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1.
JMIR Public Health Surveill ; 7(4): e25075, 2021 04 30.
Article in English | MEDLINE | ID: covidwho-2141297

ABSTRACT

BACKGROUND: Risk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit. OBJECTIVE: The goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations. METHODS: We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization. RESULTS: Providers using the risk assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; P=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; P<.001) for Tier 3. CONCLUSIONS: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.


Subject(s)
Ambulatory Care , COVID-19/therapy , Hospitalization/statistics & numerical data , Risk Assessment/methods , Telemedicine , Adolescent , Adult , Aged , Female , Humans , Male , Middle Aged , Reproducibility of Results , Retrospective Studies , Young Adult
2.
Work ; 73(3): 799-808, 2022.
Article in English | MEDLINE | ID: covidwho-2118957

ABSTRACT

BACKGROUND: Given the coronavirus 2019 (COVID-19) risk, it is essential to develop a comprehensive risk assessment method to manage the risk of the infectious diseases. OBJECTIVE: This study aimed to develop a risk assessment method for infectious diseases focusing on COVID-19. METHOD: This study was based on the fuzzy Delphi method (FDM) and fuzzy analytical hierarchical process (FAHP) in three steps: (a) designing the preliminary risk assessment algorithm by reviewing the literature, (b) corroborating the designed structure based on the majority opinions of the expert panel and assigning scores to different factors according to the Delphi method, and (c) determining the weight of components and their factors based on the FAHP. RESULTS: The COVID-19 risk index (CVRI) was found to be affected by four components and 19 factors. The four components consisted of the probability of getting sick (5 factors), disease severity (4 factors), health beliefs level (3 factors), and exposure rate (6 factors). The identified components and their relevant factors had different weights and effects on the CVIR. The weights of probability, severity, health beliefs level, and exposure rate components were 0.27, 0.20, 0.14, and 0.38, respectively. The CVRI was found to range from 0.54 to 0.82, defined in three levels. CONCLUSION: Given the significant effects of identified components, factors, and parameters on the incidence of COVID-19 on the one hand and using the FDM and FAHP on the other, the proposed method can be considered as an appropriate method for managing the risk of COVID-19 and other infectious diseases.


Subject(s)
COVID-19 , Communicable Diseases , Humans , Fuzzy Logic , Delphi Technique , Analytic Hierarchy Process , COVID-19/epidemiology , Risk Assessment/methods
3.
J Postgrad Med ; 68(4): 199-206, 2022.
Article in English | MEDLINE | ID: covidwho-2080671

ABSTRACT

Background: : Risk assessment with prognostic scoring, though important, is scarcely studied in emergency surgical patients with COVID-19 infection. Methods and Material: We conducted a retrospective cohort study on adult emergency surgical patients with COVID-19 infection in our institute from 1 May 2020 to 31 October 2021 to find the 30-day postoperative mortality and predictive accuracy of prognostic scores. We assessed the demographic data, prognostic risk scores (American Society of Anesthesiologists-Physical Classification (ASA-PS), Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scores), surgical and anesthetic factors. We assessed the postoperative morbidity using the Clavien-Dindo scale and recorded the 30-day mortality. Correlation of prognostic scores and mortality was evaluated using Univariate Cox proportional hazards regression, receiver operating characteristic curve (ROC), Youden's index and Hosmer- Lemeshow goodness of fit model. Results: Emergency surgery was performed in 67 COVID-19 patients with postoperative complication and 30-day mortality rate of 33% and 19%, respectively. A positive qSOFA and ASAPS IIIE/IVE had a 9.03- and 12.7-times higher risk of mortality compared to a negative qSOFA and ASA-PS IE/IIE (P < 0.001), respectively. Every unit increase of SOFA, POSSUM and P-POSSUM scores was associated with a 50%, 18% and 17% higher risk of mortality, respectively. SOFA, POSSUM and P-POSSUM AUCROC curves showed good discrimination between survivors and non-survivors (AUC 0.8829, 0.85 and 0.86, respectively). Conclusions: SOFA score has a higher sensitivity to predict 30-day postoperative mortality as compared to POSSUM and P-POSSUM. However, in absence of a control group of non-COVID-19 patients, actual risk attributable to COVID-19 infection could not be determined.


