Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. 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.Peer reviewe

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe

Exploring spatiotemporal distribution of under-five mortality in Ethiopia: further analysis of Ethiopian Demographic and Health Surveys 2000, 2005, 2011 and 2016

Objective The aim of this study was to explore spatiotemporal distribution of under-five mortality in Ethiopia using data from four (2000, 2005, 2011 and 2016) consecutive demographic and health surveys.Methods A total of 41 498 children were included from four consecutive Ethiopian Demographic and Health Surveys. The geospatial analysis was conducted by using ArcGIS V.10.7 and saTScan V.9.6. Thus, spatial autocorrelation, hotspot analysis, spatial interpolation and spatial scan statistics were carried out for each survey separately to show the temporal pattern of geographically risk areas of under-five mortality in Ethiopia. Finally, the most under-five mortality rate (U5MR) risk areas in each survey period were mapped.Results Under-five mortality was spatially clustered in Ethiopia (Moran’s Index: 0.046–0.096, p<0.01). The Benishangul-Gumuz region was consistently at a higher risk in the last two decades. Additional hotspot areas were detected at Afar and Amhara (in 2000, 2005, 2016), at Gambala (in 2011) and the South Nation Nationality and People’s (SNNP) Region (in 2016). Moreover, 160 primary clusters were identified. Of these, 85 clusters (log-likelihood ratio (LLR)=13.10, p<0.01) were from Benishangul-Gumuz and Amhara regions (in 2000); 67 clusters (LLR=12.93, p<0.01) were from Afar and Amhara regions (in 2005); 4 clusters (LLR=10.54, p<0.01) were from Benishangul-Gumuz region (in 2011); and another 4 clusters (LLR=11.85, p<0.01) were from Afar region (in 2016).Conclusion High-risk areas were detected mainly in the Benishangul-Gumuz and Afar regions. As a result, designing under-five population targeted intervention programmes in those high-risk geographical regions was vital to reduce under-five mortality in Ethiopia

COVID-19-related disruptions to routine vaccination services in India: a survey of paediatric providers

The COVID-19 pandemic has led to disruptions in essential health services globally. We surveyed Indian paediatric providers on their perceptions of the impact of the pandemic on routine vaccination. Among 424 (survey 1) and 141 (survey 2) respondents representing 26 of 36 Indian states and union territories, complete suspension of vaccination services was reported by 33.4% and 7.8%, respectively. In April–June 2020, 83.1% perceived that vaccination services dropped by half, followed by 32.6% in September 2020, indicating slow resumption of services. Concerns that vaccine coverage gaps can lead to mortality were expressed by 76.6%. Concerted multipronged efforts are needed to sustain gains in vaccination coverage

Perceived cause and determinants of help-seeking behavior of schizophrenia among Gondar Zuria district residents, Northwest Ethiopia

Introduction: The cause of schizophrenia could be a genetic predisposition, environment, viral infections, exposure to poison substances, living in a highly-populated area, and prenatal exposure to hunger mainly in the first three months. Evidence showed that the perceived cause of schizophrenia is supernatural, biological, spiritual, and social causes. Studies in Ethiopia showed that most of the general population perceived the causes of schizophrenia as traditional and the help they seek ranges to medical, religious, and social. Objective: This study aimed to assess perceived cause and determinants of help-seeking behavior of schizophrenia among Gondar Zuria district residents, 2020 Methods: A community-based cross-sectional study was conducted from December 3 to 25, 2020. A simple random sampling technique was employed to select 435 study participants. Data were collected through a face-to-face interview, entered into EPI DATA version 3, and analyzed using SPSS version 21. Bivariable and multivariable binary logistic regression analyses were employed to identify factors significantly associated with help-seeking behavior for schizophrenia. Statistical significance was declared at p-value < 0.05 with 95% confidence interval. Results: Nearly two-thirds (63.8%), the majority (90.8%), and more than half (52.5%) of the participants seek medical, religious, and social help for schizophrenia respectively. Being student (AOR = 3.43; 95% CI: 1.44, 8.15), unemployed (AOR = 4.87; 95% CI: 1.4, 16.40), perceived biological cause (AOR = 1.7; 95% CI: 1.01, 2.89), perceived religious cause (AOR = 0.48; 95% CI: 0.29, 0.80), and perceived social cause (AOR = 2.05; 95% CI: 1.29, 3.25) were significantly associated with medical help seeking. Attending primary school (AOR = 0.17; 95% CI: 0.04, 0.76), employed (AOR = 0.12; 95% CI: 0.02, 0.64), perceived religious cause (AOR = 2.34; 95% CI: 1.06, 5.11) were significantly associated with religious help. Being in the age group of 18–24 years (AOR = 3.5; 95% CI: 1.33, 9.18) and 25–44 years (AOR = 1.94; 95% CI: 1.03, 3.68) were significantly associated with social intervention. Conclusion: Nearly two-thirds, the majority, and more than half of the respondents seek medical, religious, and social help for schizophrenia respectively. Being student, unemployed, perceived biological case, and perceived social cause increases the odds of seeking medical help while perceived religious cause decreases it. Being unemployed, attending primary school decreases the odds of seeking religious help whereas perceived religious cause increases it. Young adults have higher odds of social help-seeking behavior. It is better to create awareness for the community and consider integrating religious and social interventions into medical interventions

