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Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards.

ABSTRACT: Disadvantaged socioeconomic position (SEP) is widely associated with disease and mortality, and there is no reason to think this will not be the case for the newly emerged coronavirus disease 2019 (COVID-19) that has reached a pandemic level. Individuals with a more disadvantaged SEP are more likely to be affected by most of the known risk factors of COVID-19. SEP has been previously established as a potential determinant of infectious diseases in general. We hypothesise that SEP plays an important role in the COVID-19 pandemic either directly or indirectly via occupation, living conditions, health-related behaviours, presence of comorbidities and immune functioning. However, the influence of socioeconomic factors on COVID-19 transmission, severity and outcomes is not yet known and is subject to scrutiny and investigation. Here we briefly review the extent to which SEP has been considered as one of the potential risk factors of COVID-19. From 29 eligible studies that reported the characteristics of patients with COVID-19 and their potential risk factors, only one study reported the occupational position of patients with mild or severe disease. This brief overview of the literature highlights that important socioeconomic characteristics are being overlooked when data are collected. As COVID-19 spreads worldwide, it is crucial to collect and report data on socioeconomic determinants as well as race/ethnicity to identify high-risk populations. A systematic recording of socioeconomic characteristics of patients with COVID-19 will be beneficial to identify most vulnerable groups, to identify how SEP relates to COVID-19 and to develop equitable public health prevention measures, guidelines and interventions.

SUBMITTER: Khalatbari-Soltani S

PROVIDER: S-EPMC7298202 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards.

Khalatbari-Soltani Saman S Cumming Robert C RC Delpierre Cyrille C Kelly-Irving Michelle M

Journal of epidemiology and community health 20200508 8

Disadvantaged socioeconomic position (SEP) is widely associated with disease and mortality, and there is no reason to think this will not be the case for the newly emerged coronavirus disease 2019 (COVID-19) that has reached a pandemic level. Individuals with a more disadvantaged SEP are more likely to be affected by most of the known risk factors of COVID-19. SEP has been previously established as a potential determinant of infectious diseases in general. We hypothesise that SEP plays an import ...[more]

PMID: 32385126

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Project description:BackgroundSocioeconomic status has been operationalised in a variety of ways, most commonly as education, social class, or income. In this study, we also use occupational complexity and a SES-index as alternative measures of socioeconomic status. Studies show that in analyses of health inequalities in the general population, the choice of indicators influence the magnitude of the observed inequalities. Less is known about the influence of indicator choice in studies of older adults. The aim of this study is twofold: i) to analyse the impact of the choice of socioeconomic status indicator on the observed health inequalities among older adults, ii) to explore whether different indicators of socioeconomic status are independently associated with health in old age.MethodsWe combined data from two nationally representative Swedish surveys, providing more than 20 years of follow-up. Average marginal effects were estimated to compare the association between the five indicators of SES, and three late-life health outcomes: mobility limitations, limitations in activities of daily living (ADL), and psychological distress.ResultsAll socioeconomic status indicators were associated with late-life health; there were only minor differences in the effect sizes. Income was most strongly associated to all indicators of late-life health, the associations remained statistically significant when adjusting for the other indicators. In the fully adjusted models, education contributed to the model fits with 0-3% (depending on the outcome), social class with 0-1%, occupational complexity with 1-8%, and income with 3-18%.ConclusionsOur results indicate overlapping properties between socioeconomic status indicators in relation to late-life health. However, income is associated to late-life health independently of all other variables. Moreover, income did not perform substantially worse than the composite SES-index in capturing health variation. Thus, if the primary objective of including an indicator of socioeconomic status is to adjust the model for socioeconomic differences in late-life health rather than to analyse these inequalities per se, income may be the preferable indicator. If, on the other hand, the primary objective of a study is to analyse specific aspects of health inequalities, or the mechanisms that drive health inequalities, then the choice of indicator should be theoretically guided.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) has triggered the disruption of health care on a global scale. With Italy tangled up in the pandemic response, oncology care has been largely diverted and cancer screenings suspended. Our multicenter Italian study aimed to evaluate whether COVID-19 has impacted access to diagnosis, staging, and treatment for patients newly diagnosed with colorectal cancer (CRC), compared with pre-pandemic time.MethodsAll consecutive new CRC patients referred to 8 Italian oncology institutions between March and December 2020 were included. Access rate and temporal intervals between date of symptoms onset, radiological and cytohistological diagnosis, treatment start and first radiological evaluation were analyzed and compared with the same months of 2019.ResultsA reduction (29%) in newly diagnosed CRC cases was seen when compared with 2019 (360 vs 506). New CRC patients in 2020 were less likely to be diagnosed with early stage (stages I-II-III) CRC (63% vs 78%, P < .01). Gender and sidedness were similar regardless of the year. The percentage of tumors with any mutation among BRAF, NRAS, and KRAS genes were significantly different between the 2 years (61% in 2020 vs 50% in 2019, P = .04). Timing of access to cancer diagnosis, staging, and treatment for patients with CRC has not been negatively affected by the pandemic. Significantly shorter temporal intervals were observed between symptom onset and first oncological appointment (69 vs 79 days, P = .01) and between histological diagnosis and first oncological appointment (34 vs 42 days, P < .01) during 2020 compared with 2019. Fewer CRC cases were discussed in multidisciplinary meetings during 2020 (38% vs 50%, P = .01).ConclusionsOur data highlight a significant drop in CRC diagnosis after COVID-19, especially for early stage disease. The study also reveals a remarkable setback in the multidisciplinary management of patients with CRC. Despite this, Italian oncologists were able to ensure diagnostic-therapeutic pathways proper operation after March 2020.