Subject(s)
COVID-19 , Adult , Humans , Retrospective Studies , Prognosis , Postoperative Period , Risk Assessment/methods , ROC Curve , Postoperative Complications/etiology , Severity of Illness Index
4.
BMJ Open ; 12(4): e057305, 2022 04 05.
Article in English | MEDLINE | ID: covidwho-1962246

ABSTRACT

OBJECTIVE: To review, inventory and compare available diagnostic tools and investigate which tool has the best performance for prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome (NSTE-ACS). METHODS: Systematic review and meta-analysis. Medline and Embase were searched up till 1 April 2021. Prospective studies with patients, suspected of NSTE-ACS, presenting in the primary care setting or by emergency medical services (EMS) were included. The most important exclusion criteria were studies including only patients with ST-elevation myocardial infarction and studies before 1995, the pretroponin era. The primary end point was the final hospital discharge diagnosis of NSTE-ACS or major adverse cardiac events (MACE) within 6 weeks. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies Criteria. MAIN OUTCOME AND MEASURES: Sensitivity, specificity and likelihood ratio of findings for risk stratification in patients suspected of NSTE-ACS. RESULTS: In total, 15 prospective studies were included; these studies reflected in total 26 083 patients. No specific variables related to symptoms, physical examination or risk factors were useful in risk stratification for NSTE-ACS diagnosis. The most useful electrocardiographic finding was ST-segment depression (LR+3.85 (95% CI 2.58 to 5.76)). Point-of-care troponin was found to be a strong predictor for NSTE-ACS in primary care (LR+14.16 (95% CI 4.28 to 46.90) and EMS setting (LR+6.16 (95% CI 5.02 to 7.57)). Combined risk scores were the best for risk assessment in an NSTE-ACS. From the combined risk scores that can be used immediately in a prehospital setting, the PreHEART score, a validated combined risk score for prehospital use, derived from the HEART score (History, ECG, Age, Risk factors, Troponin), was most useful for risk stratification in patients with NSTE-ACS (LR+8.19 (95% CI 5.47 to 12.26)) and for identifying patients without ACS (LR-0.05 (95% CI 0.02 to 0.15)). DISCUSSION: Important study limitations were verification bias and heterogeneity between studies. In the prehospital setting, several diagnostic tools have been reported which could improve risk stratification, triage and early treatment in patients suspected for NSTE-ACS. On-site assessment of troponin and combined risk scores derived from the HEART score are strong predictors. These results support further studies to investigate the impact of these new tools on logistics and clinical outcome. FUNDING: This study is funded by ZonMw, the Dutch Organisation for Health Research and Development. TRIAL REGISTRATION NUMBER: This meta-analysis was published for registration in PROSPERO prior to starting (CRD York, CRD42021254122).


Subject(s)
Acute Coronary Syndrome , Emergency Medical Services , Acute Coronary Syndrome/diagnosis , Acute Coronary Syndrome/therapy , Electrocardiography/methods , Emergency Medical Services/methods , Humans , Prospective Studies , Risk Assessment/methods
5.
Risk Anal ; 42(1): 85-96, 2022 01.
Article in English | MEDLINE | ID: covidwho-1961881

ABSTRACT

The preparedness of Indian states and union territories (UTs) against the COVID-19 pandemic has been evaluated. Ten parameters related to demographic, socioeconomic, and healthcare aspects have been considered and the performances of 27 states and three UTs have been evaluated applying the Fuzzy Analytic Hierarchy Process. Opinions of medical experts have been considered to ascertain the relative importance of decision criteria as well as subcriteria. The scores of various states and UTs in each of the decision subcriteria have been calculated by using the secondary data collected from authentic sources. It is found that Kerala and Bihar are the best prepared and worst prepared states, respectively, to combat COVID-19 pandemic. Karnataka, Goa, and Tamil Nadu have very good preparedness whereas Chhattisgarh, Jharkhand, and Bihar have very poor preparedness. Maharashtra, the most affected state in India, has average preparedness. As around 650 million people are vulnerable due to the poor and very poor preparedness of their states, the country needs to make region specific mitigation strategies to combat the COVID-19 pandemic and the preparedness map will be helpful in that direction.


