Cathepsin S is a protease important in major histocompatibility complex (MHC) class II antigen presentation and also in degrading the extracellular matrix. Studies, most of them experimental, have shown that cathepsin S is involved in different pathological conditions such as obesity, inflammation, atherosclerosis, diabetes, and cancer. The overall hypothesis of this report is that high levels of circulating cathepsin S, is a biomarker that reflects pathology induced by inflammation and obesity. The overall aim of this report was to investigate possible associations between circulating cathepsin S, inflammation, glucometabolic disturbance, and its associated diseases in the community. As cathepsin S appears to be a novel risk marker for several pathological conditions, we also wanted to examine the effect of dietary intervention on circulating cathepsin S concentrations. This thesis is based on data from three community-based cohorts, the Uppsala longitudinal study of adult men (ULSAM), the prospective investigation of the vasculature in Uppsala seniors (PIVUS), and a post-hoc study from the randomized controlled NORDIET trial. In the first study, we identified a cross-sectional positive association between serum cathepsin S and two markers of cytokine-mediated inflammation, CRP and IL-6. These associations were similar in non-obese individuals. In longitudinal analyses, higher cathepsin S at baseline was associated with higher CRP and IL-6 levels after six years of follow-up. In the second study, we identified a cross-sectional association between increased serum levels of cathepsin S and reduced insulin sensitivity. These associations were similar in non-obese individuals. No significant association was observed between cathepsin S and insulin secretion. In longitudinal analysis, higher cathepsin S levels were associated with an increased risk of developing diabetes during the six-year follow-up. In the third study, we found that higher serum levels of cathepsin S were associated with increased mortality risk. Moreover, in the ULSAM cohort, serum cathepsin S was independently associated with cause-specific mortality from cardiovascular disease and cancer. In the fourth study, we identified that adherence to an ad libitum healthy Nordic diet for 6 weeks slightly decreased the levels of plasma cathepsin S in normal or marginally overweight individuals, relative to the control group. Changes in circulating cathepsin S concentrations were correlated with changes in body weight, LDL-C, and total cholesterol. Conclusion: This thesis shows that circulating cathepsin S is a biomarker that independently reflects inflammation, insulin resistance, the risk of developing diabetes, and mortality risk. Furthermore, a Nordic diet moderately reduced cathepsin S levels in normal-weight and overweight men and women. This effect may be partially mediated by diet-induced weight loss and possibly by reduced LDL-C concentrations.

Mortality patterns and trends in post-communist countries compared with low mortality populations Zhanyl Mukhtarova Abstract This research primarily addresses mortality patterns and trends in the post-communist countries of Central Asia, Central Europe and the Baltic region together with low mortality populations such as those of France, Spain and the United States of America. The aim of this research is to analyze mortality patterns and trends in selected post-communist countries and contrast them with low mortality populations between the period of 1990 and 2006. In this study, the main age-specific mortality intensities and the excess male mortality among the selected countries were analyzed. Moreover, the historical overview of mortality development in the selected countries and population longevity was discussed. Concurrently, the influence of socioeconomic conditions and healthy lifestyles and their implications and impact on declining mortality rates were revealed. The research clearly identified several important issues encasing the field of mortality, notably that more work and financial support is necessary to improve the health status of countries within Central Asia.

Background: While death from melanoma of the skin has been gradually decreasing over the past few decades, melanoma continues to be the leading causes of death among skin cancers. Less is known about specific causes of mortality among patients with melanoma and how or whether trends in cause of death among patients diagnosed with melanoma have changed in recent years. Objective: To examine temporal trends in the cause-specific mortality among adult patients diagnosed with melanoma in the US between 2000-2013. Methods: US patients ≥ 45 years when diagnosed with melanoma were identified using data from the Surveillance, Epidemiology, and End Results Program, 18 Registries (SEER-18). Joinpoint regression analysis was used to examine the trends in cause-specific mortality among patients who were diagnosed with melanoma and died from either melanoma or other causes of death. Trends were also examined separately by age, sex, and geographic region. Results: A total of 52,675 patients diagnosed with melanoma who died from either melanoma or other cause of death (median age 74 years, 67% male) were included in the analysis. Overall, 31% of deaths were due to melanoma specifically, whereas 69% died from various other causes. A marked decline in melanoma-specific mortality was observed overall and across strata by age, sex, and region in the US beginning around 2013-2014. Among all causes of death, 55% were due to melanoma within 1 year after diagnosis and declined to 25% over the course of 6 years. A marked decline of at least 2.5% in mortality per year from other causes was observed among females, males, those 65 – 74 years or 75 years and older, and those living in northeastern, midwestern, western, and southern regions of US who were diagnosed with melanoma. Conclusions: Changes in cause-specific mortality rate among patients with melanoma were observed overall and across different subgroups. Our findings show that, among those diagnosed with melanoma, the risk of melanoma-specific death is decreasing within the last two decades, and that the deaths among those with melanoma are more likely to be from other causes such as heart disease, lung cancer, and other conditions. Future studies are needed to assess the trends in melanoma mortality as treatments and diagnostic methods continue to advance.

