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1.
Dev Psychol ; 57(10): 1681-1692, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1527993

ABSTRACT

Developmental research during COVID-19 suggests that pandemic-related disruptions in family relationships are associated with children's mental health. Most of this research has focused on 1 child per family, thereby obfuscating patterns that are differentially operative at the family-wide (i.e., between-family) versus child-specific (i.e., within-family) levels of analysis. Thus, the current study evaluates multilevel, longitudinal associations between COVID-19 disruption, family relationships, and caregiver/child mental health using a sibling comparison methodology. Caregivers (N = 549 families with 1098 children between 5 and 18 years old) were recruited from the Prolific research panel (73% White-European; 68% female; 76% United Kingdom, 19% U.S.A.; median 2019 income $50,000-$74,999). Caregiver reports of COVID-19 disruption, psychological distress, family functioning, parenting, and child mental health (for 2 children per family) were provided during May (time 1) and July (time 2) 2020. A Bayesian multilevel path analysis with random effects revealed: (a) families were experiencing difficulties across domains when COVID-19 disruption was high; (b) COVID-19 disruption corresponded to greater sibling differences in mental health; and (c) the sibling with poorer mental health received lower quality parenting over time, especially in families who reported higher levels of differential parenting. Findings suggest that understanding children's mental health difficulties during COVID-19 requires a family system lens due to the multiple ways these consequences permeate across the family unit. Comprehensive interventions for children's mental health during this time will likely require an examination of caregiver, sibling, and whole-family dynamics in the context of evidence-based telehealth practice. (PsycInfo Database Record (c) 2021 APA, all rights reserved).


Subject(s)
COVID-19 , Pandemics , Adolescent , Bayes Theorem , Child , Child, Preschool , Female , Humans , Male , Parenting , SARS-CoV-2
2.
Parasit Vectors ; 14(1): 282, 2021 May 26.
Article in English | MEDLINE | ID: covidwho-1523322

ABSTRACT

Trichinellosis is a foodborne disease caused by several Trichinella species around the world. In Chile, the domestic cycle was fairly well-studied in previous decades, but has been neglected in recent years. The aims of this study were to analyze, geographically, the incidence of trichinellosis in Chile to assess the relative risk and to analyze the incidence rate fluctuation in the last decades. Using temporal data spanning 1964-2019, as well as geographical data from 2010 to 2019, the time series of cases was analyzed with ARIMA models to explore trends and periodicity. The Dickey-Fuller test was used to study trends, and the Portmanteau test was used to study white noise in the model residuals. The Besag-York-Mollie (BYM) model was used to create Bayesian maps of the level of risk relative to that expected by the overall population. The association of the relative risk with the number of farmed swine was assessed with Spearman's correlation. The number of annual cases varied between 5 and 220 (mean: 65.13); the annual rate of reported cases varied between 0.03 and 1.9 cases per 105 inhabitants (mean: 0.53). The cases of trichinellosis in Chile showed a downward trend that has become more evident since the 1980s. No periodicities were detected via the autocorrelation function. Communes (the smallest geographical administrative subdivision) with high incidence rates and high relative risk were mostly observed in the Araucanía region. The relative risk of the commune was significantly associated with the number of farmed pigs and boar (Sus scrofa Linnaeus, 1758). The results allowed us to state that trichinellosis is not a (re)emerging disease in Chile, but the severe economic poverty rate of the Mapuche Indigenous peoples and the high number of backyard and free-ranging pigs seem to be associated with the high risk of trichinellosis in the Araucanía region.


Subject(s)
Swine Diseases/epidemiology , Trichinellosis/epidemiology , Animals , Bayes Theorem , Chile/epidemiology , Disease Outbreaks , Geographic Mapping , History, 20th Century , History, 21st Century , Incidence , Risk Assessment , Swine , Trichinella , Trichinellosis/history
3.
Clin Infect Dis ; 73(10): 1831-1839, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1522142

