In-Depth Ethical Analysis of the COVID-19 Vaccine Rollout for Migrant Workers in the Gulf Countries.

During the initial COVID-19 vaccine rollout, supplies were scarce, necessitating rationing. Gulf countries, hosting millions of migrant workers, prioritized nationals over migrants for vaccination. As it turned out, many migrant workers found themselves waiting behind nationals to get vaccinated for COVID-19. Here, we discuss the public health ethical concerns surrounding this approach and call for fair and inclusive vaccine allocation policies. First, we examine global justice through the lens of statism, where distributive justice applies only to sovereign state members, and cosmopolitanism, advocating equal justice distribution for all humans. We propose a cooperativist perspective, suggesting that new justice obligations can arise between people beyond national ties. In cases of mutually beneficial cooperation, such as migrant workers contributing to a nation's economy, equal concern for all parties is required. Second, the principle of reciprocity further supports this stance, as migrants significantly contribute to host countries' societies and economies. Additional ethical principles-equity, utilitarianism, solidarity, and nondiscrimination-are essentially violated when excluding non-nationals in vaccine distribution. Finally, we argue that prioritizing nationals over migrants is not only ethically indefensible, but it also fails to ensure full protection for nationals and hampers efforts to curb COVID-19 community spread.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran.

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis.

BACKGROUND AND AIM: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. METHODS: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. RESULTS: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. CONCLUSION: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study.

BACKGROUND: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5°â€ˆ× 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5°â€ˆ× 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING: Australian Research Council, Australian National Health & Medical Research Council.

Previous COVID-19 Infection and Antibody Levels After Vaccination.

Background: The emergence of new COVID-19 variants of concern coupled with a global inequity in vaccine access and distribution has prompted many public health authorities to circumvent the vaccine shortages by altering vaccination protocols and prioritizing persons at high risk. Individuals with previous COVID-19 infection may not have been prioritized due to existing humoral immunity. Objective: We aimed to study the association between previous COVID-19 infection and antibody levels after COVID-19 vaccination. Methods: A serological analysis to measure SARS-CoV-2 immunoglobulin (Ig)G, IgA, and neutralizing antibodies was performed on individuals who received one or two doses of either BNT162b2 or ChAdOx1 vaccines in Kuwait. A Student t-test was performed and followed by generalized linear regression models adjusted for individual characteristics and comorbidities were fitted to compare the average levels of IgG and neutralizing antibodies between vaccinated individuals with and without previous COVID-19 infection. Results: A total of 1,025 individuals were recruited. The mean levels of IgG, IgA, and neutralizing antibodies were higher in vaccinated subjects with previous COVID-19 infections than in those without previous infection. Regression analysis showed a steeper slope of decline for IgG and neutralizing antibodies in vaccinated individuals without previous COVID-19 infection compared to those with previous COVID-19 infection. Conclusion: Previous COVID-19 infection appeared to elicit robust and sustained levels of SARS-CoV-2 antibodies in vaccinated individuals. Given the inconsistent supply of COVID-19 vaccines in many countries due to inequities in global distribution, our results suggest that even greater efforts should be made to vaccinate more people, especially individuals without previous COVID-19 infection.

Robust Antibody Levels in Both Diabetic and Non-Diabetic Individuals After BNT162b2 mRNA COVID-19 Vaccination

The emergence of effective vaccines for COVID-19 has been welcomed by the world with great optimism. Given their increased susceptibility to COVID-19, the question arises whether individuals with type-2 diabetes mellitus (T2DM) and other metabolic conditions can respond effectively to the mRNA-based vaccine. We aimed to evaluate the levels of anti-SARS-CoV-2 IgG and neutralizing antibodies in people with T2DM and/or other metabolic risk factors (hypertension and obesity) compared to those without. This study included 262 people (81 diabetic and 181 non-diabetic persons) that took two doses of BNT162b2 (Pfizer–BioNTech) mRNA vaccine. Both T2DM and non-diabetic individuals had a robust response to vaccination as demonstrated by their high antibody titers. However, both SARS-CoV-2 IgG and neutralizing antibodies titers were lower in people with T2DM. The mean ( ± 1 standard deviation) levels were 154 ± 49.1 vs. 138 ± 59.4 BAU/ml for IgG and 87.1 ± 11.6 vs. 79.7 ± 19.5% for neutralizing antibodies in individuals without diabetes compared to those with T2DM, respectively. In a multiple linear regression adjusted for individual characteristics, comorbidities, previous COVID-19 infection, and duration since second vaccine dose, diabetics had 13.86 BAU/ml (95% CI: 27.08 to 0.64 BAU/ml, p=0.041) less IgG antibodies and 4.42% (95% CI: 8.53 to 0.32%, p=0.036) fewer neutralizing antibodies than non-diabetics. Hypertension and obesity did not show significant changes in antibody titers. Taken together, both type-2 diabetic and non-diabetic individuals elicited strong immune responses to SARS-CoV-2 BNT162b2 mRNA vaccine;nonetheless, lower levels were seen in people with diabetes. Continuous monitoring of the antibody levels might be a good indicator to guide personalized needs for further booster shots to maintain adaptive immunity. Nonetheless, it is important that people get their COVID-19 vaccination especially people with diabetes.

