Impact of socioeconomic status in patients hospitalised for COVID-19 in the Greater Paris area

The effect of socioeconomic status and training programmes on burnout in postgraduate trainees in the United Kingdom: a cross-sectional analysis

PURPOSE

Burnout is described as a state of mental exhaustion caused by one's professional life and is characterised by three domains: emotional exhaustion, depersonalisation, and a reduced sense of accomplishment. The prevalence of stress is high amongst doctors and varies by specialty, gender, trainee level, and socioeconomic status. The authors set out to examine the scale of the problem, as well as to determine the influence of both socioeconomic status and chosen training programme on burnout amongst postgraduate trainees. This would identify at-risk groups and aid in future targeted interventions.

METHODS

Cross-sectional data were obtained, following approval from the General Medical Council, from The National Training Survey, completed annually by all trainees in the United Kingdom. Data were then anonymised and analysed. Burnout scores were derived from the Copenhagen Burnout Inventory and are positively framed (higher scores equal lower burnout).

RESULTS

The questionnaire was completed by 63 122 participants from 2019 to 2020. Mean burnout amongst all trainees was 52.4 (SD = 19.3). Burnout scores from the most deprived quintile was significantly lower compared with those from the least deprived quintile: 51.0 (SD = 20.6) versus 52.9 (SD = 18.9), respectively (P < 0.001). The highest levels of burnout were reported in Internal Medical Training, Emergency Medicine, Obstetrics and Gynaecology, and Core Surgical Training, respectively.

CONCLUSION

Postgraduates from lower socioeconomic backgrounds are more likely to encounter burnout during training. At-risk groups who may also benefit from targeted intervention have been identified, requiring further examination through future studies.

Inequalities in healthcare use during the COVID-19 pandemic

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 - Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

Socioeconomic deprivation and illness trajectory in the Scottish population after COVID-19 hospitalization

BACKGROUND

The associations between deprivation and illness trajectory after hospitalisation for coronavirus disease-19 (COVID-19) are uncertain.

METHODS

A prospective, multicentre cohort study was conducted on post-COVID-19 patients, enrolled either in-hospital or shortly post-discharge. Two evaluations were carried out: an initial assessment and a follow-up at 28-60 days post-discharge. The study encompassed research blood tests, patient-reported outcome measures, and multisystem imaging (including chest computed tomography (CT) with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging). Primary and secondary outcomes were analysed in relation to socioeconomic status, using the Scottish Index of Multiple Deprivation (SIMD). The EQ-5D-5L, Brief Illness Perception Questionnaire (BIPQ), Patient Health Questionnaire-4 (PHQ-4) for Anxiety and Depression, and the Duke Activity Status Index (DASI) were used to assess health status.

RESULTS

Of the 252 enrolled patients (mean age 55.0 ± 12.0 years; 40% female; 23% with diabetes), deprivation status was linked with increased BMI and diabetes prevalence. 186 (74%) returned for the follow-up. Within this group, findings indicated associations between deprivation and lung abnormalities (p = 0.0085), coronary artery disease (p = 0.0128), and renal inflammation (p = 0.0421). Furthermore, patients with higher deprivation exhibited worse scores in health-related quality of life (EQ-5D-5L, p = 0.0084), illness perception (BIPQ, p = 0.0004), anxiety and depression levels (PHQ-4, p = 0.0038), and diminished physical activity (DASI, p = 0.002). At the 3-month mark, those with greater deprivation showed a higher frequency of referrals to secondary care due to ongoing COVID-19 symptoms (p = 0.0438). However, clinical outcomes were not influenced by deprivation.

CONCLUSIONS

In a post-hospital COVID-19 population, socioeconomic deprivation was associated with impaired health status and secondary care episodes. Deprivation influences illness trajectory after COVID-19.

The role of multimorbidity and socio-economic characteristics as potential risk factors for Long Covid: evidence from the multilevel analysis of the Survey of Health, Ageing and Retirement in Europe's corona surveys (2020-2021)

BACKGROUND

A substantial proportion of individuals continue experiencing persistent symptoms following the acute stage of their Covid-19 illness. However, there is a shortage of population-based studies on Long Covid risk factors.

OBJECTIVE

To estimate the prevalence of Long Covid in the population of middle-aged and older Europeans having contracted Covid-19 and to assess the role of multimorbidity and socio-economic characteristics as potential risk factors of Long Covid.