Subject(s)
COVID-19/epidemiology , Decision Making , Pandemics/prevention & control , Risk Assessment/methods , SARS-CoV-2 , Humans , India/epidemiology
6.
Risk Anal ; 42(1): 69-84, 2022 01.
Article in English | MEDLINE | ID: covidwho-1961880

ABSTRACT

We propose a Pandemic Risk Perception Scale. Our scale comprises two constructs, Dread Risk and Personal Exposure, divided into five dimensions: Infection Risk, Emotional Health Risk, Health System Risk,Financial Risk, and Alimentary Risk. Using multidimensional item response theory, confirmatory factor analysis, and structural equation modeling on two samples of respondents, our results show that Alimentary Risk, Health System Risk, and Emotional Health Risk are the main dimensions of risk perception for the COVID-19 pandemic. Furthermore, Infection Risk has a minor impact on the pandemic's risk perception, suggesting the presence of different dynamics between personal and general risk perceptions for the COVID-19 pandemic.


Subject(s)
COVID-19/epidemiology , Emotions/physiology , Pandemics , Perception , Risk Assessment/methods , SARS-CoV-2 , Surveys and Questionnaires , COVID-19/psychology , Humans
7.
Risk Anal ; 42(1): 126-142, 2022 01.
Article in English | MEDLINE | ID: covidwho-1961877

ABSTRACT

Several reports in India indicate hospitals and quarantined centers are COVID-19 hotspots. To study the transmission occurring from the hospitals and as well as from the community, we developed a mechanistic model with a lockdown effect. Using daily COVID-19 cases data from six states and overall India, we estimated several important parameters of our model. Moreover, we provided an estimation of the effective (RT ), the basic (R0 ), the community (RC ), and the hospital (RH ) reproduction numbers. We forecast COVID-19 notified cases from May 3, 2020, till May 20, 2020, under five different lockdown scenarios in the seven locations. Our analysis suggests that 65% to 99% of the new COVID-19 cases are currently asymptomatic in those locations. Besides, about 1-16% of the total COVID-19 transmission are currently occurring from hospital-based contact and these percentage can increase up to 69% in some locations. Furthermore, the hospital-based transmission rate (ß2 ) has significant positive (0.65 to 0.8) and negative (-0.58 to -0.23) correlation with R0 and the effectiveness of lockdown, respectively. Therefore, a much larger COVID-19 outbreak may trigger from the hospital-based transmission. In most of the locations, model forecast from May 3, 2020, till May 20, 2020, indicates a two-times increase in cumulative cases in comparison to total observed cases up to April 29, 2020. Based on our results, we proposed a containment policy that may reduce the threat of a larger COVID-19 outbreak in the future.


Subject(s)
COVID-19/epidemiology , Pandemics , Quarantine/organization & administration , Risk Assessment/methods , SARS-CoV-2 , COVID-19/transmission , Communicable Disease Control/methods , Humans , India/epidemiology
8.
Risk Anal ; 42(1): 105-125, 2022 01.
Article in English | MEDLINE | ID: covidwho-1961876

ABSTRACT

The COVID-19 pandemic has become a public health crisis in the Philippines and the attention of national and local health authorities is focused on managing the fluctuating COVID-19 cases. This study presents a method that integrates risk management tools into health care decision-making processes to enhance the understanding and utilization of risk-based thinking in public health decision making. The risk assessment consists of the identification of the key risk factors of the COVID-19 contagion via bow-tie diagrams. Second, the safety controls for each risk factor relevant to the Davao City context are taken into account and are identified as barriers in the bow-tie. After which, the prioritization of the identified COVID-19 risks, as well as the effectiveness of the proposed interventions, is performed using the analytic hierarchy process. Consequently, the dynamics of COVID-19 management initiatives were explored using these priorities and a system of ordinary differential equations. Our results show that reducing the number of COVID-19 fatalities should be the top priority of the health authorities. In turn, we predict that the COVID-19 contagion can be controlled and eliminated in Davao city in three-month time after prioritizing the fatalities. In order to reduce the COVID-19 fatalities, health authorities should ensure an adequate number of COVID-ready ICU facilities. The general public, on the other hand, should follow medical and science-based advice and suspected and confirmed COVID-19 patients should strictly follow isolation protocols. Overall, an informed decision-making is necessary to avoid the unwanted consequences of an uncontrolled contagion.


