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1.
JAMA ; 328(8): 772-773, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-2041181
2.
PLoS One ; 17(8): e0273344, 2022.
Article in English | MEDLINE | ID: covidwho-2002328

ABSTRACT

This study explored the roles of epidemic-spread-related behaviors, vaccination status and weather factors during the COVID-19 epidemic in 50 U.S. states since March 2020. Data from March 1, 2020 to February 5, 2022 were incorporated into panel model. The states were clustered by the k-means method. In addition to discussing the whole time period, we also took multiple events nodes into account and analyzed the data in different time periods respectively by panel linear regression method. In addition, influence of cluster grouping and different incubation periods were been discussed. Non-segmented analysis showed the rate of people staying at home and the vaccination dose per capita were significantly negatively correlated with the daily incidence rate, while the number of long-distance trips was positively correlated. Weather indicators also had a negative effect to a certain extent. Most segmental results support the above view. The vaccination dose per capita was unsurprisingly proved to be the most significant factor especially for epidemic dominated by Omicron strains. 7-day was a more robust incubation period with the best model fit while weather had different effects on the epidemic spread in different time period. The implementation of prevention behaviors and the promotion of vaccination may have a successful control effect on COVID-19, including variants' epidemic such as Omicron. The spread of COVID-19 also might be associated with weather, albeit to a lesser extent.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Regression Analysis , SARS-CoV-2 , United States/epidemiology , Weather
3.
Stat Methods Med Res ; 31(11): 2164-2188, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1968494

ABSTRACT

Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.


Subject(s)
COVID-19 , Models, Statistical , Humans , Survival Analysis , Probability , Regression Analysis , Computer Simulation
4.
J Prev Med Hyg ; 63(1): E125-E129, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1955104

ABSTRACT

Background: Globally, several measures have been taken to decrease COVID-19 mortality. However, the effectiveness of preventive measures on the mortality related to COVID-19 has not been fully assessed. Thus, the present study aimed the present study aimed to assess the success of COVID-19 epidemic management and control plan on the mortality associated with COVID-19 in Iran from February 19, 2020, to February 5, 2021. Methods: In the current quasi experimental study an interrupted time series analysis of daily collected data on confirmed deaths of COVID-19 occurred in Iran and in the world, were performed using Newey ordinary least squares regression-based methods. Results: In Iran, the trend of new deaths increased significantly every day until 24 November 2020 according to pre-intervention slope of [(OR 1.14, 95% CI 0.96-1.32,); P < 0.001]. The occurrence of new deaths had a decreasing trend after November 24, 2020, with a coefficient of [(OR -5.12, 95% CI -6.04 - -4.20), P < 0.001)]. But in the global level daily new deaths was increasing before [(OR 18.66, 95% CI 14.41-2292; P < 0.001)] and after the 24 November 2020 [(OR 57.14, 95% CI 20.80-93.49); P: 0.002]. Conclusions: Iranian COVID-19 epidemic management and control plan effectively reduced the mortality associated to COVID-19. Therefore, it is essential to continue these measures to prevent the increase in the number of deaths.


Subject(s)
COVID-19 , Epidemics , Humans , Interrupted Time Series Analysis , Iran/epidemiology , Regression Analysis
5.
Int J Mol Sci ; 21(10)2020 May 19.
Article in English | MEDLINE | ID: covidwho-1934080

ABSTRACT

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.


Subject(s)
Computer Simulation , Intestines/physiology , Support Vector Machine , Animals , Humans , Permeability , Rats , Regression Analysis , Reproducibility of Results
6.
Sci Rep ; 12(1): 11073, 2022 06 30.
Article in English | MEDLINE | ID: covidwho-1921704

ABSTRACT

Integrating data across institutions can improve learning efficiency. To integrate data efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, Distributed Algorithm for fitting Penalized (ADAP) regression models across multiple datasets. ADAP utilizes patient-level data from a lead site and incorporates the first-order (ADAP1) and second-order gradients (ADAP2) of the objective function from collaborating sites to construct a surrogate objective function at the lead site, where model fitting is then completed with proper regularizations applied. We evaluate the performance of the proposed method using both simulation and a real-world application to study risk factors for opioid use disorder (OUD) using 15,000 patient data from the OneFlorida Clinical Research Consortium. Our results show that ADAP performs nearly the same as the pooled estimator but achieves higher estimation accuracy and better variable selection than the local and average estimators. Moreover, ADAP2 successfully handles heterogeneity in covariate distributions.


