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1.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569347

ABSTRACT

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , Health Status Indicators , Adult , Aged , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Vaccines , Cross-Sectional Studies , Epidemiologic Methods , Female , Humans , Male , Middle Aged , Patient Acceptance of Health Care/statistics & numerical data , Social Media/statistics & numerical data , United States/epidemiology , Young Adult
2.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569346

ABSTRACT

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.


Subject(s)
COVID-19/epidemiology , Health Status Indicators , Models, Statistical , Epidemiologic Methods , Forecasting , Humans , Internet/statistics & numerical data , Surveys and Questionnaires , United States/epidemiology
3.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569345

ABSTRACT

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.


Subject(s)
COVID-19/epidemiology , Databases, Factual , Health Status Indicators , Ambulatory Care/trends , Epidemiologic Methods , Humans , Internet/statistics & numerical data , Physical Distancing , Surveys and Questionnaires , Travel , United States/epidemiology
4.
Int J Environ Res Public Health ; 18(16)2021 08 09.
Article in English | MEDLINE | ID: covidwho-1376808

ABSTRACT

BACKGROUND: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries' valuations to produce better valuation estimates. METHODS: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. RESULTS: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. CONCLUSIONS: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs.


Subject(s)
Health Status Indicators , Quality of Life , Bayes Theorem , Poverty , Surveys and Questionnaires
5.
JMIR Public Health Surveill ; 7(8): e26604, 2021 08 26.
Article in English | MEDLINE | ID: covidwho-1374196

ABSTRACT

BACKGROUND: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). CONCLUSIONS: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Subject(s)
COVID-19/epidemiology , Cost of Illness , Residence Characteristics/statistics & numerical data , COVID-19/mortality , Cities/epidemiology , Health Status Indicators , Humans , New York City/epidemiology , Risk Assessment/methods , Risk Factors , Social Determinants of Health , United States/epidemiology , Unsupervised Machine Learning
6.
Am J Public Health ; 111(S2): S49-S52, 2021 07.
Article in English | MEDLINE | ID: covidwho-1328025

ABSTRACT

As of March 2021, Native Hawaiians and Pacific Islanders (NHPIs) in the United States have lost more than 800 lives to COVID-19-the highest per capita death rate in 18 of 20 US states reporting NHPI deaths. However, NHPI risks are overlooked in policy discussions. We discuss the NHPI COVID-19 Data Policy Lab and dashboard, featuring the disproportionate COVID-19 mortality burden for NHPIs. The Lab democratized NHPI data, developed community infrastructure and resources, and informed testing site and outreach policies related to health equity.


Subject(s)
COVID-19/mortality , Health Status Disparities , Health Status Indicators , /statistics & numerical data , Hawaii , Humans , Risk Factors , Socioeconomic Factors
7.
J Am Heart Assoc ; 10(6): e018477, 2021 03 16.
Article in English | MEDLINE | ID: covidwho-1268159

ABSTRACT

Background The independent prognostic value of troponin and other biomarker elevation among patients with coronavirus disease 2019 (COVID-19) are unclear. We sought to characterize biomarker levels in patients hospitalized with COVID-19 and develop and validate a mortality risk score. Methods and Results An observational cohort study of 1053 patients with COVID-19 was conducted. Patients with all of the following biomarkers measured-troponin-I, B-type natriuretic peptide, C-reactive protein, ferritin, and d-dimer (n=446) -were identified. Maximum levels for each biomarker were recorded. The primary end point was 30-day in-hospital mortality. Multivariable logistic regression was used to construct a mortality risk score. Validation of the risk score was performed using an independent patient cohort (n=440). Mean age of patients was 65.0±15.2 years and 65.3% were men. Overall, 444 (99.6%) had elevation of any biomarker. Among tested biomarkers, troponin-I ≥0.34 ng/mL was the only independent predictor of 30-day mortality (adjusted odds ratio, 4.38; P<0.001). Patients with a mortality score using hypoxia on presentation, age, and troponin-I elevation, age (HA2T2) ≥3 had a 30-day mortality of 43.7% while those with a score <3 had mortality of 5.9%. Area under the receiver operating characteristic curve of the HA2T2 score was 0.834 for the derivation cohort and 0.784 for the validation cohort. Conclusions Elevated troponin and other biomarker levels are commonly seen in patients hospitalized with COVID-19. High troponin levels are a potent predictor of 30-day in-hospital mortality. A simple risk score can stratify patients at risk for COVID-19-associated mortality.


