Risk Analysis of COVID-19 Infections in Kolkata Metropolitan City: A GIS-Based Study and Policy Implications.
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ABSTRACT:
SUBMITTER: Nath B
PROVIDER: S-EPMC7995155 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Graphical abstract The higher concentration of PM10 and PM2.5 in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications. The main objective is to find out the spatiotemporal variation and dispersal of PM10 and PM2.5 along with COVID-19 infection in the dusty city Kolkata. The consecutive two years PM10 and PM2.5 data of different stations have been obtained from State Pollution Control Board, Govt. of West Bengal. Forward trajectory analysis has been done through HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model to find the path and direction of air particles. The result showed that the various meteorological or environmental factors (such as temperature, relative humidity, wind, wind speed, pressure and gusty wind) and geographical location regulate the spatiotemporal variation of PM10 and PM2.5. These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city. During summer (both pre and lockdown years), the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m. The HYSPLIT model clearly specified that the particles dispersed from south, south-west to north and north east direction due to strong wind. The constant magnification of PM10 and PM2.5 in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths. In Kolkata, the one of the crucial reasons of high infection and deaths (COVID-19) is co-morbidity of people.
| S-EPMC8383484 | biostudies-literature
Project description:Due to COVID 19 (Corona virus disease)pandemic, majority of surgeries, including surgery for cancer patients got delayed across the globe. Surgeries were limited to emergency set up only. At our institute we tried to perform colorectal cancer surgeries through out the pandemic, albeit in less numbers, as we thought cancer in itself is an emergency setting. we are planning to analyse the prospectively managed database of this particular group of patients over a period of last six 6 months and look out at 30 day post operative morbidity and mortality. Besides we will try to analyse the implications of our decision to carry on with cancer surgeries in terms of number of health care workers who got infected while being involved in primary care of these patients.
| 2354636 | ecrin-mdr-crc
Project description:BackgroundWhilst evidence of use of face masks in reducing COVID-19 cases is increasing, the impact of mandatory use across a large population has been difficult to assess. Introduction of mandatory mask use on July 22, 2020 during a resurgence of COVID-19 in Melbourne, Australia created a situation that facilitated an assessment of the impact of the policy on the epidemic growth rate as its introduction occurred in the absence of other changes to restrictions.Methods and findingsExponential epidemic growth or decay rates in daily COVID-19 diagnoses were estimated using a non-weighted linear regression of the natural logarithm of the daily cases against time, using a linear spline model with one knot (lspline package in R v 3.6.3). The model's two linear segments pivot around the hinge day, on which the mask policy began to take effect, 8 days following the introduction of the policy. We used two forms of data to assess change in mask usage: images of people wearing masks in public places obtained from a major media outlet and population-based survey data. Potential confounding factors (including daily COVID-19 tests, number of COVID-19 cases among population subsets affected differentially by the mask policy-e.g., healthcare workers) were examined for their impact on the results. Daily cases fitted an exponential growth in the first log-linear segment (k = +0.042, s.e. = 0.007), and fitted an exponential decay in the second (k = -0.023, s.e. = 0.017) log-linear segment. Over a range of reported serial intervals for SARS-CoV-2 infection, these growth rates correspond to a 22-33% reduction in an effective reproduction ratio before and after mandatory mask use. Analysis of images of people in public spaces showed mask usage rose from approximately 43% to 97%. Analysis of survey data found that on the third day before policy introduction, 44% of participants reported "often" or "always" wearing a mask; on the fourth day after, 100% reported "always" doing so. No potentially confounding factors were associated with the observed change in growth rates.ConclusionsThe mandatory mask use policy substantially increased public use of masks and was associated with a significant decline in new COVID-19 cases after introduction of the policy. This study strongly supports the use of masks for controlling epidemics in the broader community.
| S-EPMC8294480 | biostudies-literature
Project description:While the rapid expansion of telemedicine in response to the COVID-19 pandemic highlights the impressive ability of health systems to adapt quickly to new complexities, it also raises important concerns about how to implement these novel modalities equitably. As the healthcare system becomes increasingly virtual, it risks widening disparities among marginalized populations who have worse health outcomes at baseline and limited access to the resources necessary for the effective use of telemedicine. In this article, we review recent policy changes and outline important recommendations that governments and health care systems can adopt to improve access to telemedicine and to tailor the use of these technologies to best meet the needs of underserved patients. We suggest that by making health equity integral to the implementation of telemedicine now, it will help to ensure that all can benefit from its use going forward and that this will be increasingly integral to care delivery.
| S-EPMC7428456 | biostudies-literature
Project description:Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of ? (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p?<?0.05, tstat?>?tcritical) for ?, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p?<?0.1 and tstat?<?tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat?>?tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.
