Analysis of the Effects of COVID-19 Mask Mandates on Hospital Resource Consumption and Mortality at the County Level

The Effects of Non-Pharmaceutical Interventions on COVID-19 Cases, Hospitalizations, and Mortality: A Systematic Literature Review and Meta-Analysis

Introduction

To assess the effects of various non-pharmaceutical interventions (NPI) on cases, hospitalizations, and mortality during the first wave of the COVID-19 pandemic.

Methods

To empirically investigate the impacts of different NPIs on COVID-19-related health outcomes, a systematic literature review was conducted. We studied the effects of 10 NPIs on cases, hospitalizations, and mortality across three periodic lags (2, 3, and 4 weeks-or-more following implementation). Articles measuring the impact of NPIs were sourced from three databases by May 10, 2022, and risk of bias was assessed using the Newcastle-Ottawa scale.

Results

Across the 44 papers, we found that mask wearing corresponded to decreased per capita cases across all lags (up to -2.71 per 100,000). All NPIs studied except business and bar/restaurant closures corresponded to reduced case growth rates in the two weeks following implementation, while policy stringency and travelling restrictions were most effective after four. While we did not find evidence of reduced deaths in our per capita estimates, policy stringency, masks, SIPOs, limited gatherings, school and business closures were associated with decreased mortality growth rates. Moreover, the two NPIs studied in hospitalizations (SIPOs and mask wearing) showed negative estimates.

Conclusions

When assessing the impact of NPIs, considering the duration of effectiveness following implementation has paramount significance. While some NPIs may reduce the COVID-19 impact, others can disrupt the mitigative progression of containing the virus. Policymakers should be aware of both the scale of their effectiveness and duration of impact when adopting these measures for future COVID-19 waves.

Need for more robust research on the effectiveness of masks in preventing COVID-19 transmission

The COVID-19 pandemic has caused dramatic death and infection worldwide, leading to a global public health crisis. As for precautions, scientists have different opinions on the effectiveness of masks in preventing COVID-19 transmission. Published studies suggested that medical masks may help in preventing respiratory virus infection. But the currently available experimental results are too preliminary to support an informed policy. In conclusion, we need more well-designed and robust research on the effectiveness of masks in preventing COVID-19 infection.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

IMPORTANCE

Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts.

OBJECTIVES

To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations.

EXPOSURES

County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality.

MAIN OUTCOMES AND MEASURES

Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate.

RESULTS

Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

COVID-19 vaccination coverage is extremely low among older population in Bangladesh: findings from a cross-sectional study

This cross-sectional study was conducted in September 2021 among 1,045 Bangladeshi older adults aged 60 years or above to explore the COVID-19 vaccination coverage and its associated factors. We used a semi-structured questionnaire to collect data on participants' sociodemographic and lifestyle characteristics, and COVID-19 related information (selected based on an extensive literature review). A multinomial logistic regression model was used to identify the factors independently associated with vaccine receipt. Nearly, two-thirds of the participants (64.5%) were unvaccinated and 12.5% received a single dose. Among the unvaccinated, approximately 94% reported that there was a problem in accessing the vaccine. We found that participants with formal schooling had 42% lower risk of being unvaccinated (RRR (Relative Risk Ratio) = 0.58, 95% CI 0.42-0.80) or 39% lower risk of receiving a single dose (RRR = 0.61, 95% CI 0.39-0.96) than the participants having no formal schooling. The middle family monthly income groups had 65% higher risk (RRR = 1.65, 95% CI 1.17-2.32) and rural participants had 84% higher risk (RRR = 1.84, 95% CI 1.26-2.70) of not receiving vaccines compared to their counterparts. Also, the participants with non-communicable chronic conditions had a significantly lower risk of being unvaccinated (RRR = 0.49, 95% CI 0.35-0.68) or receiving a single dose (RRR = 0.49, 95% CI 0.31-0.77) compared to their counterparts. This finding may help strengthen the existing efforts to maximize vaccine coverage among older populations in Bangladesh and reach herd immunity to break the transmission chain and gain greater overall population protection more rapidly.