The well-being of gig workers in the sharing economy during COVID-19

PurposeThe purpose of this study is to deepen our understanding of the well-being of transient organizations/groups and to use this to develop a novel conceptual framework of gig worker well-being during times of crisis.Design/methodology/approachA qualitative approach was adopted combining in-depth semi-structured interviews and daily diaries. Twenty-two workers working in the sharing economy were recruited. Thematic analysis was conducted for the diary and interview data.FindingsThe findings illustrate a complex picture of sharing economy workers' four dimensions of well-being, including physical, subjective, psychological and social well-being. A number of the COVID-19 pandemic contexts, such as more time, restriction, economic recession and uncertainty, were seen to influence these workers' well-being in different ways including both positive and negative impacts. The precarious nature of gig work within the sharing economy was also found influential, which includes flexibility, uncertainty, temporality and diversity. Furthermore, the specific contexts of the hospitality, tourism and event industry (such as labor-intensive, low esteem, self-value and purpose in life) had also impacted gig workers physical and psychological well-being in various ways.Research limitations/implicationsThis study complements the gig workers' view of the sharing economy by investigating their well-being during the COVID-19 pandemic. In addition, this study reveals the complex and various influences hospitality, tourism and events industry contexts made, amplified by the pandemic. Methodologically, the daily diary approach applied in this research has captured gig workers' instant feelings and thoughts, which enriches the current understanding of gig workers' well-being.Practical implicationsFrom the findings and the newly developed conceptual framework, practical implications are proposed focusing on how the tourism, hospitality and event industries should look after their gig workers' well-being in the COVID-ized environment. From the physical well-being perspective, businesses should consider partnering with gym operators to provide corporate packages or discounted membership to their gig workers. From psychological well-being perspective, a recognition system integrating gig workers would be useful to strengthen gig workers' perception of value in their jobs. In addition, technology can be used to introduce more resources to their gig workers, particularly when distancing.Originality/valueA conceptual framework is developed, which captures the influence of both "internal” and "external” determinants of gig worker well-being during times of crisis. This research contributes to theory by developing a framework of well-being in the context of the sharing economy, as well as explicitly addressing how the uncertainty and precariousness of sharing economy work and the hospitality, tourism and event industry contexts relate to well-being. This model is likely to have applicability beyond COVID-19 as the pandemic made clear many existing challenges – rather than just simply creating new ones.

Current Challenges with the Use of Test-Negative Designs for Modeling COVID-19 Vaccination and Outcomes.

The widespread testing for SARS-CoV-2 infection has facilitated the use of test-negative designs (TND) for modeling COVID-19 vaccination and outcomes. Despite the comprehensive literature on TND, the use of TND in COVID-19 studies is relatively new and calls for robust design and analysis to adapt to a rapidly changing and dynamically evolving pandemic and to account for changes in testing and reporting practices. In this commentary, we aim to draw the attention of researchers to COVID-specific challenges in using TND as we are analyzing data amassed over more than two years of the pandemic. We first review when and why TND works, and general challenges in TND studies presented in the literature. We then discuss COVID-specific challenges which have not received adequate acknowledgment but may add to the risk of invalid conclusions in TND studies of COVID-19.

Measurement and Validation of the Comprehensive Score for Financial Toxicity (COST) in a Population With Diabetes.

OBJECTIVE: The Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT) is a validated instrument measuring financial distress among people with cancer. The reliability and construct validity of the 11-item COST-FACIT were examined in adults with diabetes and high A1C. RESEARCH DESIGN AND METHODS: We examined the factor structure (exploratory factor analysis), internal consistency reliability (Cronbach α), floor/ceiling effects, known-groups validity, and predictive validity among a sample of 600 adults with diabetes and high A1C. RESULTS: COST-FACIT demonstrated a two-factor structure with high internal consistency: general financial situation (7-items, α = 0.86) and impact of illness on financial situation (4-items, α = 0.73). The measure demonstrated a ceiling effect for 2% of participants and floor effects for 7%. Worse financial toxicity scores were observed among adults who were women, were below the poverty line, had government-sponsored health insurance, were middle-aged, were not in the workforce, and had less educational attainment (P < 0.01). Worse financial toxicity was observed for those engaging in cost coping behaviors, such as taking less or skipping medicines, delaying care, borrowing money, "maxing out" the limit on credit cards, and not paying bills (P < 0.01). In regression models for the full measure and its two factors, worse financial toxicity was correlated with higher A1C (P < 0.01), higher levels of diabetes distress (P < 0.01), more chronic conditions (P < 0.01), and more depressive symptoms (P < 0.01). CONCLUSIONS: Findings support both the reliability and validity of the COST-FACIT tool among adults with diabetes and high A1C levels. More research is needed to support the use of the COST-FACIT tool as a clinically relevant patient-centered instrument for diabetes care.

