COVID-19 admission risk tools should include multiethnic age structures, multimorbidity and deprivation metrics for air pollution, household overcrowding, housing quality and adult skills

Association of Area Deprivation Index With Mortality in Critically Ill Adults With COVID-19

BACKGROUND

Various social determinants of health have been established as significant risk factors for COVID-19 transmission, prevalence, incidence, and mortality. Area deprivation index (ADI, a composite score made up of educational, housing, and poverty markers) is an accepted multidimensional social determinants of health measure. Little is known about how structural social determinants of health before hospitalization, including ADI, may affect mortality related to COVID-19 in critically ill patients.

OBJECTIVES

To examine the association of ADI with intensive care unit (ICU) mortality in patients with COVID-19 and compare its predictive power with that of clinical factors.

METHODS

This was a retrospective cohort study of critically ill adults with COVID-19 in 3 hospitals within a single health system. Multivariable logistic regression models (adjusted for demographic and clinical variables) were used to examine the association of ADI with ICU mortality.

RESULTS

Data from 1784 patients hospitalized from 2020 to 2022 were analyzed. In multivariable models, no association was found between national ADI and ICU mortality. Notable factors associated with ICU mortality included treatment year, age, van Walraven weighted score, invasive mechanical ventilation, and body mass index.

CONCLUSION

In this study, clinical factors were more predictive of mortality than ADI and other social determinants of health. The influence of ADI may be most relevant before hospital admission. These findings could serve as a foundation for shaping targeted public health strategies and hospital interventions, enhancing care delivery, and potentially contributing to better outcomes in future pandemics.

Ancillary hospital workers experience during COVID-19: systematic review and narrative synthesis

BACKGROUND

COVID-19 overwhelmed healthcare systems worldwide. Its impact on clinical staff is well documented, but little is known about the effects on ancillary staff (cleaners, porters and caterers).

AIM

To identify the evidence of the impact of COVID-19 on ancillary staff at National Health Service (NHS) hospitals in England.

DESIGN

Systematic review and narrative synthesis.

DATA SOURCES

Databases (MEDLINE, CINAHL Ultimate, APA PsycINFO, APA PsycArticles and Academic Search Ultimate). Reference lists were searched. Four independent reviewers screened titles and abstracts against inclusion criteria. Data were extracted from included papers and studies were critically assessed using relevant critical appraisal tools.

RESULTS

8/178 studies were included, of which 5 quantitative, 2 qualitative and 1 mixed methods. Ancillary staff had higher rates of past and present COVID-19 infection. Participants felt that the work of ancillary staff had been insufficiently recognised by managers and that they had little voice within the NHS. They also experienced inequity regarding available support and safe working practices due to largely digital modes of communication which they rarely, if ever, used. In an evaluation of a personal protective equipment support 'helper' programme, ancillary workers were more positive about it than nurses, allied health practitioners, and doctors.

CONCLUSION

Few studies included ancillary staff. As reported, ancillary staff at NHS hospitals had a higher prevalence of COVID-19 infection but felt marginalised and poorly supported. They valued training when offered. Additional research is needed to understand better the impact of COVID-19 on ancillary key workers, and how best to support them in future similar circumstances.

Substandard housing and the risk of COVID-19 infection and disease severity: A retrospective cohort study

In this study we examine associations between substandard housing and the risk of COVID-19 infection and severity during the first year of the pandemic by linking individual-level housing and clinical datasets. Residents of Chelsea, Massachusetts who were tested for COVID-19 at any Mass General Brigham testing site and who lived at a property that had received a city housing inspection were included (N = 2873). Chelsea is a densely populated city with a high prevalence of substandard housing. Inspected properties with housing code violations were considered substandard; inspected properties without violations were considered adequate. COVID-19 infection was defined as any positive PCR test, and severe disease defined as hospitalization with COVID-19. We used a propensity score design to match individuals on variables including age, race, sex, and income. In the severity model, we also matched on ten comorbidities. We estimated the risk of COVID-19 infection and severity associated with substandard housing using Cox Proportional Hazards models for lockdown, the first phase of reopening, and the full study period. In our sample, 32% (919/2873) of individuals tested positive for COVID-19 and 5.9% (135/2297) had severe disease. During lockdown, substandard housing was associated with a 48% increased risk of COVID-19 infection (95%CI 1.1-2.0, p = 0.006). Through Phase 1 reopening, substandard housing was associated with a 39% increased infection risk (95%CI 1.1-1.8, p = 0.020). The difference in risk attenuated over the full study period. There was no difference in severe disease risk between the two groups. The increased risk, observed only during lockdown and early reopening - when residents were most exposed to their housing - strengthens claims that substandard housing conveys higher infection risk. The results demonstrate the value of combining cross-sector datasets. Existing city housing data can be leveraged 1) to identify and prioritize high-risk areas for future pandemic response, and 2) for longer-term housing solutions.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

