Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study.

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.

SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results.

Public health surveillance systems likely underestimate the true prevalence and incidence of SARS-CoV-2 infection due to limited access to testing and the high proportion of subclinical infections in community-based settings. This ongoing prospective, observational study aimed to generate accurate estimates of the prevalence and incidence of, and risk factors for, SARS-CoV-2 infection among residents of a central North Carolina county. From this cohort, we collected survey data and nasal swabs every two weeks and venous blood specimens every month. Nasal swabs were tested for the presence of SARS-CoV-2 virus (evidence of active infection), and serum specimens for SARS-CoV-2-specific antibodies (evidence of prior infection). As of June 23, 2021, we have enrolled a total of 153 participants from a county with an estimated 76,285 total residents. The anticipated study duration is at least 24 months, pending the evolution of the pandemic. Study data are being shared on a monthly basis with North Carolina state health authorities and future analyses aim to compare study data to state-wide metrics over time. Overall, the use of a probability-based sampling design and a well-characterized cohort will enable collection of critical data that can be used in planning and policy decisions for North Carolina and may be informative for other states with similar demographic characteristics.

Demographic profile and outcome of patients admitted to a COVID dedicated hospital in Bangladesh during the second wave.

ABSTRACT: In December 2019, with pneumonia-like clinical manifestations, a new severe acute respiratory syndrome coronavirus 2 emerged and quickly escalated into a pandemic. Since the first case detected in early March of last year, 8668 have died with an infection mortality rate of 1.52%, as of March 20, 2021. Bangladesh has been struck by the 2nd wave from mid-march 2021. As data on the second wave are sparse, the present study observed the demographic profile, symptoms, and outcomes of Coronavirus Disease 2019 (COVID-19) patients during this wave.The study was conducted at Sheikh Russel National Gastroliver Institute on 486 admitted cases during the 2nd wave of COVID-19 in Bangladesh (March 24-April 24, 2021) using a cross-sectional study design and a convenient sampling technique.Out of 486 cases, 306 (62.9%) were male, and 180 were female, with a mean age of 53.47 ±â€Š13.86. The majority of patients (32.5%) were between the ages of 51 and 60. While fever and cough being the predominant symptoms (>70% cases), the most common co-morbidities were hypertension (41.4) and diabetes mellitus (39.4). Intensive care unit utilization rate was 25%, and a half of the patients had 51% to 70% tomographic lung involvement with an overall mortality rate of 19.3%. Older age, chronic renal disease, percentage of lung involvement, and intensive care unit necessity were important mortality determinants.The present study gives an insight into the demographic profiles and outcomes of admitted patients with COVID-19 during the second wave at a covid dedicated hospital in Bangladesh.

Power-law population heterogeneity governs epidemic waves.

We generalize the Susceptible-Infected-Removed (SIR) model for epidemics to take into account generic effects of heterogeneity in the degree of susceptibility to infection in the population. We introduce a single new parameter corresponding to a power-law exponent of the susceptibility distribution at small susceptibilities. We find that for this class of distributions the gamma distribution is the attractor of the dynamics. This allows us to identify generic effects of population heterogeneity in a model as simple as the original SIR model which is contained as a limiting case. Because of this simplicity, numerical solutions can be generated easily and key properties of the epidemic wave can still be obtained exactly. In particular, we present exact expressions for the herd immunity level, the final size of the epidemic, as well as for the shape of the wave and for observables that can be quantified during an epidemic. In strongly heterogeneous populations, the herd immunity level can be much lower than in models with homogeneous populations as commonly used for example to discuss effects of mitigation. Using our model to analyze data for the SARS-CoV-2 epidemic in Germany shows that the reported time course is consistent with several scenarios characterized by different levels of immunity. These scenarios differ in population heterogeneity and in the time course of the infection rate, for example due to mitigation efforts or seasonality. Our analysis reveals that quantifying the effects of mitigation requires knowledge on the degree of heterogeneity in the population. Our work shows that key effects of population heterogeneity can be captured without increasing the complexity of the model. We show that information about population heterogeneity will be key to understand how far an epidemic has progressed and what can be expected for its future course.

Global survey-based assessment of lifestyle changes during the COVID-19 pandemic.