The Impact of Strict Public Health Measures on COVID-19 Transmission in Developing Countries: The Case of Kuwait.

Background: Many countries have succeeded in curbing the initial outbreak of COVID-19 by imposing strict public health control measures. However, little is known about the effectiveness of such control measures in curbing the outbreak in developing countries. In this study, we seek to assess the impact of various outbreak control measures in Kuwait to gain more insight into the outbreak progression and the associated healthcare burden. Methods: We use a SEIR mathematical model to simulate the first wave of the epidemic outbreak of COVID-19 in Kuwait with additional testing and hospitalization compartments. We calibrate our model by using a NBD observational framework for confirmed case and death counts. We simulate trajectories of model forecasts and assess the effectiveness of public health interventions by using maximum likelihood to estimate both the basic and effective reproduction numbers. Results: Our results indicate that the early strict control measures had the effect of delaying the intensity of the outbreak but were unsuccessful in reducing the effective reproduction number below 1. Forecasted model trajectories suggest a need to expand the healthcare system capacity to cope with the associated epidemic burden of such ineffectiveness. Conclusion: Strict public health interventions may not always lead to the same desired outcomes, particularly when population and demographic factors are not accounted for as in the case in some developing countries. Real-time dynamic modeling can provide an early assessment of the impact of such control measures as well as a forecasting tool to support outbreak surveillance and the associated healthcare expansion planning.

Disparities in excess deaths from the COVID-19 pandemic among migrant workers in Kuwait.

BACKGROUND: The actual human cost of the pandemic cannot be viewed through the COVID-19 mortality rates alone, especially when the pandemic is widening the existing health disparities among different subpopulations within the same society. In Kuwait, migrant workers were already disproportionately impacted by COVID-19 and its unintended consequences. The totality of that effect on mortality is yet to be fully understood. OBJECTIVE: To estimate excess deaths in the pandemic year of 2020 among the Kuwaiti and non-Kuwaiti migrant populations. METHODS: We analyzed publicly available retrospective data in Kuwait on total annual mortality historically (from 2005 to 2019) and in 2020. We fitted a quasi-poisson generalized linear model adjusted for yearly trend and nationality to estimate the expected deaths in 2020 in the absence of the pandemic. We calculated excess deaths as the difference between observed and expected mortality for the year of the pandemic in both Kuwaitis and non-Kuwaitis. RESULTS: In the absence of the pandemic, we expected the total mortality in Kuwait to be 6629 (95% CI: 6472 to 6789) deaths. However, the observed total mortality in 2020 was 9975 deaths; about 3346 (3186 to 3503) more deaths above the expected historical trend. Deaths among migrant workers would have been approximately 71.9% (67.8 to 76.0) lower in the absence of the pandemic. On the other hand, deaths among Kuwaitis would have been 32.4% (29.3 to 35.6) lower if the country had not been hit by the pandemic. CONCLUSION: The burden of mortality brought on by the COVID-19 pandemic is substantially higher than what the official tally might suggest. Systematically disadvantaged migrant workers shouldered a larger burden of deaths in the pandemic year. Public health interventions must consider structural and societal determinants that give rise to the health disparities seen among migrant workers.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017–2019, and also investigated the differences in PM variations between the ‘curfew’ and ‘non curfew’ hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017–2019);however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the ‘dust effects’, both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study.

