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1.
Arch Argent Pediatr ; 119(2): 76-82, 2021 04.
Article in English, Spanish | MEDLINE | ID: covidwho-1183983

ABSTRACT

INTRODUCTION: The objective of this study was to analyze available resources, guidelines in use, and preparedness to care for newborn infants at maternity centers in Argentina during the COVID-19 pandemic. METHOD: Cross-sectional study based on a survey administered to medical and nursing staff. In May 2020, Argentine facilities with more than 500 annual births were contacted; 58 % of these were from the public sector. RESULTS: In total, 104/147 facilities answered (71 %). All had guidelines for care during the pandemic, and 93 % indicated they had been trained on how to use them. A companion was not allowed during childbirth in 26 % of private facilities and in 60 % of public ones (p < 0.01). Deferred cord clamping was recommended in 87 %; rooming-in with asymptomatic newborns was promoted in 62 %; breastfeeding using protective measures was recommended in 70 %; and breast milk using a bottle, in 23 %. In 94 %, family visiting in the Neonatology Unit was restricted. Difficulties included the unavailability of individual rooms for symptomatic newborn infants and a potential shortage of health care staff and personal protective equipment. CONCLUSIONS: All facilities are aware of the national guidelines to fight the pandemic. Most have the resources to comply with the recommended protective measures. There is uncertainty as to whether personal protective equipment, staff, and physical space available at the different facilities would be enough if cases increased significantly.


Subject(s)
/prevention & control , Health Resources/supply & distribution , Infant Care/organization & administration , Infection Control/organization & administration , Maternal Health Services/organization & administration , Argentina/epidemiology , Cross-Sectional Studies , Female , Health Care Surveys , Health Policy , Humans , Infant Care/statistics & numerical data , Infant, Newborn , Infection Control/instrumentation , Infection Control/methods , Infection Control/statistics & numerical data , Male , Maternal Health Services/statistics & numerical data , Pandemics , Personal Protective Equipment/supply & distribution , Practice Guidelines as Topic , Pregnancy
3.
Pediatr Emerg Care ; 37(4): 232-236, 2021 Apr 01.
Article in English | MEDLINE | ID: covidwho-1180679

ABSTRACT

OBJECTIVES: The purposes of this study were to describe the clinical characteristics of febrile infants younger than 90 days with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, to investigate the prevalence of serious bacterial infections (SBIs) in these infants, and to compare the risk of SBI in SARS-CoV-2-positive febrile infants with sex- and age-matched SARS-CoV- 2-negative febrile infants. METHODS: This was a retrospective cohort study conducted from March to November 2020 in a tertiary children's hospital. Patients were identified by International Classification of Diseases, 10th Revision codes and included if age was younger than 90 days, a SARS-CoV-2 test was performed, and at least 1 bacterial culture was collected. Positive cases of SARS-CoV-2 were age- and sex-matched to negative controls for analysis. Serious bacterial infection was defined as a urinary tract infection, bacterial enteritis, bacteremia, and/or bacterial meningitis. RESULTS: Fifty-three SARS-CoV-2-positive infants were identified with a higher rate of respiratory symptoms and lower white blood cell and C-reactive protein values than their SARS-CoV-2 matched controls. The rate of SBI in the SARS-CoV-2-positive infants was 8% compared with 34% in the controls; the most common infections were urinary tract infections (6% vs 23%). There were no cases of bacteremia or bacterial meningitis in the COVID-19 (coronavirus disease 2019) infants and 2 (4%) cases of bacteremia in the controls. The relative risk of any SBI between the 2 groups was 0.22 (95% confidence interval, 0.1-0.6; P ≤ 0.001). CONCLUSIONS: These results suggest that febrile infants younger than 90 days with COVID-19 have lower rates of SBI than their matched SARS-CoV-2-negative controls. These data are consistent with previous studies describing lower risks of SBI in febrile infants with concomitant viral respiratory tract infections.


Subject(s)
Bacterial Infections/etiology , Emergency Service, Hospital/statistics & numerical data , Risk Assessment/methods , Bacterial Infections/epidemiology , Female , Follow-Up Studies , Humans , Infant , Infant, Newborn , Male , Prevalence , Retrospective Studies , United States
5.
Lancet Respir Med ; 9(4): 397-406, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180129

ABSTRACT

BACKGROUND: Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. METHODS: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). FINDINGS: 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30·8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1·457 [1·408-1·509]), greater deprivation (1·002 [1·001-1·003]), Asian (1·211 [1·128-1·299]) or mixed ethnicity (1·317 [1·080-1·605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5·433 [4·618-6·392]). Later date of discharge was associated with a lower odds of death (0·977 [0·976-0·978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52·2% in the first week of March to 16·8% in the last week of May. INTERPRETATION: Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours. FUNDING: None.