| S-EPMC7340861 | biostudies-literature
Project description:BackgroundData regarding the association between preexisting cardiovascular risk factors (CVRFs) and cardiovascular diseases (CVDs) and the outcomes of patients requiring hospitalization for coronavirus disease 2019 (COVID-19) are limited. Therefore, the aim of this study was to investigate the impact of preexisting CVRFs or CVDs on the outcomes of patients with COVID-19 hospitalized in a Korean healthcare system.MethodsPatients with COVID-19 admitted to 10 hospitals in Daegu Metropolitan City, Korea, were examined. All sequentially hospitalized patients between February 15, 2020, and April 24, 2020, were enrolled in this study. All patients were confirmed to have COVID-19 based on the positive results on the polymerase chain reaction testing of nasopharyngeal samples. Clinical outcomes during hospitalization, such as requiring intensive care and invasive mechanical ventilation (MV) and death, were evaluated. Moreover, data on baseline comorbidities such as a history of diabetes, hypertension, dyslipidemia, current smoking, heart failure, coronary artery disease, cerebrovascular accidents, and other chronic cardiac diseases were obtained.ResultsOf all the patients enrolled, 954 (42.0%) had preexisting CVRFs or CVDs. Among the CVRFs, the most common were hypertension (28.8%) and diabetes mellitus (17.0%). The prevalence rates of preexisting CVRFs or CVDs increased with age (P < 0.001). The number of patients requiring intensive care (P < 0.001) and invasive MV (P < 0.001) increased with age. The in-hospital death rate increased with age (P < 0.001). Patients requiring intensive care (5.3% vs. 1.6%; P < 0.001) and invasive MV (4.3% vs. 1.7%; P < 0.001) were significantly greater in patients with preexisting CVRFs or CVDs. In-hospital mortality (12.9% vs. 3.1%; P < 0.001) was significantly higher in patients with preexisting CVRFs or CVDs. Among the CVRFs, diabetes mellitus and hypertension were associated with increased requirement of intensive care and invasive MV and in-hospital death. Among the known CVDs, coronary artery disease and congestive heart failure were associated with invasive MV and in-hospital death. In multivariate analysis, preexisting CVRFs or CVDs (odds ratio [OR], 1.79; 95% confidence interval [CI], 1.07-3.01; P = 0.027) were independent predictors of in-hospital death after adjusting for confounding variables. Among individual preexisting CVRF or CVD components, diabetes mellitus (OR, 2.43; 95% CI, 1.51-3.90; P < 0.001) and congestive heart failure (OR, 2.43; 95% CI, 1.06-5.87; P = 0.049) were independent predictors of in-hospital death.ConclusionBased on the findings of this study, the patients with confirmed COVID-19 with preexisting CVRFs or CVDs had worse clinical outcomes. Caution is required in dealing with these patients at triage.
| S-EPMC7801150 | biostudies-literature
Project description:This paper examines the impact of Covid-19 lockdowns on exports by Chinese cities. We use city-level export data at a monthly frequency from January 2018 through April 2020. Differences-in-differences estimates suggest cities in lockdown experienced a ceteris paribus 34 percentage points reduction in the year-on-year growth rate of exports. The lockdown impacted the intensive and extensive margin, with higher exit and lower new entry into foreign markets. The drop in exports was smaller in (i) coastal cities; (ii) cities with better-developed ICT infrastructure; and (iii) cities with a larger share of potential teleworkers. Time-sensitive and differentiated goods experienced a more pronounced decline in export growth. Global supply chain characteristics matter, with more upstream products and industries that had accumulated larger inventories experiencing a smaller decline in export growth. Also, products that relied more on imported (domestic) intermediates experienced a sharper (flatter) slowdown in export growth. The rapid recovery in cities' exports after lockdowns were lifted suggests the policy was cost-effective in terms of its effects on trade.
| S-EPMC8447424 | biostudies-literature
Project description:BackgroundSince its appearance, COVID-19 has immensely impacted our society. Public health measures, from the initial lockdowns to vaccination campaigns, have mitigated the crisis. However, SARS-CoV-2's persistence and evolving variants continue to pose global threats, increasing the risk of reinfections. Despite vaccination progress, understanding reinfections remains crucial for informed public health responses.MethodsWe collected available data on clinical and genomic information for SARS-CoV-2 samples from patients treated in Mexico City from 2020 epidemiological week 10 to 2023 epidemiological week 06 encompassing the whole public health emergency's period. To identify clinical data we utilized the SISVER (Respiratory Disease Epidemiological Surveillance System) database for SARS-CoV-2 patients who received medical attention in Mexico City. For genomic surveillance we analyzed genomic data previously uploaded to GISAID generated by Mexican institutions. We used these data sources to generate descriptors of case number, hospitalization, death and reinfection rates, and viral variant prevalence throughout the pandemic period.FindingsThe fraction of reinfected individuals in the COVID-19 infected population steadily increased as the pandemic progressed in Mexico City. Most reinfections occurred during the fifth wave (40%). This wave was characterized by the coexistence of multiple variants exceeding 80% prevalence; whereas all other waves showed a unique characteristic dominant variant (prevalence >95%). Shifts in symptom patient care type and severity were observed, 2.53% transitioned from hospitalized to ambulatory care type during reinfection and 0.597% showed the opposite behavior; also 7.23% showed a reduction in severity of symptoms and 6.05% displayed an increase in severity. Unvaccinated individuals accounted for the highest percentage of reinfections (41.6%), followed by vaccinated individuals (31.9%). Most reinfections occurred after the fourth wave, dominated by the Omicron variant; and after the vaccination campaign was already underway.InterpretationOur analysis suggests reduced infection severity in reinfections, evident through shifts in symptom severity and care patterns. Unvaccinated individuals accounted for most reinfections. While our study centers on Mexico City, its findings may hold implications for broader regions, contributing insights into reinfection dynamics.