ABSTRACT

BACKGROUND: Monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody prevalence can complement case reporting to inform more accurate estimates of SARS-CoV-2 infection burden, but few studies have undertaken repeated sampling over time on a broad geographic scale. METHODS: We performed serologic testing on a convenience sample of residual serum obtained from persons of all ages, at 10 sites in the United States from 23 March through 14 August 2020, from routine clinical testing at commercial laboratories. We standardized our seroprevalence rates by age and sex, using census population projections and adjusted for laboratory assay performance. Confidence intervals were generated with a 2-stage bootstrap. We used bayesian modeling to test whether seroprevalence changes over time were statistically significant. RESULTS: Seroprevalence remained below 10% at all sites except New York and Florida, where it reached 23.2% and 13.3%, respectively. Statistically significant increases in seroprevalence followed peaks in reported cases in New York, South Florida, Utah, Missouri, and Louisiana. In the absence of such peaks, some significant decreases were observed over time in New York, Missouri, Utah, and Western Washington. The estimated cumulative number of infections with detectable antibody response continued to exceed reported cases in all sites. CONCLUSIONS: Estimated seroprevalence was low in most sites, indicating that most people in the United States had not been infected with SARS-CoV-2 as of July 2020. The majority of infections are likely not reported. Decreases in seroprevalence may be related to changes in healthcare-seeking behavior, or evidence of waning of detectable anti-SARS-CoV-2 antibody levels at the population level. Thus, seroprevalence estimates may underestimate the cumulative incidence of infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Viral , Bayes Theorem , Child , Humans , Seroepidemiologic Studies , United States/epidemiology , Utah
4.
JAMA Netw Open ; 4(11): e2132777, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1516694

ABSTRACT

Importance: A slow or incomplete civil registry makes it impossible to determine excess mortality due to COVID-19 and difficult to inform policy. Objective: To quantify the association of the COVID-19 pandemic with excess mortality and household income in rural Bangladesh in 2020. Design, Setting, and Participants: This repeated survey study is based on an in-person census followed by 2 rounds of telephone calls. Data were collected from a sample of 135 villages within a densely populated 350-km2 rural area of Bangladesh. Household data were obtained first in person and subsequently over the telephone. For the analysis, mortality data were stratified by month, age, sex, and household education. Mortality rates were modeled by bayesian multilevel regression, and the strata were aggregated to the population by poststratification. Data analysis was performed from February to April 2021. Exposures: Date and cause of any changes in household composition, as well as changes in income and food availability. Main Outcomes and Measures: Mortality rates were compared for 2019 and 2020, both without adjustment and after adjustment for nonresponse and differences in demographic variables between surveys. Income and food availability reported for January, May, and November 2020 were also compared. Results: Enumerators collected data from an initial 16 054 households in January 2020; 14 551 households (91%) responded when contacted again by telephone in May 2020, and 11 933 households (74%)responded when reached again over the telephone in November 2020, for a total of 58 806 individuals (29 726 female participants [50.5%]; mean [SD] age, 26.4 [19.8] years). A total of 276 deaths were reported between February and the end of October 2020 for the subset of the population that could be contacted twice over the telephone, slightly below the 289 deaths reported for the same population over the same period in 2019. After adjustment for survey nonresponse and poststratification, 2020 mortality changed by -8% (95% CI, -21% to 7%) compared with an annualized mortality of 6.1 deaths per 1000 individuals in 2019. However, in May 2020, salaried primary income earners reported a 40% decrease in monthly income (from 17 485 to 10 835 Bangladeshi Taka), and self-employed earners reported a 60% decrease in monthly income (23 083 to 8521 Bangladeshi Taka), with only a small recovery observed by November 2020. Conclusions and Relevance: In this study of households in rural Bangladesh, all-cause mortality was lower in 2020 compared with 2019. Restrictions imposed by the government may have limited the scale of the COVID-19 pandemic in rural areas, although economic data suggest that these restrictions need to be accompanied by expanded welfare programs.


Subject(s)
COVID-19 , Cause of Death , Family Characteristics , Income , Pandemics , Rural Population , Adolescent , Adult , Bangladesh , Bayes Theorem , COVID-19/mortality , Child , Educational Status , Employment , Female , Humans , Male , Middle Aged , SARS-CoV-2 , Socioeconomic Factors , Young Adult
5.
Sensors (Basel) ; 21(21)2021 Nov 08.
Article in English | MEDLINE | ID: covidwho-1512570

ABSTRACT

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.