Subject(s)
Algorithms , Opioid-Related Disorders , Computer Simulation , Datasets as Topic , Humans , Opioid-Related Disorders/epidemiology , Regression Analysis , Risk Factors
7.
Front Public Health ; 10: 877843, 2022.
Article in English | MEDLINE | ID: covidwho-1903223

ABSTRACT

Objective: To analyze the patient and visitor workplace violence (PVV) toward health workers (HWs) and identify correlations between worker characteristics, measures against violence and exposure to PVV in COVID-19 pandemic. Methods: A cross-sectional survey utilizing the international questionnaires in six public tertiary hospitals from Beijing in 2020 was conducted, and valid data from 754 respondents were collected. Multilevel logistic regression models were used to determine the association between independents and exposure to PVV. Results: During COVID-19 pandemic and regular epidemic prevention and control, doctors were 5.3 times (95% CI = 1.59~17.90) more likely to suffer from physical PVV than nurses. HWs most frequently work with infants were 7.2 times (95% CI = 2.24~23.19) more likely to suffer from psychological PVV. More than four-fifth of HWs reported that their workplace had implemented security measures in 2020, and the cross-level interactions between the security measures and profession variable indicates that doctors in the workplace without security measures were 11.3 times (95% CI = 1.09~116.39) more likely to suffer from physical PVV compared to nurses in the workplace with security measures. Conclusion: Doctors have higher risk of physical PVV in COVID-19 containment, and the security measures are very important and effective to fight against the physical PVV. Comprehensive measures should be implemented to mitigate hazards and protect the health, safety, and well-being of health workers.


Subject(s)
COVID-19 , Workplace Violence , COVID-19/epidemiology , China/epidemiology , Cross-Sectional Studies , Humans , Pandemics , Regression Analysis
8.
BMC Med Res Methodol ; 22(1): 146, 2022 05 20.
Article in English | MEDLINE | ID: covidwho-1902353

ABSTRACT

BACKGROUND: Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. METHODS: We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. RESULTS: We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation -small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. CONCLUSIONS: Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution.


Subject(s)
COVID-19 , Communicable Diseases , COVID-19/epidemiology , Communicable Diseases/epidemiology , Humans , Models, Statistical , Regression Analysis , Risk Factors
9.
Expert Rev Clin Pharmacol ; 15(6): 787-793, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1900960

ABSTRACT

BACKGROUND: The COVID-19 lockdown has resulted in limited access to most of the conventional chronic pain management services. Subsequently, changes in opioids' utilization could be expected. This study assessed the impact of the first COVID-19 lockdown on opioid utilization using aggregated-level, community dispensing dataset covering the whole English population. RESEARCH DESIGN AND METHODS: A segmented-linear regression analysis was applied to monthly dispensed opioid prescriptions from March 2019 to March 2021. Opioid utilization was measured using the number of opioids' items dispensed/1000 inhabitants and Defined Daily Dose (DDD)/1000 inhabitants/day during 12-months pre/post the lockdown in March 2020 stratified by strong and weak opioids. RESULTS: For all opioids' classes, there were nonsignificant changes in the number of opioids' items dispensed/1000 inhabitants trend pre-lockdown, small increases in their level immediately post-lockdown, and a non-significant decline in the trend post-lockdown. Similarly, a non-significant reduction in the DDD/1000 inhabitant/day baseline trend pre-lockdown, nonsignificant immediate increases in the level post-lockdown, and declines in the trend post-lockdown for all opioids' classes were observed. CONCLUSION: Unexpectedly, opioid utilization does not appear to have been significantly affected by the lockdown measures during the study period. However, patient-level data is needed to determine more accurate estimates of any changes in the opioid prescribing including incident prescribing/use.


Subject(s)
Analgesics, Opioid , COVID-19 , Analgesics, Opioid/therapeutic use , Communicable Disease Control , Drug Prescriptions , Humans , Pandemics , Practice Patterns, Physicians' , Primary Health Care , Regression Analysis
10.
J Biomed Inform ; 131: 104097, 2022 07.
Article in English | MEDLINE | ID: covidwho-1867315

ABSTRACT

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.


Subject(s)
COVID-19 , Algorithms , COVID-19/epidemiology , Humans , Likelihood Functions , Models, Statistical , Regression Analysis
11.
Science ; 376(6599): 1327-1332, 2022 06 17.
Article in English | MEDLINE | ID: covidwho-1861568

ABSTRACT

Repeated emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR0, a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many nonspike mutations within the nucleocapsid and nonstructural proteins. PyR0 forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization.


