Using Relative and Absolute Measures for Socioeconomic Inequalities in Health: Experiences from a Retrospective Cohort Study on COVID-19

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Abstract

Background:

One approach to reducing the burden of diseases can be to identify socioeconomically vulnerable groups. We aimed to estimate the socioeconomic inequality of in-hospital deaths using relative and absolute indices of socioeconomic inequality.

Methods:

In this retrospective cohort study on Covid-19 patients; age, gender, marital status, education level, date of admission, diagnostic method, and final condition were measured. Socioeconomic inequality in inhospital death was assessed using three approaches. We used the relative index of inequality (RII) to measure relative inequality. We used two approaches to evaluate absolute inequality: the slope index of inequality (SII) and the concentration index (ci).

Results:

Overall, 587 patients’ data were collected and 42 (7.2%) of these patients died in the hospital. There were statistically significant differences between the case-fatality rates of different levels of education ( P<0.001). In addition, all the inequality indices showed that the distribution of COVID-19-related deaths was higher among the lower education levels. Accordingly, after controlling the effect of age, gender, and comorbidities the RII indicated that the case fatality rate in the lowest education level was 9.42 (95% CI: 2.23 to 39.01, P<0.001) times compared to the case fatality rate in the highest level of education.

Conclusion:

The results of all three approaches indicate considerable education inequality in CFR in favor of groups of high education levels. These results can improve the prioritization and impact of public health interventions, including prevention and diagnosis of Covid-19 in favor of vulnerable groups.

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Most cited references 39

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OpenSAFELY: factors associated with COVID-19 death in 17 million patients

Elizabeth J Williamson, Alex J Walker, Krishnan Bhaskaran … (2020)

COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.

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Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China

Qiurong Ruan, Kun Yang, Wenxia Wang … (2020)

Dear Editor, The rapid emergence of COVID-19 in Wuhan city, Hubei Province, China, has resulted in thousands of deaths [1]. Many infected patients, however, presented mild flu-like symptoms and quickly recover [2]. To effectively prioritize resources for patients with the highest risk, we identified clinical predictors of mild and severe patient outcomes. Using the database of Jin Yin-tan Hospital and Tongji Hospital, we conducted a retrospective multicenter study of 68 death cases (68/150, 45%) and 82 discharged cases (82/150, 55%) with laboratory-confirmed infection of SARS-CoV-2. Patients met the discharge criteria if they had no fever for at least 3 days, significantly improved respiratory function, and had negative SARS-CoV-2 laboratory test results twice in succession. Case data included demographics, clinical characteristics, laboratory results, treatment options and outcomes. For statistical analysis, we represented continuous measurements as means (SDs) or as medians (IQRs) which compared with Student’s t test or the Mann–Whitney–Wilcoxon test. Categorical variables were expressed as numbers (%) and compared by the χ 2 test or Fisher’s exact test. The distribution of the enrolled patients’ age is shown in Fig. 1a. There was a significant difference in age between the death group and the discharge group (p < 0.001) but no difference in the sex ratio (p = 0.43). A total of 63% (43/68) of patients in the death group and 41% (34/82) in the discharge group had underlying diseases (p = 0.0069). It should be noted that patients with cardiovascular diseases have a significantly increased risk of death when they are infected with SARS-CoV-2 (p < 0.001). A total of 16% (11/68) of the patients in the death group had secondary infections, and 1% (1/82) of the patients in the discharge group had secondary infections (p = 0.0018). Laboratory results showed that there were significant differences in white blood cell counts, absolute values of lymphocytes, platelets, albumin, total bilirubin, blood urea nitrogen, blood creatinine, myoglobin, cardiac troponin, C-reactive protein (CRP) and interleukin-6 (IL-6) between the two groups (Fig. 1b and Supplementary Table 1). Fig. 1 a Age distribution of patients with confirmed COVID-19; b key laboratory parameters for the outcomes of patients with confirmed COVID-19; c interval from onset of symptom to death of patients with confirmed COVID-19; d summary of the cause of death of 68 died patients with confirmed COVID-19 The survival times of the enrolled patients in the death group were analyzed. The distribution of survival time from disease onset to death showed two peaks, with the first one at approximately 14 days (22 cases) and the second one at approximately 22 days (17 cases) (Fig. 1c). An analysis of the cause of death was performed. Among the 68 fatal cases, 36 patients (53%) died of respiratory failure, five patients (7%) with myocardial damage died of circulatory failure, 22 patients (33%) died of both, and five remaining died of an unknown cause (Fig. 1d). Based on the analysis of the clinical data, we confirmed that some patients died of fulminant myocarditis. In this study, we first reported that the infection of SARS-CoV-2 may cause fulminant myocarditis. Given that fulminant myocarditis is characterized by a rapid progress and a severe state of illness [3], our results should alert physicians to pay attention not only to the symptoms of respiratory dysfunction but also the symptoms of cardiac injury. Further, large-scale studies and the studies on autopsy are needed to confirm our analysis. In conclusion, predictors of a fatal outcome in COVID-19 cases included age, the presence of underlying diseases, the presence of secondary infection and elevated inflammatory indicators in the blood. The results obtained from this study also suggest that COVID-19 mortality might be due to virus-activated “cytokine storm syndrome” or fulminant myocarditis. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 38 kb)