The following study aims to examine the infant mortality in Kronoberg county in southern Sweden during four intervals 1820–1840, 1860–1880, 1900–1920 and 1930–1949, and thereby during the three latter stages of the demographic transition. The empirical data has been obtained from the region's central town, Växjö, and 16 different parishes on the countryside. As stated, the main issue is to study the development of infant mortality, but also to investigate whether there was any regional variation and whether the mortality was higher among the illegitimate children. The results show that infant mortality decreased from 173 to 35 per mille and that the urban parts of the study area initially had the highest mortality, but until the last interval it was lo­west in the urban environment. The highest infant mortality rate was observed among those born out of wedlock, which also declined from 262 to 65 per thousand throughout the studied periods.

Pang Suet-Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 85-87). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.1.1 --- Survival Analysis --- p.2 / Chapter 1.1.2 --- Tree、-structured Statistical Method --- p.2 / Chapter 1.1.3 --- Mortality Study --- p.3 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.3 --- Background Information --- p.4 / Chapter 1.4 --- Data Content --- p.7 / Chapter 1.5 --- Thesis Outline --- p.8 / Chapter 2 --- Imputation and File Splitting --- p.10 / Chapter 2.1 --- Imputation of Missing Values --- p.10 / Chapter 2.1.1 --- Purpose of Imputation --- p.10 / Chapter 2.1.2 --- Procedure of Hot Deck Imputation --- p.11 / Chapter 2.1.3 --- List of Variables for Imputation --- p.12 / Chapter 2.2 --- File Splitting --- p.14 / Chapter 2.2.1 --- Splitting by Gender --- p.14 / Chapter 2.3 --- Splitting for Validation Check --- p.1G / Chapter 3 --- Cox Hazard Model --- p.17 / Chapter 3.1 --- Basic Idea --- p.17 / Chapter 3.1.1 --- Survival Analysis --- p.17 / Chapter 3.1.2 --- Survivor Function --- p.18 / Chapter 3.1.3 --- Hazard Function --- p.18 / Chapter 3.2 --- The Cox Proportional Hazards Model --- p.19 / Chapter 3.2.1 --- Kaplan-Meier Estimate and Log-Rank Test --- p.20 / Chapter 3.2.2 --- Hazard Ratio --- p.23 / Chapter 3.2.3 --- Partial Likelihood --- p.24 / Chapter 3.3 --- Extension of the Cox Proportional Hazards Model for Time-dependent Variables --- p.25 / Chapter 3.3.1 --- Modification of the Cox's Model --- p.25 / Chapter 3.4 --- Results of Model Fitting --- p.26 / Chapter 3.4.1 --- Extract the Significant Covariates from the Models --- p.31 / Chapter 3.5 --- Model Interpretation --- p.32 / Chapter 4 --- CART --- p.37 / Chapter 4.1 --- CART Procedure --- p.38 / Chapter 4.2 --- Selection of the Splits --- p.39 / Chapter 4.2.1 --- Goodness of Split --- p.39 / Chapter 4.2.2 --- Type of Variables --- p.40 / Chapter 4.2.3 --- Estimation --- p.40 / Chapter 4.3 --- Pruning the Tree --- p.41 / Chapter 4.3.1 --- Misclassification Cost --- p.42 / Chapter 4.3.2 --- Class Assignment Rule --- p.44 / Chapter 4.3.3 --- Minimal Cost Complexity Pruning --- p.44 / Chapter 4.4 --- Cross Validation --- p.47 / Chapter 4.4.1 --- V-fold Cross-validation --- p.47 / Chapter 4.4.2 --- Selecting the right sized tree --- p.49 / Chapter 4.5 --- Missing Value --- p.49 / Chapter 4.6 --- Results of CART program --- p.51 / Chapter 4.7 --- Model Interpretation --- p.53 / Chapter 5 --- Model Prediction --- p.58 / Chapter 5.1 --- Application to Test Sample --- p.58 / Chapter 5.1.1 --- Fitting test sample to Cox's Model --- p.59 / Chapter 5.1.2 --- Fitting test sample to CART model --- p.61 / Chapter 5.2 --- Comparison of Model Prediction --- p.62 / Chapter 5.2.1 --- Misclassification Rate --- p.62 / Chapter 5.2.2 --- Misclassification Rate of Cox's model --- p.63 / Chapter 5.2.3 --- Misclassification Rate of CART model --- p.64 / Chapter 5.2.4 --- Prediction Result --- p.64 / Chapter 6 --- Conclusion --- p.67 / Chapter 6.1 --- Comparison of Results --- p.67 / Chapter 6.2 --- Comparison of the Two Statistical Techniques --- p.68 / Chapter 6.3 --- Limitation --- p.70 / Appendix A: Coding Description for the Health Factors --- p.72 / Appendix B: Log-rank Test --- p.75 / Appendix C: Longitudinal Plot of Time Dependent Variables --- p.76 / Appendix D: Hypothesis Testing of Suspected Covariates --- p.78 / Appendix E: Terminal node report for both gender --- p.81 / Appendix F: Calculation of Critical Values --- p.83 / Appendix G: Distribution of Missing Value in Learning sample and Test Sample --- p.84 / Bibliography --- p.85