Subject(s)
COVID-19/epidemiology , Pandemics , Risk Assessment/methods , SARS-CoV-2 , Urban Population , Humans , Philippines/epidemiology
9.
J Stroke Cerebrovasc Dis ; 31(8): 106589, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1945834

ABSTRACT

OBJECTIVES: To derive models that identify patients with COVID-19 at high risk for stroke. MATERIALS AND METHODS: We used data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry to generate models for predicting stroke risk among adults hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our models, we used data on demographics, comorbidities, medications, and vital sign and laboratory values at admission. The outcome was a cerebrovascular event (stroke, TIA, or cerebral vein thrombosis). First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable and multivariable analyses. Then, we assigned points for each variable based on corresponding coefficients to create a prediction score. Second, we used machine learning techniques to create risk estimators using all available covariates. RESULTS: Among 21,420 patients hospitalized with COVID-19, 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created/validated a COVID-19 stroke risk score with a C-statistic of 0.66 (95% CI, 0.60-0.72). The CANDLE score assigns 1 point each for prior cerebrovascular disease, afebrile temperature, no prior pulmonary disease, history of hypertension, leukocytosis, and elevated systolic blood pressure. CANDLE stratified risk of an acute cerebrovascular event according to low- (0-1: 0.2% risk), medium- (2-3: 1.1% risk), and high-risk (4-6: 2.1-3.0% risk) groups. Machine learning estimators had similar discriminatory performance as CANDLE: C-statistics, 0.63-0.69. CONCLUSIONS: We developed a practical clinical score, with similar performance to machine learning estimators, to help stratify stroke risk among patients hospitalized with COVID-19.


Subject(s)
COVID-19 , Stroke , Adult , COVID-19/complications , COVID-19/diagnosis , Hospitalization , Humans , Risk Assessment/methods , Risk Factors , Stroke/diagnosis , Stroke/epidemiology , Stroke/therapy
10.
Int J Environ Res Public Health ; 19(12)2022 06 07.
Article in English | MEDLINE | ID: covidwho-1887191

ABSTRACT

BACKGROUND: At an international level, the risk assessment and management process of violent offenders follows a standard method that implies well-defined theoretical models and the use of scientifically validated tools. In Italy, this process is still highly discretionary. The aim of this study is to highlight the advantages deriving from the use of risk assessment tools within the framework of a single case study; Methods: Recidivism risk and social dangerousness of an Italian woman perpetrator of filicide were assessed through the administration of the Level of Service/Case Management Inventory (LS/CMI) instrument supported by Historical Clinical Risk-20 Version 3 (HCR-20 V3); Results: The administration of LS/CMI showed that, in this single case, the subcomponents represent a criminogenic risk/need factor are: Family/Marital, Companions, Alcohol and Drug Problem and Leisure; while constituting strengths: employment and the absence of a Pro-criminal Orientation and an Antisocial Pattern; Conclusions: Data collected through LS/CMI indicated life areas of a single case, which should be emphasised not only to assess the risk of re-offending and social dangerousness but also for a social rehabilitation programme more suited to the subject. This study demonstrates that the LS/CMI assessment tool is suitable for the Italian context.


