Retrospective methodology to estimate daily infections from deaths (REMEDID) in COVID-19: the Spain case study.
Ontology highlight
ABSTRACT:
SUBMITTER: Garcia-Garcia D
PROVIDER: S-EPMC8163852 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesThe purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries.Study designFor 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic.MethodsUnivariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables.ResultsThe proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak.ConclusionsOur analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.
| S-EPMC7980183 | biostudies-literature
Project description:The COVID-19 has caused the deadliest pandemic around the globe, emerged from the city of Wuhan, China by the end of 2019 and affected all continents of the world, with severe health implications and as well as financial-damage. Pakistan is also amongst the top badly effected countries in terms of casualties and financial loss due to COVID-19. By 20th March, 2021, Pakistan reported 623,135 total confirmed cases and 13,799 deaths. A state space model called 'Bayesian Dynamic Linear Model' (BDLM) was used for the forecast of daily new infections, deaths and recover cases regarding COVID-19. For the estimation of states of the models and forecasting new observations, the recursive Kalman filter was used. Twenty days ahead forecast show that the maximum number of new infections are 4,031 per day with 95% prediction interval (3,319-4,743). Death forecast shows that the maximum number of the deaths with 95% prediction interval are 81 and (67-93), respectively. Maximum daily recoveries are 3,464 with 95% prediction interval (2,887-5,423) in the next 20 days. The average number of new infections, deaths and recover cases are 3,282, 52 and 1,840, respectively, in the upcoming 20 days. As the data generation processes based on the latest data has been identified, therefore it can be updated with the availability of new data to provide latest forecast.
| S-EPMC8211153 | biostudies-literature
Project description:BackgroundWith the widespread outbreak of novel coronavirus diseases 2019(COVID-19), more and more death cases were reported, however, limited data are available for the patients who died. We aimed to explore the clinical characteristics of deaths with COVID-19 pneumonia.MethodsWe abstracted and analyzed epidemiological, demographic, clinical, and laboratory data from 83 death cases with COVID-19 pneumonia in East Hospital of Wuhan University Renmin Hospital, between January 26, 2020, and February 28, 2020.ResultsOf the 83 deaths, none was the medical staff. The mean age was 71.8 years (SD 13.2; range, 34-97 years) and 53(63.9%) were male. The median from onset to admission was 10 days (IQR 7-14: range, 2-43 days), to death was 17 days (IQR 14-21: range, 6-54 days). Most deaths (66[80%]) had underlying comorbid diseases, the most of which was hypertension [47(57%)]. The main initial symptoms of these 83 deaths were shortness of breath(98.8%), fever(94%), and myalgia or fatigue(90.4%). Laboratory analyses showed the lymphocytopenia in 69(83%) deaths, hypoalbuminemia in 77(93%) deaths, the elevation of lactate dehydrogenase in 79(95%) deaths, procalcitonin in 69(83%) deaths and C-reactive protein in 79(95%) deaths. All 83 patients received antiviral treatment, 81(97.6%) deaths received antibiotic therapy, 54(65.1%) deaths received glucocorticoid therapy, and 20(24.1%) patients received invasive mechanical ventilation.ConclusionMost of the deaths with COVID-19 pneumonia were elderly patients with underlying comorbid diseases, especially those over 70 years of age. The time of death after the onset of the disease was mostly 15-21 days. More care should be given to the elderly in further prevention and control strategies of COVID-19.