METHODS

A population-based longitudinal prospective study involving a sample of respondents 50 years and older (n = 4,004) from 27 countries who participated in the 2020 and 2021 Survey of Health, Ageing and Retirement in Europe (SHARE), in particular the Corona Surveys. Analyses were conducted by a multilevel (random intercept) hurdle negative binomial model.

RESULTS

Overall, 71.6% (95% confidence interval = 70.2-73.0%) of the individuals who contracted Covid-19 had at least one symptom of Long Covid up to 12 months after the infection, with an average of 3.06 (standard deviation = 1.88) symptoms. There were significant cross-country differences in the prevalence of Long Covid and number of symptoms. Higher education and being a man were associated with a lower risk of Long Covid, whilst being employed was associated with a higher risk of having Long Covid. Multimorbidity was associated with a higher number of symptoms and older age was associated with a lower number of symptoms.

CONCLUSION

Our results provide evidence on the substantial burden of Long Covid in Europe. Individuals who contracted Covid-19 may require long-term support or further medical intervention, putting additional pressure on national health care systems.

Associations of psychological wellbeing with COVID-19 hospitalization and mortality in adults aged 50 years or older from 25 European countries and Israel

BACKGROUND

Lower psychological wellbeing is associated with poor outcomes in a variety of diseases and healthy populations. However, no study has investigated whether psychological wellbeing is associated with the outcomes of COVID-19. This study aimed to determine whether individuals with lower psychological wellbeing are more at risk for poor outcomes of COVID-19.

METHODS

Data were from the Survey of Health, Aging, and Retirement in Europe (SHARE) in 2017 and SHARE's two COVID-19 surveys in June-September 2020 and June-August 2021. Psychological wellbeing was measured using the CASP-12 scale in 2017. The associations of the CASP-12 score with COVID-19 hospitalization and mortality were assessed using logistic models adjusted for age, sex, body mass index, smoking, physical activity, household income, education level, and chronic conditions. Sensitivity analyses were performed by imputing missing data or excluding cases whose diagnosis of COVID-19 was solely based on symptoms. A confirmatory analysis was conducted using data from the English Longitudinal Study of Aging (ELSA). Data analysis took place in October 2022.

RESULTS

In total, 3,886 individuals of 50 years of age or older with COVID-19 were included from 25 European countries and Israel, with 580 hospitalized (14.9%) and 100 deaths (2.6%). Compared with individuals in tertile 3 (highest) of the CASP-12 score, the adjusted odds ratios (ORs) of COVID-19 hospitalization were 1.81 (95% CI, 1.41-2.31) for those in tertile 1 (lowest) and 1.37 (95% CI, 1.07-1.75) for those in tertile 2. As for COVID-19 mortality, the adjusted ORs were 2.05 (95% CI, 1.12-3.77) for tertile 1 and 1.78 (95% CI, 0.98-3.23) for tertile 2, compared with tertile 3. The results were relatively robust to missing data or the exclusion of cases solely based on symptoms. This inverse association of the CASP-12 score with COVID-19 hospitalization risk was also observed in ELSA.

CONCLUSION

This study shows that lower psychological wellbeing is independently associated with increased risks of COVID-19 hospitalization and mortality in European adults aged 50 years or older. Further study is needed to validate these associations in recent and future waves of the COVID-19 pandemic and other populations.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Seroprevalence of the SARS-CoV-2 antibody in healthcare workers: a multicentre cross-sectional study in 10 Colombian cities

BACKGROUND

Healthcare workers are at increased risk of infection due to occupational exposure to SARS-CoV-2-infected patients. The objective of this study was to determine the seroprevalence of SARS-CoV-2 in healthcare workers in Colombia.

METHODS

This study is a cross-sectional study focused on estimating the seroprevalence of SARS-CoV-2 antibodies in healthcare workers from 65 hospitals in 10 cities in Colombia during the second semester of 2020. The seroprevalence was determined using an automated immunoassay (Abbott SARS-CoV-2 CLIA IgG). The study included a survey to establish the sociodemographic variables and the risk of infection. A multivariate model was used to evaluate the association between the results of seroprevalence and risk factors.

RESULTS

The global seroprevalence of antibodies against SARS-CoV-2 was 35% (95% Bayesian CI 33% to 37%). All the personnel reported the use of protective equipment. General services personnel and nurses presented the highest ratios of seroprevalence among the healthcare workers. Low socioeconomic strata have shown a strong association with seropositivity.