Subject(s)
COVID-19 , Genetic Fitness , SARS-CoV-2 , Bayes Theorem , COVID-19/virology , Genome, Viral , Humans , Mutation , Regression Analysis , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics
13.
Clin Imaging ; 88: 4-8, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1819456

ABSTRACT

BACKGROUND: COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. METHODS: The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. RESULTS: While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. CONCLUSION: Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.


Subject(s)
COVID-19 , Sepsis , APACHE , Creatinine , Female , Humans , Intensive Care Units , Linear Models , Male , Organ Dysfunction Scores , Procalcitonin , Prognosis , ROC Curve , Regression Analysis , Retrospective Studies , Sepsis/therapy , Tomography, X-Ray Computed
14.
J Prev Med Public Health ; 55(2): 144-152, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1786149

ABSTRACT

OBJECTIVES: This study aimed to identify the social and policy determinants of coronavirus disease 2019 (COVID-19) infection across 23 countries. METHODS: COVID-19 indicators (incidence, mortality, and fatality) for each country were calculated by direct and indirect standardization. Multivariable regression analyses were used to identify the social and policy determinants of COVID-19 infection. RESULTS: A higher number of doctors per population was related to lower incidence, mortality, and fatality rates of COVID-19 in 23 countries (ß=-0.672, -0.445, and -0.564, respectively). The number of nurses/midwives per population was associated with lower mortality and fatality rates of COVID-19 in 23 countries (ß=-0.215 and -0.372, respectively). Strengthening of policy restriction indicators, such as restrictions of public gatherings, was related to lower COVID-19 incidence (ß=-0.423). A national Bacillus Calmette-Guérin vaccination policy conducted among special groups or in the past was associated with a higher incidence of COVID-19 in 23 countries (ß=0.341). The proportion of the elderly population (aged over 70 years) was related to higher mortality and fatality rates (ß=0.209 and 0.350, respectively), and income support was associated with mortality and fatality rates (ß=-0.362 and -0.449, respectively). CONCLUSIONS: These findings do not imply causality because this was a country-based correlation study. However, COVID-19 transmission can be influenced by social and policy determinants such as integrated health systems and policy responses to COVID-19. Various social and policy determinants should be considered when planning responses to COVID-19.


Subject(s)
COVID-19 , Aged , COVID-19/epidemiology , Humans , Incidence , Policy , Regression Analysis , Research
15.
Geospat Health ; 17(s1)2022 03 18.
Article in English | MEDLINE | ID: covidwho-1780143

ABSTRACT

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Incidence , Pandemics , Regression Analysis , Satellite Imagery
16.
PLoS One ; 17(3): e0263603, 2022.
Article in English | MEDLINE | ID: covidwho-1745323

ABSTRACT

BACKGROUND: Burnout is a work-related stress syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment. Nurse burnout is related to nurses' deteriorating mental health and poorer patient care quality and thus, is a significant concern in healthcare. The Coronavirus Disease 2019 (COVID-19) pandemic has swept the world and distressed the healthcare systems. Because of the body's stress mechanism, it is vital to examine the current prevalence of nurse burnout and understand it at a biological level, using an epigenetic biomarker, telomere length. PURPOSE: To determine the prevalence of burnout among nurses in the Peri-Operative and Labor & Delivery settings pre and during the COVID-19 pandemic and to examine the effects of burnout on absolute telomere length. METHODS: This is a cross-sectional study assessing the prevalence of nurses' burnout and the relationships between nurses' burnout and telomere length. Due to the COVID-19 pandemic, we had to stop the study during the mid of data collection. Even though the study was not designed to capture changes before and during the pandemic, we analyzed two groups' data before and during the pandemic. The study took place in a US hospital. Nurses in the hospital's Operating Room, Post-Anesthesia Care Unit, and Labor & Delivery Unit participated in the study. Maslach Burnout Inventory survey and nurses' demographics were administered online. Telomere length was measured via finger-prick blood. RESULTS: 146 nurses participated in the study, with 120 participants' blood samples collected. The high-level burnout rate was 70.5%. Correlation analysis did not reveal a direct correlation between nurse burnout and telomere length. However, in a multiple regression analysis, the final model contained the burnout subscale of emotional exhaustion, years as an RN, and work unit's nursing care quality. There was a low degree of departure from normality of the mean absolute telomere length in the pre-pandemic group and a substantial degree of departure in the during-pandemic group. CONCLUSIONS: Nurse burnout is a prevalent phenomenon in healthcare, and this study indicates that nurses currently experience high levels of burnout. Nurses' cellular biomarker, telomere length, is shorter in the group of nurses during the COVID-19 pandemic than before. Appropriate measures should be implemented to decrease nurses' burnout symptoms and improve nurses' psychological and physical health. Nurses, especially those younger than 60, report higher burnout symptoms, particularly emotional exhaustion. This study indicates the need for intervention to promote nurses' health during the pandemic and beyond. If not appropriately managed, nurse burnout may continue to be a significant issue facing the healthcare system.