Subject(s)
/mortality , Hospital Mortality/trends , Minority Groups/statistics & numerical data , Pandemics/statistics & numerical data , Patient Acceptance of Health Care/statistics & numerical data , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Comorbidity , Datasets as Topic , Electronic Health Records/statistics & numerical data , England/epidemiology , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Retrospective Studies , Risk Factors , Severity of Illness Index , Sex Factors , Young Adult
6.
Lancet Respir Med ; 9(4): 407-418, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180128

ABSTRACT

BACKGROUND: Most low-income and middle-income countries (LMICs) have little or no data integrated into a national surveillance system to identify characteristics or outcomes of COVID-19 hospital admissions and the impact of the COVID-19 pandemic on their national health systems. We aimed to analyse characteristics of patients admitted to hospital with COVID-19 in Brazil, and to examine the impact of COVID-19 on health-care resources and in-hospital mortality. METHODS: We did a retrospective analysis of all patients aged 20 years or older with quantitative RT-PCR (RT-qPCR)-confirmed COVID-19 who were admitted to hospital and registered in SIVEP-Gripe, a nationwide surveillance database in Brazil, between Feb 16 and Aug 15, 2020 (epidemiological weeks 8-33). We also examined the progression of the COVID-19 pandemic across three 4-week periods within this timeframe (epidemiological weeks 8-12, 19-22, and 27-30). The primary outcome was in-hospital mortality. We compared the regional burden of hospital admissions stratified by age, intensive care unit (ICU) admission, and respiratory support. We analysed data from the whole country and its five regions: North, Northeast, Central-West, Southeast, and South. FINDINGS: Between Feb 16 and Aug 15, 2020, 254 288 patients with RT-qPCR-confirmed COVID-19 were admitted to hospital and registered in SIVEP-Gripe. The mean age of patients was 60 (SD 17) years, 119 657 (47%) of 254 288 were aged younger than 60 years, 143 521 (56%) of 254 243 were male, and 14 979 (16%) of 90 829 had no comorbidities. Case numbers increased across the three 4-week periods studied: by epidemiological weeks 19-22, cases were concentrated in the North, Northeast, and Southeast; by weeks 27-30, cases had spread to the Central-West and South regions. 232 036 (91%) of 254 288 patients had a defined hospital outcome when the data were exported; in-hospital mortality was 38% (87 515 of 232 036 patients) overall, 59% (47 002 of 79 687) among patients admitted to the ICU, and 80% (36 046 of 45 205) among those who were mechanically ventilated. The overall burden of ICU admissions per ICU beds was more pronounced in the North, Southeast, and Northeast, than in the Central-West and South. In the Northeast, 1545 (16%) of 9960 patients received invasive mechanical ventilation outside the ICU compared with 431 (8%) of 5388 in the South. In-hospital mortality among patients younger than 60 years was 31% (4204 of 13 468) in the Northeast versus 15% (1694 of 11 196) in the South. INTERPRETATION: We observed a widespread distribution of COVID-19 across all regions in Brazil, resulting in a high overall disease burden. In-hospital mortality was high, even in patients younger than 60 years, and worsened by existing regional disparities within the health system. The COVID-19 pandemic highlights the need to improve access to high-quality care for critically ill patients admitted to hospital with COVID-19, particularly in LMICs. FUNDING: National Council for Scientific and Technological Development (CNPq), Coordinating Agency for Advanced Training of Graduate Personnel (CAPES), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ), and Instituto de Salud Carlos III.


Subject(s)
/epidemiology , Epidemiological Monitoring , Healthcare Disparities/statistics & numerical data , Hospital Mortality/trends , Pandemics/statistics & numerical data , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , /therapy , Comorbidity , Female , Geography , Health Services Accessibility/organization & administration , Health Services Accessibility/statistics & numerical data , Health Services Needs and Demand/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Patient Admission/statistics & numerical data , Respiration, Artificial/statistics & numerical data , Retrospective Studies , Young Adult
7.
Lancet Respir Med ; 9(4): 349-359, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180127

ABSTRACT

BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.