Along with the major impact on public health, the COVID-19 outbreak has caused unprecedented concerns ranging from sudden loss of employment to mental stress and anxiety. We implemented a survey-based data collection platform to characterize how the COVID-19 pandemic has affected the socio-economic, physical and mental health conditions of individuals. We focused on three broad areas, namely, changes in social interaction during home confinement, economic impact and their health status. We identified a substantial increase in virtual interaction among individuals, which might be a way to alleviate the sudden unprecedented mental health burden, exacerbated by general awareness about viral infections or other manifestations associated with them. The majority of participants (85%) lived with one or more companions and unemployment issues did not affect 91% of the total survey takers, which was one of the crucial consequences of the pandemic. Nevertheless, measures such as an increased frequency of technology-aided distant social interaction, focus on physical fitness and leisure activities were adopted as coping mechanisms during this period of home isolation. Collectively, these metrics provide a succinct and informative summary of the socio-economic and health impact of the COVID-19 pandemic on the individuals. Findings from our study reflect that continuous surveillance of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide. Given the limitations of analyzing the large number of variables, we have made the raw data publicly available on the OMF ME/CFS Data Center server to facilitate further analyses (https://igenomed.stanford.edu/dataset/survey-study-on-lifestyle-changes-during-covid-19-pandemic).

Understanding American Public Support for COVID-19 Risk Mitigation: The Role of Political Orientation, Socio-Demographic characteristics, Personal Concern, and Experience, the United States, 2020.

Objectives: COVID-19 is the most challenging public health crisis in decades in the United States. It is imperative to enforce social distancing rules before any safe and effective vaccines are widely available. Policies without public support are destined to fail. This study aims to reveal factors that determine the American public support for six mitigation measures (e.g., cancel gatherings, close schools, restrict non-essential travel). Methods: Based on a nationally representative survey, this study uses Structural Equation Modelling to reveal the relationships between various factors and public support for COVID-19 mitigation. Results: 1). Democrats are more likely than Republicans to support mitigation measures; 2).Favorability towards the political leader (Biden or Trump) can slant public support for COVID-19 mitigation measures among different segments of the public.; 3). Indirect experience, rather than direct experience with COVID-19 can motivate people to support mitigation; 4). Concern for COVID-19 is a strong motivator of support for mitigation. Conclusion: Political polarization poses an enormous challenge to societal well-being during a pandemic. Indirect experience renders COVID-19 an imminent threat.

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes.

BACKGROUND: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. METHODS AND FINDINGS: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. CONCLUSIONS: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).

Medical resources and coronavirus disease (COVID-19) mortality rate: Evidence and implications from Hubei province in China.

In light of the ongoing coronavirus disease (COVID-19) pandemic, this study aims to examine the relationship between the availability of public health resources and the mortality rate of this disease. We conducted empirical analyses using linear regression, a time-varying effect model, and a regression discontinuity design to investigate the association of medical resources with the mortality rate of the COVID-19 patients in Hubei, China. The results showed that the numbers of hospital beds, healthcare system beds, and medical staff per confirmed cases all had significant negative effects on the coronavirus disease mortality rate. Furthermore, in the context of the severe pandemic currently being experienced worldwide, the present study summarized the experience and implications in pandemic prevention and control in Hubei province from the perspective of medical resource integration as follows: First, hospitals' internal medical resources were integrated, breaking interdepartmental barriers. Second, joint pandemic control was realized by integrating regional healthcare system resources. Finally, an external medical resource allocation system was developed.

Model-based forecasting for Canadian COVID-19 data.

BACKGROUND: Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. METHOD: We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. FINDING: The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

COVID-19 misinformation: Mere harmless delusions or much more? A knowledge and attitude cross-sectional study among the general public residing in Jordan.

