Exploring root rot pathogens in wheat-pea rotations in Kansas

In 2018, over 277,000 bushels of wheat were produced on 7.7 million acres of land in Kansas alone. Based on the price of wheat by the end of 2018, this accounted for $1.44 million. This wheat is normally rotated with soybeans or fallow, but recent interest has arisen regarding the growth of peas in northern Kansas. As of 2019, there are both research and commercial growing operations underway. Many plant diseases have been especially prevalent during the summer because of the high rainfall and heat. In order to assess the severity of pea disease in Kansas, as well as explore potential interconnectivity between wheat and pea pathogens, a survey was conducted, and efforts were made to isolate and culture fungal pathogens of both wheat and pea

The Influence of Universal Health Coverage on Life Expectancy at Birth (LEAB) and Healthy Life Expectancy (HALE): A Multi-Country Cross-Sectional Study

Background: There are substantial differences in long term health outcomes across countries, particularly in terms of both life expectancy at birth (LEAB) and healthy life expectancy (HALE). Socio-economic status, disease prevention approaches, life style and health financing systems all influence long-term health goals such as life expectancy. Within this context, universal health coverage (UHC) is expected to influence life expectancy as a comprehensive health policy. The aim of the study is to investigate this relationship between Universal Health Coverage (UHC) and life expectancy.Method: A multi-country cross-sectional study was performed drawing on different sources of data (World Health Organization, UNDP-Education and World Bank) from 193 UN member countries, applying administrative record linkage theory. Descriptive statistics, t-tests, Pearson correlations, hierarchical linear regressions were utilized as appropriate.Result: Global average healthy life years was shown to be 61.34 ± 8.40 and life expectancy at birth was 70.00 ± 9.3. Standardized coefficients from regression analysis found UHC (0.34), child vaccination (Diphtheria Pertussis Tetanus−3: 0.17) and sanitation coverage (0.31) were associated with significantly increased life expectancy at birth. In contrast, population growth was associated with a decrease (0.29). Likewise, unit increases in child vaccination (DPT 3), sanitation and UHC would increase healthy life expectancy considerably (0.18, 0.31, and 0.40 respectively), whereas the same for population growth reduces healthy life expectancy by 0.28.Conclusion: Universal Health Coverage (UHC) is a comprehensive health system approach that facilitates a wide range of health services and significantly improves the life expectancy at birth and healthy life expectancy. This study suggests that specific programs to achieve UHC should be considered for countries that have not seen sufficient gains in life expectancy as part of the wider push to achieve the Sustainable Development Goal (SDG)

Disseminated cryptococcosis in a deceased with HIV-1 diagnosed by minimally invasive tissue sampling technique.

Minimal invasive tissue sampling (MITS) technique detected HIV infection and disseminated cryptococcosis in an adult female with sudden death. A proper autopsy is essential to diagnose the exact cause of death and MITS can suffice in natural deaths

Burden of injuries in Nepal, 1990–2017: Findings from the Global Burden of Disease Study 2017

Background: Nepal is a low-income country undergoing rapid political, economic and social development. To date, there has been little evidence published on the burden of injuries during this period of transition.Methods: The Global Burden of Disease Study (GBD) is a comprehensive measurement of population health outcomes in terms of morbidity and mortality. We analysed the GBD 2017 estimates for deaths, years of life lost, years lived with disability, incidence and disability-adjusted life years (DALYs) from injuries to ascertain the burden of injuries in Nepal from 1990 to 2017.Results: There were 16 831 (95% uncertainty interval 13 323 to 20 579) deaths caused by injuries (9.21% of all-cause deaths (7.45% to 11.25%)) in 2017 while the proportion of deaths from injuries was 6.31% in 1990. Overall, the injury-specific age-standardised mortality rate declined from 88.91 (71.54 to 105.31) per 100 000 in 1990 to 70.25 (56.75 to 85.11) per 100 000 in 2017. In 2017, 4.11% (2.47% to 6.10%) of all deaths in Nepal were attributed to transport injuries, 3.54% (2.86% to 4.08%) were attributed to unintentional injuries and 1.55% (1.16% to 1.85%) were attributed to self-harm and interpersonal violence. From 1990 to 2017, road injuries, falls and self-harm all rose in rank for all causes of death.Conclusions: The increase in injury-related deaths and DALYs in Nepal between 1990 and 2017 indicates the need for further research and prevention interventions. Injuries remain an important public health burden in Nepal with the magnitude and trend of burden varying over time by cause-specific, sex and age group. Findings from this study may be used by the federal, provincial and local governments in Nepal to prioritise injury prevention as a public health agenda and as evidence for country-specific interventions

Diseases, Injuries, and Risk Factors in Child and Adolescent Health, 1990 to 2017: Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2017 Study.