Project description:BackgroundThe COVID-19 pandemic brings unprecedented uncertainty and stress. This study aimed to characterize general sleep status among Chinese residents during the early stage of the outbreak and to explore the network relationship among COVID-19 uncertainty, intolerance of uncertainty, perceived stress, and sleep status.MethodsA cross-sectional correlational survey was conducted online. A total of 2534 Chinese residents were surveyed from 30 provinces, municipalities, autonomous regions of China and regions abroad during the period from February 7 to 14, 2020, the third week of lockdown. Final valid data from 2215 participants were analyzed. Self-report measures assessed uncertainty about COVID-19, intolerance of uncertainty, perceived stress, and general sleep status. Serial mediation analysis using the bootstrapping method and path analysis were applied to test the mediation role of intolerance of uncertainty and perceived stress in the relationship between uncertainty about COVID-19 and sleep status.ResultsThe total score of sleep status was 4.82 (SD = 2.72). Age, place of residence, ethnicity, marital status, infection, and quarantine status were all significantly associated with general sleep status. Approximately half of participants (47.1%) reported going to bed after 12:00 am, 23.0% took 30 min or longer to fall asleep, and 30.3% slept a total of 7 h or less. Higher uncertainty about COVID-19 was significantly positively correlated with higher intolerance of uncertainty (r = 0.506, p < 0.001). The mediation analysis found a mediating role of perceived stress in the relationship between COVID-19 uncertainty and general sleep status (β = 0.015, 95%C.I. = 0.009-0.021). However, IU was not a significant mediator of the relationship between COVID-19 uncertainty and sleep (β = 0.009, 95%C.I. = - 0.002-0.020). Moreover, results from the path analysis further showed uncertainty about COVID-19 had a weak direct effect on poor sleep (β = 0.043, p < 0.05); however, there was a robust indirect effect on poor sleep through intolerance of uncertainty and perceived stress.ConclusionsThese findings suggest that intolerance of uncertainty and perceived stress are critical factors in the relationship between COVID-19 uncertainty and sleep outcomes. Results are discussed in the context of the COVID-19 pandemic, and practical policy implications are also provided.

Project description:BACKGROUND:In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. OBJECTIVE:The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. METHODS:We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. RESULTS:After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics' themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. CONCLUSIONS:Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media's actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

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Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards.

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Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards.

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