Since the emergence of the recent coronavirus disease 2019 (COVID-19) and its spread as a pandemic, media was teeming with misinformation that led to psychologic, social and economic consequences among the global public. Probing knowledge and anxiety regarding this novel infectious disease is necessary to identify gaps in knowledge and sources of misinformation which can help public health efforts to design and implement more focused interventional measures. The aim of this study was to evaluate the knowledge, attitude and effects of misinformation about COVID-19 on anxiety level among the general public residing in Jordan. This cross-sectional study was conducted using an online-based questionnaire that took place in April 2020, which targeted people residing in Jordan, aged 18 and above. The questionnaire included items on the following: demographic characteristics of the participants, knowledge about COVID-19, anxiety level and misconceptions regarding the origin of the pandemic. The total number of participants included in final analysis was 3150. The study population was predominantly females (76.0%), with mean age of 31 years. The overall knowledge of COVID-19 was satisfactory. Older age, males, lower monthly income and educational levels, smoking and history of chronic disease were associated with perceiving COVID-19 as a very dangerous disease. Variables that were associated with a higher anxiety level during the pandemic included: lower monthly income and educational level, residence outside the capital (Amman) and history of smoking. Misinformation about the origin of the pandemic (being part of a conspiracy, biologic warfare and the 5G networks role) was also associated with higher anxiety levels. Social media platforms, TV and news releases were the most common sources of information about the pandemic. The study showed the potential harmful effects of misinformation on the general public and emphasized the need to meticulously deliver timely and accurate information about the pandemic to lessen the health, social and psychological impact of the disease.

Lockdown, relaxation, and acme period in COVID-19: A study of disease dynamics in Hermosillo, Sonora, Mexico.

Lockdown and social distancing measures have been implemented for many countries to mitigate the impacts of the COVID-19 pandemic and prevent overwhelming of health services. However, success on this strategy depends not only on the timing of its implementation, but also on the relaxation measures adopted within each community. We developed a mathematical model to evaluate the impacts of the lockdown implemented in Hermosillo, Mexico. We compared this intervention with some hypothetical ones, varying the starting date and also the population proportion that is released, breaking the confinement. A Monte Carlo study was performed by considering three scenarios to define our baseline dynamics. Results showed that a hypothetical delay of two weeks, on the lockdown measures, would result in an early acme around May 9 for hospitalization prevalence and an increase on cumulative deaths, 42 times higher by May 31, when compared to baseline. On the other hand, results concerning relaxation dynamics showed that the acme levels depend on the proportion of people who gets back to daily activities as well as the individual behavior with respect to prevention measures. Analysis regarding different relaxing mitigation measures were provided to the Sonoran Health Ministry, as requested. It is important to stress that, according to information provided by health authorities, the acme occurring time was closed to the one given by our model. Hence, we considered that our model resulted useful for the decision-making assessment, and that an extension of it can be used for the study of a potential second wave.

Clinicians, cooks, and cashiers: Examining health equity and the COVID-19 risks to essential workers.

In Spring/Summer 2020, most individuals living in the United States experienced several months of social distancing and stay-at-home orders because of the coronavirus (COVID-19) pandemic. Clinicians, restaurant cooks, cashiers, transit operators, and other essential workers (EWs), however, continued to work outside the home during this time in order to keep others alive and maintain a functioning society. In the United States, EWs are often low-income persons of color who are more likely to face socioeconomic vulnerabilities, systemic racism, and health inequities. To assess the various impacts of COVID-19 on EWs, an online survey was distributed to a representative sample of individuals residing in six states during May/June 2020. The sample included 990 individuals who identified as EWs and 736 nonessential workers (NWs). We assessed differences between EW and NW respondents according to three categories related to health equity and social determinants of health: (1) demographics (e.g. race/ethnicity); (2) COVID-19 exposure risk pathways (e.g. ability to social distance); and (3) COVID-19 risk perceptions (e.g. perceived risk of contracting COVID-19). EWs were more likely to be Black or Hispanic than NWs and also had lower incomes and education levels on average. Unsurprisingly, EWs were substantially more likely to report working outside the home and less likely to report social distancing and wearing masks indoors as compared to NWs. EWs also perceived a slightly greater risk of contracting COVID-19. These findings, which we discuss in the context of persistent structural inequalities, systemic racism, and health inequities within the United States, highlight ways in which COVID-19 exacerbates existing socioeconomic vulnerabilities faced by EWs.

Community's perceived high risk of coronavirus infections during early phase of epidemics are significantly influenced by socio-demographic background, in Gondar City, Northwest Ethiopia: A cross-sectional -study.

