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

Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines

BACKGROUND

Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.

METHODS

To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models.

RESULTS

After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (n = 11), sex and gender (n = 5), and socioeconomic status (n = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges.

CONCLUSION

This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics.

HIGHLIGHTS

Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.

The impact of smoking on COVID-19-related mortality: a Brazilian national cohort study

INTRODUCTION

Current evidence suggests the potential heightened vulnerability of smokers to severe coronavirus disease (COVID-19) outcomes.

AIMS

This study aimed to analyze the clinical outcomes and mortality related to tobacco use in a cohort of hospitalized Brazilian COVID-19 patients.

METHODS

This retrospective cohort study analyzed adults hospitalized for COVID-19 in Brazil using the SIVEP-Gripe database (official data reported by public and private healthcare facilities for monitoring severe acute respiratory syndrome cases in Brazil). The inclusion criteria were patients over 18 years of age with a positive RT-qPCR test for SARS-CoV-2. The analysis focused on in-hospital mortality, considering smoking as an exposure variable, and included covariates such as age, gender, and comorbidities. Smoking history was collected from the self-reported field in the database. Statistical analyses included descriptive statistics, crude Odds Ratios, and multivariable binary logistic regression.

RESULTS

This study included 2,124,285 COVID-19 patients, among whom 44,774 (2.1 %) were smokers. The average age of the smokers was higher than that of the never-smokers (65.3 years vs. 59.7 years). The clinical outcomes revealed that smokers had higher rates of intensive care unit admission (51.6 % vs. 37.2 % for never-smokers), invasive ventilatory support (31.5 % vs. 20.2 % for never-smokers), and higher mortality (42.7 % vs. 31.8 % for never smokers). In the multivariable analysis, smokers demonstrated a heightened risk of death (aOR 1.23; 95 % CI 1.19-1.25).

CONCLUSIONS

This large populational-based cohort study confirms the current evidence and underscore the critical importance of recognizing smoking as a substantial risk factor for adverse outcomes in COVID-19 patients.

Mortality risk in patients with obesity and COVID-19 infection: a systematic review and meta-analysis

Obesity is a risk factor for severe respiratory diseases, including COVID-19 infection. Meta-analyses on mortality risk were inconsistent. We systematically searched 3 databases (Medline, Embase, CINAHL) and assessed the quality of studies using the Newcastle-Ottawa tool (CRD42020220140). We included 199 studies from US and Europe, with a mean age of participants 41.8-78.2 years, and a variable prevalence of metabolic co-morbidities of 20-80 %. Exceptionally, one third of the studies had a low prevalence of obesity of <20 %. Compared to patients with normal weight, those with obesity had a 34 % relative increase in the odds of mortality (p-value 0.002), with a dose-dependent relationship. Subgroup analyses showed an interaction with the country income. There was a high heterogeneity in the results, explained by clinical and methodologic variability across studies. We identified one trial only comparing mortality rate in vaccinated compared to unvaccinated patients with obesity; there was a trend for a lower mortality in the former group. Mortality risk in COVID-19 infection increases in parallel to an increase in BMI. BMI should be included in the predictive models and stratification scores used when considering mortality as an outcome in patients with COVID-19 infections. Furthermore, patients with obesity might need to be prioritized for COVID-19 vaccination.

Who is most at risk of dying if infected with SARS-CoV-2? A mortality risk factor analysis using machine learning of patients with COVID-19 over time: a large population-based cohort study in Mexico

OBJECTIVE

COVID-19 would kill fewer people if health programmes can predict who is at higher risk of mortality because resources can be targeted to protect those people from infection. We predict mortality in a very large population in Mexico with machine learning using demographic variables and pre-existing conditions.

DESIGN

Cohort study.

SETTING

March 2020 to November 2021 in Mexico, nationally represented.

PARTICIPANTS

1.4 million laboratory-confirmed patients with COVID-19 in Mexico at or over 20 years of age.

PRIMARY AND SECONDARY OUTCOME MEASURES

Analysis is performed on data from March 2020 to November 2021 and over three phases: (1) from March to October in 2020, (2) from November 2020 to March 2021 and (3) from April to November 2021. We predict mortality using an ensemble machine learning method, super learner, and independently estimate the adjusted mortality relative risk of each pre-existing condition using targeted maximum likelihood estimation.

RESULTS

Super learner fit has a high predictive performance (C-statistic: 0.907), where age is the most predictive factor for mortality. After adjusting for demographic factors, renal disease, hypertension, diabetes and obesity are the most impactful pre-existing conditions. Phase analysis shows that the adjusted mortality risk decreased over time while relative risk increased for each pre-existing condition.