COVID-19 vaccine effectiveness against omicron (B.1.1.529) variant infection and hospitalisation in patients taking immunosuppressive medications: a retrospective cohort study.

Background: There is a scarcity of research regarding the effectiveness of the mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech) COVID-19 vaccines in patients taking immunosuppressant medications, and no data are published to date pertaining to their effectiveness against omicron (B.1.1.529) variant SARS-CoV-2 infection and hospitalisation. We aimed to assess the relationship between immunosuppressive medications, mRNA vaccination, omicron infection, and severe COVID-19 outcomes (ie, hospitalisation, ICU admission, death). Methods: We did a retrospective cohort study and included vaccinated and unvaccinated people aged 18 years or older in the Michigan Medicine health-care system, USA, during the omicron-dominant period of the pandemic (Dec 16, 2021-March 4, 2022). We collected data from electronic health records (demographics, diagnoses, medications) combined with immunisation data from the Michigan State Registry to determine vaccination status, and we collected COVID-19-related hospitalisation data by chart review. We used a Cox proportional hazards model based on calendar time to assess the effectiveness of the mRNA-1273 and BNT162b2 vaccines in people taking immunosuppressive medications (conventional synthetic disease-modifying antirheumatic drugs [DMARDs], biologic DMARDs, or glucocorticoids within the past 3 months), while controlling for participant characteristics. Using the same model, we assessed the effect of different classes of medication such as immunosuppressive DMARDs, immunomodulatory DMARDs, and glucocorticoids on SARS-CoV-2 infection and hospitalisation due to COVID-19. All analyses were done using complete cases after removing participants with missing covariates. Findings: 209 492 people were identified in Michigan Medicine, including 165 913 who were vaccinated and 43 579 who were unvaccinated. 41 078 people were excluded because they were younger than 18 years, partially vaccinated, had received a vaccine other than the two vaccines studied, or had incomplete covariate data. 168 414 people were included in the analysis; 97 935 (58%) were women, 70 479 (42%) were men, and 129 816 (77%) were White. 5609 (3%) people were taking immunosuppressive medications. In patients receiving immunosuppressants, three doses of BNT162b2 had a vaccine effectiveness of 50% (95% CI 31-64; p<0·0001) and three doses of mRNA-1273 had a vaccine effectiveness of 60% (42-73; p<0·0001) against SARS-CoV-2 infection. Three doses of either vaccine had an effectiveness of 87% (95% CI 73-93; p<0·0001) against hospitalisation due to COVID-19. Receipt of immunosuppressive DMARDs (hazard ratio 2·32, 95% CI 1·23-4·38; p=0·0097) or glucocorticoids (2·93, 1·77-4·86; p<0·0001) and a history of organ or bone marrow transplantation (3·52, 2·01-6·16; p<0·0001) were associated with increased risk of hospitalisation due to COVID-19 compared with those who had not received immunosuppressive medications or transplant. Interpretation: People taking immunosuppressive DMARDs or glucocorticoids are at substantially higher risk of hospitalisation due to COVID-19 than the general population. However, the mRNA-1273 and BNT162b2 vaccines remain effective within this group, and it is important that patients taking these medications remain up to date with vaccinations to mitigate their risk. Funding: National Institute of Allergy and Infectious Diseases, National Institutes of Health.

A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns.