CONCLUSION

This study estimates the prevalence of SARS-CoV-2 infection among healthcare workers. Even though all the personnel reported the use of protective equipment, the seroprevalence in the general services personnel and nurses was high. Also, a significant difference by cities was observed.

Relationship Between Income Level and Hospitalization Rate in COVID-19 Cases; an Example of Social Factors Affecting Health

Introduction

Considering the population's socioeconomic status and clinical features is essential in planning and performing interventions related to disease control. The main purpose of this study was to investigate the relationship between income level and hospitalization rate of COVID-19 patients‌.

Methods

A cross-sectional study was performed on 198,944 hospitalized COVID-19 patients in Tehran province between March 2020 and March 2021. Data of hospitalized COVID-19 patients was obtained from the Hospital Intelligent Management System (HIM). The income data of patients were obtained from the Iranian Database on Targeted Subsidies belonging to the Ministry of Cooperatives, Labor, and Social Welfare. Data analyses were performed using SPSS software.

Results

About 2.5% of the inpatients were from the first decile, while 20.6% were from the tenth. The share of the lower three deciles of total hospitalization was about 11%, while the share of the upper three deciles was 50%. There was a big difference between the upper- and lower-income deciles regarding death rates. In the first decile, 30% of inpatients died, while the proportion was 10% in the tenth decile. There was a significant and positive relationship between income decline and hospitalization (r = 0.75; p = 0.02). Also, there was a significant and negative relationship between income decline and death rate (r = -0.90; p = 0.01).

Conclusion

Low-income groups use fewer inpatient services, are more prone to severe illness and death from COVID-19‌, and treatment in this group has a lower chance of success. Using a systemic approach to address socioeconomic factors in healthcare planning is crucial.

Spatial determinants of excess all-cause mortality during the first wave of the COVID-19 epidemic in France

BACKGROUND

The first wave of the COVID-19 pandemic in France was associated with high excess mortality, and anecdotal evidence pointed to differing excess mortality patterns depending on social and environmental determinants. In this study we aimed to investigate the spatial distribution of excess mortality during the first wave of the COVID-19 pandemic in France and relate it at the subnational level to contextual determinants from various dimensions (socioeconomic, population density, overall health status, healthcare access etc.). We also explored whether the determinants identified at the national level varied depending on geographical location.

METHODS

We used available national data on deaths in France to calculate excess mortality by department for three age groups: 0-49, 50-74 and > 74 yrs. between March 1st and April 27th, 2020. We selected 15 variables at the department level that represent four dimensions that may be related to overall mortality at the ecological level, two representing population-level vulnerabilities (morbidity, social deprivation) and two representing environmental-level vulnerabilities (primary healthcare supply, urbanization). We modelled excess mortality by age group for our contextual variables at the department level. We conducted both a global (i.e., country-wide) analysis and a multiscale geographically weighted regression (MGWR) model to account for the spatial variations in excess mortality.

RESULTS

In both age groups, excess all-cause mortality was significantly higher in departments where urbanization was higher (50-74 yrs.: β = 15.33, p < 0.001; > 74 yrs.: β = 18.24, p < 0.001) and the supply of primary healthcare providers lower (50-74 yrs.: β = - 8.10, p < 0.001; > 74 yrs.: β = - 8.27, p < 0.001). In the 50-74 yrs. age group, excess mortality was negatively associated with the supply of pharmacists (β = - 3.70, p < 0.02) and positively associated with work-related mobility (β = 4.62, p < 0.003); in the > 74 yrs. age group our measures of deprivation (β = 15.46, p < 0.05) and morbidity (β = 0.79, p < 0.008) were associated with excess mortality. Associations between excess mortality and contextual variables varied significantly across departments for both age groups.

CONCLUSIONS

Public health strategies aiming at mitigating the effects of future epidemics should consider all dimensions involved to develop efficient and locally tailored policies within the context of an evolving, socially and spatially complex situation.