by Siu-Fai Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 80-81). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Mortality and Socioeconomic Status --- p.1 / Chapter 1.2 --- Research Plan and Difficulties Encountered in the Study --- p.4 / Chapter 2 --- Imputation and File Merging --- p.8 / Chapter 2.1 --- Structure and Contents of Data Sets --- p.8 / Chapter 2.2 --- Imputation of Missing Values --- p.14 / Chapter 2.3 --- Merging Data Sets --- p.22 / Chapter 2.3.1 --- Merging Death Data and Census Data --- p.22 / Chapter 2.3.2 --- Merging Two Census Data Sets --- p.29 / Chapter 2.3.3 --- Final Data Set Used in Modeling --- p.31 / Chapter 3 --- Modeling and Estimation --- p.33 / Chapter 3.1 --- Discrete-Time Hazard Function Analysis --- p.33 / Chapter 3.1.1 --- The Hazard Function --- p.34 / Chapter 3.1.2 --- Logistic Regression --- p.36 / Chapter 3.2 --- Application of Discrete-Time Hazard Model on the Death Data Set --- p.37 / Chapter 3.2.1 --- Preparing the Person-Period Data Set --- p.38 / Chapter 3.2.2 --- Modeling the Person-Period Data Set --- p.41 / Chapter 3.3 --- Combining Results from different imputed data sets --- p.47 / Chapter 3.4 --- Estimation of Cell Probabilities --- p.51 / Chapter 4 --- Model Adequacy Checking --- p.52 / Chapter 4.1 --- The Definition of Residuals in Multiple Imputation --- p.52 / Chapter 4.2 --- Residual Analysis of The Cancer Mortality Model --- p.59 / Chapter 5 --- Conclusion --- p.63 / Chapter 5.1 --- The Cancer Mortality --- p.63 / Chapter 5.2 --- Competing Risk --- p.68 / Chapter 5.3 --- Discussion --- p.72 / Appendix A: Coding Description of District --- p.75 / Appendix B: Results of the Heart Diseases Mortality Model --- p.76 / Bibliography --- p.80