Subject(s)
COVID-19/diagnosis , Cardiovascular Diseases/diagnosis , Health Status Indicators , Hospitalization , Troponin I/blood , Aged , Aged, 80 and over , Biomarkers/blood , C-Reactive Protein/analysis , COVID-19/blood , COVID-19/mortality , Cardiovascular Diseases/blood , Cardiovascular Diseases/mortality , Female , Ferritins/blood , Fibrin Fibrinogen Degradation Products/analysis , Hospital Mortality , Humans , Male , Middle Aged , Natriuretic Peptide, Brain/blood , Predictive Value of Tests , Prognosis , Reproducibility of Results , Retrospective Studies , Risk Assessment , Risk Factors , Time Factors , Up-Regulation
8.
Salud Publica Mex ; 63(3 May-Jun): 444-451, 2021 May 03.
Article in Spanish | MEDLINE | ID: covidwho-1259814

ABSTRACT

Objetivo. Describir el diseño y los resultados de campo de la Encuesta Nacional de Salud y Nutrición (Ensanut) 2020 so-bre Covid-19. Material y métodos. La Ensanut Covid-19 es una encuesta probabilística de hogares. En este artículo se describen los siguientes elementos del diseño: alcance, muestreo, medición, inferencia y logística. Resultados. Se obtuvieron 10 216 entrevistas de hogar completas y 9 464 resultados sobre seropositividad a SARS-CoV-2. La tasa de respuesta de hogar fue 80% y la de prueba de seropositividad de 44%. Conclusiones. El diseño probabilístico de la Ensa-nut Covid-19 permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y regional; en particular, permitirá hacer inferencias de utilidad práctica sobre la prevalencia de seropositividad a SARS-CoV-2 en México. Además, la Ensanut Covid-19 podrá ser comparada con Ensanut previas para identificar potenciales cambios en los estados de salud y nutrición de la población mexicana.


Subject(s)
COVID-19/epidemiology , Health Status Indicators , Nutrition Surveys/methods , Age Distribution , COVID-19/transmission , Censuses , Humans , Mexico/epidemiology , Nutrition Surveys/statistics & numerical data , Prevalence , Rural Health/statistics & numerical data , Sample Size , Urban Health/statistics & numerical data
9.
Epidemiol. serv. saúde ; 30(2): e2020722, 2021. graf
Article in English, Portuguese | LILACS (Americas) | ID: covidwho-1234612

ABSTRACT

Objetivo: Analisar como a testagem da população influencia os indicadores de saúde usados para monitorar a pandemia de COVID-19 nos 50 países com maior número de casos diagnosticados. Métodos: Estudo ecológico sobre dados secundários, extraídos em 19/08/2020. Foram calculadas incidência acumulada, taxa de mortalidade, letalidade e proporção de testes positivos. Os dados foram descritos e apresentados graficamente, com o respectivo coeficiente de correlação de Spearman. Resultados: A taxa de testagem variou enormemente entre os países. A incidência acumulada e a proporção de testes positivos foram correlacionadas ao número de testes, enquanto a taxa de mortalidade e a letalidade apresentaram correlação baixa com esse indicador. Conclusão: A maioria dos países não testa o suficiente para garantir adequado monitoramento da pandemia, com reflexo na qualidade dos indicadores. A ampliação do número de testes é fundamental; porém, ela deve ser acompanhada de outras medidas, como isolamento de casos diagnosticados e rastreamento de contatos.


Objetivo: Analizar cómo el testeo poblacional influye en los indicadores de salud utilizados para monitorear la pandemia de COVID-19 en los 50 países con mayor número de casos diagnosticados. Métodos: Estudio ecológico, con datos secundarios, recogidos el 19/8/2020. Se calcularon la incidencia acumulada, la tasa de mortalidad, la letalidad y la proporción de pruebas positivas. Los datos fueron descritos y presentados gráficamente, con el respectivo Coeficiente de Correlación de Spearman. Resultados: La tasa de testeo varió enormemente entre los países. La incidencia acumulada y la proporción de pruebas positivas se correlacionaron con el número de pruebas, mientras que la tasa de mortalidad y de letalidad mostraron una baja correlación con este indicador. Conclusión: La mayoría de los países no realizan suficientes pruebas para garantizar un seguimiento adecuado de la pandemia, lo que se refleja en la calidad de los indicadores. La ampliación del número de pruebas es fundamental, y debe ir acompañada de aislamiento de casos y seguimiento de contactos.