The Effects of Income Level on Susceptibility to COVID-19 and COVID-19 Morbidity/Mortality: A Nationwide Cohort Study in South Korea

This study aimed to investigate the association of income level with susceptibility to coronavirus disease 2019 (COVID-19) and COVID-19 morbidity and mortality. Using the Korean National Health Insurance COVID-19 Database cohort, medical claim data from 2015 through 2020 were collected. A total of 7943 patients who were diagnosed with COVID-19 from 1 January 2020 to 4 June 2020 were included. A total of 118,914 participants had negative COVID-19 PCR tests. Income levels were classified by 20th percentiles based on 2019 Korean National Health Insurance premiums. The 20th percentile income levels were categorized into three groups (low, middle, and high). The relationship of income level with susceptibility to COVID-19 and COVID-19 morbidity and mortality was analyzed using logistic regression analysis. A high income level was related to lower odds of COVID-19 infection (adjusted odds ratio (aOR) = 0.79, 95% confidence interval (CI) = 0.75-0.83, p < 0.001). The negative association between income level and COVID-19 infection was maintained in all subgroups. Patients with low income levels were susceptible to COVID-19 infection; however, there was no relation of COVID-19 morbidity and mortality with income level in the Korean population.

Epidemiological Characteristics of COVID-19 Patients in Kütahya Province in Turkey

Aim: It is aimed to evaluate the epidemiological features of COVID-19 patients and risk factors affecting hospitalization. Methods: This cross-sectional study included 883 adult patients whose Polymerase Chain Reaction tests were positive for SARS-CoV-2 in Kütahya province until July 2020. The patients were questioned in terms of their socio-demographic characteristics, drugs, comorbidities, and symptoms. They were divided into two groups according to their hospitalization status and outpatient treatment status. Results: There were 473 female and 410 male participants in the study. 532 of 883 adult patients were hospitalized. The most common symptoms were fatigue (47.9%), myalgia (44.7%), and loss of smell and taste (32.4%). Hospitalization was associated with advanced age, low income, presence of additional disease, several symptoms, smoking, comorbidities including diabetes mellitus, chronic kidney diseases, cardiovascular and respiratory system. In multivariant analyses, advance age, low income, fever, dyspnea and chronic lung diseases were associated with increased odds of hospital admission. Conclusion: In our study, it was found that independent risk factors for hospitalization were advanced age, low income, fever, shortness of breath, and chronic lung diseases. We think that determining risk factors for hospitalization may be a guide for clinicians in predicting patient prognosis. Keywords: COVID-19, epidemiology, hospitalization, symptoms, comorbidity

Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020

Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143-1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606-1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.

Elevated COVID19 mortality risk in detroit area hospitals among patients from census tracts with extreme socioeconomic vulnerability

BACKGROUND

the incidence of novel coronavirus disease (COVID19) is elevated in areas with heightened socioeconomic vulnerability. Early reports from US hospitals also implicated social disadvantage and chronic disease history as COVID19 mortality risk factors. However, the relationship between race and COVID19 mortality remains unclear.

METHODS

we examined in-hospital COVID19 mortality risk factors in a multi-hospital tertiary health care system that serves greater Detroit, Michigan, a predominantly African American city with high rates of poverty and chronic disease. Consecutive adult patients who presented to emergency departments and tested positive for COVID19 from 3/11/2020 through 4/18/2020 were included. Using log-binomial regression, we assessed the relationship between in-hospital mortality and residence in census tracts that were flagged for extreme socioeconomic vulnerability, patient-level demographics, and clinical comorbidities.

FINDINGS

a total of 1,015 adults tested positive for COVID19 during the study period; 80% identified as Black people, 52% were male and 53% were ≥ 65 years of age. The median body mass index was 30•4 and the median Charlson Comorbidity Index score was 4. Patients from census tracts that were flagged for vulnerability related to socioeconomic status had a higher mortality rate than their peers who resided in less vulnerable census tracts (β 0.26, standard error (SE) 0.11, degrees of freedom (df) 378, t-value (t) 2.27, exp(β) 1.29, p-value 0.02). Adjustment for age category, Black race, sex and/or the Charlson Comorbidity Index score category reduced the magnitude of association by less than 10% [exp(β) 1.29 vs. 1.21]. Black race [p = 0.38] and sex [p = 0.62] were not associated with mortality in this sample.

INTERPRETATION

people who lived in areas flagged for extreme socioeconomic vulnerability had elevated mortality risk in our predominantly African-American cohort of COVID19 patients who were able to seek hospital care during the so-called 'first wave' of the pandemic. By contrast, Black race was not associated with mortality in our sample.