8 research outputs found
Application of mixed model and spatial analysis methods in multi-environmental and agricultural field trials.
No full textDoctor of Philosophy in Statistics. University of KwaZulu-Natal, Pietermaritzburg 2015.Agricultural experimentation involves selection of experimental materials,
selection of experimental units, planning of experiments, and collection of
relevant information, analysis and interpretation of the results. An overall
work of this thesis is on the importance, improvement and efficiency of variety
contrast by using linear mixed mode with spatial-variance covariance compare
to the usual ANOVA methods of analysis. A need of some considerations on the
recently widely usage of a bi-plot analysis of genotype plus genotype by
environment interaction (GEE) on the analysis of multi-environmental crop
trials. An application of some parametric bootstrap method for testing and
selecting multiplicative terms in GGE and AMMI models and to show some
statistical methods for handling missing data using multiple imputations
principal component and other deterministic approaches.
Multi-environment agricultural experiments are unbalanced because several
genotypes are not tested in some environments or missing of a
measurement from some plot during the experimental stage. A need for
imputation of the missing values sometimes is necessary. Multiple
imputation of missing data using the cross-validation by eigenvector method
and PCA methods are applied. We can see the advantage of these methods
having easy computational implementation, no need of any distributional or
structural assumptions and do not have any restrictions regarding the pattern
or mechanism of missing data in experiments.
Genotype by environment (G×E) interaction is associated with the differential
performance of genotypes tested at different locations and in different years,
and influences selection and recommendation of cultivars. Wheat genotypes
were evaluated in six environments to determine the G×E interactions and
stability of the genotypes. Additive main effects and multiplicative interactions
(AMMI) was conducted for grain yield of both year and it showed that grain
yield variation due to environments, genotypes and (G×E) were highly
significant. Stability for grain yield was determined using genotype plus
genotype by environment interaction (GGE) biplot analysis. The first two
principal components (PC1 and PC2) were used to create a 2-dimensional GGE
biplot. Which-won where pattern was based on six locations in the first and five
locations in the second year for all the twenty genotypes? The resulting pattern
is one realization among many possible outcomes, and its repeatability in the
second was different and a future year is quite unknown. A repeatability of
which won-where pattern over years is the necessary and sufficient condition
for mega-environment delineations and genotype recommendation.
The advantages of mixed models with spatial variance-covariance structures,
and direct implications of model choice on the inference of varietal
performance, ranking and testing based on two multi-environmental data sets
from realistic national trials. A model comparison with a ᵪ2-test for the trials in
the two data sets (wheat and barley data) suggested that selected spatial
variance-covariance structures fitted the data significantly better than the
ANOVA model. The forms of optimally-fitted spatial variance-covariance,
ranking and consistency ratio test were not the same from one trial (location) to
the other. Linear mixed models with single stage analysis including spatial
variance-covariance structure with a group factor of location on the random
model also improved the real genotype effect estimation and their ranking. The
model also improved varietal performance estimation because of its capacity to
handle additional sources of variation, location and genotype by location
(environment) interaction variation and accommodating of local stationary
trend. The knowledge and understanding of statistical methods for analysis of
multi-environmental data analysis is particularly important for plant breeders
and those who are working on the improvement of plant variety for proper
selection and decision making of the next level of improvement for country
agricultural development.Institute of Agricultural Research (EIAR) is acknowledged on p1039
Application of statistical multivariate techniques to wood quality data.
No full textThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.Sappi is one of the leading producer and supplier of Eucalyptus pulp to the world market. It is also a great contributor to South Africa economy in terms of employment opportunity to the rural people through its large plantation and export earnings. Pulp mills production of quality wood pulp is mainly affected by the supply of non uniform raw material namely Eucalyptus tree supply from various plantations. Improvement in quality of the pulp depends directly on the improvement on the quality of the raw materials. Knowing factors which affect the pulp quality is important for tree breeders. Thus, the main objective of this research is first to determine which of the anatomical, chemical and pulp properties of wood are significant factors that affect pulp properties namely viscosity, brightness and yield. Secondly the study will also investigate the effect of the difference in plantation location and site quality, trees age and species type difference on viscosity, brightness and yield of wood pulp. In order to meet the above mentioned objectives, data for this research was obtained from Sappi’s P186 trial and other two published reports from the Council for Scientific and Industrial Research (CSIR). Principal component analysis, cluster analysis, multiple regression analysis and multivariate linear regression analysis were used. These statistical analysis methods were used to carry out mean comparison of pulp quality measurements based on viscosity, brightness and yield of trees of different age, location, site quality and hybrid type and the results indicate that these four factors (age, location, site quality and hybrid type) and some anatomical and chemical measurements (fibre lumen diameter, kappa number, total hemicelluloses and total lignin) have significant effect on pulp quality measurements
Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment
No full textThe most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance–covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a chi-square test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend
The burden of injuries in Ethiopia from 1990-2017: evidence from the global burden of disease study
No full textBackground
Mortality caused by injuries is increasing and becoming a significant global public health concern. Limited evidence from Ethiopia on road traffic, unintentional and intentional injuries indicate the potential public health impact of problems resulting from such injuries. However, there is a significant evidence gap about the actual national burden of all injuries in Ethiopia. This data base study aimed to reveal the national burden of different injuries in Ethiopia.
