conclusion

This study's findings highlight the diversity in the pandemic mental health experiences of ethnic minorities in the UK and has implications for policy, practice and further research. To enable moving forward beyond the pandemic, our study surfaced the need for culturally appropriate mental health support, financial support (as a key mental health determinant), accurate media representation, and clear communication messaging from the Governments of the UK.

Impact of influenza vaccination on GP-diagnosed COVID-19 and all-cause mortality: a Dutch cohort study

OBJECTIVES

As clinical presentation and complications of both viruses overlap, it was hypothesised that influenza vaccination was associated with lower general practitioner (GP)-diagnosed COVID-19 rates and lower all-cause mortality rates.

STUDY DESIGN

From a primary care population-based cohort in the Netherlands, GP-diagnosed COVID-19 (between 10 March and 22 November 2020) and all-cause mortality events (between 30 December 2019 and 22 November 2020) were recorded. 223 580 persons were included, representing the influenza vaccination 2019 target group (all aged ≥60 years, and those <60 years with a medical indication). Proportional hazards regression analyses evaluated associations between influenza vaccination in 2019 and two outcomes: GP-diagnosed COVID-19 and all-cause mortality. Covariables were sex, age, comorbidities and number of acute respiratory infection primary care consultations in 2019.

RESULTS

A slightly positive association (HR 1.15; 95% CI 1.08 to 1.22) was found between influenza vaccination in 2019 and GP-diagnosed COVID-19, after adjusting for covariables. A slightly protective effect for all-cause mortality rates (HR 0.90; 95% CI 0.83 to 0.97) was found for influenza vaccination, after adjusting for covariables. A subgroup analysis among GP-diagnosed COVID-19 cases showed no significant association between influenza vaccination in 2019 and all-cause mortality.

CONCLUSIONS

Our hypothesis of a possibly negative association between influenza vaccination in 2019 and GP-diagnosed COVID-19 was not confirmed as we found a slightly positive association. A slightly protective effect on all-cause mortality was found after influenza vaccination, possibly by a wider, overall protective effect on health. Future research designs should include test-confirmed COVID-19 cases and controls, adjustments for behavioural, socioeconomic and ethnic factors and validated cause-specific mortality cases.

Quality of Life in Post-COVID-19 Patients after Hospitalization

The COVID-19 pandemic has had a strong impact on people’s quality of life (QoL), which is affected by social and economic changes as well as by mental and physical health. The aim of this study was to determine QoL in post-COVID-19 patients who had required hospitalization, and to identify relevant sociodemographic data. We used questionnaires which considered demographic and socioeconomic data, health and vaccination status, the pandemic situation, and EQ-5D scoring. The interactions of all data and the scores of EQ-5D were analyzed. Multivariate logistic regression analysis was applied to the five dimensions of EQ-5D. In this single-hospital-cohort study, the average times elapsed since initial diagnosis and hospital admission were 2.5 (76.3 ± 18.1 days) and 5 months (155.4 ± 33.9 days), respectively. Post-COVID-19 females were 3–5 times more likely to be affected in terms of anxiety/depression, and in negative impact upon their usual activities, at 5 months after diagnosis. At the same time, reductions in mobility were 3–4 times more likely in elderly post-COVID-19 patients, whose levels of pain and discomfort increased. Single patients, those with low incomes, and those with severe clinical outcomes were 2–4 times more likely to experience a reduction in their usual activities, while the presence of co-morbidities and lower levels of education were associated with increased pain and discomfort. Aging-induced pain/discomfort and anxiety/depression were significantly exacerbated in elderly patients with widespread vaccination. Our study revealed effects of demographic and socioeconomic factors upon lower QoL in post-COVID-19 patients in four dimensions of EQ-5D: mobility, usual activity, pain/discomfort, and anxiety/depression, 5 months after first diagnosis and hospitalization.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Higher risk, higher protection. COVID-19 risk among immigrants in France: results from the population-based EpiCov survey

Background

Immigrants and ethnic/racialized minorities have been identified as being at higher risk of coronavirus disease-19 (COVID-19) infection, but few studies report on their exposures and prevention behaviours. This study aims to examine the social distribution of COVID-19 exposure (overcrowding, working outside the home, use of public transport to go to work) and prevention behaviours (use of face masks, washing hands, respect for physical distance) in France during the first wave of the epidemic.