| S-EPMC10899476 | biostudies-literature
Project description:BackgroundIn response to the disaster of coronavirus disease 2019 (COVID-19) pandemic, Seoul Metropolitan Government (SMG) established a patient facility for mild condition patients other than hospital. This study was conducted to investigate the operation and necessary resources of a community treatment center (CTC) operated in Seoul, a metropolitan city with a population of 10 million.MethodsTo respond COVID-19 epidemic, the SMG designated 5 municipal hospitals as dedicated COVID-19 hospitals and implemented one CTC cooperated with the Boramae Municipal Hospital for COVID-19 patients in Seoul. As a retrospective cross-sectional observational study, retrospective medical records review was conducted for patients admitted to the Seoul CTC. The admission and discharge route of CTC patients were investigated. The patient characteristics were compared according to route of discharge whether the patient was discharged to home or transferred to hospital. To report the operation of CTC, the daily mean number of tests (reverse transcription polymerase chain reaction and chest X-ray) and consultations by medical staffs were calculated per week. The list of frequent used medications and who used medication most frequently were investigated.ResultsUntil May 27 when the Seoul CTC was closed, 26.5% (n = 213) of total 803 COVID-19 patients in Seoul were admitted to the CTC. It was 35.7% (n = 213) of 597 newly diagnosed patients in Seoul during the 11 weeks of operation. The median length of stay was 21 days (interquartile range, 12-29 days). A total of 191 patients (89.7%) were discharged to home after virologic remission and 22 (10.3%) were transferred to hospital for further treatment. Fifty percent of transferred patients were within a week since CTC admission. Daily 2.5-3.6 consultations by doctors or nurses and 0.4-0.9 tests were provided to one patient. The most frequently prescribed medication was symptomatic medication for COVID-19 (cough/sputum and rhinorrhea). The next ranking was psychiatric medication for sleep problem and depression/anxiety, which was prescribed more than digestive drug.ConclusionIn the time of an infectious disease disaster, a metropolitan city can operate a temporary patient facility such as CTC to make a surge capacity and appropriately allocate scarce medical resource.
| S-EPMC7572227 | biostudies-literature
Project description:BackgroundA coronavirus disease 2019 (COVID-19) outbreak started in February 2020 and was controlled at the end of March 2020 in Daegu, the epicenter of the coronavirus outbreak in Korea. The aim of this study was to describe the clinical course and outcomes of patients with COVID-19 in Daegu.MethodsIn collaboration with Daegu Metropolitan City and Korean Center for Diseases Control, we conducted a retrospective, multicenter cohort study. Demographic, clinical, treatment, and laboratory data, including viral RNA detection, were obtained from the electronic medical records and cohort database and compared between survivors and non-survivors. We used univariate and multi-variable logistic regression methods and Cox regression model and performed Kaplan-Meier analysis to determine the risk factors associated with the 28-day mortality and release from isolation among the patients.ResultsIn this study, 7,057 laboratory-confirmed patients with COVID-19 (total cohort) who had been diagnosed from February 18 to July 10, 2020 were included. Of the total cohort, 5,467 were asymptomatic to mild patients (77.4%) (asymptomatic 30.6% and mild 46.8%), 985 moderate (14.0%), 380 severe (5.4%), and 225 critical (3.2%). The mortality of the patients was 2.5% (179/7,057). The Cox regression hazard model for the patients with available clinical information (core cohort) (n = 2,254) showed the risk factors for 28-day mortality: age > 70 (hazard ratio [HR], 4.219, P = 0.002), need for O₂ supply at admission (HR, 2.995; P = 0.001), fever (> 37.5°C) (HR, 2.808; P = 0.001), diabetes (HR, 2.119; P = 0.008), cancer (HR, 3.043; P = 0.011), dementia (HR, 5.252; P = 0.008), neurological disease (HR, 2.084; P = 0.039), heart failure (HR, 3.234; P = 0.012), and hypertension (HR, 2.160; P = 0.017). The median duration for release from isolation was 33 days (interquartile range, 24.0-46.0) in survivors. The Cox proportional hazard model for the long duration of isolation included severity, age > 70, and dementia.ConclusionOverall, asymptomatic to mild patients were approximately 77% of the total cohort (asymptomatic, 30.6%). The case fatality rate was 2.5%. Risk factors, including older age, need for O₂ supply, dementia, and neurological disorder at admission, could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.
| S-EPMC7781854 | biostudies-literature