Subject(s)
Burnout, Professional/epidemiology , COVID-19/epidemiology , Nursing Staff, Hospital/psychology , Telomere/genetics , Adult , Burnout, Professional/genetics , Burnout, Professional/psychology , COVID-19/complications , COVID-19/psychology , Clinical Competence , Cross-Sectional Studies , Female , Humans , Job Satisfaction , Male , Middle Aged , Prevalence , Quality of Health Care , Regression Analysis , Telomere Homeostasis , Young Adult
17.
PLoS One ; 17(3): e0264655, 2022.
Article in English | MEDLINE | ID: covidwho-1745320

ABSTRACT

BACKGROUND: Isolation is an indispensable measure to contain the SARS-CoV-2 virus, but it may have a negative impact on mental health and overall wellbeing. Evidence on the isolation experience, facilitating and complicating factors is needed to mitigate negative effects. METHODS AND FINDINGS: This observational, population-based cohort study enrolled 1547 adults from the general population with SARS-CoV-2 infection reported to authorities between 27 February 2020 and 19 January 2021 in Zurich, Switzerland. We assessed the proportion of individuals reporting symptoms of depression and anxiety before, during and after isolation (by DASS-21), and queried worries, positive experiences, and difficulties. We analyzed the association of these outcomes with socio-demographics using ordinal regression. Additionally, we report free-text statements by participants to capture most important aspects of isolation. The proportion of participants affected by depression or anxiety increased during isolation from 10·0% to 17·1% and 9·1% to 17·6%, respectively. Ordinal regression showed that taking care of children increased the difficulty of isolation (OR 2·10, CI 1·43-3·08) and risk of non-compliance (OR 1·63, CI 1·05-2·53), especially in younger participants. A facilitating factor that individuals commonly expressed was receiving more support during isolation. CONCLUSION: Isolation due to SARS-CoV-2 presents a mental burden, especially for younger individuals and those taking care of children. Public health authorities need to train personnel and draw from community-based resources to provide targeted support, information, and guidance to individuals during isolation. Such efforts could alleviate the negative impact isolation has on the mental and physical health of individuals and ensure compliance of the population with recommendations.


Subject(s)
Anxiety Disorders/epidemiology , COVID-19/psychology , Depression/epidemiology , Social Isolation/psychology , Adolescent , Adult , Aged , Aged, 80 and over , Anxiety Disorders/psychology , Cohort Studies , Depression/psychology , Female , Humans , Male , Middle Aged , Patient Compliance/psychology , Patient Compliance/statistics & numerical data , Regression Analysis , Switzerland/epidemiology , Young Adult
18.
JAMA Netw Open ; 5(3): e220984, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1729076

ABSTRACT

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.


Subject(s)
COVID-19/ethnology , COVID-19/mortality , Health Status Disparities , Spatial Analysis , Cross-Sectional Studies , District of Columbia/epidemiology , Humans , Regression Analysis , SARS-CoV-2 , Social Determinants of Health
19.
JAMA ; 327(7): 639-651, 2022 02 15.
Article in English | MEDLINE | ID: covidwho-1718172