| S-EPMC7506806 | biostudies-literature
Project description:ObjectivesTo evaluate the effectiveness of widespread adoption of masks or face coverings to reduce community transmission of the SARS-CoV-2 virus that causes COVID-19.MethodsWe created an agent-based stochastic network simulation using a variant of the standard SEIR dynamic infectious disease model. We considered a mask order that was initiated 3.5 months after the first confirmed COVID-19 case. We varied the likelihood of individuals wearing masks from 0-100% in steps of 20% (mask adherence) and considered 25% to 90% mask-related reduction in viral transmission (mask efficacy). Sensitivity analyses assessed early (by week 13) versus late (by week 42) adoption of masks and geographic differences in adherence (highest in urban and lowest in rural areas).ResultsIntroduction of mask use with 50% efficacy worn by 50% of individuals reduces the cumulative infection attack rate (IAR) by 27%, the peak prevalence by 49%, and population-wide mortality by 29%. If 90% of individuals wear 50% efficacious masks, this decreases IAR by 54%, peak prevalence by 75%, and population-wide mortality by 55%; similar improvements hold if 70% of individuals wear 75% efficacious masks. Late adoption reduces IAR and deaths by 18% or more compared to no adoption. Lower adoption in rural areas than urban would lead to rural areas having the highest IAR.ConclusionsEven after community transmission of SARS-CoV-2 has been established, adoption of mask-wearing by a majority of community-dwelling individuals can meaningfully reduce the number and outcome of COVID-19 infections over and above physical distancing interventions.HighlightsThis paper shows the impact of widespread adoption of masks in response to the COVID-19 pandemic, with varying levels of population adherence, mask efficacy, and timing of mask adoption.The paper's findings help inform messaging to policymakers at the state or local level considering adding or keeping mask mandates, and to communities to promote widespread adoption of high-quality masks.Adoption of masks by at least half of the population can reduce cumulative infections and population deaths by more than 25%, while decreasing peak prevalence by about 50%. Even greater marginal improvements arise with adoption rates above 70%. The benefits of adopting high-quality masks is above that achieved by mobility changes and distancing alone.Rural and suburban areas are at higher relative risk than urban areas, due to less distancing and lower adoption of masks.
| S-EPMC7852241 | biostudies-literature
Project description:The US government imposed two travel restriction policies to prevent the spread of the COVID-19 but may have funneled asymptomatic air travelers to selected major airports and transportation hubs. Using the most recent JHU COVID-19 database, American Community Survey, Airport and Amtrak data form Bureau of Transportation Statistics from 3132 US counties we ran negative binomial regressions and Cox regression models to explore the associations between COVID-19 cases and death rates and proximity to airports, train stations, and public transportation. Counties within 25 miles of an airport had 1.392 times the rate of COVID-19 cases and 1.545 times the rate of COVID-19 deaths in comparison to counties that are more than 50 miles from an airport. More effective policies to detect and isolate infected travelers are needed. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to protect travelers and transportation workers from COVID-19.
| S-EPMC7714661 | biostudies-literature
Project description:BackgroundHealth care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths.ObjectiveThis study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong.MethodsThis was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits.ResultsCompared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths.ConclusionsWe demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences.
| S-EPMC10866203 | biostudies-literature
Project description:Measuring mortality has been a challenge during the COVID-19 pandemic. Here, we compared the results from the Spanish daily mortality surveillance system (MoMo) of excess mortality estimates, using a time series analysis, with those obtained for the confirmed COVID-19 deaths reported to the National Epidemiological Surveillance Network (RENAVE). The excess mortality estimated at the beginning of March 2020 was much greater than what has been observed in previous years, and clustered in a very short time. The cumulated excess mortality increased with age. In the first epidemic wave, the excess mortality estimated by MoMo was 1.5 times higher than the confirmed COVID-19 deaths reported to RENAVE, but both estimates were similar in the following pandemic waves. Estimated excess mortality and confirmed COVID-19 mortality rates were geographically distributed in a very heterogeneous way. The greatest increase in mortality that has taken place in Spain in recent years was detected early by MoMo, coinciding with the spread of the COVID-19 pandemic. MoMo is able to identify risk situations for public health in a timely manner, relying on mortality in general as an indirect indicator of various important public health problems.
| S-EPMC8703729 | biostudies-literature
Project description:As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.