Subject(s)
/diagnosis , Clinical Decision Rules , Clinical Decision-Making/methods , Clinical Deterioration , Aged , Aged, 80 and over , /therapy , Critical Care/statistics & numerical data , Female , Hospital Mortality , Humans , Intensive Care Units/statistics & numerical data , Logistic Models , Male , Middle Aged , Patient Admission/statistics & numerical data , Prognosis , Prospective Studies , Reproducibility of Results , Respiration, Artificial/statistics & numerical data , Severity of Illness Index , United Kingdom/epidemiology
8.
JMIR Mhealth Uhealth ; 8(7): e17216, 2020 07 09.
Article in English | MEDLINE | ID: covidwho-1177909

ABSTRACT

BACKGROUND: Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. OBJECTIVE: This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. METHODS: The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. RESULTS: The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). CONCLUSIONS: This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. TRIAL REGISTRATION: Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t.


Subject(s)
Home Care Services , Internet , Stroke Rehabilitation/methods , Stroke/physiopathology , Telerehabilitation , Upper Extremity/physiopathology , Wearable Electronic Devices , Aged , Chronic Disease , Exercise Therapy/statistics & numerical data , Humans , Machine Learning , Middle Aged , Mobile Applications , Models, Theoretical , Prospective Studies , Survivors , Treatment Outcome
9.
Health Aff (Millwood) ; 39(10): 1792-1798, 2020 10.
Article in English | MEDLINE | ID: covidwho-1177766

ABSTRACT

Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.


Subject(s)
Automobile Driving/legislation & jurisprudence , Coronavirus Infections/prevention & control , Licensure/legislation & jurisprudence , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Safety Management/organization & administration , User-Computer Interface , Adolescent , Coronavirus Infections/epidemiology , Feasibility Studies , Female , Humans , Male , Motor Vehicles/statistics & numerical data , Ohio , Pandemics/statistics & numerical data , Philadelphia , Pilot Projects , Pneumonia, Viral/epidemiology , Transportation/methods , Young Adult
12.
PLoS One ; 16(3): e0249247, 2021.
Article in English | MEDLINE | ID: covidwho-1175427

ABSTRACT

OBJECTIVES: We aimed to estimate the seroprevalence of COVID-19 in a rural district of South India, six months after the index case. METHODOLOGY: We conducted a cross-sectional study of 509 adults aged more than 18 years. From all the four subdistricts, two grampanchayats (administrative cluster of 5-8 villages) were randomly selected followed by one village through convenience. The participants were invited for the study to the community-based study kiosk set up in all the eight villages through village health committees. We collected socio-demographic characteristics and symptoms using a mobile application-based questionnaire, and we tested samples for the presence of IgG antibodies for SARS CoV-2 using an electro chemiluminescent immunoassay. We calculated age-gender adjusted and test performance adjusted seroprevalence. RESULTS: The age-and gender-adjusted seroprevalence was 8.5% (95% CI 6.9%- 10.8%). The unadjusted seroprevalence among participants with hypertension and diabetes was 16.3% (95% CI:9.2-25.8) and 10.7% (95% CI: 5.5-18.3) respectively. When we adjusted for the test performance, the seroprevalence was 6.1% (95% CI 4.02-8.17). The study estimated 7 (95% CI 1:4.5-1:9) undetected infected individuals for every RT-PCR confirmed case. Infection Fatality Rate (IFR) was calculated as 12.38 per 10000 infections as on 22 October 2020. History of self-reported symptoms and education were significantly associated with positive status (p < 0.05). CONCLUSION: A significant proportion of the rural population in a district of south India remains susceptible to COVID-19. A higher proportion of susceptible, relatively higher IFR and a poor tertiary healthcare network stress the importance of sustaining the public health measures and promoting early access to the vaccine are crucial to preserving the health of this population. Low population density, good housing, adequate ventilation, limited urbanisation combined with public, private and local health leadership are critical components of curbing future respiratory pandemics.


