Socioeconomic Inequalities in COVID-19 Vaccination and Infection in Adults, Catalonia, Spain

Socioeconomic inequalities in COVID-19 infection and vaccine uptake among children and adolescents in Catalonia, Spain: a population-based cohort study

Introduction

This study aims to investigate the relationship between deprivation, as measured by a socioeconomic deprivation index (SDI) score for census tract urban areas, and COVID-19 infections and vaccine uptake among children and adolescents before and after the vaccination rollout in Catalonia, Spain.

Methods

We conducted a population-based cohort study using primary care records. Individuals were followed 3 months before the start of the vaccination campaign in Spain and 3 months after. Children (5-11 years) and adolescents (12-15 years) with at least 1 year of prior history observation available and without missing deprivation data. For each outcome, we estimated cumulative incidence and crude Cox proportional-hazard models by SDI quintiles, and hazard ratios (HRs) of COVID-19 infection and vaccine uptake relative to the least deprived quintile, Q1.

Results

Before COVID-19 vaccination rollout, 290,625 children and 179,685 adolescents were analyzed. Increased HR of deprivation was associated with a higher risk of COVID-19 infection in both children [Q5: 1.55 (95% CI, 1.47-1.63)] and adolescents [Q5: 1.36 (95% CI, 1.29-1.43)]. After the rollout, this pattern changed among children, with lower risk of infection in more deprived areas [Q5: 0.62 (95% CI, 0.61-0.64)]. Vaccine uptake was higher among adolescents than children, but in both age groups, non-vaccination was more common among those living in more deprived areas (39.3% and 74.6% in Q1 vs. 26.5% and 66.9% in Q5 among children and adolescents, respectively).

Conclusions

Children and adolescents living in deprived areas were at higher risk of COVID-19 non-vaccination. Socioeconomic disparities in COVID-19 infection were also evident before vaccine rollout, with a higher infection risk in deprived areas across age groups. Our findings suggest that changes in the association between deprivation and infections among children after the vaccine rollout were likely due to testing disparities.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

Socioeconomic Inequalities in SARS-CoV-2 Infection and COVID-19 Health Outcomes in Urban Italy During the COVID-19 Vaccine Rollout, January-November 2021

This study analysed the evolution of the association of socioeconomic deprivation (SED) with SARS-CoV-2 infection and COVID-19 outcomes in urban Italy during the vaccine rollout in 2021. We conducted a retrospective cohort analysis between January and November 2021, comprising of 16,044,530 individuals aged ≥ 20 years, by linking national COVID-19 surveillance system data to the Italian SED index calculated at census block level. We estimated incidence rate ratios (IRRs) of infection and severe COVID-19 outcomes by SED tercile relative to the least deprived tercile, over three periods defined as low (0-10%); intermediate (> 10-60%) and high (> 60-74%) vaccination coverage. We found patterns of increasing relative socioeconomic inequalities in infection, hospitalisation and death as COVID-19 vaccination coverage increased. Between the low and high coverage periods, IRRs for the most deprived areas increased from 1.09 (95%CI 1.03-1.15) to 1.28 (95%CI 1.21-1.37) for infection; 1.48 (95%CI 1.36-1.61) to 2.02 (95%CI 1.82-2.25) for hospitalisation and 1.57 (95%CI 1.36-1.80) to 1.89 (95%CI 1.53-2.34) for death. Deprived populations in urban Italy should be considered as vulnerable groups in future pandemic preparedness plans to respond to COVID-19 in particular during mass vaccination roll out phases with gradual lifting of social distancing measures.

Socioeconomic inequalities in healthcare system efficiency in Japan during COVID-19 pandemic: an analysis of the moderating role of vaccination

Background

In the context of the COVID-19 pandemic, limited research has focused on socioeconomic disparities in Local Healthcare System Efficiency (LHSE) among Japanese prefectures. This study seeks to investigate the moderating impact of vaccination on the relationship between LHSE and socioeconomic characteristics and endowments.

Methods

To explore these relationships, we first utilized the Data Envelopment Analysis with Slack-Based Measure to measure the LHSE, based on data from Japanese prefectures during waves 2 to 5 of the pandemic. Then estimating the impact of socioeconomic variables on LHSE. Finally, we assessed the changes in the way socioeconomic variables affect LHSE before and after vaccine deployment using the Seemingly Unrelated Estimation t-test methodology.

Results

The research findings suggest an overall reduction in LHSE disparities across various regions due to the utilization of vaccines. Particularly in areas with relatively nsufficient bed resources, a significant improvement in LHSE was observed in most regions. However, there was no evidence supporting the role of vaccine deployment in mitigating socioeconomic inequalities in LHSE. Conversely, the utilization of vaccines showed a positive correlation between the improvement in LHSE and the proportion of older adult population in regions with sufficient bed resources. In regions facing bed shortages, the enhancement of LHSE became more reliant on reducing the occupancy rate of secured beds for severe cases after the introduction of vaccination.

Discussion

In regions facing bed shortages, the enhancement of LHSE became more reliant on reducing the occupancy rate of secured beds for severe cases. This underscores the importance for policymakers and implementers to prioritize the treatment of severe cases and ensure an effective supply of medical resources, particularly secured beds for severe cases, in their efforts to improve LHSE, in the post-COVID-19 era with rising vaccine coverage.