BACKGROUND: Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. METHODS: We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ([Formula: see text]). We estimated the basic reproduction number ([Formula: see text]) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated [Formula: see text] values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. RESULTS: We estimate [Formula: see text] at 1·08 (95% CI: 1·00-1·26) for P1 and 2·36 (2·03-2·71) for P2. On March 22nd, [Formula: see text] for P1 and P2 are estimated at 1·19 (1·04-1·34) and 1·75 (1·26-2·11) respectively. After the partial curfew had taken effect, [Formula: see text] for P1 dropped modestly to 1·05 (0·82-1·26) but almost doubled for P2 to 2·89 (2·30-3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. CONCLUSION: Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

Clinical characteristics and outcomes of COVID-19 patients with diabetes mellitus in Kuwait.

BACKGROUND: COVID-19 has a highly variable clinical presentation, ranging from asymptomatic to severe respiratory symptoms and death. Diabetes seems to be one of the main comorbidities contributing to a worse COVID-19 outcome. OBJECTIVE: In here we analyze the clinical characteristics and outcomes of diabetic COVID-19 patients Kuwait. METHODS: In this single-center, retrospective study of 417 consecutive COVID-19 patients, we analyze and compare disease severity, outcome, associated complications, and clinical laboratory findings between diabetic and non-diabetic COVID-19 patients. RESULTS: COVID-19 patients with diabetes had more ICU admission than non-diabetic COVID-19 patients (20.1% vs. 16.8%, p < 0.001). Diabetic COVID-19 patients also recorded higher mortality in comparison to non-diabetic COVID-19 patients (16.7% vs. 12.1%, p < 0.001). Diabetic COVID-19 patients had significantly higher prevalence of comorbidities, such as hypertension. Laboratory investigations also highlighted notably higher levels of C-reactive protein in diabetic COVID019 patients and lower estimated glomerular filtration rate. They also showed a higher incidence of complications. logistic regression analysis showed that every 1 mmol/L increase in fasting blood glucose in COVID-19 patients is associated with 1.52 (95% CI: 1.34-1.72, p < 0.001) times the odds of dying from COVID-19. CONCLUSION: Diabetes is a major contributor to worsening outcomes in COVID-19 patients. Understanding the pathophysiology underlining these findings could provide insight into better management and improved outcome of such cases.

Levels and Particle Size Distribution of Airborne SARS-CoV-2 at a Healthcare Facility in Kuwait

The Coronavirus Disease 2019 (COVID-19) pandemic spread rapidly despite extraordinary screening and social distancing measures. Such rapid spread was due in part to the fact that the disease transmission, particularly via airborne spread, is poorly understood. Characterizing the airborne size distribution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential to understanding the risk of airborne transmission. We collected size-fractionated (≤2.5, 2.5-10, and ≥10 μm) samples using a cascade impactor at more than 30 locations inside and outside Jaber Hospital and the nearby Temporary Quarantine Facility in Kuwait from April to July 2020. We hypothesized that airborne SARS-CoV-2 would be present in all size fractions, including fine particles, and in a size distribution that differed by sampling location. We found 6% of the samples (13 out of 210) were positive for SARS-CoV-2 RNA. Concentrations ranged from 3 to 25 copies/m3. The size distribution of particle-associated SARS-CoV-2 was different for each location. Large (≥10 μm) particles with the virus were found in symptomatic patient rooms. Fine (≤2.5 μm) particle-associated SARS-CoV-2 was detected in rooms with intubated patients and outside the hospital entrance gates. Coarse (2.5-10 μm) virus-laden particles were present in all locations with positive samples. This is the most comprehensive study to date on size-fractionated airborne SARS-CoV-2 RNA. Our findings support location-specific precautions that mitigate the spread of particles including fine particulate matter over distances greater than 1 meter, including in locations outside the hospital.

The impact of outdoor air pollution on COVID-19: a review of evidence from in vitro, animal, and human studies.