Subject(s)
/epidemiology , Rural Population/statistics & numerical data , Adult , Female , Humans , India/epidemiology , Male , Middle Aged , Seroepidemiologic Studies , Young Adult
13.
PLoS One ; 16(3): e0248590, 2021.
Article in English | MEDLINE | ID: covidwho-1175426

ABSTRACT

The present paper investigates factors contributing to the home advantage, by using the exceptional opportunity to study professional football matches played in the absence of spectators due to the COVID-19 pandemic in 2020. More than 40,000 matches before and during the pandemic, including more than 1,000 professional matches without spectators across the main European football leagues, have been analyzed. Results support the notion of a crowd-induced referee bias as the increased sanctioning of away teams disappears in the absence of spectators with regard to fouls (p < .001), yellow cards (p < .001), and red cards (p < .05). Moreover, the match dominance of home teams decreases significantly as indicated by shots (p < .001) and shots on target (p < .01). In terms of the home advantage itself, surprisingly, only a non-significant decrease is found. While the present paper supports prior research with regard to a crowd-induced referee bias, spectators thus do not seem to be the main driving factor of the home advantage. Results from amateur football, being naturally played in absence of a crowd, provide further evidence that the home advantage is predominantly caused by factors not directly or indirectly attributable to a noteworthy number of spectators.


Subject(s)
/epidemiology , Crowding , Pandemics , Soccer , Athletic Performance/statistics & numerical data , Decision Making , Europe , Humans
14.
BMC Public Health ; 21(1): 685, 2021 04 08.
Article in English | MEDLINE | ID: covidwho-1175313

ABSTRACT

BACKGROUND: People with chronic conditions are disproportionately prone to be affected by the COVID-19 pandemic but there are limited data documenting this. We aimed to assess the health, psychosocial and economic impacts of the COVID-19 pandemic on people with chronic conditions in India. METHODS: Between July 29, to September 12, 2020, we telephonically surveyed adults (n = 2335) with chronic conditions across four sites in India. Data on participants' demographic, socio-economic status, comorbidities, access to health care, treatment satisfaction, self-care behaviors, employment, and income were collected using pre-tested questionnaires. We performed multivariable logistic regression analysis to examine the factors associated with difficulty in accessing medicines and worsening of diabetes or hypertension symptoms. Further, a diverse sample of 40 participants completed qualitative interviews that focused on eliciting patient's experiences during the COVID-19 lockdowns and data analyzed using thematic analysis. RESULTS: One thousand seven hundred thirty-four individuals completed the survey (response rate = 74%). The mean (SD) age of respondents was 57.8 years (11.3) and 50% were men. During the COVID-19 lockdowns in India, 83% of participants reported difficulty in accessing healthcare, 17% faced difficulties in accessing medicines, 59% reported loss of income, 38% lost jobs, and 28% reduced fruit and vegetable consumption. In the final-adjusted regression model, rural residence (OR, 95%CI: 4.01,2.90-5.53), having diabetes (2.42, 1.81-3.25) and hypertension (1.70,1.27-2.27), and loss of income (2.30,1.62-3.26) were significantly associated with difficulty in accessing medicines. Further, difficulties in accessing medicines (3.67,2.52-5.35), and job loss (1.90,1.25-2.89) were associated with worsening of diabetes or hypertension symptoms. Qualitative data suggest most participants experienced psychosocial distress due to loss of job or income and had difficulties in accessing in-patient services. CONCLUSION: People with chronic conditions, particularly among poor, rural, and marginalized populations, have experienced difficulties in accessing healthcare and been severely affected both socially and financially by the COVID-19 pandemic.


Subject(s)
Chronic Disease , Pandemics , Aged , /epidemiology , Chronic Disease/epidemiology , Chronic Disease/therapy , Female , Health Services Accessibility/statistics & numerical data , Health Status , Humans , India/epidemiology , Male , Middle Aged , Qualitative Research , Quarantine , Socioeconomic Factors , Surveys and Questionnaires
15.
BMC Public Health ; 21(1): 684, 2021 04 08.
Article in English | MEDLINE | ID: covidwho-1175312