Subject(s)
COVID-19 , Bayes Theorem , Biometry , Humans , Iris , SARS-CoV-2 , Support Vector Machine
6.
Stud Health Technol Inform ; 285: 112-117, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1502264

ABSTRACT

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.


Subject(s)
COVID-19 , Pneumonia , Bayes Theorem , COVID-19/diagnosis , Forecasting , Humans , Pneumonia/diagnosis
7.
J Acquir Immune Defic Syndr ; 88(2): 125-131, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1494135

ABSTRACT

BACKGROUND: Limited empirical evidence exists about the extent to which the current HIV epidemic intersects with COVID-19 infections at the area/geographic level. Moreover, little is known about how demographic, social, economic, behavioral, and clinical determinants are jointly associated with these infectious diseases. SETTING: Contiguous US counties (N = 3108). METHODS: We conducted a cross-sectional analysis and investigated the joint association between new HIV infection prevalence in 2018 and COVID-19 infections (January 22, 2020 and October 7, 2020) and explore the contribution of factors such as income inequality, binge drinking, and socioeconomic deprivation. We used Bayesian multivariate spatial models to estimate the cross-disease correlations between these diseases and identified hotspots, which we defined as a county with a posterior probability greater than 80% of being in the top decile of that disease. RESULTS: New HIV infection prevalence and COVID-19 infection moderately and significantly intersect [spatial correlation = 0.37, 95% credible interval (CrI) = 0.36-0.37]. Seventy-five counties, mostly in the south, were at elevated burden for HIV and COVID-19 infections. Higher income inequality was positively associated with both COVID-19 (relative risk 1.05, 95% CrI = 1.03-1.07) and HIV infection (relative risk = 1.12, 95% CrI = 1.09-1.15). CONCLUSIONS: We found that there is a considerable intersection between the current distribution of HIV burden with COVID-19 infections at the area level. We identified areas that federal funding and vaccination campaigns should prioritize for prevention and care efforts.


Subject(s)
COVID-19/epidemiology , HIV Infections/epidemiology , Adult , Bayes Theorem , COVID-19/virology , Cross-Sectional Studies , HIV Infections/virology , Humans , Income , Middle Aged , Prevalence , Socioeconomic Factors , United States/epidemiology
8.
Am J Ther ; 28(5): e576-e579, 2021.
Article in English | MEDLINE | ID: covidwho-1494105
9.
Nat Commun ; 12(1): 6250, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1493099

ABSTRACT

Understanding the trajectory, duration, and determinants of antibody responses after SARS-CoV-2 infection can inform subsequent protection and risk of reinfection, however large-scale representative studies are limited. Here we estimated antibody response after SARS-CoV-2 infection in the general population using representative data from 7,256 United Kingdom COVID-19 infection survey participants who had positive swab SARS-CoV-2 PCR tests from 26-April-2020 to 14-June-2021. A latent class model classified 24% of participants as 'non-responders' not developing anti-spike antibodies, who were older, had higher SARS-CoV-2 cycle threshold values during infection (i.e. lower viral burden), and less frequently reported any symptoms. Among those who seroconverted, using Bayesian linear mixed models, the estimated anti-spike IgG peak level was 7.3-fold higher than the level previously associated with 50% protection against reinfection, with higher peak levels in older participants and those of non-white ethnicity. The estimated anti-spike IgG half-life was 184 days, being longer in females and those of white ethnicity. We estimated antibody levels associated with protection against reinfection likely last 1.5-2 years on average, with levels associated with protection from severe infection present for several years. These estimates could inform planning for vaccination booster strategies.


Subject(s)
Antibodies, Viral/immunology , Antibody Formation/immunology , COVID-19/immunology , SARS-CoV-2/pathogenicity , Adult , Aged , Antibody Formation/physiology , Bayes Theorem , Female , Humans , Immunoglobulin G/metabolism , Male , Middle Aged , SARS-CoV-2/immunology
10.
BMC Infect Dis ; 21(1): 1119, 2021 Oct 30.
Article in English | MEDLINE | ID: covidwho-1486557

ABSTRACT

BACKGROUND: Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. METHODS: Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. RESULTS: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. CONCLUSIONS:  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.