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Gender Differences in Patients With COVID-19: Focus on Severity and Mortality

Jian-Min Jin, Peng Bai, Wei He … (2020)

Objective: The recent outbreak of Novel Coronavirus Disease (COVID-19) is reminiscent of the SARS outbreak in 2003. We aim to compare the severity and mortality between male and female patients with COVID-19 or SARS. Study Design and Setting: We extracted the data from: (1) a case series of 43 hospitalized patients we treated, (2) a public data set of the first 37 cases of patients who died of COVID-19 and 1,019 patients who survived in China, and (3) data of 524 patients with SARS, including 139 deaths, from Beijing in early 2003. Results: Older age and a high number of comorbidities were associated with higher severity and mortality in patients with both COVID-19 and SARS. Age was comparable between men and women in all data sets. In the case series, however, men's cases tended to be more serious than women's (P = 0.035). In the public data set, the number of men who died from COVID-19 is 2.4 times that of women (70.3 vs. 29.7%, P = 0.016). In SARS patients, the gender role in mortality was also observed. The percentage of males were higher in the deceased group than in the survived group (P = 0.015). Conclusion: While men and women have the same prevalence, men with COVID-19 are more at risk for worse outcomes and death, independent of age.

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Author and article information

Journal

Journal ID (nlm-ta): Iran J Public Health

Journal ID (iso-abbrev): Iran J Public Health

Journal ID (publisher-id): IJPH

Title: Iranian Journal of Public Health

Publisher: Tehran University of Medical Sciences

ISSN (Print): 2251-6085

ISSN (Electronic): 2251-6093

Publication date (Print): June 2023

Volume: 52

Issue: 6

Pages: 1269-1277

Affiliations

[1. ]Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran

[2. ]Department of Geriatric Medicine, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran

Author notes

[* ] Corresponding Author: Email: ehsanteymoorzadeh@ 123456yahoo.com

Article

Publisher ID: IJPH-52-1269

DOI: 10.18502/ijph.v52i6.12993

PMC ID: 10362811

SO-VID: 59eba3da-96a4-4cae-a99c-aa4c0e260db8

License:

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license ( https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial uses of the work are permitted, provided the original work is properly cited.

Using Relative and Absolute Measures for Socioeconomic Inequalities in Health: Experiences from a Retrospective Cohort Study on COVID-19

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Novel Coronavirus Disease COVID-19

Most cited references 39

OpenSAFELY: factors associated with COVID-19 death in 17 million patients

Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China

Gender Differences in Patients With COVID-19: Focus on Severity and Mortality

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