ABSTRACT

BACKGROUND: We investigated if people's response to the official recommendations during the COVID-19 pandemic is associated with conspiracy beliefs related to COVID-19, a distrust in the sources providing information on COVID-19, and an endorsement of complementary and alternative medicine (CAM). METHODS: The sample consisted of 1325 Finnish adults who filled out an online survey marketed on Facebook. Structural regression analysis was used to investigate whether: 1) conspiracy beliefs, a distrust in information sources, and endorsement of CAM predict people's response to the non-pharmaceutical interventions (NPIs) implemented by the government during the COVID-19 pandemic, and 2) conspiracy beliefs, a distrust in information sources, and endorsement of CAM are related to people's willingness to take a COVID-19 vaccine. RESULTS: Individuals with more conspiracy beliefs and a lower trust in information sources were less likely to have a positive response to the NPIs. Individuals with less trust in information sources and more endorsement of CAM were more unwilling to take a COVID-19 vaccine. Distrust in information sources was the strongest and most consistent predictor in all models. Our analyses also revealed that some of the people who respond negatively to the NPIs also have a lower likelihood to take the vaccine. This association was partly related to a lower trust in information sources. CONCLUSIONS: Distrusting the establishment to provide accurate information, believing in conspiracy theories, and endorsing treatments and substances that are not part of conventional medicine, are all associated with a more negative response to the official guidelines during COVID-19. How people respond to the guidelines, however, is more strongly and consistently related to the degree of trust they feel in the information sources, than to their tendency to hold conspiracy beliefs or endorse CAM. These findings highlight the need for governments and health authorities to create communication strategies that build public trust.


Subject(s)
Health Knowledge, Attitudes, Practice , Patient Acceptance of Health Care , Adolescent , Adult , Aged , /prevention & control , Complementary Therapies , Female , Finland/epidemiology , Humans , Male , Middle Aged , Patient Acceptance of Health Care/statistics & numerical data , Trust , Vaccination/psychology , Young Adult
16.
Health Secur ; 19(1): 31-43, 2021.
Article in English | MEDLINE | ID: covidwho-1174869

ABSTRACT

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition. Finally, we modelled the message retransmission process using a negative binomial regression, which allowed us to quantify the extent to which particular message features amplify or suppress retransmission, net of controls related to timing and properties of the sending account. In addition to identifying other predictors of retransmission, we show that the impact of images is strongly driven by content, with textual information in messages and embedded images operating in similar ways. We offer potential recommendations for crafting and deploying social media messages that can "cut through the noise" of an infodemic.


Subject(s)
Information Dissemination/methods , Public Health Informatics/methods , Social Media/statistics & numerical data , Communication , Humans , Social Marketing
17.
JMIR Public Health Surveill ; 6(3): e19969, 2020 07 17.
Article in English | MEDLINE | ID: covidwho-1172934

ABSTRACT

BACKGROUND: In the absence of vaccines and established treatments, nonpharmaceutical interventions (NPIs) are fundamental tools to control coronavirus disease (COVID-19) transmission. NPIs require public interest to be successful. In the United States, there is a lack of published research on the factors that influence public interest in COVID-19. Using Google Trends, we examined the US level of public interest in COVID-19 and how it correlated to testing and with other countries. OBJECTIVE: The aim of this study was to determine how public interest in COVID-19 in the United States changed over time and the key factors that drove this change, such as testing. US public interest in COVID-19 was compared to that in countries that have been more successful in their containment and mitigation strategies. METHODS: In this retrospective study, Google Trends was used to analyze the volume of internet searches within the United States relating to COVID-19, focusing on dates between December 31, 2019, and March 24, 2020. The volume of internet searches related to COVID-19 was compared to that in other countries. RESULTS: Throughout January and February 2020, there was limited search interest in COVID-19 within the United States. Interest declined for the first 21 days of February. A similar decline was seen in geographical regions that were later found to be experiencing undetected community transmission in February. Between March 9 and March 12, 2020, there was a rapid rise in search interest. This rise in search interest was positively correlated with the rise of positive tests for SARS-CoV-2 (6.3, 95% CI -2.9 to 9.7; P<.001). Within the United States, it took 52 days for search interest to rise substantially after the first positive case; in countries with more successful outbreak control, search interest rose in less than 15 days. CONCLUSIONS: Containment and mitigation strategies require public interest to be successful. The initial level of COVID-19 public interest in the United States was limited and even decreased during a time when containment and mitigation strategies were being established. A lack of public interest in COVID-19 existed in the United States when containment and mitigation policies were in place. Based on our analysis, it is clear that US policy makers need to develop novel methods of communicating COVID-19 public health initiatives.