CONCLUSIONS

While age is the most important predictor of mortality, younger individuals with hypertension, diabetes and obesity are at comparable mortality risk as individuals who are 20 years older without any of the three conditions. Our model can be continuously updated to identify individuals who should most be protected against infection as the pandemic evolves.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

OBJECTIVES

To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs).

METHODS

A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake.

RESULTS

A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives.

CONCLUSIONS

We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective

BACKGROUND

The COVID-19 pandemic is an epidemiological and psychological crisis; what it does to the body is quite well known by now, and more research is underway, but the syndemic impact of COVID-19 and mental health on underlying chronic illnesses among the general population is not completely understood.

METHODS

We carried out a literature review to identify the potential impact of COVID-19 and related mental health issues on underlying comorbidities that could affect the overall health of the population.

RESULTS

Many available studies have highlighted the impact of COVID-19 on mental health only, but how complex their interaction is in patients with comorbidities and COVID-19, the absolute risks, and how they connect with the interrelated risks in the general population, remain unknown. The COVID-19 pandemic can be recognized as a syndemic due to; synergistic interactions among different diseases and other health conditions, increasing overall illness burden, emergence, spread, and interactions between infectious zoonotic diseases leading to new infectious zoonotic diseases; this is together with social and health interactions leading to increased risks in vulnerable populations and exacerbating clustering of multiple diseases.

CONCLUSION

There is a need to develop evidence to support appropriate and effective interventions for the overall improvement of health and psychosocial wellbeing of at-risk populations during this pandemic. The syndemic framework is an important framework that can be used to investigate and examine the potential benefits and impact of codesigning COVID-19/non-communicable diseases (NCDs)/mental health programming services which can tackle these epidemics concurrently.

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran

BACKGROUND

The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients.

METHODS

This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models.

RESULTS

Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features.

CONCLUSIONS

Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2

BACKGROUND

This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data.

METHODS

This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models.

RESULTS

We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation.

CONCLUSIONS

The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.

Social determinants and adherence to recommended COVID-19 vaccination among the Arab ethnic minority: A syndemics framework

Background

Since the mass vaccination against SARS-CoV-2 was launched in Israel, the Arab ethnicity minority had lower vaccine uptake. The syndemics theory suggests a closely interrelated complex of health and social crises among vulnerable societies results in an increased disease burden or in more adverse health conditions. Syndemics may explain the health disparities between different people or communities. Likewise, acculturation was found to be associated with different health outcomes among minority populations. The purpose of the study is to explore the association between syndemic construct, acculturation style, and adherence to recommended COVID-19 vaccination among the Arab ethnicity in Israel.

Methods

A cross-sectional study among 305 participants who completed a self-report questionnaire. Syndemic construct (syndemics score and syndemics severity) was calculated from the participants' health behavior index, self-rated health status, and adherence to flu vaccination. Four acculturation strategies were defined according to Barry's acculturation model: assimilation, integration, separation, and marginalization style. Linear regression (stepwise method) was conducted to determine the explanatory factors for COVID-19 vaccine adherence.

Results

Assimilation and separation acculturation styles and syndemics severity were significantly associated with higher adherence to the recommended COVID-19 vaccination (B = 1.12, 95%CI = 0.34-1.98; B = 0.45, 95%CI = 0.10-0.80; B = 0.18, 95%CI = 0.09-0.28; respectively). The explained variance of the model (R ²) was 19.9%.

Conclusion

Syndemics severity, assimilation and separation acculturation styles were associated with higher adherence to recommended COVID-19 vaccination in the Israeli Arab minority population. Syndemics score was not associated with recommended COVID-19 vaccination. To encourage COVID-19 vaccination among minority communities, campaigns should be tailored to the social determinants in a sensitive and individualized manner.

Clinical indicators, nursing diagnoses, and mortality risk in critically ill patients with COVID-19: a retrospective cohort

OBJECTIVE

To identify clinical indicators and nursing diagnoses with the highest risk of mortality in critically ill patients with COVID-19.

METHOD

Retrospective cohort with the population of adults and elderly people with COVID-19 from an Intensive Care Unit. Categorical variables were described using absolute and relative frequencies and risk factors for mortality using Cox regression, with a confidence interval of 95%.

RESULTS

The main clinical indicators of COVID-19 patients were dyspnea, fever, fatigue, cough, among others, and the Nursing Diagnoses at higher risk of mortality were Ineffective protection, Ineffective tissue perfusion, Contamination, Ineffective Breathing Pattern, Impaired spontaneous ventilation, Acute confusion, Frailty syndrome, Obesity, and Decreased cardiac output. It is worth mentioning that there was little information about the diagnoses of Domains 9, 10, and 12.