Objective: To analyse how testing the population influences the health indicators used to monitor the COVID-19 pandemic in the 50 countries with the highest number of diagnosed cases. Methods:This was an ecological study using secondary data retrieved on 8/19/2020. Cumulative incidence, mortality rate, case-fatality rate, and proportion of positive tests were calculated. The data were described and presented graphically, with their respective Spearman Correlation Coefficients. Results: The testing rate varied enormously between countries. Cumulative incidence and the proportion of positive tests were correlated with the number of tests, while the mortality rate and case-fatality rate showed low correlation with this indicator. Conclusion: Most countries do not test enough to ensure adequate monitoring of the pandemic, and this is reflected in the quality of the indicators. Expanding the number of tests is essential, but it needs to be accompanied by other measures, such as isolation of diagnosed cases and contact tracing.


Subject(s)
Humans , Incidence , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Coronavirus Infections/epidemiology , Laboratory Test/statistics & numerical data , Serologic Tests/statistics & numerical data , Global Health/statistics & numerical data , Health Status Indicators , Reverse Transcriptase Polymerase Chain Reaction , Pandemics/statistics & numerical data
10.
Health Secur ; 19(S1): S41-S49, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1219235

ABSTRACT

Vulnerable refugee communities are disproportionately affected by the ongoing COVID-19 pandemic; existing longstanding health inequity in these communities is exacerbated by ineffective risk communication practices about COVID-19. Culturally and linguistically appropriate health communication following health literacy guidelines is needed to dispel cultural myths, social stigma, misinformation, and disinformation. For refugee communities, the physical, mental, and social-related consequences of displacement further complicate understanding of risk communication practices grounded in a Western cultural ethos. We present a case study of Clarkston, Georgia, the "most diverse square mile in America," where half the population is foreign born and majority refugee. Supporting marginalized communities in times of risk will require a multipronged, systemic approach to health communication including: (1) creating a task force of local leaders and community members to deal with emergent issues; (2) expanding English-language education and support for refugees; (3) including refugee perspectives on risk, health, and wellness into risk communication messaging; (4) improving cultural competence and health literacy training for community leaders and healthcare providers; and (5) supporting community health workers. Finally, better prepared public health programs, including partnerships with trusted community organizations and leadership, can ensure that appropriate and supportive risk communication and health education and promotion are in place long before the next emergency.


Subject(s)
COVID-19/therapy , Community Health Workers/organization & administration , Culturally Competent Care/organization & administration , Health Promotion/organization & administration , Health Status Indicators , Refugees/statistics & numerical data , COVID-19/epidemiology , Georgia , Humans , Needs Assessment/organization & administration
11.
Scand J Public Health ; 49(1): 79-87, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1207559

ABSTRACT

Aims: There is a need to document the mental-health effects of the COVID-19 pandemic and its associated societal lockdowns. We initiated a large mixed-methods data collection, focusing on crisis-specific worries and mental-health indicators during the lockdown in Denmark. Methods: The study incorporated five data sources, including quantitative surveys and qualitative interviews. The surveys included a time series of cross-sectional online questionnaires starting on 20 March 2020, in which 300 (3×100) Danish residents were drawn every three days from three population groups: the general population (N=1046), families with children (N=1032) and older people (N=1059). These data were analysed by trend analysis. Semi-structured interviews were conducted with 32 people aged 24-83 throughout Denmark to provide context to the survey results and to gain insight into people's experiences of the lockdown. Results: Absolute level of worries, quality of life and social isolation were relatively stable across all population groups during the lockdown, although there was a slight deterioration in older people's overall mental health. Many respondents were worried about their loved ones' health (74-76%) and the potential long-term economic consequences of the pandemic (61-66%). The qualitative interviews documented significant variation in people's experiences, suggesting that the lockdown's effect on everyday life had not been altogether negative. Conclusions: People in Denmark seem to have managed the lockdown without alarming changes in their mental health. However, it is important to continue investigating the effects of the pandemic and various public-health measures on mental health over time and across national contexts.