BACKGROUND: Post COVID-19 condition (PCC) is known to affect a large proportion of COVID-19 survivors. Robust study design and methods are needed to understand post-COVID-19 diagnosis patterns in all survivors, not just those clinically diagnosed with PCC. METHODS: We applied a case-crossover Phenome-Wide Association Study (PheWAS) in a retrospective cohort of COVID-19 survivors, comparing the occurrences of 1,671 diagnosis-based phenotype codes (PheCodes) pre- and post-COVID-19 infection periods in the same individual using a conditional logistic regression. We studied how this pattern varied by COVID-19 severity and vaccination status, and we compared to test negative and test negative but flu positive controls. RESULTS: In 44,198 SARS-CoV-2-positive patients, we foundenrichment in respiratory,circulatory, and mental health disorders post-COVID-19-infection. Top hits included anxiety disorder (p = 2.8e-109, OR = 1.7 [95 % CI: 1.6-1.8]), cardiac dysrhythmias (p = 4.9e-87, OR = 1.7 [95 % CI: 1.6-1.8]), and respiratory failure, insufficiency, arrest (p = 5.2e-75, OR = 2.9 [95 % CI: 2.6-3.3]). In severe patients, we found stronger associations with respiratory and circulatory disorders compared to mild/moderate patients. Fully vaccinated patients had mental health and chronic circulatory diseases rise to the top of the association list, similar to the mild/moderate cohort. Both control groups (test negative, test negative and flu positive) showed a different pattern of hits to SARS-CoV-2 positives. CONCLUSIONS: Patients experience myriad symptoms more than 28 days after SARS-CoV-2 infection, but especially respiratory, circulatory, and mental health disorders. Our case-crossover PheWAS approach controls for within-person confounders that are time-invariant. Comparison to test negatives and test negative but flu positive patients with a similar design helped identify enrichment specific to COVID-19. This design may be applied other emerging diseases with long-lasting effects other than a SARS-CoV-2 infection. Given the potential for bias from observational data, these results should be considered exploratory. As we look into the future, we must be aware of COVID-19 survivors' healthcare needs.

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization.

OBJECTIVE: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. METHODS: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. RESULTS: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. CONCLUSIONS: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review.

BACKGROUND: This study aims to examine the worldwide prevalence of post-coronavirus disease 2019 (COVID-19) condition, through a systematic review and meta-analysis. METHODS: PubMed, Embase, and iSearch were searched on July 5, 2021 with verification extending to March 13, 2022. Using a random-effects framework with DerSimonian-Laird estimator, we meta-analyzed post-COVID-19 condition prevalence at 28+ days from infection. RESULTS: Fifty studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post-COVID-19 condition was 0.43 (95% confidence interval [CI], .39-.46). Hospitalized and nonhospitalized patients had estimates of 0.54 (95% CI, .44-.63) and 0.34 (95% CI, .25-.46), respectively. Regional prevalence estimates were Asia (0.51; 95% CI, .37-.65), Europe (0.44; 95% CI, .32-.56), and United States of America (0.31; 95% CI, .21-.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI, .26-.49), 0.25 (95% CI, .15-.38), 0.32 (95% CI, .14-.57), and 0.49 (95% CI, .40-.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI, .17-.30), followed by memory problems (0.14; 95% CI, .10-.19). CONCLUSIONS: This study finds post-COVID-19 condition prevalence is substantial; the health effects of COVID-19 seem to be prolonged and can exert stress on the healthcare system.

Impacts of the COVID-19 pandemic on unmet social needs, self-care, and outcomes among people with diabetes and poor glycemic control.

AIMS: The purpose of this study was to examine whether pandemic exposure impacted unmet social and diabetes needs, self-care behaviors, and diabetes outcomes in a sample with diabetes and poor glycemic control. METHODS: This was a cross-sectional analysis of participants with diabetes and poor glycemic control in an ongoing trial (n = 353). We compared the prevalence of unmet needs, self-care behaviors, and diabetes outcomes in successive cohorts of enrollees surveyed pre-pandemic (prior to March 11, 2020, n = 182), in the early stages of the pandemic (May-September, 2020, n = 75), and later (September 2020-January 2021, n = 96) stratified by income and gender. Adjusted multivariable regression models were used to examine trends. RESULTS: More participants with low income reported food insecurity (70% vs. 83%, p < 0.05) and needs related to access to blood glucose supplies (19% vs. 67%, p < 0.05) during the pandemic compared to pre-pandemic levels. In adjusted models among people with low incomes, the odds of housing insecurity increased among participants during the early pandemic months compared with participants pre-pandemic (OR 20.2 [95% CI 2.8-145.2], p < 0.01). A1c levels were better among participants later in the pandemic than those pre-pandemic (ß = -1.1 [95% CI -1.8 to -0.4], p < 0.01), but systolic blood pressure control was substantially worse (ß = 11.5 [95% CI 4.2-18.8, p < 0.001). CONCLUSION: Adults with low-incomes and diabetes were most impacted by the pandemic. A1c may not fully capture challenges that people with diabetes are facing to manage their condition; systolic blood pressures may have worsened and problems with self-care may forebode longer-term challenges in diabetes control.