Methodology
Data for this study were extracted from the estimates of the Global Burden of Diseases (GBD) 2017 study. Estimates of metrics such as Disability-Adjusted Life Years (DALYs), death rates, incidence, and prevalence were extracted. The metrics were then examined at different injury types, socio-demographic categories such as age groups and sex. Trends of the metrics were also explored for these categories across years from 2007 to 2017. The DALYs and deaths due to injuries in Ethiopia were also compared with other East African countries (specifically Kenya, Tanzania, Uganda, and Zambia) in order to evaluate regional differences across years, by sex and by different injury types such as transport injuries, unintentional injuries, self-harm and interpersonal violence.
Results
The age-standardized injury death rate has decreased to 69.4; 95% UI: (63.0–76.9) from 90.11; 95% UI: (82.41–97.73) in 2017 as compared with 2007. Road injury, falls, self-harm and interpersonal violence were the leading causes of mortality from injuries occurring in 2017. The age-standardized injury DALYs rate has decreased to 3328.2; 95% UI: (2981.7-3707.8) from 4265.55; 95% UI: (3898.11–4673.64) in 2017 as compared with 2007. The number of deaths resulting from injuries in 2017 was highest for males, children under 5 years, people aged 15–24.
Conclusion
The current age-standardized death rate and DALYs from injuries is high and the observed annual reduction is not satisfactory. There is a difference in gender and age regarding the number of deaths resulting from injuries. The data indicates that the current national efforts to address the public health impact of injuries in Ethiopia are not sufficient enough to bring a marked reduction. As a result, a more holistic approach to address all injuries is recommended in Ethiopia.publishedVersio
The burden of cardiovascular diseases in Ethiopia from 1990 to 2017: evidence from the Global Burden of Disease Study
No full textIn Ethiopia, evidence on the national burden of cardiovascular diseases (CVDs) is limited. To address this gap, this systematic analysis estimated the burden of CVDs in Ethiopia using the Global Burden of Disease (GBD) 2017 study data. The age-standardized CVD prevalence, disability-adjusted life years (DALYs) and mortality rates in Ethiopia were 5534 (95% uncertainty interval [UI] 5310.09 - 5774.0), 3549.6 (95% UI 3229.0 - 3911.9) and 182.63 (95% UI 165.49 - 203.9) per 100 000 population, respectively. Compared with 1990, the age-standardized CVD prevalence rate in 2017 showed no change. But significant reductions were observed in CVD mortality (54.7%), CVD DALYs (57.7%) and all-cause mortality (53.4%). The top three prevalent CVDs were ischaemic heart disease, rheumatic heart disease and stroke in descending order. The reduction in the mortality rate due to CVDs is slower than for communicable, maternal, neonatal and nutritional disease mortalities. As a result, CVDs are the leading cause of mortality in Ethiopia. These findings urge Ethiopia to consider CVDs as a priority public health problem
The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019
No full text© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). Interpretation: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Funding: Bill & Melinda Gates Foundation
Global, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990-2023
No full textBackground: Cardiovascular diseases (CVDs) are the leading cause of mortality and are among the foremost causes of disability globally. CVD burden has continued to increase in most countries since 1990, with trends driven by changing exposures to harmful risk factors, population growth, and population aging. Objectives: We report estimates of global, national, and subnational CVD burden, including 18 subdiseases and 12 associated modifiable risk factors. We analyzed change in CVD burden from 1990 to 2023 and identified drivers of change including population growth, population aging, and risk factor exposure. Methods: The Global Burden of Disease (GBD) 2023 study, a multinational collaborative research study, quantified burden due to 375 diseases including CVD burden and identified drivers of change from 1990 to 2023 using all available data and statistical models. GBD 2023 estimated the population-level burden of diseases in 204 countries and territories from 1990 to 2023. Results: CVDs were the leading cause of disability-adjusted life years (DALYs) and deaths estimated in the GBD. As of 2023, there were 437 million (95% UI: 401 to 465 million) CVD DALYs globally, a 1.4-fold increase from the number in 1990 of 320 million (292 to 344 million). Ischemic heart disease, intracerebral hemorrhage, ischemic stroke, and hypertensive heart disease were the leading cardiovascular causes of DALYs in 2023 globally. As of 2023, age-standardized CVD DALY rates were highest in low and low-middle Socio-demographic Index (SDI) settings and lowest in high SDI settings. The number of CVD deaths increased globally from 13.1 million (95% UI: 12.2 to 14.0 million) in 1990 to 19.2 million (95% UI: 17.4 to 20.4 million) in 2023. The number of prevalent cases of CVD more than doubled since 1990, with 311 million (95% UI: 294 to 333 million) prevalent cases of CVD in 1990 and 626 million (95% UI: 591 to 672 million) prevalent cases in 2023 globally. A total of 79.6% (95% UI: 75.7% to 82.5%) of CVD burden is attributable to modifiable risk factors 347 million [95% UI: 318 to 373 million] DALYs in 2023). Globally, high systolic