Subject(s)
COVID-19/psychology , Health Status Indicators , Mental Health , Adolescent , Adult , Aged , Aged, 80 and over , Anxiety/epidemiology , COVID-19/prevention & control , Cross-Sectional Studies , Denmark/epidemiology , Female , Humans , Male , Middle Aged , Physical Distancing , Quarantine/legislation & jurisprudence , Quarantine/psychology , Young Adult
12.
BMJ Glob Health ; 6(3)2021 03.
Article in English | MEDLINE | ID: covidwho-1153676

ABSTRACT

Worldwide, approximately 11 million people are currently being held in prison, a number that has steadily grown since the turn of the 21st century. The prison population is more likely to suffer from physical and mental ailments both during and prior to their imprisonment due to poverty, social exclusion and chaotic lifestyles. Recognition of people in prison is noticeably absent from the Sustainable Development Goals (SDGs), despite the goals' ethos of 'leaving no one behind'.We present the first analysis of how improving the health of people in prison can contribute to achieving 15 SDGs. Relevant indicators are proposed to fulfil these goals while meeting the existing international prison health standards. We also assess the political, economic and social challenges, alongside the unparalleled COVID-19 pandemic that can thwart the realisation of the SDGs. To reach the 'furthest behind first', prison health must be at the forefront of the SDGs.


Subject(s)
Delivery of Health Care , Goals , Prisons , Sustainable Development , COVID-19 , Health Status Indicators , Humans , SARS-CoV-2 , World Health Organization
13.
J Am Geriatr Soc ; 69(6): 1441-1447, 2021 06.
Article in English | MEDLINE | ID: covidwho-1153546

ABSTRACT

BACKGROUND/OBJECTIVES: The safety and immunogenicity of the BNT162b2 coronavirus disease 2019 (COVID-19) vaccine in older adults with different frailty and disability profiles have not been well determined. Our objective was to analyze immunogenicity of the BNT162b2 mRNA COVID-19 vaccine in older adults across frailty and disability profiles. DESIGN: Multicenter longitudinal cohort study. SETTING AND PARTICIPANTS: A total of 134 residents aged ≥65 years with different frailty and disability profiles in five long-term care facilities (LTCFs) in Albacete, Spain. INTERVENTION AND MEASUREMENTS: Residents were administered two vaccine doses as per the label, and antibody levels were determined 21.9 days (SD 9.3) after both the first and second dose. Functional variables were assessed using activities of daily living (Barthel Index), and frailty status was determined with the FRAIL instrument. Cognitive status and comorbidity were also evaluated. RESULTS: Mean age was 82.9 years (range 65-99), and 71.6% were female. The mean antibody titers in residents with and without previous COVID-19 infection were 49,878 AU/ml and 15,274 AU/ml, respectively (mean difference 34,604; 95% confidence interval [CI]: 27,699-41,509). No severe adverse reactions were observed, after either vaccine dose. Those with prevaccination COVID-19 had an increased antibody level after the vaccine (B = 31,337; 95% CI: 22,725-39,950; p < 0.001). Frailty, disability, older age, sex, cognitive impairment, or comorbidities were not associated with different antibody titers. CONCLUSIONS: The BNT162b2 mRNA COVID-19 vaccine in older adults is safe and produces immunogenicity, independently of the frailty and disability profiles. Older adults in LTCFs should receive a COVID-19 vaccine.


Subject(s)
Antibody Formation , COVID-19 Vaccines/immunology , COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , Disabled Persons , Frail Elderly , Activities of Daily Living , Aged , Aged, 80 and over , COVID-19 Serological Testing , Comorbidity , Female , Health Status Indicators , Humans , Longitudinal Studies , Male , Nursing Homes , SARS-CoV-2 , Spain
14.
Bone Joint J ; 103-B(4): 672-680, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1146934