Understanding the Patterns of Serological Testing for COVID-19 Pre- and Post-Vaccination Rollout in Michigan.

Testing for SARS-CoV-2 antibodies is commonly used to determine prior COVID-19 infections and to gauge levels of infection- or vaccine-induced immunity. Michigan Medicine, a primary regional health center, provided an ideal setting to understand serologic testing patterns over time. Between 27 April 2020 and 3 May 2021, characteristics for 10,416 individuals presenting for SARS-CoV-2 antibody tests (10,932 tests in total) were collected. Relative to the COVID-19 vaccine roll-out date, 14 December 2020, the data were split into a pre- (8026 individuals) and post-vaccine launch (2587 individuals) period and contrasted with untested individuals to identify factors associated with tested individuals and seropositivity. Exploratory analysis of vaccine-mediated seropositivity was performed in 347 fully vaccinated individuals. Predictors of tested individuals included age, sex, smoking, neighborhood variables, and pre-existing conditions. Seropositivity in the pre-vaccine launch period was 9.2% and increased to 46.7% in the post-vaccine launch period. In the pre-vaccine launch period, seropositivity was significantly associated with age (10 year; OR = 0.80 (0.73, 0.89)), ever-smoker status (0.49 (0.35, 0.67)), respiratory disease (4.38 (3.13, 6.12)), circulatory disease (2.09 (1.48, 2.96)), liver disease (2.06 (1.11, 3.84)), non-Hispanic Black race/ethnicity (2.18 (1.33, 3.58)), and population density (1.10 (1.03, 1.18)). Except for the latter two, these associations remained statistically significant in the post-vaccine launch period. The positivity rate of fully vaccinated individual was 296/347(85.3% (81.0%, 88.8%)).

Exposure and risk factors for COVID-19 and the impact of staying home on Michigan residents

COVID-19 has had a substantial impact on clinical care and lifestyles globally. The State of Michigan reports over 80,000 positive COVID-19 tests between March 1, 2020 and July 29, 2020. We surveyed 8,041 Michigan Medicine biorepository participants in late June 2020. We found that 55% of COVID-19 cases reported no known exposure to family members or to someone outside the house diagnosed with COVID-19. A significantly higher rate of COVID-19 cases were employed as essential workers (45% vs 19%, p = 9x10-12). COVID-19 cases reporting a fever were more likely to require hospitalization (categorized as severe;OR = 4.4 [95% CI: 1.6-12.5, p = 0.005]) whereas respondents reporting rhinorrhea was less likely to require hospitalization (categorized as mild-to-moderate;OR = 0.16 [95% CI: 0.04-0.73, p = 0.018]). African-Americans reported higher rates of being diagnosed with COVID-19 (OR = 4.0 [95% CI: 2.2-7.2, p = 5x10-6]), as well as higher rates of exposure to family or someone outside the household diagnosed with COVID-19, an annual household income < $40,000, living in rental housing, and chronic diseases. During the Executive Order in Michigan, African Americans, women, and the lowest income group reported worsening health behaviors and higher overall concern for the potential detrimental effects of the pandemic. The higher risk of contracting COVID-19 observed among African Americans may be due to the increased rates of working as essential employees, lower socioeconomic status, and exposure to known positive cases. Continued efforts should focus on COVID-19 prevention and mitigation strategies, as well as address the inequality gaps that result in higher risks for both short-term and long-term health outcomes.

[Impacts of Meteorology and Emission Variations on PM2.5 Concentration Throughout the Country During the 2020 Epidemic Period].

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.