RESUMO
Background: High prevalence, severity, and formidable morbidity have marked the recent emergence of the novel coronavirus disease (COVID-19) pandemic. The significant association with the pre-existing co-morbid conditions has increased the disease burden of this global health emergency, pushing the patients, healthcare workers and facilities to the verge of complete disruption. Methods: Meta-analysis of pooled data was undertaken to assess the cumulative risk assessment of multiple co-morbid conditions associated with severe COVID-19. PubMed, Scopus, and Google Scholar were searched from January 1st to June 27th 2020 to generate a well-ordered, analytical, and critical review. The exercise began with keying in requisite keywords, followed by inclusion and exclusion criteria, data extraction, and quality evaluation. The final statistical meta-analysis of the risk factors of critical/severe and non-critical COVID-19 infection was carried out on Microsoft Excel (Ver. 2013), MedCalc (Ver.19.3), and RevMan software (Ver.5.3). Results: We investigated 19 eligible studies, comprising 12037 COVID-19 disease patients, representing the People's Republic of China (PRC), USA, and Europe. 18.2% (n = 2200) of total patients had critical/severe COVID-19 disease. The pooled analysis showed a significant association of COVID-19 disease severity risk with cardiovascular disease (RR: 3.11, p < 0.001), followed by diabetes (RR: 2.06, p < 0.001), hypertension (RR: 1.54, p < 0.001), and smoking (RR: 1.52, p < 006). Conclusion: The review involved a sample size of 12037 COVID-19 patients across a wide geographical distribution. The reviewed reports have focussed on the association of individual risk assessment of co-morbid conditions with the heightened risk of COVID-19 disease. The present meta-analysis of cumulative risk assessment of co-morbidity from cardiovascular disease, diabetes, hypertension, and smoking signals a novel interpretation of inherent risk factors exacerbating COVID-19 disease severity. Consequently, there exists a definite window of opportunity for increasing survival of COVID-19 patients (with high risk and co-morbid conditions) by timely identification and implementation of appropriately suitable treatment modalities.
RESUMO
The escalating demand for anthropic needs and urban development has highlighted the importance of monitoring and change detection of land use land cover (LULC). With an increase in agricultural expansion and infrastructural development, every land surface on earth calls for a long-term investigation of land modification patterns and their underlying contributory factors. The present investigation monitors the LULC changes and assesses the process controls in Kohima and Dimapur districts of Nagaland, India. Currently, these two districts encompassing the main urban cities of the hilly state are experiencing rapid urbanization and unplanned developmental activities. Alike any other LULC changes observed in unplanned and developing cities, these districts are likely to face environmental degradation, and particularly, the occurrence of frequent landslides and flash floods. The study has three objectives-(i) LULC mapping of Kohima and Dimapur districts for three periods (1998, 2008, and 2018), (ii) comparative analysis of LULC change patterns in the two districts during the three epochs (1998-2008, 2008-2018, and 1998-2018), and (iii) assessment of the contributory factors. For the study, remotely sensed LANDSAT images (TM and OLI) in Geographical Information System (GIS) platform were utilized along with field surveys. Supervised classification technique was employed and four major LULC classes were identified using Landsat level-1 classification system. The overall accuracy of the classification varied between 91 and 98%. Results showed that Built Up areas have increased significantly in both the districts at the rate of 322.6 ha/year in Kohima and 301.9 ha/year in Dimapur during 1998-2018. On the other hand, Agricultural Land and Forest Land declined in both districts. Changes in LULC were mainly due to marginalization of shifting cultivation, deforestation, infrastructural development, urban migration, and flourishing of aquaculture farming. This study furnishes baseline information on LULC in the data-scarce region of Northeast India and is an insinuation to the policy-makers to ensure sustainable land use planning in the face of rapid urbanization.