CONCLUSION

This research infers the need to monitor the clinical indicators dyspnea, fever, fatigue, cough, among others, and the Nursing Diagnoses with the highest risk of mortality Ineffective protection, Ineffective tissue perfusion, Contamination, Ineffective Breathing Pattern, Impaired spontaneous ventilation in critically ill patients.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Prehospital COVID19-related Encounters Predict Future Hospital Utilization

Objective: Identify if prehospital patient encounters can predict SARS-CoV-2 (COVID19) related hospital utilization. Methods: EMS data from COVID19-related prehospital encounters was pulled from NEMSIS systems in Minnesota. This data was plotted against hospital general medical-surgical bed and ICU bed usage during the initial COVID19 surge and again during a second surge. A validation dataset from 2019 was also utilized. Results: There was a total of 6,460 influenza-like-illness calls, and 2,161 COVID19-specific calls during the studied timeframe. A total of 24,806 medical-surgical bed-days and 20,208 ICU bed-days were analyzed. During initial COVID surge (April-July 2020), EMS encounters best correlated with medical-surgical bed utilization 10 days in the future (r² = 0.85, N = 113, p = <0.001), with each encounter correlating with a utilization of 7.1 beds. ICU bed utilization was best predicted 16 days in the future (r² = 0.86, N = 107, p = <0.001) with each encounter correlating with the use of 4.5 ICU beds. Similarly strong and clinically significant correlations were found during the second surged during July and August. There was no significant correlation when comparing to a similar dataset using 2019 ILI calls. Conclusion: Minnesota prehospital COVID19-related prehospital encounters are shown to accurately predict hospital bed utilization 1-2 weeks in advance. This was reproducible across two COVID19 surges. Trends in EMS patient encounters could serve as a valuable data point in predicting COVID19 surges and their effects on hospital utilization.

A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization

Background and aim

COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization.

Methods

Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant.

Results

A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST.

Conclusion

SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Simple scoring tool to estimate risk of hospitalization and mortality in ambulatory and emergency department patients with COVID-19

BACKGROUND

Accurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.

METHODS

All consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.

RESULTS

22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n = 16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n = 6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).

CONCLUSION

A prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources

OBJECTIVE

To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries.

DESIGN

Epidemiological study.

SETTING

Country-based data from publicly available online databases of international organisations.

PARTICIPANTS

The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia).

PRIMARY AND SECONDARY OUTCOME MEASURES

The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19.

RESULTS

In the model for the COVID-19 cases (R²=0.45), obesity (β=0.460), hypertension (β=0.214), sunshine (β=-0.157) and transparency (β=0.147); whereas in the model for COVID-19 deaths (R²=0.41), obesity (β=0.279), hypertension (β=0.285), alcohol consumption (β=0.173) and urbanisation (β=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index.

CONCLUSIONS

This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04486508).

Vaccine effectiveness of ChAdOx1 nCoV-19 against COVID-19 in a socially vulnerable community in Rio de Janeiro, Brazil: a test-negative design study

Objectives

To estimate vaccine effectiveness after the first and second dose of ChAdOx1 nCoV-19 against symptomatic COVID-19 and infection in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating.

Methods

We conducted a test-negative study in the community Complexo da Maré, the largest group of slums (n = 16) in Rio de Janeiro, Brazil, from January 17, 2021 to November 27, 2021. We selected RT-qPCR positive and negative tests from a broad community testing program. The primary outcome was symptomatic COVID-19 (positive RT-qPCR test with at least one symptom) and the secondary outcome was infection (any positive RT-qPCR test). Vaccine effectiveness was estimated as 1 – OR, which was obtained from adjusted logistic regression models.

Results

We included 10 077 RT-qPCR tests (6,394, 64% from symptomatic and 3,683, 36% from asymptomatic individuals). The mean age was 40 (SD: 14) years, and the median time between vaccination and RT-qPCR testing among vaccinated was 41 (25–75 percentile: 21–62) days for the first dose and 36 (25–75 percentile: 17–59) days for the second dose. Adjusted vaccine effectiveness against symptomatic COVID-19 was 31.6% (95% CI, 12.0–46.8) 21 days after the first dose and 65.1% (95% CI, 40.9–79.4) 14 days after the second dose. Adjusted vaccine effectiveness against COVID-19 infection was 31.0% (95% CI, 12.7–45.5) 21 days after the first dose and 59.0% (95% CI, 33.1–74.8) 14 days after the second dose.