Subject(s)
Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Public Opinion , Search Engine/trends , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Cross-Cultural Comparison , Humans , Pneumonia, Viral/epidemiology , Retrospective Studies , United States/epidemiology
18.
JMIR Public Health Surveill ; 6(3): e19831, 2020 07 30.
Article in English | MEDLINE | ID: covidwho-1172930

ABSTRACT

Before the coronavirus disease (COVID-19), 1 in 3 women and girls, globally, were victimized by an abusive partner in intimate relationships. However, the current pandemic has amplified cases of domestic violence (DV) against women and girls, with up to thrice the prevalence in DV cases compared to the same time last year. Evidence of the adverse effects of the pandemic on DV is still emerging, even as violence prevention strategies are iteratively being refined by service providers, advocacy agencies, and survivors to meet stay-at-home mandates. Emotional and material support for survivors is a critical resource increasingly delivered using digital and technology-based modalities, which offer several advantages and challenges. This paper rapidly describes current DV mitigation approaches using digital solutions, signaling emerging best practices to support survivors, their children, and abusers during stay-at-home advisories. Some examples of technology-based strategies and solutions are presented. An immediate priority is mapping out current digital solutions in response to COVID-19-related DV and outlining issues with uptake, coverage, and meaningful use of digital solutions.


Subject(s)
Coronavirus Infections/epidemiology , Domestic Violence/prevention & control , Intimate Partner Violence/prevention & control , Pandemics , Pneumonia, Viral/epidemiology , Telemedicine/methods , Coronavirus Infections/prevention & control , Domestic Violence/statistics & numerical data , Female , Global Health/statistics & numerical data , Humans , Intimate Partner Violence/statistics & numerical data , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Prevalence , Social Support , Survivors/psychology
19.
JMIR Public Health Surveill ; 6(3): e19354, 2020 07 17.
Article in English | MEDLINE | ID: covidwho-1172926

ABSTRACT

BACKGROUND: Coronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care. OBJECTIVE: This study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States. METHODS: The search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre-COVID-19 (prior to March 1) and post-COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre-COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship. RESULTS: The relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts. CONCLUSIONS: Google searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks.


Subject(s)
Coronavirus Infections/diagnosis , Gastrointestinal Diseases/epidemiology , Pandemics , Pneumonia, Viral/diagnosis , Public Health Surveillance/methods , Search Engine/statistics & numerical data , Coronavirus Infections/epidemiology , Humans , Pneumonia, Viral/epidemiology , United States/epidemiology
20.
MMWR Morb Mortal Wkly Rep ; 70(14): 519-522, 2021 Apr 09.
Article in English | MEDLINE | ID: covidwho-1173072

ABSTRACT

CDC's National Vital Statistics System (NVSS) collects and reports annual mortality statistics using data from U.S. death certificates. Because of the time needed to investigate certain causes of death and to process and review data, final annual mortality data for a given year are typically released 11 months after the end of the calendar year. Daily totals reported by CDC COVID-19 case surveillance are timely but can underestimate numbers of deaths because of incomplete or delayed reporting. As a result of improvements in timeliness and the pressing need for updated, quality data during the global COVID-19 pandemic, NVSS expanded provisional data releases to produce near real-time U.S. mortality data.* This report presents an overview of provisional U.S. mortality data for 2020, including the first ranking of leading causes of death. In 2020, approximately 3,358,814 deaths† occurred in the United States. From 2019 to 2020, the estimated age-adjusted death rate increased by 15.9%, from 715.2 to 828.7 deaths per 100,000 population. COVID-19 was reported as the underlying cause of death or a contributing cause of death for an estimated 377,883 (11.3%) of those deaths (91.5 deaths per 100,000). The highest age-adjusted death rates by age, race/ethnicity, and sex occurred among adults aged ≥85 years, non-Hispanic Black or African American (Black) and non-Hispanic American Indian or Alaska Native (AI/AN) persons, and males. COVID-19 death rates were highest among adults aged ≥85 years, AI/AN and Hispanic persons, and males. COVID-19 was the third leading cause of death in 2020, after heart disease and cancer. Provisional death estimates provide an early indication of shifts in mortality trends and can guide public health policies and interventions aimed at reducing numbers of deaths that are directly or indirectly associated with the COVID-19 pandemic.


Subject(s)
/mortality , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Cause of Death/trends , Child , Child, Preschool , Continental Population Groups/statistics & numerical data , Ethnic Groups/statistics & numerical data , Female , Health Status Disparities , Humans , Infant , Male , Middle Aged , Mortality/ethnology , United States/epidemiology , Vital Statistics , Young Adult
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