| S-EPMC7877657 | biostudies-literature
Project description:Background/objectivesTo determine racial/ethnic disparities in weekly counts of new COVID-19 cases and deaths among nursing home residents or staff.DesignCross-sectional analysis of national nursing home COVID-19 reports linked to other data. Multivariable two-part models modeled disparities in count of cases or deaths, and logistic regressions modeled disparities in self-reported shortages in staff and personal protective equipment (PPE), across nursing home groups with varying proportions of racial/ethnic minority residents, defined as low-, medium-, medium-high-, and high-proportion groups.SettingA total of 12,576 nursing homes nationally.ParticipantsNone.InterventionNone.MeasurementsNumbers of incident COVID-19 confirmed cases among residents and staff, and incident COVID-19 related deaths among residents (primary outcomes); and nursing home reported shortages in staff and PPE (secondary outcomes). All outcomes were reported for the week of May 25, 2020.ResultsThe number of weekly new COVID-19 confirmed cases among residents ranged from an average of 0.4 cases per facility (standard deviation (SD) = 2.5) for the low-proportion group (93.0% had zero new cases) to 1.5 cases per facility (SD = 6.3) for the high-proportion group (78.9% had zero new cases). Multivariable regression estimated that compared with the low-proportion group, the likelihood of having at least one new resident case was 76% higher (odds ratio = 1.76; 95% confidence interval = 1.38-2.25; P = .000) for the high-proportion group. Similar across-facility disparities were found for the weekly count of new COVID-19 deaths among residents (ranging from 0.1 deaths per facility (SD = 1.1) for the low-proportion group to 0.4 deaths (SD = 2.0) for the high-proportion group) and in the weekly count of new COVID-19 confirmed cases among staff (ranging from 0.3 cases (SD = 1.4] to 1.3 cases (SD = 4.4) per facility). No substantial disparities in self-reported shortages of staff or PPE were found.ConclusionNursing homes caring for disproportionately more racial/ethnic minority residents reported more weekly new COVID-19 confirmed cases and/or deaths. Immediate actions are needed to address these system-level disparities.
| S-EPMC7537079 | biostudies-literature
Project description:ObjectivesTo measure the effect of social distancing on reducing daily deaths, infections and hospital resources needed for coronavirus disease 2019 (COVID-19) patients during the first wave of the pandemic in Nordic countries.MethodsThe observations of social distancing, daily deaths, infections along with the needed hospital resources for COVID-19 patient hospitalizations including the numbers of all hospital beds, beds needed in ICUs and infection wards, nursing staffs needed in ICUs and infection wards were collected from the Institute for Health Metrics and Evaluation (IHME) by the University of Washington. The observations of social distancing were based on the reduction in human contact relative to background levels for each location quantified by cell phone mobility data collected from IHME. The weighted data per 100,000 population gathered in a 40-day period of the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden. Statistical technique of panel data analysis is used to measure the associations between social distancing and COVID-19 indicators in long-run.ResultsResults of dynamic long-run models confirm that a 1% rise in social distancing by reducing human contacts may decline daily deaths, daily infections, all hospital beds needed, beds/nurses needed in ICUs and beds/nurses needed in infection wards due COVID-19 pandemic by 1.13%, 15.26%, 1.10%, 1.17% and 1.89%, respectively. Moreover, results of error correction models verify that if the equilibriums between these series are disrupted by a sudden change in social distancing, the lengths of restoring back to equilibrium are 67, 62, 40, 22 and 49 days for daily deaths, daily infections, all hospital beds needed, nurses/beds needed in ICUs and nurses/beds needed in infection wards, respectively.ConclusionProper social distancing was a successful policy for tackling COVID-19 with falling mortality and infection rates as well as the needed hospital resources for patient hospitalizations in Nordic countries. The results alert governments of the need for continuously implementing social distancing policies while using vaccines to prevent national lockdowns and reduce the burden of patient hospitalizations.
| S-EPMC7975574 | biostudies-literature