Subject(s)
COVID-19 , Pandemics , Bayes Theorem , Humans , Multivariate Analysis , New Zealand/epidemiology , SARS-CoV-2
11.
BMC Infect Dis ; 21(1): 871, 2021 Aug 25.
Article in English | MEDLINE | ID: covidwho-1477269

ABSTRACT

BACKGROUND: Epidemic projections and public health policies addressing Coronavirus disease (COVID)-19 have been implemented without data reporting on the seroconversion of the population since scalable antibody testing has only recently become available. METHODS: We measured the percentage of severe acute respiratory syndrome- Coronavirus-2 (SARS-CoV-2) seropositive individuals from 2008 blood donors drawn in the state of Rhode Island (RI). We utilized multiple antibody testing platforms, including lateral flow immunoassays (LFAs), enzyme-linked immunosorbent assays (ELISAs) and high throughput serological assays (HTSAs). To estimate seroprevalence, we utilized the Bayesian statistical method to adjust for sensitivity and specificity of the commercial tests used. RESULTS: We report than an estimated seropositive rate of RI blood donors of approximately 0.6% existed in April-May of 2020. Daily new case rates peaked in RI in late April 2020. We found HTSAs and LFAs were positively correlated with ELISA assays to detect antibodies specific to SARS-CoV-2 in blood donors. CONCLUSIONS: These data imply that seroconversion, and thus infection, is likely not widespread within this population. We conclude that IgG LFAs and HTSAs are suitable to conduct seroprevalence assays in random populations. More studies will be needed using validated serological tests to improve the precision and report the kinetic progression of seroprevalence estimates.


Subject(s)
Antibodies, Viral/blood , Blood Donors , COVID-19/epidemiology , SARS-CoV-2 , Bayes Theorem , Humans , Rhode Island/epidemiology , Seroepidemiologic Studies
12.
Sci Rep ; 11(1): 20687, 2021 10 19.
Article in English | MEDLINE | ID: covidwho-1475486

ABSTRACT

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.


Subject(s)
COVID-19/drug therapy , Drug Repositioning/methods , Gene Regulatory Networks/drug effects , Protein Interaction Maps/genetics , SARS-CoV-2 , Antiviral Agents/pharmacology , Bayes Theorem , Computational Biology/methods , Drug Delivery Systems , Drug Discovery , Humans , Polypharmacology , Protein Interaction Mapping , United States , United States Food and Drug Administration
13.
Front Public Health ; 9: 729559, 2021.
Article in English | MEDLINE | ID: covidwho-1470772

ABSTRACT

Background: We provided a comprehensive evaluation of efficacy of available treatments for coronavirus disease 2019 (COVID-19). Methods: We searched for candidate COVID-19 studies in WHO COVID-19 Global Research Database up to August 19, 2021. Randomized controlled trials for suspected or confirmed COVID-19 patients published on peer-reviewed journals were included, regardless of demographic characteristics. Outcome measures included mortality, mechanical ventilation, hospital discharge and viral clearance. Bayesian network meta-analysis with fixed effects was conducted to estimate the effect sizes using posterior means and 95% equal-tailed credible intervals (CrIs). Odds ratio (OR) was used as the summary measure for treatment effect. Bayesian hierarchical models were used to estimate effect sizes of treatments grouped by the treatment classifications. Results: We identified 222 eligible studies with a total of 102,950 patients. Compared with the standard of care, imatinib, intravenous immunoglobulin and tocilizumab led to lower risk of death; baricitinib plus remdesivir, colchicine, dexamethasone, recombinant human granulocyte colony stimulating factor and tocilizumab indicated lower occurrence of mechanical ventilation; tofacitinib, sarilumab, remdesivir, tocilizumab and baricitinib plus remdesivir increased the hospital discharge rate; convalescent plasma, ivermectin, ivermectin plus doxycycline, hydroxychloroquine, nitazoxanide and proxalutamide resulted in better viral clearance. From the treatment class level, we found that the use of antineoplastic agents was associated with fewer mortality cases, immunostimulants could reduce the risk of mechanical ventilation and immunosuppressants led to higher discharge rates. Conclusions: This network meta-analysis identified superiority of several COVID-19 treatments over the standard of care in terms of mortality, mechanical ventilation, hospital discharge and viral clearance. Tocilizumab showed its superiority compared with SOC on preventing severe outcomes such as death and mechanical ventilation as well as increasing the discharge rate, which might be an appropriate treatment for patients with severe or mild/moderate illness. We also found the clinical efficacy of antineoplastic agents, immunostimulants and immunosuppressants with respect to the endpoints of mortality, mechanical ventilation and discharge, which provides valuable information for the discovery of potential COVID-19 treatments.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/therapy , Humans , Immunization, Passive , Network Meta-Analysis , Randomized Controlled Trials as Topic , SARS-CoV-2
14.
Lancet Public Health ; 6(11): e805-e816, 2021 11.
Article in English | MEDLINE | ID: covidwho-1467001

