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10.1136/bmjopen-2020-039749 http://scihub22266oqcxt.onion/10.1136/bmjopen-2020-039749 32994257!7526277!32994257     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMJ+Open 2020 ; 10 (9): e039749 Nephropedia Template TP {{tp|p=32994257|t=2020. Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability |pdf=|usr=}}{{32994257}} gab.com Text Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability JO: BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=32994257 #FOAvip OBJECTIVES: The growth of COVID-19 infections in England raises questions about system vulnerability. Several factors that vary across geographies, such as age, existing disease prevalence, medical resource availability and deprivation, can trigger adverse effects on the National Health System during a pandemic. In this paper, we present data on these factors and combine them to create an index to show which areas are more exposed. This technique can help policy makers to moderate the impact of similar pandemics. DESIGN: We combine several sources of data, which describe specific risk factors linked with the outbreak of a respiratory pathogen, that could leave local areas vulnerable to the harmful consequences of large-scale outbreaks of contagious diseases. We combine these measures to generate an index of community-level vulnerability. SETTING: 91 Clinical Commissioning Groups (CCGs) in England. MAIN OUTCOME MEASURES: We merge 15 measures spatially to generate an index of community-level vulnerability. These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities. RESULTS: We find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). Here, vulnerability stems from a faster rate of population ageing and from the widespread presence of underlying at-risk diseases. These same areas, especially the North-East Coast areas of Lancashire, also appear vulnerable to adverse shocks to healthcare supply due to tighter labour markets for healthcare personnel. Importantly, our index correlates with a measure of social deprivation, indicating that these communities suffer from long-standing lack of economic opportunities and are characterised by low public and private resource endowments. CONCLUSIONS: Evidence-based policy is crucial to mitigate the health impact of pandemics such as COVID-19. While current attention focuses on curbing rates of contagion, we introduce a vulnerability index combining data that can help policy makers identify the most vulnerable communities. We find that this index is positively correlated with COVID-19 deaths and it can thus be used to guide targeted capacity building. These results suggest that a stronger focus on deprived and vulnerable communities is needed to tackle future threats from emerging and re-emerging infectious disease. Twit Text FOAVip Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability ????BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=32994257 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32994257 #FOAvip English Wikipedia <ref>{{Cite pmid|32994257}}</ref> Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability #MMPMID32994257 Nicodemo C; Barzin S; Cavalli N; Lasserson D; Moscone F; Redding S; Shaikh M BMJ Open 2020[Sep]; 10 (9): e039749 PMID32994257show ga OBJECTIVES: The growth of COVID-19 infections in England raises questions about system vulnerability. Several factors that vary across geographies, such as age, existing disease prevalence, medical resource availability and deprivation, can trigger adverse effects on the National Health System during a pandemic. In this paper, we present data on these factors and combine them to create an index to show which areas are more exposed. This technique can help policy makers to moderate the impact of similar pandemics. DESIGN: We combine several sources of data, which describe specific risk factors linked with the outbreak of a respiratory pathogen, that could leave local areas vulnerable to the harmful consequences of large-scale outbreaks of contagious diseases. We combine these measures to generate an index of community-level vulnerability. SETTING: 91 Clinical Commissioning Groups (CCGs) in England. MAIN OUTCOME MEASURES: We merge 15 measures spatially to generate an index of community-level vulnerability. These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities. RESULTS: We find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). Here, vulnerability stems from a faster rate of population ageing and from the widespread presence of underlying at-risk diseases. These same areas, especially the North-East Coast areas of Lancashire, also appear vulnerable to adverse shocks to healthcare supply due to tighter labour markets for healthcare personnel. Importantly, our index correlates with a measure of social deprivation, indicating that these communities suffer from long-standing lack of economic opportunities and are characterised by low public and private resource endowments. CONCLUSIONS: Evidence-based policy is crucial to mitigate the health impact of pandemics such as COVID-19. While current attention focuses on curbing rates of contagion, we introduce a vulnerability index combining data that can help policy makers identify the most vulnerable communities. We find that this index is positively correlated with COVID-19 deaths and it can thus be used to guide targeted capacity building. These results suggest that a stronger focus on deprived and vulnerable communities is needed to tackle future threats from emerging and re-emerging infectious disease. |*Communicable Disease Control/methods/organization & administration[MESH] |*Coronavirus Infections/epidemiology/prevention & control/transmission[MESH] |*Pandemics/prevention & control[MESH] |*Pneumonia, Viral/epidemiology/prevention & control/transmission[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Disease Transmission, Infectious/*prevention & control[MESH] |England/epidemiology[MESH] |Health Resources/*supply & distribution[MESH] |Health Services Accessibility/*standards[MESH] |Health Status Disparities[MESH] |Humans[MESH] |Needs Assessment[MESH] |Prevalence[MESH] |Public Health/methods/trends[MESH] |Quality Improvement/organization & administration[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH]Measuring geographical disparities in England at the time of COVID-19:(epmc).pdf DeepDyve Pubget Overpricing