blood pressure, dietary risks, high low-density lipoprotein cholesterol, and air pollution were the modifiable risks responsible for most attributable CVD burden in 2023. Since 1990, changes in exposure to modifiable risk factors have had mixed effects on CVD burden, with increases in high body mass index, high fasting plasma glucose, and low physical activity leading to higher burden, while reductions in tobacco usage have mitigated some of these increases. Population growth and population aging were the main drivers of the increasing burden since 1990, adding 128 million (95% UI: 115 to 139 million) and 139 million (95% UI: 126 to 151 million) CVD DALYs to the increase in CVD burden since 1990. Conclusions: CVD remains the leading cause of disease burden and death worldwide with the greatest burden in low, low-middle, and middle SDI regions. Large variation exists in CVD burden even for countries at similar levels of development, a gap explained substantially by known, modifiable risk factors that are inadequately controlled. The decades-long increase in CVD burden was the result of population growth, population aging, and increased exposure to a subset of risk factors led by metabolic risks. Countries will need to adopt effective health system and public health strategies if they are to progress in achieving global goals to reduce the burden of CVD
Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950–2023: a demographic analysis for the Global Burden of Disease Study 2023
No full textComprehensive, comparable, and timely estimates of demographic metrics—including life expectancy and age-specific mortality—are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study—part of the latest GBD release, GBD 2023—aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time. We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950–2023. For the first time, we used complete birth history data for ages 5–14 years, age-specific sibling history data for ages 15–49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution. In 2023, 60·1 million (95% UI 59·0–61·1) deaths occurred globally, of which 4·67 million (4·59–4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2–38·4) over the 1950–2023 study period, during which the global age-standardised all-cause mortality rate declined by 66·6% (65·8–67·3). Trends in age-specific mortality rates between 2011 and 2023 varied by age group and location, with the largest decline in under-5 mortality occurring in east Asia (67·7% decrease); the largest increases in mortality for those aged 5–14 years, 25–29 years, and 30–39 years occurring in high-income North America (11·5%, 31·7%, and 49·9%, respectively); and the largest increases in mortality for those aged 15–19 years and 20–24 years occurring in Eastern Europe (53·9% and 40·1%, respectively). We also identified higher than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 5–14 years (87·3% higher in GBD 2023 than GBD 2021 on average across countries and territories over the 1950–2021 period) and for females aged 15–29 years (61·2% higher), as well as lower than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 50 years and older (13·2% lower), reflecting advances in our modelling approach. Global life expectancy followed three distinct trends over the study period. First, between 1950 and 2019, there were considerable improvements, from 51·2 (50·6–51·7) years for females and 47·9 (47·4–48·4) years for males in 1950 to 76·3 (76·2–76·4) years for females and 71·4 (71·3–71·5) years for males in 2019. Second, this period was followed by a decrease in life expectancy during the COVID-19 pandemic, to 74·7 (74·6–74·8) years for females and 69·3 (69·2–69·4) years for males in 2021. Finally, the world experienced a period of post-pandemic recovery in 2022 and 2023, wherein life expectancy generally returned to pre-pandemic (2019) levels in 2023 (76·3 [76·0–76·6] years for females and 71·5 [71·2–71·8] years for males). 194 (95·1%) of 204 countries and territories experienced at least partial post-pandemic recovery in age-standardised mortality rates by 2023, with 61·8% (126 of 204) recovering to or falling below pre-pandemic levels. There were several mortality trajectories during and following the pandemic across countries and territories. Long-term mortality trends also varied considerably between age groups and locations, demonstrating the diverse landscape of health outcomes globally. This analysis identified several key differences in mortality trends from previous estimates, including higher rates of adolescent mortality, higher rates of young adult mortality in females, and lower rates of mortality in older age groups in much of sub-Saharan Africa. The findings also highlight stark differences across countries and territories in the timing and scale of changes in all-cause mortality trends during and following the COVID-19 pandemic (2020–23). Our estimates of evolving trends in mortality and life expectancy across locations, ages, sexes, and SDI levels in recent years as well as over the entire 1950–2023 study period provide crucial information for governments, policy makers, and the public to ensure that health-care systems, economies, and societies are prepared to address the world's health needs, particularly in populations with higher rates of mortality than previously known. The estimates from this study provide a robust framework for GBD and a valuable foundation for policy development, implementation, and evaluation around the world. Gates Foundation