ABSTRACT

AIMS: The aim of this study was to assess the quality of life of patients on the waiting list for a total hip (THA) or knee arthroplasty (KA) during the COVID-19 pandemic. Secondary aims were to assess whether length of time on the waiting list influenced quality of life and rate of deferral of surgery. METHODS: During the study period (August and September 2020) 843 patients (THA n = 394, KA n = 449) from ten centres in the UK reported their EuroQol five dimension (EQ-5D) scores and completed a waiting list questionnaire (2020 group). Patient demographic details, procedure, and date when listed were recorded. Patients scoring less than zero for their EQ-5D score were defined to be in a health state "worse than death" (WTD). Data from a retrospective cohort (January 2014 to September 2017) were used as the control group. RESULTS: The 2020 group had a significantly worse EQ-5D score compared to the control group for both THA (p < 0.001) and KA (p < 0.001). Over one-third (35.0%, n = 138/394) of patients waiting for a THA and nearly a quarter (22.3%, n = 100/449) for KA were in a health state WTD, which was significantly greater than the control group (odds ratio 2.30 (95% confidence interval (CI) 1.83 to 2.93) and 2.08 (95% CI 1.61 to 2.70), respectively; p < 0.001). Over 80% (n = 680/843) of the 2020 group felt that their quality of life had deteriorated while waiting. Each additional month spent on the waiting list was independently associated with a decrease in quality of life (EQ-5D: -0.0135, p = 0.004). There were 117 (13.9%) patients who wished to defer their surgery and the main reason for this was health concerns for themselves and or their family (99.1%, n = 116/117). CONCLUSION: Over one-third of patients waiting for THA and nearly one-quarter waiting for a KA were in a state WTD, which was approaching double that observed prior to the pandemic. Increasing length of time on the waiting list was associated with decreasing quality of life. Level of evidence: Level III retrospective case control study Cite this article: Bone Joint J 2021;103-B(4):672-680.


Subject(s)
Arthroplasty, Replacement, Hip , Arthroplasty, Replacement, Knee , COVID-19 , Health Services Accessibility , Health Status Indicators , Quality of Life/psychology , Waiting Lists , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/psychology , Cross-Sectional Studies , Female , Humans , Linear Models , Male , Medical Audit , Middle Aged , Multivariate Analysis , Pandemics , Patient Acceptance of Health Care , Quality Improvement , Time Factors , United Kingdom/epidemiology
16.
Curr Probl Cardiol ; 46(5): 100819, 2021 May.
Article in English | MEDLINE | ID: covidwho-1083060

ABSTRACT

OBJECTIVES AND METHODS: the current understanding of the interplay between cardiovascular (CV) risk and Covid-19 is grossly inadequate. CV risk-prediction models are used to identify and treat high risk populations and to communicate risk effectively. These tools are unexplored in Covid-19. The main objective is to evaluate the association between CV scoring systems and chest X ray (CXR) examination (in terms of severity of lung involvement) in 50 Italian Covid-19 patients. Results only the Framingham Risk Score (FRS) was applicable to all patients. The Atherosclerotic Cardiovascular Disease Score (ASCVD) was applicable to half. 62% of patients were classified as high risk according to FRS and 41% according to ASCVD. Patients who died had all a higher FRS compared to survivors. They were all hypertensive. FRS≥30 patients had a 9.7 higher probability of dying compared to patients with a lower FRS. We found a strong correlation between CXR severity and FRS and ASCVD (P < 0.001). High CV risk patients had consolidations more frequently. CXR severity was significantly associated with hypertension and diabetes. 71% of hypertensive patients' CXR and 88% of diabetic patients' CXR had consolidations. Patients with diabetes or hypertension had 8 times greater risk of having consolidations. CONCLUSIONS: High CV risk correlates with more severe CXR pattern and death. Diabetes and hypertension are associated with more severe CXR. FRS offers more predictive utility and fits best to our cohort. These findings may have implications for clinical practice and for the identification of high-risk groups to be targeted for the vaccine precedence.


Subject(s)
COVID-19/diagnostic imaging , Cardiovascular Diseases/diagnosis , Health Status Indicators , Radiography, Thoracic , Adult , Aged , COVID-19/mortality , COVID-19/therapy , Cardiovascular Diseases/mortality , Cardiovascular Diseases/therapy , Comorbidity , Diabetes Mellitus/diagnosis , Diabetes Mellitus/mortality , Female , Heart Disease Risk Factors , Humans , Hypertension/diagnosis , Hypertension/mortality , Italy , Male , Middle Aged , Predictive Value of Tests , Prognosis , Risk Assessment , Severity of Illness Index
17.
Indian Pediatr ; 58(1): 75-76, 2021 01 15.
Article in English | MEDLINE | ID: covidwho-1052686

ABSTRACT

Pediatric symptom checklist (PSC)-youth self-report short version was administered telephonically to children between 11-15 years to study the impact on mental health. Out of 423 children, 130 (30.7%) had psychosocial problems, of which 107 (25.2%) had anxiety or depressive symptoms. The common reasons were fear of acquiring COVID-19 infection (60%), not able to attend school (56%), and not able to meet friends (80%).


