METHODS: World Spine Care convened the GSCI to develop an evidence-based, practical, and sustainable healthcare model for spinal care. The initiative aims to improve the management, prevention, and public health for spine-related disorders worldwide; thus, global representation was essential. A series of meetings established the initiative's mission and goals. Electronic surveys collected contributorship and demographic information, and experiences with spinal conditions to better understand perceptions and potential biases that were contributing to the model of care.
RESULTS: Sixty-eight clinicians and scientists participated in the deliberations and are authors of one or more of the GSCI articles. Of these experts, 57 reported providing spine care in 34 countries, (i.e., low-, middle-, and high-income countries, as well as underserved communities in high-income countries.) The majority reported personally experiencing or having a close family member with one or more spinal concerns including: spine-related trauma or injury, spinal problems that required emergency or surgical intervention, spinal pain referred from non-spine sources, spinal deformity, spinal pathology or disease, neurological problems, and/or mild, moderate, or severe back or neck pain. There were no substantial reported conflicts of interest.
CONCLUSION: The GSCI participants have broad professional experience and wide international distribution with no discipline dominating the deliberations. The GSCI believes this set of papers has the potential to inform and improve spine care globally. These slides can be retrieved under Electronic Supplementary Material.
METHODS: Leading spine clinicians and scientists around the world were invited to participate. The interprofessional, international team consisted of 68 members from 24 countries, representing most disciplines that study or care for patients with spinal symptoms, including family physicians, spine surgeons, rheumatologists, chiropractors, physical therapists, epidemiologists, research methodologists, and other stakeholders.
RESULTS: Literature reviews on the burden of spinal disorders and six categories of evidence-based interventions for spinal disorders (assessment, public health, psychosocial, noninvasive, invasive, and the management of osteoporosis) were completed. In addition, participants developed a stratification system for surgical intervention, a classification system for spinal disorders, an evidence-based care pathway, and lists of resources and recommendations to implement the GSCI model of care.
CONCLUSION: The GSCI proposes an evidence-based model that is consistent with recent calls for action to reduce the global burden of spinal disorders. The model requires testing to determine feasibility. If it proves to be implementable, this model holds great promise to reduce the tremendous global burden of spinal disorders. These slides can be retrieved under Electronic Supplementary Material.
METHODS: Using a snowball sampling approach, we conducted an online cross-sectional study in 20 countries across four continents from February to May 2021.
RESULTS: A total of 10,477 participants were included in the analyses with a mean age of 36±14.3 years. The findings revealed the prevalence of perceptions towards COVID-19 vaccine's effectiveness (78.8%), acceptance (81.8%), hesitancy (47.2%), and drivers of vaccination decision-making (convenience [73.3%], health providers' advice [81.8%], and costs [57.0%]). The county-wise distribution included effectiveness (67.8-95.9%; 67.8% in Egypt to 95.9% in Malaysia), acceptance (64.7-96.0%; 64.7% in Australia to 96.0% in Malaysia), hesitancy (31.5-86.0%; 31.5% in Egypt to 86.0% in Vietnam), convenience (49.7-95.7%; 49.7% in Austria to 95.7% in Malaysia), advice (66.1-97.3%; 66.1% in Austria to 97.3% in Malaysia), and costs (16.0-91.3%; 16.0% in Vietnam to 91.3% in Malaysia). In multivariable regression analysis, several socio-demographic characteristics were identified as associated factors of outcome variables including, i) vaccine effectiveness: younger age, male, urban residence, higher education, and higher income; ii) acceptance: younger age, male, urban residence, higher education, married, and higher income; and iii) hesitancy: male, higher education, employed, unmarried, and lower income. Likewise, the factors associated with vaccination decision-making including i) convenience: younger age, urban residence, higher education, married, and lower income; ii) advice: younger age, urban residence, higher education, unemployed/student, married, and medium income; and iii) costs: younger age, higher education, unemployed/student, and lower income.
CONCLUSIONS: Most participants believed that vaccination would effectively control and prevent COVID-19, and they would take vaccinations upon availability. Determinant factors found in this study are critical and should be considered as essential elements in developing COVID-19 vaccination campaigns to boost vaccination uptake in the populations.
METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced.
RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes.
CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.
METHODS: We used results from the Global Burden of Disease (GBD) 2017 study to report incidence, prevalence, years lived with disability, deaths, years of life lost and disability-adjusted life years for all locations in the GBD 2017 hierarchy from 1990 to 2017 for road injuries. Second, we measured mortality-to-incidence ratios by location. Third, we assessed the distribution of the natures of injury (eg, traumatic brain injury) that result from each road injury.
RESULTS: Globally, 1 243 068 (95% uncertainty interval 1 191 889 to 1 276 940) people died from road injuries in 2017 out of 54 192 330 (47 381 583 to 61 645 891) new cases of road injuries. Age-standardised incidence rates of road injuries increased between 1990 and 2017, while mortality rates decreased. Regionally, age-standardised mortality rates decreased in all but two regions, South Asia and Southern Latin America, where rates did not change significantly. Nine of 21 GBD regions experienced significant increases in age-standardised incidence rates, while 10 experienced significant decreases and two experienced no significant change.
CONCLUSIONS: While road injury mortality has improved in recent decades, there are worsening rates of incidence and significant geographical heterogeneity. These findings indicate that more research is needed to better understand how road injuries can be prevented.
OBJECTIVE: With a descriptive epidemiological framing, we assessed whether recent historical patterns of regional influenza burden are reflected in the observed heterogeneity in COVID-19 cases across regions of the world.
METHODS: Weekly surveillance data reported by the World Health Organization from January 2017 to December 2019 for influenza and from January 1, 2020 through October 31, 2020, for COVID-19 were used to assess seasonal and temporal trends for influenza and COVID-19 cases across the seven World Bank regions.
RESULTS: In regions with more pronounced influenza seasonality, COVID-19 epidemics have largely followed trends similar to those seen for influenza from 2017 to 2019. COVID-19 epidemics in countries across Europe, Central Asia, and North America have been marked by a first peak during the spring, followed by significant reductions in COVID-19 cases in the summer months and a second wave in the fall. In Latin America and the Caribbean, COVID-19 epidemics in several countries peaked in the summer, corresponding to months with the highest influenza activity in the region. Countries from regions with less pronounced influenza activity, including South Asia and sub-Saharan Africa, showed more heterogeneity in COVID-19 epidemics seen to date. However, similarities in COVID-19 and influenza trends were evident within select countries irrespective of region.
CONCLUSIONS: Ecological consistency in COVID-19 trends seen to date with influenza trends suggests the potential for shared individual, structural, and environmental determinants of transmission. Using a descriptive epidemiological framework to assess shared regional trends for rapidly emerging respiratory pathogens with better studied respiratory infections may provide further insights into the differential impacts of nonpharmacologic interventions and intersections with environmental conditions. Ultimately, forecasting trends and informing interventions for novel respiratory pathogens like COVID-19 should leverage epidemiologic patterns in the relative burden of past respiratory pathogens as prior information.
METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.
RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.
CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.