The determinants of COVID-19 morbidity and mortality across countries.
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ABSTRACT:
SUBMITTER: Chang D
PROVIDER: S-EPMC8989117 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
The determinants of COVID-19 morbidity and mortality across countries.
Chang Dianna D Chang Xin X He Yu Y Tan Kelvin Jui Keng KJK
Scientific reports 20220407 1
We identify 21 predetermined country-level factors that explain marked variations in weekly COVID-19 morbidity and mortality across 91 countries between January and the end of 2020. Besides factors commonly associated with infectious diseases (e.g., population and tourism activities), we discover a list of country characteristics that shape COVID-19 outcomes. Among demographic-geographic factors, the male-to-female ratio, population density, and urbanization aggravate the severity of COVID-19, w ...[more]
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Project description:BackgroundThis work aimed to identify the mathematical model and ecological determinants of COVID-19 infection and mortality across different countries during the first six months of the pandemic.MethodologyIn this study, authors used the online available data sources of randomly selected 18 countries to figure out potential determinants of COVID-19 transmissibility and mortality. The studied variables were environmental factors (daily average temperature, daily humidity), socioeconomic attributes (population age structure, count and density, human development index, per capita income (PCI), gross domestic product, internet coverage) mobility trends and chronic diseases. Researchers used the linear and exponential time series analysis, and further utilized multivariate techniques to explain the variance in the monthly increase in cases and deaths.ResultsIn the first two months, the R2 of linear models for the cases and deaths were higher than that of the corresponding R2 of the exponential model. Later one, R2 of the exponential model was occasionally relatively higher than that of the linear model. The exponential growth rate of new cases was significantly associated with mobility trends (β = 0.00398, P = 0.002), temperature (β = 0.000679, P = 0.011), humidity (β = 0.000249, P < 0.001), and the proportion of population aged ≥65 years (β = -0.000959, P = 0.012). Similarly, the exponential growth rate of deaths was significantly associated with mobility trends (β = 0.0027, P = 0.049), temperature (β = 0.0014, P < 0.001), humidity (β = -0.0026, P < 0.001), and PCI of countries. During this period, COVID-19 transmissibility was evident to be controlled as soon as social mobility is decreased by about 40% of the baseline over 3 months controlling for the other predictors.ConclusionControlling of COVID-19 pandemic is based mainly on controlling social mobility. Role of environmental determinants like temperature and humidity was well noticed on disease fatality and transmissibility. Socio-demographic determinants of COVID-19 spread and fatality included modifiable risk factors like PCI and non-modifiable risk factors like ageing.
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Project description:The interaction of coronavirus disease (COVID-19) with the majority of common prescriptions is broadly unknown. The purpose of this study is to identify medications associated with altered disease outcomes in COVID-19. A retrospective cohort composed of all adult inpatient admissions to our center with COVID-19 was analyzed. Data concerning all antecedent prescriptions were collected and agents brought forward for analysis if prescribed to at least 20 patients in our cohort. Forty-two medications and 22 classes of medication were examined. Groups were propensity score matched and analyzed by logistic and linear regression. The majority of medications did not show a statistically significant relationship with altered disease outcomes. Lower mortality was associated with use of pregabalin (hazard ratio [HR], 0.10; 95% confidence interval [CI], 0.01-0.92; P = .049) and inhalers of any type (HR, 0.33; 95%CI, 0.14-0.80; P = .015), specifically beclomethasone (HR, 0.10; 95%CI, 0.01-0.82; P = .032), tiotropium (HR, 0.07; 95%CI, 0.01-0.83; P = .035), and steroid-containing inhalers (HR, 0.35; 95%CI, 0.15-0.79; P = .013). Gliclazide (HR, 4.37; 95%CI, 1.26-15.18; P = .020) and proton pump inhibitor (HR, 1.72; 95%CI, 1.06-2.79; P = .028) use was associated with greater mortality. Diuretic (HR, 0.07; 95%CI, 0.01-0.37; P = .002) and statin (HR, 0.35; 95%CI, 0.17-0.73; P = .006) use was associated with lower rates of critical care admission. Our data lends confidence to observing usual practice in patients with COVID-19 by continuing antecedent prescriptions in the absence of an alternative acute contraindication. We highlight potential benefits in investigation of diuretics, inhalers, pregabalin, and statins as therapeutic agents for COVID-19 and support further assessment of the safety of gliclazide and proton pump inhibitors in the acute illness.