Importance:Understanding causes and correlates of health loss among children and adolescents can identify areas of success, stagnation, and emerging threats and thereby facilitate effective improvement strategies. Objective:To estimate mortality and morbidity in children and adolescents from 1990 to 2017 by age and sex in 195 countries and territories. Design, Setting, and Participants:This study examined levels, trends, and spatiotemporal patterns of cause-specific mortality and nonfatal health outcomes using standardized approaches to data processing and statistical analysis. It also describes epidemiologic transitions by evaluating historical associations between disease indicators and the Socio-Demographic Index (SDI), a composite indicator of income, educational attainment, and fertility. Data collected from 1990 to 2017 on children and adolescents from birth through 19 years of age in 195 countries and territories were assessed. Data analysis occurred from January 2018 to August 2018. Exposures:Being under the age of 20 years between 1990 and 2017. Main Outcomes and Measures:Death and disability. All-cause and cause-specific deaths, disability-adjusted life years, years of life lost, and years of life lived with disability. Results:Child and adolescent deaths decreased 51.7% from 13.77 million (95% uncertainty interval [UI], 13.60-13.93 million) in 1990 to 6.64 million (95% UI, 6.44-6.87 million) in 2017, but in 2017, aggregate disability increased 4.7% to a total of 145 million (95% UI, 107-190 million) years lived with disability globally. Progress was uneven, and inequity increased, with low-SDI and low-middle-SDI locations experiencing 82.2% (95% UI, 81.6%-82.9%) of deaths, up from 70.9% (95% UI, 70.4%-71.4%) in 1990. The leading disaggregated causes of disability-adjusted life years in 2017 in the low-SDI quintile were neonatal disorders, lower respiratory infections, diarrhea, malaria, and congenital birth defects, whereas neonatal disorders, congenital birth defects, headache, dermatitis, and anxiety were highest-ranked in the high-SDI quintile. Conclusions and Relevance:Mortality reductions over this 27-year period mean that children are more likely than ever to reach their 20th birthdays. The concomitant expansion of nonfatal health loss and epidemiological transition in children and adolescents, especially in low-SDI and middle-SDI countries, has the potential to increase already overburdened health systems, will affect the human capital potential of societies, and may influence the trajectory of socioeconomic development. Continued monitoring of child and adolescent health loss is crucial to sustain the progress of the past 27 years

Evolution and patterns of global health financing 1995-2014 : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background An adequate amount of prepaid resources for health is important to ensure access to health services and for the pursuit of universal health coverage. Previous studies on global health financing have described the relationship between economic development and health financing. In this study, we further explore global health financing trends and examine how the sources of funds used, types of services purchased, and development assistance for health disbursed change with economic development. We also identify countries that deviate from the trends. Methods We estimated national health spending by type of care and by source, including development assistance for health, based on a diverse set of data including programme reports, budget data, national estimates, and 964 National Health Accounts. These data represent health spending for 184 countries from 1995 through 2014. We converted these data into a common inflation-adjusted and purchasing power-adjusted currency, and used non-linear regression methods to model the relationship between health financing, time, and economic development. Findings Between 1995 and 2014, economic development was positively associated with total health spending and a shift away from a reliance on development assistance and out-of-pocket (OOP) towards government spending. The largest absolute increase in spending was in high-income countries, which increased to purchasing power-adjusted $5221 per capita based on an annual growth rate of 3.0$914 and $267 per capita in 2014, respectively. Spending in low-income countries grew nearly as fast, at 4.6$51 to $120 per capita. In 2014, 59.2$37.6 billion in 2016. Nonetheless, there is a great deal of variation revolving around these averages. 29 countries spend at least 50% more than expected per capita, based on their level of economic development alone, whereas 11 countries spend less than 50% their expected amount. Interpretation Health spending remains disparate, with low-income and lower-middle-income countries increasing spending in absolute terms the least, and relying heavily on OOP spending and development assistance. Moreover, tremendous variation shows that neither time nor economic development guarantee adequate prepaid health resources, which are vital for the pursuit of universal health coverage.Peer reviewe

Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3

Sustainable Development Goal (SDG) 3 aims to “ensure healthy lives and promote well-being for all at all ages”. While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available

Future and potential spending on health 2015-40 : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential.Peer reviewe

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation: Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Funding: Bill & Melinda Gates Foundation