Socioeconomic and immigration status and COVID-19 testing in Toronto, Ontario: retrospective cross-sectional study

Have immigrant children been left behind in COVID-19 testing rates? - A quantitative study in the Lisbon metropolitan area between march 2020 and may 2023

Immigrant children often encounter additional barriers in accessing health care than their peers. However, there is a lack of evidence globally regarding how migrant status may have affected access to COVID-19 testing during the pandemic. This study aimed to analyze migrant status as a determinant of COVID-19 testing rates among children in the Lisbon metropolitan area, Portugal. This cross-sequential study included 722 children aged 2-8 years (47% non-immigrants; 53% immigrants). We collected data from a national surveillance system on laboratory-confirmed COVID-19 tests conducted between March 2020 and May 2023 and assessed whether children were ever tested for COVID-19 and testing frequency. We employed robust and standard Poisson regression models to estimate Adjusted Prevalence Ratios and Relative Risks with 95% confidence intervals. A total of 637 tests were performed. Immigrant children had lower testing rates (53% vs. 48%) and fewer tests per child (median: 2 vs. 3). Moreover, they were 17% less likely to be ever tested (PR = 0.83, 95% CI: 0.76-0.89) and performed 26% fewer tests (RR = 0.74, 95% CI: 0.67-0.82) compared to non-immigrant children. Caregiver's age, education, employment status, child's birth weight, and perceived health status were associated factors. Our findings suggest that the COVID-19 pandemic has left immigrant children somewhat behind. We conclude that specific interventions targeting vulnerable populations, such as immigrant children, are needed in future health crises.

A command centre implementation before and during the COVID-19 pandemic in a community hospital

INTRODUCTION

The objective of the study was to assess the effects of high-reliability system by implementing a command centre (CC) on clinical outcomes in a community hospital before and during COVID-19 pandemic from the year 2016 to 2021.

METHODS

A descriptive, retrospective study was conducted at an acute care community hospital. The administrative data included monthly average admissions, intensive care unit (ICU) admissions, average length of stay, total ICU length of stay, and in-hospital mortality. In-hospital acquired events were recorded and defined as one of the following: cardiac arrest, cerebral infarction, respiratory arrest, or sepsis after hospital admissions. A subgroup statistical analysis of patients with in-hospital acquired events was performed. In addition, a subgroup statistical analysis was performed for the department of medicine.

RESULTS

The rates of in-hospital acquired events and in-hospital mortality among all admitted patients did not change significantly throughout the years 2016 to 2021. In the subgroup of patients with in-hospital acquired events, the in-hospital mortality rate also did not change during the years of the study, despite the increase in the ICU admissions during the COVID-19 pandemic.Although the in-hospital mortality rate did not increase for all admitted patients, the in-hospital mortality rate increased in the department of medicine.

CONCLUSION

Implementation of CC and centralized management systems has the potential to improve quality of care by supporting early identification and real-time management of patients at risk of harm and clinical deterioration, including COVID-19 patients.

Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?

COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.

Socio-economic determinants of SARS-CoV-2 infection: Results from a population-based cross-sectional serosurvey in Geneva, Switzerland

Background

SARS-CoV-2 infection and its health consequences have disproportionally affected disadvantaged socio-economic groups globally. This study aimed to analyze the association between socio-economic conditions and having developed antibodies for-SARS-CoV-2 in a population-based sample in the canton of Geneva, Switzerland.

Methods

Data was obtained from a population-based serosurvey of adults in Geneva and their household members, between November and December, 2020, toward the end of the second pandemic wave in the canton. Participants were tested for antibodies for-SARS-CoV-2. Socio-economic conditions representing different dimensions were self-reported. Mixed effects logistic regressions were conducted for each predictor to test its association with seropositive status as the main outcome.

Results

Two thousand eight hundred and eighty-nine adults completed the study questionnaire and were included in the final analysis. Retired participants and those living in suburban areas had lower odds of a seropositive result when compared to employed participants (OR: 0.42, 95% CI: 0.20-0.87) and those living in urban areas (OR: 0.67, 95% CI: 0.46-0.97), respectively. People facing financial hardship for less than a year had higher odds of a seropositive result compared to those who had never faced them (OR: 2.23, 95% CI: 1.01-4.95). Educational level, occupational position, and household income were not associated with being seropositive, nor were ethnicity or country of birth.

Discussion

While conventional measures of socio-economic position did not seem to be related to the risk of being infected in this sample, this study sheds lights on the importance of examining the broader social determinants of health when evaluating the differential impact of the pandemic within the population.