Discussion

ChAdOx1 nCoV-19 was effective in reducing symptomatic COVID-19 in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating.

Coresidence increases the risk of testing positive for COVID-19 among older Brazilians

BACKGROUND

Brazil is among the countries hit hardest by COVID-19, and older adults are among the vulnerable groups. Intergenerational coresidence and interdependence among family members, both prevalent in Brazil, likely increase social and physical contact and thus potential infection.

METHODS

Using nationally representative data from the COVID-19 module of the Brazilian National Household Sample Survey (Pesquisa Nacional por Amostra de Domicílios), collected between July and November of 2020, we examined the association between living arrangements and exposure to and testing for COVID-19 among 63,816 Brazilians aged 60 years and older. We examine whether living arrangements influence self-reported COVID-19 symptoms as an indicator of subjective health assessment, testing as an indicator of health care service use, and a positive COVID-19 test result as an objective indicator of exposure to the disease.

RESULTS

Living arrangements shape older adults' vulnerabilities to COVID-19 exposure and testing. Specifically, those living alone were more likely to report having symptoms and having had a test for COVID-19. However, older adults in multigenerational and skipped generation households were more likely than solo-dwellers to test positive for COVID-19. Those with symptoms were more likely to test, regardless of their living arrangement. Among older adults without symptoms, those living alone had a higher probability of testing than those living in multigenerational or skipped-generation households.

CONCLUSIONS

Overall, our findings suggest that coresidence with younger family members puts older adults' health at risk in the context of COVID-19. As younger Brazilians are increasingly vulnerable to COVID-19 and experiencing severe outcomes, policy makers need to be more attentive to the health needs of households that comprise older and younger cohorts, which are also more prevalent in poor and marginalized segments of the population.

Indicadores clínicos, diagnósticos de enfermagem e risco de mortalidade em pacientes críticos com COVID-19: coorte retrospectiva

RESUMO Objetivo: Identificar os indicadores clínicos e os diagnósticos de enfermagem com maior risco de mortalidade em pacientes críticos com COVID-19. Método: Coorte retrospectiva com a população de adultos e idosos com COVID-19 de uma Unidade de Terapia Intensiva. As variáveis categóricas foram descritas por frequências absoluta e relativa e os fatores de risco para mortalidade, pela regressão de Cox, com intervalo de confiança de 95%. Resultados: Os principais indicadores clínicos de pacientes com COVID-19 foram dispneia, febre, fadiga, tosse, entre outros, e os Diagnósticos de Enfermagem de maior risco de mortalidade: Proteção ineficaz; Perfusão tissular ineficaz; Contaminação; Padrão Respiratório Ineficaz; Ventilação espontânea prejudicada; Confusão aguda; Síndrome do idoso frágil; Obesidade e Débito cardíaco diminuído. Vale ressaltar que havia poucas informações sobre os diagnósticos dos Domínios 9, 10 e 12. Conclusão: Esta pesquisa infere a necessidade de vigiar os indicadores clínicos dispneia, febre, fadiga, tosse, entre outros e os Diagnósticos de Enfermagem de maior risco de mortalidade Proteção ineficaz; Perfusão tissular ineficaz; Contaminação; Padrão Respiratório Ineficaz; Ventilação espontânea prejudicada em pacientes críticos.

OUP accepted manuscript

Objective

To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.

Materials and Methods

Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models.

Results

Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively.

Discussion

The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories.

Conclusions

This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.

COVID-19 within families amplifies the prosociality gap between adolescents of high and low socioeconomic status

COVID-19 has had worse health, education, and labor market effects on groups with low socioeconomic status (SES) than on those with high SES. Little is known, however, about whether COVID-19 has also had differential effects on noncognitive skills that are important for life outcomes. Using panel data from before and during the pandemic, we show that COVID-19 affects one key noncognitive skill, that is, prosociality. While prosociality is already lower for low-SES students prior to the pandemic, we show that COVID-19 infections within families amplify the prosociality gap between French high school students of high and low SES by almost tripling its size in comparison to pre-COVID-19 levels.

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies

OBJECTIVES

We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions.

METHODS

Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework.

RESULTS

A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively.

CONCLUSION

Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths.

TRIAL REGISTRATION NUMBER

CRD42020218115.

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study

Background:

In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.

Methods:

We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using

An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.