Studies have pointed out that air pollution may be a contributing factor to the coronavirus disease 2019 (COVID-19) pandemic. However, the specific links between air pollution and severe acute respiratory syndrome-coronavirus-2 infection remain unclear. Here we provide evidence from in vitro, animal and human studies from the existing literature. Epidemiological investigations have related various air pollutants to COVID-19 morbidity and mortality at the population level, however, those studies suffer from several limitations. Air pollution may be linked to an increase in COVID-19 severity and lethality through its impact on chronic diseases, such as cardiopulmonary diseases and diabetes. Experimental studies have shown that exposure to air pollution leads to a decreased immune response, thus facilitating viral penetration and replication. Viruses may persist in air through complex interactions with particles and gases depending on: 1) chemical composition; 2) electric charges of particles; and 3) meteorological conditions such as relative humidity, ultraviolet (UV) radiation and temperature. In addition, by reducing UV radiation, air pollutants may promote viral persistence in air and reduce vitamin D synthesis. Further epidemiological studies are needed to better estimate the impact of air pollution on COVID-19. In vitro and in vivo studies are also strongly needed, in particular to more precisely explore the particle-virus interaction in air.

Fasting Blood Glucose and COVID-19 Severity: Nonlinearity Matters.

OBJECTIVE: Fasting blood glucose (FBG) could be an independent predictor for coronavirus disease 2019 (COVID-19) morbidity and mortality. However, when included as a predictor in a model, it is conventionally modeled linearly, dichotomously, or categorically. We comprehensively examined different ways of modeling FBG to assess the risk of being admitted to the intensive care unit (ICU). RESEARCH DESIGN AND METHODS: Utilizing COVID-19 data from Kuwait, we fitted conventional approaches to modeling FBG as well as a nonlinear estimation using penalized splines. RESULTS: For 417 patients, the conventional linear, dichotomous, and categorical approaches to modeling FBG missed key trends in the exposure-response relationship. A nonlinear estimation showed a steep slope until about 10 mmol/L before flattening. CONCLUSIONS: Our results argue for strict glucose management on admission. Even a small incremental increase within the normal range of FBG was associated with a substantial increase in risk of ICU admission for COVID-19 patients.

COVID-19 clinical outcomes and nationality: results from a Nationwide registry in Kuwait.

BACKGROUND: In light of the COVID-19 pandemic, many have flagged racial and ethnic differences in health outcomes in western countries as an urgent global public health priority. Kuwait has a unique demographic profile with two-thirds of the population consisting of non-nationals, most of which are migrant workers. We aimed to explore whether there is a significant difference in health outcomes between non-Kuwaiti and Kuwaiti patients diagnosed with COVID-19. METHODS: We used a prospective COVID-19 registry of all patients (symptomatic and asymptomatic) in Kuwait who tested positive from February 24th to April 20th, 2020, collected from Jaber Al-Ahmad Al-Sabah Hospital, the officially-designated COVID-19 healthcare facility in the country. We ran separate logistic regression models comparing non-Kuwaitis to Kuwaitis for death, intensive care unit (ICU) admission, acute respiratory distress syndrome (ARDS) and pneumonia. RESULTS: The first 1123 COVID-19 positive patients in Kuwait were all recruited in the study. About 26% were Kuwaitis and 73% were non-Kuwaiti. With adjustments made to age, gender, smoking and selected co-morbidities, non-Kuwaitis had two-fold increase in the odds of death or being admitted to the intensive care unit compared to Kuwaitis (OR: 2.14, 95% CI 1.12-4.32). Non-Kuwaitis had also higher odds of ARDS (OR:2.44, 95% CI 1.23-5.09) and pneumonia (OR: 2.24, 95% CI 1.27-4.12). CONCLUSION: This is the first study to report on COVID-19 outcomes between Kuwaiti and non-Kuwaiti patients. The current pandemic may have amplified the differences of health outcomes among marginalized subpopulations. A number of socioeconomic and environmental factors could explain this health disparity. More research is needed to advance the understanding of policymakers in Kuwait in order to make urgent public health interventions.

COVID-19 stressors on migrant workers in Kuwait: cumulative risk considerations.

As a marginalised subpopulation, migrant workers often fall short from protection by public policies, they take precarious jobs with unsafe working and living conditions and they grapple with cultural and linguistic barriers. In light of the current COVID-19 pandemic, migrant workers are now exposed to additional stressors of the virus and related responses. We applied a comprehensive qualitative cumulative risk assessment framework for migrant workers living in Kuwait. This pandemic could be one of the few examples where the stressors overlap all domains of migrant workers' lives. No single intervention can solve all the problems; there must be a set of interventions to address all domains. Local authorities and employers must act quickly to stop the spread, ensure easy access to testing and treatment, provide adequate housing and clear communication, encourage wide social support, safeguard financial protection and mental well-being and continuously re-evaluate the situation as more data are collected.