Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of the SARS-CoV-2 virus that causes the COVID-19 disease, it is essential to enquire if an outbreak of the epidemic might have been anticipated, given the well-documented history of SARS and MERS, among other infectious diseases. If various issues directly related to health security risks could have been predicted accurately, public health and medical contingency plans might have been prepared and activated in advance of an epidemic such as COVID-19. This paper evaluates an important source of health security, the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly in an effective and timely manner. The GHS index numerical scores are calculated as the arithmetic (AM), geometric (GM), and harmonic (HM) means of six categories, where AM uses equal weights for each category. The GHS Index scores are regressed on the numerical score rankings of the six categories to check if the use of equal weights of 0.167 in the calculation of the GHS Index using AM is justified, with GM and HM providing a check of the robustness of the arithmetic mean. The highest weights are determined to be around 0.244-0.246, while the lowest weights are around 0.186-0.187 for AM. The ordinal GHS Index is regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal Global Health Security (GHS) Index, where the highest weight is 0.368, while the lowest is 0.142, so the estimated results are wider apart than for the numerical score rankings. Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, whereas Risk Environment and Prevention have the smallest effects. The quantitative and qualitative results are different when GM and HM are used.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246562 | PMC |
http://dx.doi.org/10.3390/ijerph17093161 | DOI Listing |
PLoS One
January 2025
Equity Research and Innovation Center, Section of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Background: Accurate assessment of cardiovascular disease (CVD) risk is crucial for effective prevention and resource allocation. However, few CVD risk estimation tools consider social determinants of health (SDoH), despite their known impact on CVD risk. We aimed to estimate 10-year CVD risk in the Eastern Caribbean Health Outcomes Research Network Cohort Study (ECS) across multiple risk estimation instruments and assess the association between SDoH and CVD risk.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Nutritional Physiology, National Institute of Medical and Nutritional Sciences "Salvador Zubirán", Mexico City, Mexico.
Childhood obesity increases the risk of developing metabolic diseases in adulthood, since environmental stimuli during critical windows of development can impact on adult metabolic health. Studies demonstrating the effect of prepubertal diet on adult metabolic disease risk are still limited. We hypothesized that a prepubertal control diet (CD) protects the adult metabolic phenotype from diet-induced obesity (DIO), while a high-fat diet (HFD) would predispose to adult metabolic alterations.
View Article and Find Full Text PDFSci Adv
January 2025
Doerr School of Sustainability, Stanford University, Stanford, CA, USA.
Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations.
View Article and Find Full Text PDFPLoS One
January 2025
School of Information Science and Engineering, Xinjiang University, Urumqi, China.
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
View Article and Find Full Text PDFAm J Health Promot
January 2025
Health Promotion Research Center, Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle, WA, USA.
: Social determinants of health (SDOH), such as food security and healthcare access, are key to maintaining and improving health. Publicly funded safety-net programs, such as Medicaid and the Supplemental Nutrition Assistance Program, address SDOH. Many low-wage employees are program-eligible, but there are substantial participation gaps.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!