Methods

We used the EpiCov population-based survey from a random sample of individuals aged 15 years or more. We determined the distribution of the self-reported outcomes according to migratory status and sex, using χ² tests. We modelled the probability of outcomes with logistic regression. Finally, we focused the analysis on the Greater Paris area and accounted for neighbourhood characteristics.

Results

A total of 111 824 participants were included in the study. Overall, immigrant groups from non-European countries were more exposed to COVID-19-related factors and more respectful of prevention measures. The probability of overcrowding and the use of public transport was higher for immigrants from sub-Saharan Africa [adjusted odds ratio (aOR) = 3.71 (3.19; 4.32), aOR = 6.36 (4.86; 8.32)] than for the majority population. Immigrant groups were less likely to have a non-systematic use of face masks and to breach physical distancing than the majority population [for immigrants from sub-Saharan Africa, aOR = 0.32 (0.28; 0.37) and aOR = 0.71 (0.61; 0.81), respectively]. Living in a neighbourhood with a higher share of immigrants was associated with higher exposure and better prevention behaviours.

Conclusions

In France, immigrants had a higher exposure to COVID-19-related factors and more systematic prevention behaviours.

Racial disparities in COVID-19 pandemic cases, hospitalisations, and deaths: A systematic review and meta-analysis

Background

People from racial minority groups in western countries experience disproportionate socioeconomic and structural determinants of health disadvantages. These disadvantages have led to inequalities and inequities in health care access and poorer health outcomes. We report disproportionate disparities in prevalence, hospitalisation, and deaths from COVID-19 by racial minority populations.

Methods

We conducted a systematic literature search of relevant databases to identify studies reporting on prevalence, hospitalisations, and deaths from COVID-19 by race groups between 01 January 2020 – 15 April 2021. We grouped race categories into Blacks, Hispanics, Whites and Others. Random effects model using the method of DerSimonian and Laird were fitted, and forest plot with respective ratio estimates and 95% confidence interval (CI) for each race category, and subgroup meta-regression analyses and the overall pooled ratio estimates for prevalence, hospitalisation and mortality rate were presented.

Results

Blacks experienced significantly higher burden of COVID-19: prevalence ratio 1.79 (95% confidence interval (CI) = 1.59-1.99), hospitalisation ratio 1.87 (95% CI = 1.69-2.04), mortality ratio 1.68 (95% CI = 1.52-1.83), compared to Whites: prevalence ratio 0.70 (95% CI = 0.0.64-0.77), hospitalisation ratio 0.74 (95% CI = 0.65-0.82), mortality ratio 0.82 (95% CI = 0.78-0.87). Also, Hispanics experienced a higher burden: prevalence ratio 1.78 (95% CI = 1.63-1.94), hospitalisation ratio 1.32 (95% CI = 1.08-1.55), mortality ratio 0.94 (95% CI = 0.84-1.04) compared to Whites. A higher burden was also observed for Other race groups: prevalence ratio 1.43 (95% CI = 1.19-1.67), hospitalisation ratio 1.12 (95% CI = 0.89-1.35), mortality ratio 1.06 (95% CI = 0.89-1.23) compared to Whites. The disproportionate burden among Blacks and Hispanics remained following correction for publication bias.

Conclusions

Blacks and Hispanics have been disproportionately affected by COVID-19. This is deeply concerning and highlights the systemically entrenched disadvantages (social, economic, and political) experienced by racial minorities in western countries; and this study underscores the need to address inequities in these communities to improve overall health outcomes.