Subject(s)
Criminals , Recidivism , Female , Humans , Italy , Risk Assessment/methods , Violence
11.
Cornea ; 41(3): 339-346, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1865000

ABSTRACT

PURPOSE: The purpose of this study was to assess the medical history of adenoviral keratoconjunctivitis (AK) and subepithelial infiltrates (SEIs) among French ophthalmologists and orthoptists and the frequency of unreported occupational diseases. We also described short-term and long-term consequences of AK and evaluated associated factors. METHODS: The REDCap questionnaire was diffused online several times over 7 consecutive months, from October 2019 to May 2020, through mailing lists (French Society of Ophthalmology, residents, and hospital departments), social networks, and by word of mouth. RESULTS: Seven hundred ten participants were included with a response rate of 6.2% for ophthalmologists, 3.8% for orthoptists, and 28.3% for ophthalmology residents. The medical history of AK was found in 24.1% (95% confidence interval 21%-27.2%) of respondents and SEI in 43.9% (36.5%-51.3%) of the AK population. In total, 87.1% (82.1%-92.1%) of AK occupational diseases were not declared. In total, 57.7% of respondents took 9.4 ± 6.2 days of sick leave, mostly unofficial, and 95.7% stopped surgeries for 13.0 ± 6.6 days. Among the AK population, 39.8% had current sequelae, with 17.5% having persistent SEIs, 19.9% using current therapy, and 16.4% experiencing continuing discomfort. SEIs were associated with wearing contact lenses (odds ratio 3.31, 95% confidence interval 1.19-9.21) and smoking (4.07, 1.30-12.8). Corticosteroid therapy was associated with a greater number of sequelae (3.84, 1.51-9.75). CONCLUSIONS: AK and SEI affect a large proportion of ophthalmologists and orthoptists, possibly for years, with high morbidity leading to occupational discomfort. Few practitioners asked for either to be recognized as an occupational disease. Associated factors would require a dedicated study.


Subject(s)
Adenovirus Infections, Human/complications , Eye Infections, Viral/complications , Keratoconjunctivitis/complications , Ophthalmologists/statistics & numerical data , Orthoptics/statistics & numerical data , Risk Assessment/methods , Vision, Low/etiology , Adenovirus Infections, Human/epidemiology , Adult , Aged , Cross-Sectional Studies , Eye Infections, Viral/epidemiology , Female , Follow-Up Studies , France/epidemiology , Humans , Keratoconjunctivitis/epidemiology , Male , Middle Aged , Morbidity/trends , Retrospective Studies , Risk Factors , Surveys and Questionnaires , Time Factors , Vision, Low/epidemiology , Visual Acuity , Young Adult
13.
Vascul Pharmacol ; 145: 106999, 2022 08.
Article in English | MEDLINE | ID: covidwho-1852225

ABSTRACT

Inflammatory response following SARS-CoV-2 infection results in substantial increase of amounts of intravascular pro-coagulant extracellular vesicles (EVs) expressing tissue factor (CD142) on their surface. CD142-EV turned out to be useful as diagnostic biomarker in COVID-19 patients. Here we aimed at studying the prognostic capacity of CD142-EV in SARS-CoV-2 infection. Expression of CD142-EV was evaluated in 261 subjects admitted to hospital for pneumonia and with a positive molecular test for SARS-CoV-2. The study population consisted of a discovery cohort of selected patients (n = 60) and an independent validation cohort including unselected consecutive enrolled patients (n = 201). CD142-EV levels were correlated with post-hospitalization course of the disease and compared to the clinically available 4C Mortality Score as referral. CD142-EV showed a reliable performance to predict patient prognosis in the discovery cohort (AUC = 0.906) with an accuracy of 81.7%, that was confirmed in the validation cohort (AUC = 0.736). Kaplan-Meier curves highlighted a high discrimination power in unselected subjects with CD142-EV being able to stratify the majority of patients according to their prognosis. We obtained a comparable accuracy, being not inferior in terms of prediction of patients' prognosis and risk of mortality, with 4C Mortality Score. The expression of surface vesicular CD142 and its reliability as prognostic marker was technically validated using different immunocapture strategies and assays. The detection of CD142 on EV surface gains considerable interest as risk stratification tool to support clinical decision making in COVID-19.