BACKGROUND: Epidemiological studies during the early phase of the coronavirus (COVID-19) pandemics reported different level of people's risk perception in different countries. There is a paucity of data on perceived high risk of COVID-19 and associated factors in Ethiopia. We sought to assess the prevalence of community's perceived high risk about COVID-19 infections and associated factors among Gondar town community. METHODS: A cross-sectional study was carried out from April 20 to 27, 2020 in Gondar town community, Northwest Ethiopia. Multistage cluster sampling technique was used to recruit 635 participants. Structured and pre-tested questionnaire was used to collect the data. Descriptive statistics, bivariate and multivariable binary logistic regression were used to summarize the results. RESULTS: A total of 623 participants were considered in the analysis with a response rate of 98.1%. The prevalence of coronavirus high risk perceptions of the respondents was found to be 23.11% (95% CI; 19.80%-26.43%). Age above 45 years (AOR = 1.41, 95%CI; 1.19-2.66), college and above educational level (AOR = 0.28, 95%CI; 0.21-0.98), and poor knowledge towards COVID-19 virus (AOR = 1.57, 95%CI; 1.09-2.23) were significantly associated with perceived high risk about COVID-19. CONCLUSIONS: The prevalence of perceived high risk of COVID-19 was found to be low. Factors such as age, educational status, and knowledge about COVID-19 virus were found to be independent predictors of perceived high risk towards COVID-19. Government and non-government organizations should use formal and informal means of educating the community.

Investigating duration and intensity of Covid-19 social-distancing strategies.

The exponential character of the recent Covid-19 outbreak requires a change in strategy from containment to mitigation. Meanwhile, most countries apply social distancing with the objective to keep the number of critical cases below the capabilities of the health care system. Due to the novelty and rapid spread of the virus, an a priori assessment of this strategy was not possible. In this study, we present a model-based systems analysis to assess the effectiveness of social distancing measures in terms of intensity and duration of application. Results show a super-linear scaling between intensity (percent contact reduction) and required duration of application to have an added value (a lower number of fatalities). This holds true for an effective reproduction of [Formula: see text] and is reverted for [Formula: see text]. If R is not reduced below 1, secondary effects of required long-term isolation are likely to unravel the added value of disease mitigation. If an extinction is not feasible, we recommend moderate social-distancing that is well balanced against capability limits of national health-care systems.

Preparing for a future COVID-19 wave: insights and limitations from a data-driven evaluation of non-pharmaceutical interventions in Germany.

To contain the COVID-19 pandemic, governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPIs on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. While this quasi-experiment does not allow for causal identification, each policy's effect on reducing disease spread provides meaningful insights. We adapt the Susceptible-Exposed-Infected-Recovered model for disease propagation to include data on daily confirmed cases, interstate movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that in Germany policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), closure of educational institutions (e.g., schools), and retail outlet closures are associated with the sharpest drops in movement within and across states. Contact restrictions appear to be most effective at lowering COVID-19 cases, while border closures appear to have only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loosened and gives policymakers better data for making informed decisions.

Impact of COVID-19 predicts perceived risk more strongly than known demographic risk factors.

OBJECTIVE: To identify the factors associated with perceived COVID-19 risk among people living in the US. METHODS: A cross-sectional representative sample of 485 US residents was collected in mid-April 2020. Participants were asked about (a) perceptions of COVID-19 risk, (b) demographic factors known to be associated with increased COVID-19 risk, and (c) the impact of COVID-19 on different life domains. We used a three-step hierarchical linear regression model to assess the differential contribution of the factors listed above on perceived COVID-19 risk. RESULTS: The final model accounted for 16% of variability in perceived risk, F(18,458) = 4.8, p < .001. Participants who were White reported twice as much perceived risk as participants of color (B = -2.1, 95% CI[-3.4,-0.8]. Higher perceived risk was observed among those who reported a negative impact of the pandemic on their sleep (B = 1.5, 95% CI[0.8,2.1]) or work (B = 0.7, 95%CI[0.1,1.3]). The number of cases per capita in their state of residence, age, or proximity to someone with a COVID-19 diagnosis were not found to meaningfully predict perceived risk. CONCLUSIONS: Perceived risk was not found to be associated with known demographic risk factors, except that the effect of race/ethnicity was in the opposite direction of existing evidence. Perception of COVID-19 risk was associated with the perceived personal impact of the pandemic.