SARS-CoV-2 infections in migrants and the role of household overcrowding: a causal mediation analysis of Virus Watch data

BACKGROUND

Migrants are over-represented in SARS-CoV-2 infections globally; however, evidence is limited for migrants in England and Wales. Household overcrowding is a risk factor for SARS-CoV-2 infection, with migrants more likely to live in overcrowded households than UK-born individuals. We aimed to estimate the total effect of migration status on SARS-CoV-2 infection and to what extent household overcrowding mediated this effect.

METHODS

We included a subcohort of individuals from the Virus Watch prospective cohort study during the second SARS-CoV-2 wave (1 September 2020-30 April 2021) who were aged ≥18 years, self-reported the number of rooms in their household and had no evidence of SARS-CoV-2 infection pre-September 2020. We estimated total, indirect and direct effects using Buis' logistic decomposition regression controlling for age, sex, ethnicity, clinical vulnerability, occupation, income and whether they lived with children.

RESULTS

In total, 23 478 individuals were included. 9.07% (187/2062) of migrants had evidence of infection during the study period vs 6.27% (1342/21 416) of UK-born individuals. Migrants had 22% higher odds of infection during the second wave (total effect; OR 1.22, 95% CI 1.01 to 1.47). Household overcrowding accounted for approximately 36% (95% CI -4% to 77%) of these increased odds (indirect effect, OR 1.07, 95% CI 1.03 to 1.12; proportion accounted for: indirect effect on log odds scale/total effect on log odds scale=0.36).

CONCLUSION

Migrants had higher odds of SARS-CoV-2 infection during the second wave compared with UK-born individuals and household overcrowding explained 36% of these increased odds. Policy interventions to reduce household overcrowding for migrants are needed as part of efforts to tackle health inequalities during the pandemic and beyond.

Ethnic inequalities in COVID-19 infection, hospitalisation, intensive care admission, and death: a global systematic review and meta-analysis of over 200 million study participants

Background

COVID-19 has exacerbated existing ethnic inequalities in health. Little is known about whether inequalities in severe disease and deaths, observed globally among minoritised ethnic groups, relates to greater infection risk, poorer prognosis, or both. We analysed global data on COVID-19 clinical outcomes examining inequalities between people from minoritised ethnic groups compared to the ethnic majority group.

Methods

Databases (MEDLINE, EMBASE, EMCARE, CINAHL, Cochrane Library) were searched from 1st December 2019 to 3rd October 2022, for studies reporting original clinical data for COVID-19 outcomes disaggregated by ethnicity: infection, hospitalisation, intensive care unit (ICU) admission, and mortality. We assessed inequalities in incidence and prognosis using random-effects meta-analyses, with Grading of Recommendations Assessment, Development, and Evaluation (GRADE) use to assess certainty of findings. Meta-regressions explored the impact of region and time-frame (vaccine roll-out) on heterogeneity. PROSPERO: CRD42021284981.