Subject(s)
COVID-19 , Extracellular Vesicles , Biomarkers/metabolism , COVID-19/diagnosis , Extracellular Vesicles/metabolism , Humans , Reproducibility of Results , Risk Assessment/methods , SARS-CoV-2 , Thromboplastin/metabolism
14.
BJOG ; 129(2): 221-231, 2022 01.
Article in English | MEDLINE | ID: covidwho-1840295

ABSTRACT

OBJECTIVE: The primary aim of this article was to describe SARS-CoV-2 infection among pregnant women during the wild-type and Alpha-variant periods in Italy. The secondary aim was to compare the impact of the virus variants on the severity of maternal and perinatal outcomes. DESIGN: National population-based prospective cohort study. SETTING: A total of 315 Italian maternity hospitals. SAMPLE: A cohort of 3306 women with SARS-CoV-2 infection confirmed within 7 days of hospital admission. METHODS: Cases were prospectively reported by trained clinicians for each participating maternity unit. Data were described by univariate and multivariate analyses. MAIN OUTCOME MEASURES: COVID-19 pneumonia, ventilatory support, intensive care unit (ICU) admission, mode of delivery, preterm birth, stillbirth, and maternal and neonatal mortality. RESULTS: We found that 64.3% of the cohort was asymptomatic, 12.8% developed COVID-19 pneumonia and 3.3% required ventilatory support and/or ICU admission. Maternal age of 30-34 years (OR 1.43, 95% CI 1.09-1.87) and ≥35 years (OR 1.62, 95% CI 1.23-2.13), citizenship of countries with high migration pressure (OR 1.75, 95% CI 1.36-2.25), previous comorbidities (OR 1.49, 95% CI 1.13-1.98) and obesity (OR 1.72, 95% CI 1.29-2.27) were all associated with a higher occurrence of pneumonia. The preterm birth rate was 11.1%. In comparison with the pre-pandemic period, stillbirths and maternal and neonatal deaths remained stable. The need for ventilatory support and/or ICU admission among women with pneumonia increased during the Alpha-variant period compared with the wild-type period (OR 3.24, 95% CI 1.99-5.28). CONCLUSIONS: Our results are consistent with a low risk of severe COVID-19 disease among pregnant women and with rare adverse perinatal outcomes. During the Alpha-variant period there was a significant increase of severe COVID-19 illness. Further research is needed to describe the impact of different SARS-CoV-2 viral strains on maternal and perinatal outcomes.


Subject(s)
COVID-19 , Intensive Care Units/statistics & numerical data , Pregnancy Complications, Infectious , Premature Birth/epidemiology , SARS-CoV-2 , Adult , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/therapy , COVID-19 Testing/methods , COVID-19 Testing/statistics & numerical data , Cohort Studies , Comorbidity , Female , Hospitalization/statistics & numerical data , Hospitals, Maternity/statistics & numerical data , Humans , Italy/epidemiology , Pregnancy , Pregnancy Complications, Infectious/diagnosis , Pregnancy Complications, Infectious/epidemiology , Pregnancy Complications, Infectious/therapy , Pregnancy Outcome/epidemiology , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Severity of Illness Index
15.
PLoS One ; 16(4): e0251060, 2021.
Article in English | MEDLINE | ID: covidwho-1833536

ABSTRACT

During the COVID-19 lockdown in the US, many businesses were shut down temporarily. Essential businesses, most prominently grocery stores, remained open to ensure access to food and household essentials. Grocery shopping presents increased potential for COVID-19 infection because customers and store employees are in proximity to each other. This study investigated shoppers' perceptions of COVID-19 infection risks and put them in context by comparing grocery shopping to other activities outside home, and examined whether a proactive preventive action by grocery stores influence shoppers' perceived risk of COVID-19 infection. Our data were obtained via an anonymous online survey distributed between April 2 and 10, 2020 to grocery shoppers in New York State (the most affected by the pandemic at the time of the study) and Washington State (the first affected by the pandemic). We found significant factors associated with high levels of risk perception on grocery shoppers. We identified some effective preventive actions that grocery stores implement to alleviate anxiety and risk perception. We found that people are generally more concerned about in-store grocery shopping relative to other out-of-home activities. Findings suggest that a strict policy requiring grocery store employees to use facemasks and gloves greatly reduced shoppers' perceived risk rating of infection of themselves by 37.5% and store employees by 51.2%. Preventive actions by customers and businesses are critical to reducing the unwitting transmission of COVID-19 as state governments prepare to reopen the economy and relax restrictions on activities outside home.