Explaining among-country variation in COVID-19 case fatality rate.

While the epidemic of SARS-CoV-2 has spread worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate had varied temporally (as the epidemic has progressed) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries. We used data on the temporal trajectory of case fatality rate provided by the European Center for Disease Prevention and Control, and country-specific data on different metrics describing the incidence of known comorbidity factors associated with an increased risk of COVID-19 mortality at the individual level. We also compiled data on demography, economy and political regimes for each country. We found that temporal trajectories of case fatality rate greatly vary among countries. We found several factors associated with temporal changes in case fatality rate both among variables describing comorbidity risk and demographic, economic and political variables. In particular, countries with the highest values of DALYs lost to cardiovascular, cancer and chronic respiratory diseases had the highest values of COVID-19 CFR. CFR was also positively associated with the death rate due to smoking in people over 70 years. Interestingly, CFR was negatively associated with share of death due to lower respiratory infections. Among the demographic, economic and political variables, CFR was positively associated with share of the population over 70, GDP per capita, and level of democracy, while it was negatively associated with number of hospital beds ×1000. Overall, these results emphasize the role of comorbidity and socio-economic factors as possible drivers of COVID-19 case fatality rate at the population level.

Concerns and adjustments: How the Portuguese population met COVID-19.

BACKGROUND: The COVID-19 pandemic has led to disruptive changes worldwide, with different implications across countries. The evolution of citizens' concerns and behaviours over time is a central piece to support public policies. OBJECTIVE: To unveil perceptions and behaviours of the Portuguese population regarding social and economic impacts of the COVID-19 pandemic, allowing for more informed public policies. METHODS: Online panel survey distributed in three waves between March 13th and May 6th 2020. Data collected from a non-representative sample of 7,448 respondents includes socio-demographic characteristics and self-reported measures on levels of concern and behaviours related to COVID-19. We performed descriptive analysis and probit regressions to understand relationships between the different variables. RESULTS: Most participants (85%) report being at least very concerned with the consequences of the COVID-19 pandemic and social isolation reached a high level of adherence during the state of emergency. Around 36% of the sample anticipated consumption decisions, stockpiling ahead of the state of emergency declaration. Medical appointments suffered severe consequences, being re-rescheduled or cancelled. We find important variation in concerns with the economic impact across activity sectors. CONCLUSION: We show that high level of concern and behaviour adaptation in our sample preceded the implementation of lockdown measures in Portugal around mid-March. One month later, a large share of individuals had suffered disruption in their routine health care and negative impacts in their financial status.

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.

BACKGROUND: Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems. METHODS AND FINDINGS: We use nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. The dataset contains hospitalized patients who tested positive for COVID-19 and had either recovered or were deceased between March 1 and June 30, 2020. A total of 113,214 patients with 50,387 deceased, were included. Both interpretable (sparse versions of Logistic Regression and Support Vector Machines) and state-of-the-art non-interpretable (Gradient Boosted Decision Trees and Random Forest) classification methods are employed. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Variables highly predictive of mortality included geographic location of the hospital (OR = 2.2 for Northeast region, OR = 2.1 for North region); renal (OR = 2.0) and liver (OR = 1.7) chronic disease; immunosuppression (OR = 1.7); obesity (OR = 1.7); neurological (OR = 1.6), cardiovascular (OR = 1.5), and hematologic (OR = 1.2) disease; diabetes (OR = 1.4); chronic pneumopathy (OR = 1.4); immunosuppression (OR = 1.3); respiratory symptoms, ranging from respiratory discomfort (OR = 1.4) and dyspnea (OR = 1.3) to oxygen saturation less than 95% (OR = 1.7); hospitalization in a public hospital (OR = 1.2); and self-reported patient illiteracy (OR = 1.1). Validation accuracies (AUC) for predicting mortality and ventilation need reach 79% and 70%, respectively, when using only pre-admission variables. Models that use post-admission disease progression information reach accuracies (AUC) of 86% and 87% for predicting mortality and ventilation use, respectively. CONCLUSIONS: The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic.

Social determinants of COVID-19 mortality at the county level.

As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.