Results:

We observed that

Conclusions:

Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

Socioeconomic inequalities associated with mortality for COVID-19 in Colombia: a cohort nationwide study

BACKGROUND

After 8 months of the COVID-19 pandemic, Latin American countries have some of the highest rates in COVID-19 mortality. Despite being one of the most unequal regions of the world, there is a scarce report of the effect of socioeconomic conditions on COVID-19 mortality in their countries. We aimed to identify the effect of some socioeconomic inequality-related factors on COVID-19 mortality in Colombia.

METHODS

We conducted a survival analysis in a nation-wide retrospective cohort study of confirmed cases of COVID-19 in Colombia from 2 March 2020 to 26 October 2020. We calculated the time to death or recovery for each confirmed case in the cohort. We used an extended multivariable time-dependent Cox regression model to estimate the HR by age groups, sex, ethnicity, type of health insurance, area of residence and socioeconomic strata.

RESULTS

There were 1 033 218 confirmed cases and 30 565 deaths for COVID-19 in Colombia between 2 March and 26 October. The risk of dying for COVID-19 among confirmed cases was higher in males (HR 1.68 95% CI 1.64 to 1.72), in people older than 60 years (HR 296.58 95% CI 199.22 to 441.51), in indigenous people (HR 1.20 95% CI 1.08 to 1.33), in people with subsidised health insurance regime (HR 1.89 95% CI 1.83 to 1.96) and in people living in the very low socioeconomic strata (HR 1.44 95% CI 1.24 to 1.68).

CONCLUSION

Our study provides evidence of socioeconomic inequalities in COVID-19 mortality in terms of age groups, sex, ethnicity, type of health insurance regimen and socioeconomic status.

COVID-19 pandemic as a risk factor for the reactivation of herpes viruses

The appearance on the skin of herpes virus lesions, concomitantly with the coronavirus disease 2019 (COVID-19) pandemic, leads us to suspect an underlying infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Diagnostic reverse transcriptase polymerase chain reaction tests and immunoglobulin M (IgM) and IgG seroconversion studies have therefore been carried out. We present three cases of herpes virus infections in immunocompetent patients: one of the infections was herpes simplex 1 in a 40-year-old woman, and the other two were herpes varicella-zoster infections in a 62-year-old man and a 25-year-old woman. The patients were in the care of the southern health district of Seville of the SAS (Andalusian Health Service) during the Spanish state of alarm over the COVID-19 pandemic. The SARS-CoV-2 infection was confirmed in only one of the three cases. In this study, we briefly review the etiopathogenic role of the COVID-19 pandemic situation, whereby immunodeficiencies are generated that favour the appearance of other viral infections, such as herpes virus infections.

Hospital load and increased COVID-19 related mortality in Israel

The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality of patients with COVID-19 by analyzing records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250–500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load.

COVID-19 has caused many healthcare systems to become overwhelmed, potentially impacting patient care. Here, the authors show that COVID-19-related in-hospital mortality rates in Israel increased in periods of moderate or high hospital load, independent of patient characteristics.

The Relevance of a Physical Active Lifestyle and Physical Fitness on Immune Defense: Mitigating Disease Burden, With Focus on COVID-19 Consequences

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a fast spreading virus leading to the development of Coronavirus Disease-2019 (COVID-19). Severe and critical cases are characterized by damage to the respiratory system, endothelial inflammation, and multiple organ failure triggered by an excessive production of proinflammatory cytokines, culminating in the high number of deaths all over the world. Sedentarism induces worse, continuous, and progressive consequences to health. On the other hand, physical activity provides benefits to health and improves low-grade systemic inflammation. The aim of this review is to elucidate the effects of physical activity in physical fitness, immune defense, and its contribution to mitigate the severe inflammatory response mediated by SARS-CoV-2. Physical exercise is an effective therapeutic strategy to mitigate the consequences of SARS-CoV-2 infection. In this sense, studies have shown that acute physical exercise induces the production of myokines that are secreted in tissues and into the bloodstream, supporting its systemic modulatory effect. Therefore, maintaining physical activity influence balance the immune system and increases immune vigilance, and also might promote potent effects against the consequences of infectious diseases and chronic diseases associated with the development of severe forms of COVID-19. Protocols to maintain exercise practice are suggested and have been strongly established, such as home-based exercise (HBE) and outdoor-based exercise (OBE). In this regard, HBE might help to reduce levels of physical inactivity, bed rest, and sitting time, impacting on adherence to physical activity, promoting all the benefits related to exercise, and attracting patients in different stages of treatment for COVID-19. In parallel, OBE must improve health, but also prevent and mitigate COVID-19 severe outcomes in all populations. In conclusion, HBE or OBE models can be a potent strategy to mitigate the progress of infection, and a coadjutant therapy for COVID-19 at all ages and different chronic conditions.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.