Spatiotemporal variability in socioeconomic inequalities in COVID-19 vaccination in Catalonia, Spain

OBJECTIVES

Socioeconomic inequalities have played a significant role in the unequal coverage of the COVID-19 vaccine. The objectives of this study were to (1) assess the socioeconomic inequalities in COVID-19 vaccination coverage in Catalonia, Spain; (2) analyse the spatial variation over time of these inequalities; and (3) assess variations in time and space in the effect of vaccination on inequalities in COVID-19 outcomes.

STUDY DESIGN

A mixed longitudinal ecological study design was used.

METHODS

Catalonia is divided in to 373 Basic Health Areas. Weekly data from these Basic Health Areas were obtained from the last week of December 2020 until the first week of March of 2022. A joint spatio-temporal model was used with the dependent variables of vaccination and COVID-19 outcomes, which were estimated using a Bayesian approach. The study controlled for observed confounders, unobserved heterogeneity, and spatial and temporal dependencies. The study allowed the effect of the explanatory variables on the dependent variables to vary in space and in time.

RESULTS

Areas with lower socioeconomic level were those with the lowest vaccination rates and the highest risk of COVID-19 outcomes. In general, individuals in areas that were located in the upper two quartiles of average net income per person and in the lower two quartiles of unemployment rate (i.e., the least economically disadvantaged) had a higher propensity to be vaccinated than those in the most economically disadvantaged areas. In the same sense, the greater the percentage of the population aged ≥65 years, the higher the propensity to be vaccinated, while areas located in the two upper quartiles of population density and areas with a high percentage of poor housing had a lower propensity to be vaccinated. Higher vaccination rates reduced the risk of COVID-19 outcomes, while COVID-19 outcomes did not influence the propensity to be vaccinated. The effects of the explanatory variables were not the same in all areas or between the different waves of the pandemic, and clusters of excess risk of low vaccination in the most disadvantaged areas were detected.

CONCLUSIONS

COVID-19 vaccination inequalities in the most disadvantaged areas could be a result of structural barriers, such as the lack of access to information about the vaccination process, and/or logistical challenges, such as the lack of transportation, limited Internet access or difficulty in scheduling appointments. Public health strategies should be developed to mitigate these barriers and reduce vaccination inequalities.

Socioeconomic inequalities in COVID-19 incidence during the first six waves in Barcelona

BACKGROUND

The emergence of SARS-CoV-2 affected urban areas. In Barcelona, six waves of COVID-19 hit the city between March 2020 and March 2022. Inequalities in the incidence of COVID-19 have been described. However, no studies have examined the daily trends of socioeconomic inequalities and how they changed during the different phases of the pandemic. The aim of this study is to analyse the dynamic socioeconomic inequalities in the incidence of COVID-19 during the six waves in Barcelona.

METHODS

We examined the proportion of daily cases observed in the census tracts in the lower income tercile compared with the proportion of daily cases observed in the sum of the lower and higher income terciles. Daily differences in these proportions were assessed as a function of the epidemic waves, sex, age group, daily incidence and daily change in the incidence. A logistic regression model with an autoregressive term was used for statistical analysis.

RESULTS

A time-dynamic effect was found for socioeconomic inequalities in the incidence of COVID-19. In fact, belonging to a lower-income area changed from being a risk factor (Waves 1, 2, 4 and 5) to being a protective factor in the sixth wave of the pandemic. Age also had a significant effect on incidence, which also changed over the different waves of the pandemic. Finally, the lower-income areas showed a comparatively lower incidence during the ascending phase of the epidemic waves.

CONCLUSION

Socioeconomic inequalities in COVID-19 changed by wave, age group and wave phase.

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research

Purpose

The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.

Patients and Methods

We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.

Results

After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.

Conclusion

We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

Social and ideological determinants of COVID-19 vaccination status in Spain

OBJECTIVES

This study analysed the association between social and ideological determinants with COVID-19 vaccine accessibility and hesitancy in the Spanish adult population.

STUDY DESIGN

This was a repeated cross-sectional study.

METHODS

The data analysed are based on monthly surveys conducted by the Centre for Sociological Research between May 2021 and February 2022. Individuals were classified according to their COVID-19 vaccination status into (1) vaccinated (reference group); (2) willing to vaccinate but not vaccinated, proxy of lack of vaccine accessibility; and (3) hesitant, proxy of vaccine hesitancy. Independent variables included social (educational attainment, gender) and ideological determinants (voting in the last elections, importance attached to the health vs the economic impact of the pandemic, and political self-placement). We estimated odds ratio (OR) and 95% confidence interval (CI) conducting one age-adjusted multinomial logistic regression model for each determinant and then stratified them by gender.

RESULTS

Both social and ideological determinants had a weak association with the lack of vaccine accessibility. Individuals with medium educational attainment had higher odds of vaccine hesitancy (OR = 1.44, CI 1.08-1.93) compared with those with high educational attainment. People self-identified as conservative (OR = 2.90; CI 2.02-4.15) and those who prioritised the economic impact (OR = 3.80; CI 2.62-5.49) and voted for parties opposed to the Government (OR = 2.00; CI 1.54-2.60) showed higher vaccine hesitancy. The stratified analysis showed a similar pattern for both men and women.

CONCLUSIONS

Considering the determinants of vaccine uptake and hesitancy could help to design strategies that increase immunisation at the population level and minimise health inequities.