An integrated risk and epidemiological model to estimate risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020-March 1, 2021

Modeling impact of vaccination on COVID-19 dynamics in St. Louis

The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

The Effect of Socio-Demographic Factors in Health-Seeking Behaviors among Bangladeshi Residents during the First Wave of COVID-19

Background: Socio-demographic variability among nations and cultures highly influences health-seeking behavior (HSB) in managing endemic or pandemic diseases. The present study aimed to investigate the influence of socio-demographic factors on HSB among Bangladeshi residents during the first wave of COVID-19. Methods: A cross-sectional online survey was performed with Bangladeshi residents (60% male and 40% female; age range: 10−60 years or above) from May to July 2020. Information was collected from the participants who resided in slum areas or did not have internet access through face-to-face surveys, maintaining spatial distancing and proper preventive measures. A self-reported and structured questionnaire, including socio-demographic and HSB, was undertaken, and the data was analyzed using a convenience sampling method. Finally, among the 947 participants, 20 were selected using a stratified random sampling technique for in-depth-interview (IDI). The linear regression analysis was performed to determine the influence of socio-demographic factors on HSB. Results: Only about 4.2% of respondents did not wear masks, but nearly half of the participants (46.6%) did not use hand gloves. The mean score of HSB was 9.98 (SD = 2.01) out of 16, with a correct overall rate of 62.4%. As per regression analysis, higher HSBs were found among participants who reported older age (>50 years) (9.96 ± 2.45), educated unemployed students (10.1 ± 1.95), higher education (10.5 ± 1.76), and higher-income (10.4 ± 1.59); in contrast, participants living in slum areas (8.18 ± 2.34) and whose source of income was business (8.46 ± 2.04) exhibited lower HSBs. Females, compared to men, showed better HSB in every aspect, apart from online food ordering. Qualitative data showed that the younger generation is more aware because of their access to information and persuaded the older generations to follow health-seeking protocols. The results also showed that some lower-income slum-dwellers have access to information and healthcare through their employers. IDIs also found cultural, religious, and mental-health affect people’s adherence to health-seeking guidelines and regulations. Conclusions: The findings suggest that socio-demographic factors significantly influenced HSBs during COVID-19 in the Bangladeshi population. Authorities can use these observations to systematically manage future endemics or pandemics.