Subject(s)
COVID-19/psychology , Mental Disorders/etiology , Adolescent , COVID-19/epidemiology , COVID-19/prevention & control , Child , Female , Health Status Indicators , Humans , India/epidemiology , Male , Mental Disorders/diagnosis , Mental Disorders/epidemiology , Pandemics , Physical Distancing , Regression Analysis , Risk Factors , Self Report
18.
Clin Respir J ; 15(5): 467-471, 2021 May.
Article in English | MEDLINE | ID: covidwho-1015532

ABSTRACT

BACKGROUND: The unprecedented COVID-19 pandemic has put a serious burden on the healthcare system worldwide. Due to varied manifestations of SARS-CoV-2 infection, many scoring systems, which were earlier used for community acquired pneumonia (CAP) are in use to determine the disease severity and the need of ICU admissions for proper management. COVID-19 is a relatively new disease and the validity of these scoring systems in SARS-CoV-2 infection is not completely known. This study aimed to validate these scoring systems in cases of COVID-19 pneumonia in an Indian setup. The study has also tried to find the most accurate indicator of disease severity and 14-day mortality among these scoring systems. MATERIALS AND METHODS: This study included 122 SARS-CoV-2 infected patients at a tertiary hospital in Ranchi, Jharkhand. The severity of the disease according to ICMR protocol for COVID-19, the PSI/PORT score, the CURB-65 score and the SCAP score were calculated in all the patients and analysed with the disease outcome, that is, 14-day mortality. RESULTS: SCAP score, PSI/PORT score and CURB-65 criteria, all were good indicators of disease severity and 14-day mortality. However, when compared to other scoring systems, SCAP score was a more accurate marker of disease severity and 14-day mortality. CONCLUSION: The PSI/PORT scoring system, the CURB-65 criteria and the SCAP scoring system can be used to assess the COVID-19 severity and predict the 14-day mortality risk in cases of COVID-19 pneumonia.


Subject(s)
COVID-19/mortality , Severity of Illness Index , Adolescent , Adult , Aged , Aged, 80 and over , Child , Female , Health Status Indicators , Humans , India/epidemiology , Male , Middle Aged , Risk Assessment , Young Adult
20.
Diabetes Obes Metab ; 23(2): 589-598, 2021 02.
Article in English | MEDLINE | ID: covidwho-969453

ABSTRACT

AIM: To assess predictors of in-hospital mortality in people with prediabetes and diabetes hospitalized for COVID-19 infection and to develop a risk score for identifying those at the greatest risk of a fatal outcome. MATERIALS AND METHODS: A combined prospective and retrospective, multicentre, cohort study was conducted at 10 sites in Austria in 247 people with diabetes or newly diagnosed prediabetes who were hospitalized with COVID-19. The primary outcome was in-hospital mortality and the predictor variables upon admission included clinical data, co-morbidities of diabetes or laboratory data. Logistic regression analyses were performed to identify significant predictors and to develop a risk score for in-hospital mortality. RESULTS: The mean age of people hospitalized (n = 238) for COVID-19 was 71.1 ± 12.9 years, 63.6% were males, 75.6% had type 2 diabetes, 4.6% had type 1 diabetes and 19.8% had prediabetes. The mean duration of hospital stay was 18 ± 16 days, 23.9% required ventilation therapy and 24.4% died in the hospital. The mortality rate in people with diabetes was numerically higher (26.7%) compared with those with prediabetes (14.9%) but without statistical significance (P = .128). A score including age, arterial occlusive disease, C-reactive protein, estimated glomerular filtration rate and aspartate aminotransferase levels at admission predicted in-hospital mortality with a C-statistic of 0.889 (95% CI: 0.837-0.941) and calibration of 1.000 (P = .909). CONCLUSIONS: The in-hospital mortality for COVID-19 was high in people with diabetes but not significantly different to the risk in people with prediabetes. A risk score using five routinely available patient variables showed excellent predictive performance for assessing in-hospital mortality.


Subject(s)
COVID-19/mortality , Diabetes Mellitus, Type 2/mortality , Health Status Indicators , Patient Admission/statistics & numerical data , Prediabetic State/mortality , Aged , Austria , COVID-19/virology , Diabetes Mellitus, Type 2/virology , Female , Hospital Mortality , Hospitals , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Prediabetic State/virology , Prospective Studies , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2
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