METHODS: We developed the International Diet-Health Index (IDHI) to measure health impacts of dietary intake across 186 countries in 2010, using age-specific and sex-specific data on country-level dietary intake, effects of dietary factors on cardiometabolic diseases and country-specific cardiometabolic disease profiles. The index encompasses the impact of 11 foods/nutrients on 12 cardiometabolic diseases, the mediation of health effects of specific dietary intakes through blood pressure and body mass index and background disease prevalence in each country-age-sex group. We decomposed the index into IDHIbeneficial for risk-reducing factors, and IDHIadverse for risk-increasing factors. The flexible functional form of the IDHI allows inclusion of additional risk factors and diseases as data become available.
RESULTS: By sex, women experienced smaller detrimental cardiometabolic effects of diet than men: (females IDHIadverse range: -0.480 (5th percentile, 95th percentile: -0.932, -0.300) to -0.314 (-0.543, -0.213); males IDHIadverse range: (-0.617 (-1.054, -0.384) to -0.346 (-0.624, -0.222)). By age, middle-aged adults had highest IDHIbeneficial (females: 0.392 (0.235, 0.763); males: 0.415 (0.243, 0.949)) and younger adults had most extreme IDHIadverse (females: -0.480 (-0.932, -0.300); males: -0.617 (-1.054, -0.384)). Regionally, Central Latin America had the lowest IDHIoverall (-0.466 (-0.892, -0.159)), while Southeast Asia had the highest IDHIoverall (0.272 (-0.224, 0.903)). IDHIoverall was highest in low-income countries and lowest in upper middle-income countries (-0.039 (-0.317, 0.227) and -0.146 (-0.605, 0.303), respectively). Among 186 countries, Honduras had lowest IDHIoverall (-0.721 (-0.916, -0.207)), while Malaysia had highest IDHIoverall (0.904 (0.435, 1.190)).
CONCLUSION: IDHI encompasses dietary intakes, health effects and country disease profiles into a single index, allowing policymakers a useful means of assessing/comparing health impacts of diet quality between populations.
METHODS: This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced.
RESULTS: Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide.
CONCLUSION: Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease's first 90 days, especially in the United States of America.
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.
Methods: We searched PubMed, EMBASE, Cochrane library, and Econlit for articles published from inception to 31 July 2019. Original articles reporting costs or full economic evaluation related with snakebites were included. The methods and reporting quality were assessed. Costs were presented in US dollars (US$) in 2018.
Results: Twenty-three cost of illness studies and three economic evaluation studies related to snakebites were included. Majority of studies (18/23, 78.26%) were conducted in Low- and Middle-income countries. Most cost of illness studies (82.61%) were done using hospital-based data of snakebite patients. While, four studies (17.39%) estimated costs of snakebites in communities. Five studies (21.74%) used societal perspective estimating both direct and indirect costs. Only one study (4.35%) undertook incidence-based approach to estimate lifetime costs. Only three studies (13.04%) estimated annual national economic burdens of snakebite which varied drastically from US$126 319 in Burkina Faso to US$13 802 550 in Sri Lanka. Quality of the cost of illness studies were varied and substantially under-reported. All three economic evaluation studies were cost-effectiveness analysis using decision tree model. Two of them assessed cost-effectiveness of having full access to antivenom and reported cost-effective findings.
Conclusions: Economic burdens of snakebite were underestimated and not extensively studied. To accurately capture the economic burdens of snakebites at both the global and local level, hospital data should be collected along with community survey and economic burdens of snakebites should be estimated both in short-term and long-term period to incorporate the lifetime costs and productivity loss due to premature death, disability, and consequences of snakebites.
METHODS: As data on policy indicators were straightforward and fully available, we focused on studying 25 non-policy indicators: 23 GMFs and 2 PMIs. Gathering data availability of the target indicators was conducted among NCD surveillance experts from the six selected countries during May-June 2020. Our research team found information regarding whether the country had no data at all, was using WHO estimates, was providing 'expert judgement' for the data, or had actual data available for each target indicator. We triangulated their answers with several WHO data sources, including the WHO Health Observatory Database and various WHO Global Reports on health behaviours (tobacco, alcohol, diet, and physical activity) and NCDs. We calculated the percentages of the indicators that need improvement by both indicator category and country.
RESULTS: For all six studied countries, the health-service indicators, based on responses to the facility survey, are the most lacking in data availability (100% of this category's indicators), followed by the health-service indicators, based on the population survey responses (57%), the mortality and morbidity indicators (50%), the behavioural risk indicators (30%), and the biological risk indicators (7%). The countries that need to improve their NCD surveillance data availability the most are Cambodia (56% of all indicators) and Lao PDR (56%), followed by Malaysia (36%), Vietnam (36%), Myanmar (32%), and Thailand (28%).
CONCLUSION: Some of the non-policy GMF and PMI indicators lacked data among the six studied countries. To achieve the global NCDs targets, in the long run, the six countries should collect their own data for all indicators and begin to invest in and implement the facility survey and the population survey to track NCDs-related health services improvements once they have implemented the behavioural and biological Health Risks Population Survey in their countries.