Findings

77 studies comprising over 200,000,000 participants were included. Compared with White majority populations, we observed an increased risk of testing positive for infection for people from Black (adjusted Risk Ratio [aRR]:1.78, 95% CI:1.59-1.99, I2 = 99.1), South Asian (aRR:3.00, 95% CI:1.59-5.66, I2 = 99.1), Mixed (aRR:1.64, 95% CI:1.02-1.67, I2 = 93.2) and Other ethnic groups (aRR:1.36, 95% CI:1.01-1.82, I2 = 85.6). Black, Hispanic, and South Asian people were more likely to be seropositive. Among population-based studies, Black and Hispanic ethnic groups and Indigenous peoples had an increased risk of hospitalisation; Black, Hispanic, South Asian, East Asian and Mixed ethnic groups and Indigenous peoples had an increased risk of ICU admission. Mortality risk was increased for Hispanic, Mixed, and Indigenous groups. Smaller differences were seen for prognosis following infection. Following hospitalisation, South Asian, East Asian, Black and Mixed ethnic groups had an increased risk of ICU admission, and mortality risk was greater in Mixed ethnic groups. Certainty of evidence ranged from very low to moderate.

Interpretation

Our study suggests that systematic ethnic inequalities in COVID-19 health outcomes exist, with large differences in exposure risk and some differences in prognosis following hospitalisation. Response and recovery interventions must focus on tackling drivers of ethnic inequalities which increase exposure risk and vulnerabilities to severe disease, including structural racism and racial discrimination.

Funding

ESRC:ES/W000849/1.

Impact of social deprivation on clinical outcomes of adults hospitalised with community-acquired pneumonia in England: a retrospective cohort study

INTRODUCTION

Socioeconomic deprivation has been associated with an increased incidence of infection and poorer clinical outcomes during influenza pandemics and the COVID-19 pandemic. The aim of this study was to determine the relationship between deprivation and adverse clinical outcomes following hospital admission with community-acquired pneumonia (CAP), specifically 30-day all-cause mortality and non-elective hospital readmission.

METHODS

Data from the British Thoracic Society national CAP audit on patients admitted to hospital with CAP in England between 1 December 2018 and 31 January 2019 were linked to patient-level Hospital Episode Statistics data and Index of Multiple Deprivation (IMD) scores. Multivariable logistic regression models were used to examine the association between deprivation and (a) 30-day mortality and (b) 30-day readmission with p values for trend reported. Age was examined as a potential effect modifier on the effect of IMD quintile on mortality and subsequent subanalysis in those <65 and ≥65 years was performed.

RESULTS

Of 9165 adults admitted with CAP, 24.7% (n=2263) were in the most deprived quintile. No significant trend between deprivation and mortality was observed (p trend=0.38); however, the association between deprivation and mortality differed by age group. In adults aged<65 years, 30-day mortality was highest in the most deprived and lowest in the least deprived quintiles (4.4% vs 2.5%, aOR 1.83, 95% CI 0.84 to 4.0) with a significant trend across groups (p trend=0.04). Thirty-day readmission was highest in the most deprived quintile (17.1%) with a significant p trend across groups (p trend 0.003). Age-adjusted odds of readmission were highest in the most deprived compared with the least deprived (aOR 1.41, 95% CI 1.16 to 1.73).

CONCLUSIONS

In adults aged<65 years hospitalised with CAP in England, mortality varied inversely with indices of social deprivation. There was also a significant association between deprivation and 30-day readmission. Strategies are required to decrease health inequalities in pneumonia mortality and hospital readmissions associated with deprivation.

Impact of ethnicity on the accuracy of measurements of oxygen saturations: A retrospective observational cohort study

Background

Pulse oximeters are routinely used in community and hospital settings worldwide as a rapid, non-invasive, and readily available bedside tool to approximate blood oxygenation. Potential racial biases in peripheral oxygen saturation (SpO2) measurements may influence the accuracy of pulse oximetry readings and impact clinical decision making. We aimed to assess whether the accuracy of oxygen saturation measured by SpO2, relative to arterial blood gas (SaO2), varies by ethnicity.

Methods

In this large retrospective observational cohort study covering four NHS Hospitals serving a large urban population in Birmingham, United Kingdom, consecutive pairs of SpO2 and SaO2 measurements taken on the same patient within an interval of less than 20 min were identified from electronic patient records. Where multiple pairs of measurements were recorded in a spell, only the first was included in the analysis. The differences between SpO2 and SaO2 measurements were compared across groups of self-identified ethnicity. These differences were subsequently adjusted for age, sex, bilirubin, systolic blood pressure, carboxyhaemaglobin saturations and the time interval between SpO2 and SaO2 measurements.