ABSTRACT

BACKGROUND: High-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England. METHODS: We performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. FINDINGS: In 2002-06 and 2006-10, all but a few (0-1%) MSOAs had a life expectancy increase for female and male sexes. In 2010-14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014-19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4-10·9) in 2002 to reach 14·2 years (13·9-14·5) in 2019, and for men increased from 11·5 years (11·3-11·7) in 2002 to 13·6 years (13·4-13·9) in 2019. INTERPRETATION: In the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country. FUNDING: Wellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.


Subject(s)
Life Expectancy/trends , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Child , Child, Preschool , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Registries , Residence Characteristics/statistics & numerical data , Risk Assessment , Spatio-Temporal Analysis , Young Adult
15.
Int J Environ Res Public Health ; 18(19)2021 09 22.
Article in English | MEDLINE | ID: covidwho-1463628

ABSTRACT

The spatial accessibility of prehospital EMS is particularly important for the elderly population's physiological functions. Due to the recent expansion of aging populations all over the globe, elderly people's spatial accessibility to prehospital EMS presents a serious challenge. An efficient strategy to address this issue involves using geographic information systems (GIS)-based tools to evaluate the spatial accessibility in conjunction with the spatial distribution of aging people, available road networks, and prehospital EMS facilities. This study employed gravity model and empirical Bayesian Kriging (EBK) interpolation analysis to evaluate the elderly's spatial access to prehospital EMS in Ningbo, China. In our study, we aimed to solve the following specific research questions: In the study area, "what are the characteristics of the prehospital EMS demand of the elderly?" "Do the elderly have equal and convenient spatial access to prehospital EMS?" and "How can we satisfy the prehospital EMS demand of an aging population, improve their spatial access to prehospital EMS, and then ensure their quality of life?" The results showed that 37.44% of patients admitted to prehospital EMS in 2020 were 65 years and older. The rate of utilization of ambulance services by the elderly was 27.39 per 1000 elderly residents. Ambulance use by the elderly was the highest in the winter months and the lowest in the spring months (25.90% vs. 22.38%). As for the disease spectrum, the main disease was found to be trauma and intoxication (23.70%). The mean accessibility score was only 1.43 and nearly 70% of demand points had scored lower than 1. The elderly's spatial accessibility to prehospital EMS had a central-outward gradient decreasing trend from the central region to the southeast and southwest of the study area. Our proposed methodology and its spatial equilibrium results could be taken as a benchmark of prehospital care capacity and help inform authorities' efforts to develop efficient, aging-focused spatial accessibility plans.


Subject(s)
Emergency Medical Services , Quality of Life , Aged , Ambulances , Bayes Theorem , China , Humans
16.
Am J Obstet Gynecol MFM ; 3(3): 100312, 2021 05.
Article in English | MEDLINE | ID: covidwho-1453982