ABSTRACT

Importance: Assessing COVID-19 vaccine performance against the rapidly spreading SARS-CoV-2 Omicron variant is critical to inform public health guidance. Objective: To estimate the association between receipt of 3 doses of Pfizer-BioNTech BNT162b2 or Moderna mRNA-1273 vaccine and symptomatic SARS-CoV-2 infection, stratified by variant (Omicron and Delta). Design, Setting, and Participants: A test-negative case-control analysis among adults 18 years or older with COVID-like illness tested December 10, 2021, through January 1, 2022, by a national pharmacy-based testing program (4666 COVID-19 testing sites across 49 US states). Exposures: Three doses of mRNA COVID-19 vaccine (third dose ≥14 days before test and ≥6 months after second dose) vs unvaccinated and vs 2 doses 6 months or more before test (ie, eligible for a booster dose). Main Outcomes and Measures: Association between symptomatic SARS-CoV-2 infection (stratified by Omicron or Delta variants defined using S-gene target failure) and vaccination (3 doses vs unvaccinated and 3 doses vs 2 doses). Associations were measured with multivariable multinomial regression. Among cases, a secondary outcome was median cycle threshold values (inversely proportional to the amount of target nucleic acid present) for 3 viral genes, stratified by variant and vaccination status. Results: Overall, 23 391 cases (13 098 Omicron; 10 293 Delta) and 46 764 controls were included (mean age, 40.3 [SD, 15.6] years; 42 050 [60.1%] women). Prior receipt of 3 mRNA vaccine doses was reported for 18.6% (n = 2441) of Omicron cases, 6.6% (n = 679) of Delta cases, and 39.7% (n = 18 587) of controls; prior receipt of 2 mRNA vaccine doses was reported for 55.3% (n = 7245), 44.4% (n = 4570), and 41.6% (n = 19 456), respectively; and being unvaccinated was reported for 26.0% (n = 3412), 49.0% (n = 5044), and 18.6% (n = 8721), respectively. The adjusted odds ratio for 3 doses vs unvaccinated was 0.33 (95% CI, 0.31-0.35) for Omicron and 0.065 (95% CI, 0.059-0.071) for Delta; for 3 vaccine doses vs 2 doses the adjusted odds ratio was 0.34 (95% CI, 0.32-0.36) for Omicron and 0.16 (95% CI, 0.14-0.17) for Delta. Median cycle threshold values were significantly higher in cases with 3 doses vs 2 doses for both Omicron and Delta (Omicron N gene: 19.35 vs 18.52; Omicron ORF1ab gene: 19.25 vs 18.40; Delta N gene: 19.07 vs 17.52; Delta ORF1ab gene: 18.70 vs 17.28; Delta S gene: 23.62 vs 20.24). Conclusions and Relevance: Among individuals seeking testing for COVID-like illness in the US in December 2021, receipt of 3 doses of mRNA COVID-19 vaccine (compared with unvaccinated and with receipt of 2 doses) was less likely among cases with symptomatic SARS-CoV-2 infection compared with test-negative controls. These findings suggest that receipt of 3 doses of mRNA vaccine, relative to being unvaccinated and to receipt of 2 doses, was associated with protection against both the Omicron and Delta variants, although the higher odds ratios for Omicron suggest less protection for Omicron than for Delta.


Subject(s)
2019-nCoV Vaccine mRNA-1273/administration & dosage , BNT162 Vaccine/administration & dosage , COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , SARS-CoV-2 , Vaccine Efficacy , Adolescent , Adult , Aged , COVID-19/epidemiology , COVID-19/virology , Case-Control Studies , Dose-Response Relationship, Immunologic , Humans , Immunization, Secondary , Middle Aged , Odds Ratio , Regression Analysis , Retrospective Studies , Risk Factors , Young Adult
20.
PLoS One ; 17(2): e0263245, 2022.
Article in English | MEDLINE | ID: covidwho-1708180

ABSTRACT

In low- and middle-income countries (LMICs), economic downturns can lead to increased child mortality by affecting dietary, environmental, and care-seeking factors. This study estimates the potential loss of life in children under five years old attributable to economic downturns in 2020. We used a multi-level, mixed effects model to estimate the relationship between gross domestic product (GDP) per capita and under-5 mortality rates (U5MRs) specific to each of 129 LMICs. Public data were retrieved from the World Bank World Development Indicators database and the United Nations World Populations Prospects estimates for the years 1990-2020. Country-specific regression coefficients on the relationship between child mortality and GDP were used to estimate the impact on U5MR of reductions in GDP per capita of 5%, 10%, and 15%. A 5% reduction in GDP per capita in 2020 was estimated to cause an additional 282,996 deaths in children under 5 in 2020. At 10% and 15%, recessions led to higher losses of under-5 lives, increasing to 585,802 and 911,026 additional deaths, respectively. Nearly half of all the potential under-5 lives lost in LMICs were estimated to occur in Sub-Saharan Africa. Because most of these deaths will likely be due to nutrition and environmental factors amenable to intervention, countries should ensure continued investments in food supplementation, growth monitoring, and comprehensive primary health care to mitigate potential burdens.


Subject(s)
Child Mortality/trends , Developing Countries , Gross Domestic Product/trends , Africa South of the Sahara , Child, Preschool , Dietary Supplements , Environment , Female , Humans , Infant , Infant, Newborn , Male , Poverty , Primary Health Care , Regression Analysis , Uncertainty
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