Findings

Paired O2 saturation measurements from 16,818 inpatient spells between 1st January 2017 and 18th February 2021 were analysed. The cohort self-identified as being of White (81.2%), Asian (11.7%), Black (4.0%), or Other (3.2%) ethnicities. Across the cohort, SpO2 was statistically significantly higher than SaO2 (p < 0.0001), with medians of 98% (interquartile range [IQR]: 95-100%) vs. 97% (IQR: 96-99%), and a median difference of 0.5% points (pps; 95% confidence interval [CI]: 0.5-0.6). However, the size of this difference varied considerably with the magnitude of SaO2, with SpO2 overestimating by a median by 3.8pp (IQR: 0.4, 8.8) for SaO2 values <90% but underestimating by a median of 0.4pp (IQR: -2.0, 1.4) for an SaO2 of 95%. The differences between SpO2 and SaO2 were also found to vary by ethnicity, with this difference being 0.8pp (95% CI: 0.6-1.0, p < 0.0001) greater in those of Black vs. White ethnicity. These differences resulted in 8.7% vs. 6.1% of Black vs. White patients who were classified as normoxic on SpO2 actually being hypoxic on the gold standard SaO2 (odds ratio: 1.47, 95% CI: 1.09-1.98, p = 0.012).

Interpretation

Pulse oximetry may overestimate O2 saturation, and this is possibly more pronounced in patients of Black ethnicity. Prospective studies are urgently warranted to assess the impact of ethnicity on the accuracy of pulse oximetry, to ensure care is optimised for all.

Funding

PIONEER, the Health Data Research UK (HDR-UK) Health Data Research Hub in acute care.

Predictors of Mortality for Patients with COVID-19 in the Rural Appalachian Region

Background

The prevalence and outcome of coronavirus disease 2019 (COVID-19) in rural areas is unknown.

Methods

This is a multi-center retrospective cohort study of hospitalized patients diagnosed with COVID-19 from April 5, 2020 to December 31, 2020. The data were extracted from 13 facilities in the Appalachian Regional Healthcare system that share the same electronic health record using ICD-10-CM codes.

Results

The number of patients diagnosed with COVID-19 per facility ranged from 5 to 535 with a median of 106 patients. Total mortality was 11.4% and ranged from 0% to 22.6% by facility (median: 9.0%). Non-survivors had a greater prevalence of congestive heart failure (CHF), hypertension, type 2 diabetes mellitus, stroke, transient ischemic attack (TIA), and pulmonary embolism. Patients who died were also more likely to have had chronic obstructive pulmonary disease (COPD), acute respiratory failure (ARF), liver cirrhosis, chronic kidney disease (CKD), dementia, cancer, anemia, and opiate dependence.

Conclusion

The aging population, multiple co-morbidities, and health-related behaviors make rural patients vulnerable to COVID-19. A better understanding of the disease in rural areas is crucial, given its heightened vulnerability to adverse outcomes.

Utility of severity assessment tools in COVID-19 pneumonia: a multicentre observational study

BACKGROUND

Severity scores in pneumonia and sepsis are being applied to SARS-CoV-2 infection. We aimed to assess whether these severity scores are accurate predictors of early adverse outcomes in COVID-19.

METHODS

We conducted a multicentre observational study of hospitalised SARS-CoV-2 infection. We assessed risk scores (CURB65, qSOFA, Lac-CURB65, MuLBSTA and NEWS2) in relation to admission to intensive care or death within 7 days of admission, defined as early severe adverse events (ESAE). The 4C Mortality Score was also assessed in a sub-cohort of patients.

FINDINGS

In 2,387 participants, the overall mortality was 18%. In all scores examined, increasing score was associated with increased risk of ESAE. Area under the curve (AUC) to predict ESAE for CURB65, qSOFA, Lac-CURB65, MuLBSTA and NEWS2 were 0.61, 0.62, 0.59, 0.59 and 0.68, respectively. AUC to predict ESAE was 0.60 with ISARIC 4C Mortality Score.

CONCLUSION

None of the scores examined accurately predicted ESAE in SARS-CoV-2 infection. Non-validated scores should not be used to inform clinical decision making in COVID-19.