ABSTRACT

OBJECTIVE: This study aimed to evaluate the comparative clinical effectiveness and safety of dexamethasone vs betamethasone for preterm birth. DATA SOURCES: The sources searched were MEDLINE, EMBASE, Cochrane Library, LILACS, ClinicalTrials.gov, and International Clinical Trials Registry Platform without language restrictions until October 2019 in addition to the reference lists of included studies. Field experts were also contacted. STUDY ELIGIBILITY CRITERIA: Randomized or quasi-randomized controlled trials comparing any corticosteroids against each other or against placebo at any dose for preterm birth were included in the study. METHODS: Three researchers independently selected and extracted data and assessed the risk of bias of the included studies by using Early Review Organizing Software and Covidence software. Random-effects pairwise meta-analysis and Bayesian network meta-analysis were performed. The primary outcomes were chorioamnionitis, endometritis or puerperal sepsis, neonatal death, respiratory distress syndrome, and neurodevelopmental disability. RESULTS: A total of 45 trials (11,227 women and 11,878 infants) were included in the study. No clinical or statistical difference was found between dexamethasone and betamethasone in neonatal death (odds ratio, 1.05; 95% confidence interval, 0.62-1.84; moderate-certainty evidence), neurodevelopmental disability (odds ratio, 1.03; 95% confidence interval, 0.80-1.33; moderate-certainty evidence), intraventricular hemorrhage (odds ratio, 1.04; 95% confidence interval, 0.56-1.78); low-certainty evidence), or birthweight (+5.29 g; 95% confidence interval, -49.79 to 58.97; high-certainty evidence). There was no statistically significant difference, but a potentially clinically important effect was found between dexamethasone and betamethasone in chorioamnionitis (odds ratio, 0.70; 95% confidence interval, 0.45-1.06; moderate-certainty evidence), fetal death (odds ratio, 0.81; 95% confidence interval, 0.24-2.41; low-certainty evidence), puerperal sepsis (odds ratio, 2.04; 95% confidence interval, 0.72-6.06; low-certainty evidence), and respiratory distress syndrome (odds ratio, 1.34; 95% confidence interval, 0.96-2.11; moderate-certainty evidence). Meta-regression, subgroup, and sensitivity analyses did not reveal important changes regarding the main analysis. CONCLUSION: Corticosteroids have proven effective for most neonatal and child-relevant outcomes compared with placebo or no treatment for women at risk of preterm birth. No important difference was found on neonatal death, neurodevelopmental disability, intraventricular hemorrhage, and birthweight between corticosteroids, and there was no statistically significant difference, but a potentially important difference was found in chorioamnionitis, fetal death, endometritis or puerperal sepsis, and respiratory distress syndrome. Further research is warranted to improve the certainty of evidence and inform health policies.


Subject(s)
Premature Birth , Bayes Theorem , Betamethasone , Child , Dexamethasone/therapeutic use , Female , Humans , Infant , Infant, Newborn , Network Meta-Analysis , Pregnancy , Premature Birth/epidemiology
17.
Clin Infect Dis ; 73(7): e1774-e1775, 2021 10 05.
Article in English | MEDLINE | ID: covidwho-1455272
18.
PLoS One ; 15(2): e0229658, 2020.
Article in English | MEDLINE | ID: covidwho-1453108

ABSTRACT

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.


Subject(s)
Disease Outbreaks , Models, Biological , Bayes Theorem , Humans
19.
Sci Rep ; 11(1): 19617, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1450290

ABSTRACT

Successive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic. These generalisations have not been able until today to represent the potential of the epidemic to overwhelm hospital capacity until today. This work builds on previous generalisations, including a new compartment for hospital occupancy that allows accounting for the infected patients that need specialised medical attention. Consequently, a deeper understanding of the hospitalisations rate and probability as well as of the recovery rates for hospitalised and non-hospitalised individuals is achieved, offering new information and predictions of crucial importance for the planning of the health systems and global epidemic response. Additionally, a new methodology to calibrate epidemic flows between compartments is proposed. We conclude that the two-step calibration procedure is able to recalibrate non-error-free data and showed crucial to reconstruct the series in a specific situation characterised by significant errors over the official recovery cases. The performed modelling also allowed us to understand how effective the several interventions (lockdown or other mobility restriction measures) were, offering insight for helping public authorities to set the timing and intensity of the measures in order to avoid the implosion of the health systems.


Subject(s)
COVID-19/epidemiology , Hospitalization/statistics & numerical data , Models, Statistical , Bayes Theorem , COVID-19/pathology , COVID-19/virology , Humans , Pandemics , Portugal/epidemiology , Quarantine , SARS-CoV-2/isolation & purification
20.
J R Soc Interface ; 18(182): 20210179, 2021 09.
Article in English | MEDLINE | ID: covidwho-1441850

ABSTRACT

The time-dependent reproduction number, Rt, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of Rt, together with their standard deviations, are obtained as a by-product.


Subject(s)
COVID-19 , Epidemics , Bayes Theorem , Forecasting , Humans , Models, Statistical , SARS-CoV-2
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