Subject(s)
COVID-19/psychology , Consumer Behavior/statistics & numerical data , Perception/ethics , Consumer Behavior/economics , Family Characteristics , Food , Food Supply/statistics & numerical data , Health Risk Behaviors , Humans , New York , Perception/physiology , Risk Assessment/methods , SARS-CoV-2/pathogenicity , Supermarkets , Surveys and Questionnaires , United States/epidemiology , Washington
16.
BJOG ; 129(2): 282-290, 2022 01.
Article in English | MEDLINE | ID: covidwho-1831885

ABSTRACT

OBJECTIVE: To assess associations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and pregnancy outcomes considering testing policy and test-positivity-to-delivery interval. DESIGN: Nationwide cohort study. SETTING: Sweden. POPULATION: From the Pregnancy-Register we identified 88 593 singleton births, 11 March 2020-31 January 2021, linked to data on SARS-CoV-2-positivity from the Public Health Agency, and information on neonatal care admission from the Neonatal Quality Register. Adjusted odds ratios (aORs) were estimated stratified by testing-policy and test-positivity-to-delivery interval. MAIN OUTCOME MEASURES: Five-minute Apgar score, neonatal care admission, stillbirth and preterm birth. RESULTS: During pregnancy, SARS-CoV-2 test-positivity was 5.4% (794/14 665) under universal testing and 1.9% (1402/73 928) under non-universal testing. There were generally lower risks associated with SARS-CoV-2 under universal than non-universal testing. In women testing positive >10 days from delivery, generally no significant differences in risk were observed under either testing policy. Neonatal care admission was more common (15.3% versus 8.0%; aOR 2.24, 95% CI 1.62-3.11) in women testing positive ≤10 days before delivery under universal testing. There was no significant association with 5-minute Apgar score below 7 (1.0% versus 1.7%; aOR 0.64, 95% CI 0.24-1.72) or stillbirth (0.3% versus 0.4%; aOR 0.72, 95% CI 0.10-5.20). Compared with term births (2.1%), test-positivity was higher in medically indicated preterm birth (5.7%; aOR 2.70, 95% CI 1.60-4.58) but not significantly increased in spontaneous preterm birth (2.3%; aOR 1.12, 95% CI 0.62-2.02). CONCLUSIONS: Testing policy and timing of test-positivity impact associations between SARS-CoV-2-positivity and pregnancy outcomes. Under non-universal testing, women with complications near delivery are more likely to be tested than women without complications, thereby inflating any association with adverse pregnancy outcomes compared with findings under universal testing. TWEETABLE ABSTRACT: Testing policy and time from SARS-CoV-2 infection to delivery influence the association with pregnancy outcomes.


Subject(s)
COVID-19 Testing , COVID-19 , Intensive Care Units, Neonatal/statistics & numerical data , Pregnancy Complications, Infectious , Pregnancy Outcome/epidemiology , SARS-CoV-2/isolation & purification , Apgar Score , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/therapy , COVID-19 Testing/methods , COVID-19 Testing/statistics & numerical data , Cohort Studies , Female , Humans , Infant, Newborn , Pregnancy , Pregnancy Complications, Infectious/diagnosis , Pregnancy Complications, Infectious/epidemiology , Pregnancy Complications, Infectious/therapy , Premature Birth/epidemiology , Prenatal Care/methods , Prenatal Care/standards , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Stillbirth/epidemiology , Sweden/epidemiology
17.
BMC Med Inform Decis Mak ; 22(1): 59, 2022 03 04.
Article in English | MEDLINE | ID: covidwho-1808363