Progress in health among regions of Ethiopia, 1990-2019: a subnational country analysis for the Global Burden of Disease Study 2019.

BACKGROUND: Previous Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) studies have reported national health estimates for Ethiopia. Substantial regional variations in socioeconomic status, population, demography, and access to health care within Ethiopia require comparable estimates at the subnational level. The GBD 2019 Ethiopia subnational analysis aimed to measure the progress and disparities in health across nine regions and two chartered cities. METHODS: We gathered 1057 distinct data sources for Ethiopia and all regions and cities that included census, demographic surveillance, household surveys, disease registry, health service use, disease notifications, and other data for this analysis. Using all available data sources, we estimated the Socio-demographic Index (SDI), total fertility rate (TFR), life expectancy, years of life lost, years lived with disability, disability-adjusted life-years, and risk-factor-attributable health loss with 95% uncertainty intervals (UIs) for Ethiopia's nine regions and two chartered cities from 1990 to 2019. Spatiotemporal Gaussian process regression, cause of death ensemble model, Bayesian meta-regression tool, DisMod-MR 2.1, and other models were used to generate fertility, mortality, cause of death, and disability rates. The risk factor attribution estimations followed the general framework established for comparative risk assessment. FINDINGS: The SDI steadily improved in all regions and cities from 1990 to 2019, yet the disparity between the highest and lowest SDI increased by 54% during that period. The TFR declined from 6·91 (95% UI 6·59-7·20) in 1990 to 4·43 (4·01-4·92) in 2019, but the magnitude of decline also varied substantially among regions and cities. In 2019, TFR ranged from 6·41 (5·96-6·86) in Somali to 1·50 (1·26-1·80) in Addis Ababa. Life expectancy improved in Ethiopia by 21·93 years (21·79-22·07), from 46·91 years (45·71-48·11) in 1990 to 68·84 years (67·51-70·18) in 2019. Addis Ababa had the highest life expectancy at 70·86 years (68·91-72·65) in 2019; Afar and Benishangul-Gumuz had the lowest at 63·74 years (61·53-66·01) for Afar and 64.28 (61.99-66.63) for Benishangul-Gumuz. The overall increases in life expectancy were driven by declines in under-5 mortality and mortality from common infectious diseases, nutritional deficiency, and war and conflict. In 2019, the age-standardised all-cause death rate was the highest in Afar at 1353·38 per 100 000 population (1195·69-1526·19). The leading causes of premature mortality for all sexes in Ethiopia in 2019 were neonatal disorders, diarrhoeal diseases, lower respiratory infections, tuberculosis, stroke, HIV/AIDS, ischaemic heart disease, cirrhosis, congenital defects, and diabetes. With high SDIs and life expectancy for all sexes, Addis Ababa, Dire Dawa, and Harari had low rates of premature mortality from the five leading causes, whereas regions with low SDIs and life expectancy for all sexes (Afar and Somali) had high rates of premature mortality from the leading causes. In 2019, child and maternal malnutrition; unsafe water, sanitation, and handwashing; air pollution; high systolic blood pressure; alcohol use; and high fasting plasma glucose were the leading risk factors for health loss across regions and cities. INTERPRETATION: There were substantial improvements in health over the past three decades across regions and chartered cities in Ethiopia. However, the progress, measured in SDI, life expectancy, TFR, premature mortality, disability, and risk factors, was not uniform. Federal and regional health policy makers should match strategies, resources, and interventions to disease burden and risk factors across regions and cities to achieve national and regional plans, Sustainable Development Goals, and universal health coverage targets. FUNDING: Bill & Melinda Gates Foundation