Area-level indicators of income and total mortality during the COVID-19 pandemic

Increased mortality in socioeconomic disadvantaged municipalities during the first phase of the COVID-19 pandemic in Lombardy region

BACKGROUND

Lombardy was the first European region most severely affected by the coronavirus disease 2019 pandemic in the spring of 2020. During that period, a substantial increase in socioeconomic inequality in total mortality was observed. This study aims to evaluate mortality data in the region up to September 2023 to verify whether the increased disparities between the poorest and the wealthiest municipalities persisted in the subsequent phases of the pandemic.

METHODS

This study analyzed mortality data from January 2019 to September 2023 in Lombardy's municipalities by month and pandemic phases characterized by the predominance of the different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Municipalities were grouped according to the average income or pension of their residents. Age-standardized mortality rates (ASMRs) and the ASMR ratio between the poorest and the wealthiest municipalities were compared throughout the study period.

RESULTS

In the pre-pandemic period (January 2019 - February 2020), the ASMR ratio at all ages between the poorest and the wealthiest municipalities fluctuated between 1.12 [95% confidence interval (CI): 1.07-1.16] and 1.29 (95% CI: 1.25-1.34). In March 2020, the ASMR ratio increased to 1.49 (95% CI: 1.45-1.52 95%) and returned to values registered before the pandemic thereafter. A similar pattern was observed in the analysis of mortality ≥ 65, using the average pension for group municipalities.

CONCLUSIONS

During the dramatic circumstances that the region faced in March 2020, pre-existing socioeconomic inequalities substantially widened. With the reorganization of the health system and the availability of vaccines, these disparities returned to the levels recorded before the pandemic.

Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which is the source of the coronavirus disease 2019 (COVID-19), was declared a pandemic in the March of 2020. Travel and tourism were severely impacted as restrictions were imposed to help slow the disease spread, but some states took alternative approaches to travel restrictions. This study investigated the spread of COVID-19 in South Dakota during the early pandemic period to better understand how tourism affected the movement of the virus within the region. Sequences from the fall of 2020 were retrieved from public sources. CDC and other sources were used to determine infections, deaths, and tourism metrics during this time. The data were analyzed using correlation and logistic regression. This study found that the number of unique variants per month was positively correlated with hotel occupancy, but not with the number of cases or deaths. Interestingly, the emergence of the B.1.2 variant in South Dakota was positively correlated with increased case numbers and deaths. Data show that states with a shelter-in-place order were associated with a slower emergence of the B.1.2 variant compared to states without such an order, including South Dakota. Findings suggest complex relationships between tourism, SARS-CoV-2 infections, and mitigation strategies. The unique approach that South Dakota adopted provided insights into the spread of the disease in areas without state-wide restrictions. Our results suggest both positive and negative aspects of this approach. Finally, our data highlight the need for future surveillance efforts, including efforts focused on identifying variants with known increased transmission potential to produce effective population health management.

Risks of mortality and severe coronavirus disease 19 (COVID-19) outcomes in patients with or without systemic lupus erythematosus

OBJECTIVES

We compared the outcomes of patients with or without systemic lupus erythematosus (SLE) who were diagnosed with coronavirus disease 19 (COVID-19) and evaluated factors within patients with SLE associated with severe outcomes.

METHODS

This retrospective cohort study used the deidentified Optum COVID-19 electronic health record dataset to identify patients with COVID-19 from 1/1/2020 to 31/12/2020. Cases with SLE were matched with general controls at a ratio of 1:10 by age, sex, race and ethnicity and COVID-19 diagnosis date. Outcomes included 30-day mortality, mechanical ventilation, hospitalisation and intensive care unit admission. We evaluated the relationship between COVID-19-related outcomes and SLE using multivariable logistic regression. In addition, within SLE cases, we examined factors associated with COVID-19 related outcomes, including disease activity and SLE therapy.

RESULTS

We included 687 patients matched with 6870 controls. Unadjusted rates of outcomes for patients with SLE were significantly worse than for matched controls including mortality (3.6% vs 1.8%), mechanical ventilation (6% vs 2.5%) and hospitalisation (31% vs 17.7%) (all p<0.001). After multivariable adjustment, patients with SLE had increased risks of mechanical ventilation (OR 1.81, 95% CI 1.16 to 2.82) and hospitalisation (OR 1.32, 95% CI 1.05 to 1.65). Among patients with SLE, severe disease activity was associated with increased risks of mechanical ventilation (OR 5.83, 95% CI 2.60 to 13.07) and hospitalisation (OR 3.97, 95% CI 2.37 to 6.65). Use of glucocorticoids, mycophenolate and tacrolimus before COVID-19 was associated with worse outcomes.

CONCLUSION

Patients with SLE had increased risk of severe COVID-19-related outcomes compared with matched controls. Patients with severe SLE disease activity or prior use of corticosteroids experienced worse outcomes.

Socioeconomic differences in COVID-19 infection, hospitalisation and mortality in urban areas in a region in the South of Europe

BACKGROUND

To analyse differences in confirmed cases, hospitalisations and deaths due to COVID-19 related to census section socioeconomic variables.  METHODS: Ecological study in the 12 largest municipalities in Andalusia (Spain) during the first three epidemic waves of the COVID-19 (02/26/20-03/31/21), covering 2,246 census sections (unit of analysis) and 3,027,000 inhabitants. Incidence was calculated, standardised by age and sex, for infection, hospitalisation and deaths based on average gross income per household (AGI) for the census tracts in each urban area. Association studied using a Poisson Bayesian regression model with random effects for spatial smoothing.

RESULTS

There were 140,743 cases of COVID-19, of which 12,585 were hospitalised and 2,255 died. 95.2% of cases were attributed to the second and third waves, which were jointly analysed. We observed a protective effect of income for infection in 3/12 cities. Almeria had the largest protective effect (smoothed relative risk (SRR) = 0.84 (0.75-0.94 CI 95%). This relationship reappeared with greater magnitude in 10/12 cities for hospitalisation, lowest risk in Algeciras SRR = 0.41 (0.29-0.56). The pattern was repeated for deaths in all urban areas and reached statistical significance in 8 cities. Lowest risk in Dos Hermanas SRR = 0.35 (0.15-0.81).

CONCLUSIONS

Income inequalities by geographical area were found in the incidence of COVID-19. The strengths of the association increased when analysing the severe outcomes of hospitalisations and, above all, deaths.

Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review

Objectives: International evidence of socioeconomic inequalities in COVID-19 outcomes is extensive and growing, but less is known about the temporal dynamics of these inequalities over the course of the pandemic.

Methods: We systematically searched the Embase and Scopus databases. Additionally, several relevant journals and the reference lists of all included articles were hand-searched. This study follows the PRISMA guidelines for scoping reviews.

Results: Forty-six studies were included. Of all analyses, 91.4% showed stable or increasing socioeconomic inequalities in COVID-19 outcomes over the course of the pandemic, with socioeconomically disadvantaged populations being most affected. Furthermore, the study results showed temporal dynamics in socioeconomic inequalities in COVID-19, frequently initiated through higher COVID-19 incidence and mortality rates in better-off populations and subsequent crossover dynamics to higher rates in socioeconomically disadvantaged populations (41.9% of all analyses).

Conclusion: The identified temporal dynamics of socioeconomic inequalities in COVID-19 outcomes have relevant public health implications. Socioeconomic inequalities should be monitored over time to enable the adaption of prevention and interventions according to the social particularities of specific pandemic phases.