ABSTRACT

BACKGROUND: Venous thromboembolism (VTE) risk assessment in surgical patients is important for the appropriate diagnosis and treatment of patients. The commonly used Caprini model is limited by its inadequate ability to discriminate between risk stratums on the surgical population in southwest China and lengthy risk factors. The purpose of this study was to establish an improved VTE risk assessment model that is accurate and simple. METHODS: This study is based on the clinical data from 81,505 surgical patients hospitalized in the Southwest Hospital of China between January 1, 2019 and June 18, 2021. Among the population, 559 patients developed VTE. An improved VTE risk assessment model, SW-model, was established through Logistic Regression, with comparisons to both Caprini and Random Forest. RESULTS: The SW-model incorporated eight risk factors. The area under the curve (AUC) of SW-model (0.807 [0.758, 0.853], 0.804 [0.765, 0.840]), are significantly superior (p = 0.001 and p = 0.044) to those of the Caprini (0.705 [0.652, 0.757], 0.758 [0.719, 0795]) on two test sets, but inferior (p < 0.001 and p = 0.002) to Random Forest (0.854 [0.814, 0.890], 0.839 [0.806, 0.868]). In decision curve analysis, within threshold range from 0.015 to 0.04, the DCA curves of the SW-model are superior to Caprini and two default strategies. CONCLUSIONS: The SW-model demonstrated a higher discriminative capability to distinguish VTE positive in surgical patients compared with the Caprini model. Compared to Random Forest, Logistic Regression based SW-model provided interpretability which is essential in guarantee the procedure of risk assessment transparent to clinicians.


Subject(s)
Venous Thromboembolism , Hospitalization , Humans , Retrospective Studies , Risk Assessment/methods , Risk Factors , Venous Thromboembolism/diagnosis , Venous Thromboembolism/epidemiology , Venous Thromboembolism/etiology
18.
BMJ Open ; 12(3): e053864, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1765122

ABSTRACT

OBJECTIVES: The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive. DESIGN: Prospective observational cohort study. SETTING: Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020. PARTICIPANTS: Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately. RESULTS: Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities. CONCLUSIONS: IMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.


Subject(s)
COVID-19 , Adolescent , Adult , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Cohort Studies , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Prospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2
19.
PLoS One ; 17(3): e0264785, 2022.
Article in English | MEDLINE | ID: covidwho-1745317

ABSTRACT

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , COVID-19/epidemiology , COVID-19/etiology , Child , China/epidemiology , Female , Humans , India/epidemiology , Machine Learning , Male , Middle Aged , Models, Statistical , Risk Assessment/methods , Risk Factors , Young Adult
20.
Int J Environ Res Public Health ; 17(9)2020 05 01.
Article in English | MEDLINE | ID: covidwho-1725597

ABSTRACT

Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of the SARS-CoV-2 virus that causes the COVID-19 disease, it is essential to enquire if an outbreak of the epidemic might have been anticipated, given the well-documented history of SARS and MERS, among other infectious diseases. If various issues directly related to health security risks could have been predicted accurately, public health and medical contingency plans might have been prepared and activated in advance of an epidemic such as COVID-19. This paper evaluates an important source of health security, the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly in an effective and timely manner. The GHS index numerical scores are calculated as the arithmetic (AM), geometric (GM), and harmonic (HM) means of six categories, where AM uses equal weights for each category. The GHS Index scores are regressed on the numerical score rankings of the six categories to check if the use of equal weights of 0.167 in the calculation of the GHS Index using AM is justified, with GM and HM providing a check of the robustness of the arithmetic mean. The highest weights are determined to be around 0.244-0.246, while the lowest weights are around 0.186-0.187 for AM. The ordinal GHS Index is regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal Global Health Security (GHS) Index, where the highest weight is 0.368, while the lowest is 0.142, so the estimated results are wider apart than for the numerical score rankings. Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, whereas Risk Environment and Prevention have the smallest effects. The quantitative and qualitative results are different when GM and HM are used.


Subject(s)
Coronavirus Infections/epidemiology , Global Health , Pneumonia, Viral/epidemiology , COVID-19 , Humans , Pandemics , Risk Assessment/methods
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