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Project description:We examine the relationship between contemporaneous fine particulate matter exposure and COVID-19 morbidity and mortality using an instrumental variable approach. Harnessing daily changes in county-level wind direction, we show that arguably exogenous fluctuations in local air quality impact the incidence of confirmed COVID-19 cases and deaths. We find that a one μ g/m 3 increase in PM 2.5, or 15% of the average PM 2.5 concentration in a county, increases the number of same-day confirmed cases by 1.8% from the mean case incidence in a county. A one μ g/m 3 increase in PM 2.5 increases the same-day death rate by just over 4% from the mean. These effects tend to increase in magnitude over longer time horizons and are robust to a host of sensitivity tests. When analyzing potential mechanisms, we also demonstrate that an additional unit of PM 2.5 increases COVID-19-related hospitalizations by 0.8% and use of intensive care units by 0.5% on the same day. Using individual case records, we also show that higher PM 2.5 exposure at the time of case confirmation increases risk of later mechanical ventilation and mortality. These results suggest that air pollution plays an important role in mediating the severity of respiratory syndromes such as COVID-19.
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Project description:There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.
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Project description:BackgroundThere are data on the safety of cancer surgery and the efficacy of preventive strategies on the prevention of postoperative symptomatic COVID-19 in these patients. But there is little such data for any elective surgery. The main objectives of this study were to examine the safety of bariatric surgery (BS) during the coronavirus disease 2019 (COVID-19) pandemic and to determine the efficacy of perioperative COVID-19 protective strategies on postoperative symptomatic COVID-19 rates.MethodsWe conducted an international cohort study to determine all-cause and COVID-19-specific 30-day morbidity and mortality of BS performed between 01/05/2020 and 31/10/2020.ResultsFour hundred ninety-nine surgeons from 185 centres in 42 countries provided data on 7704 patients. Elective primary BS (n = 7084) was associated with a 30-day morbidity of 6.76% (n = 479) and a 30-day mortality of 0.14% (n = 10). Emergency BS, revisional BS, insulin-treated type 2 diabetes, and untreated obstructive sleep apnoea were associated with increased complications on multivariable analysis. Forty-three patients developed symptomatic COVID-19 postoperatively, with a higher risk in non-whites. Preoperative self-isolation, preoperative testing for SARS-CoV-2, and surgery in institutions not concurrently treating COVID-19 patients did not reduce the incidence of postoperative COVID-19. Postoperative symptomatic COVID-19 was more likely if the surgery was performed during a COVID-19 peak in that country.ConclusionsBS can be performed safely during the COVID-19 pandemic with appropriate perioperative protocols. There was no relationship between preoperative testing for COVID-19 and self-isolation with symptomatic postoperative COVID-19. The risk of postoperative COVID-19 risk was greater in non-whites or if BS was performed during a local peak.
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Project description:Death rates due to COVID-19 pandemic vary considerably across regions and countries. Case Mortality Rates (CMR) per 100,000 population are more reliable than case-fatality rates per 100 test-positive cases, which are heavily dependent on the extent of viral case testing carried out in a country. We aimed to study the variations in CMR against population risk factors such as aging, underlying chronic diseases and social determinants such as poverty and overcrowding. Data on COVID-19 CMR in 93 countries was analyzed for associations with preexisting prevalence rates of eight diseases [asthma, lung cancer, Chronic Obstructive Pulmonary Disease (COPD), Alzheimer's Disease (AD), hypertension, ischemic heart disease, depression and diabetes], and six socio-demographic factors [Gross Domestic Product (GDP) per capita, unemployment, age over 65 years, urbanization, population density, and socio-demographic index]. These data were analyzed in three steps: correlation analysis, bivariate comparison of countries, and multivariate modelling. Bivariate analysis revealed that COVID-19 CMR were higher in countries that had high prevalence of population risk factors such as AD, lung cancer, asthma and COPD. On multivariate modeling however, AD, COPD, depression and higher GDP predicted increased death rates. Comorbid illnesses such as AD and lung diseases may be more influential than aging alone.
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