AIDS mortality, its linkages as a determinant and consequence of food security and its impact on natural resource utilisation by mainly rural populations, has not been well researched, especially their effects on rural livelihoods. With the high epidemic prevalence and persistent food insecurity, natural resources are and will continue to play a key role as a buffer against stresses and shocks in rural livelihoods. Determining linkages between household food security, adult AIDS mortality, and how these affect natural resource utilisation at the village level was the objective of this research. The overarching goal of sustainable natural resource utilisation in Agincourt Demographic Surveillance Site (DSS) was determined through three research questions outlined as follows; What is the status of food security, AIDS mortality and Natural resource utilisation in Agincourt?; What is the relationship between dependence on natural resources as a source of food and or livelihood to resource degradation?; and What are the household and community drivers of household food security? Statistical analysis was used to evaluate the prevalence of food insecurity and the reliance on natural resources while remote sensing was used to assess resource availability and identification of possible natural resource degradation hotspots. More than half of the population in the DSS is food-secure, in 2004, with an even smaller hungry population in 2007. HIV/AIDS and non-HIV/AIDS adult mortality, analysed at village level are underlying drivers and determinants, affecting availability of income which is a direct driver of food insecurity. Availability of income, through social grants, remittances or wages, and delay or non-receipt of this income results in food insecurity in some households. Food production, affected and constrained by climate variability, is a less stable and less popular means of attaining food. More than half of the Agincourt population utilises natural resources to supplement dietary diversity and household income, although there is a significant reduction in households using natural resources in 2007 compared to 2004. Resource degradation is noted in the village commons especially between the highly food-insecure villages and are identified as environmental degradation hot spots. The identification of synergies among these factors in policy design and for interventions is essential for poverty alleviation, improved health and sustainable utilisation of natural resources and rural livelihoods. Glory be to GOD for making this work possible “Commit your work to the Lord and then your plans will succeed” (Proverbs 16:3) “Material poverty doesn‟t necessarily lead to a lack of capacity for creativeness and Inventiveness. Poor people survival by their wits and have much more to contribute to address complex problems than we tend to credit them with.” Dr. Maphela Ramphele (Destiny Magazine, 2010)

<b>Background:</b> 'Competing risks' are events that either preclude or alter the probability of experiencing the primary study outcome(s). Many standard survival models fail to account for competing risks, introducing an unknown level of bias in their measures of absolute and relative risk. Individuals with type 2 diabetes mellitus (T2DM) and albuminuria are at increased risk of multiple competing causes of mortality, including cardiovascular disease (CVD), cancer and renal disease, yet studies to date have not implemented competing risks methodology. <b>Aim:</b> Using albuminuria in T2DM as a case study, this Thesis set out to quantify differences between standard- and competing-risks-adjusted survival analysis estimates of absolute and relative risk for the outcomes of cardiovascular and cancer mortality. <b>Methods:</b> 86,962 patients aged &ge;35 years with T2DM present on or before 2005 were identified in the Clinical Practice Research Datalink. To quantify differences in measures of absolute risk, cumulative risk estimates for cardiovascular and cancer mortality from standard survival analysis methods (Kaplan-Meier estimator) were compared to those from competing-risks-adjusted methods (cumulative incidence competing risk estimator). Cumulative risk estimates were stratified by patient albuminuria level (normoalbuminuria vs albuminuria). To quantify differences in measures of relative risk, estimates for the effect of albuminuria on the relative hazards of cardiovascular and cancer mortality were compared between standard cause-specific hazard (CSH) models (Cox-proportional-hazards regression), competing risk CSH models (unstratified Lunn-McNeil model), and competing risk subdistribution hazard (SDH) models (Fine-Gray model). <b>Results:</b> Patients with albuminuria, compared to those with normoalbuminuria, were older (p&LT;0.001), had higher systolic blood pressure (p&LT;0.001), had worse glycaemic control (p&LT;0.001), and were more likely to be current or ex-smokers (p&LT;0.001). Over the course of nine years of follow-up 22,512 patients died; 8,800 from CVD, 5,239 from cancer, and 8,473 from other causes. Median follow-up was 7.7 years. In patients with normoalbuminuria, nine-year standard and competing-risks-adjusted cumulative risk estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI): 10.8-11.5%) and 10.2% (95% CI: 9.9-10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI: 7.7-8.3%) and 7.2% (95% CI: 6.9-7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI: 20.9-22.7%) and 18.5% (95% CI: 17.8-19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI: 10.0-11.5%) and 8.6% (8.1-9.2%). For the effect of albuminuria on cardiovascular mortality, hazard ratios from multivariable standard CSH, competing risks CSH, and subdistribution hazard ratios from competing risks SDH models were 1.75 (95% CI: 1.63-1.87), 1.75 (95% CI: 1.64-1.87), and 1.58 (95% CI: 1.48-1.69), respectively. For the effect of albuminuria on cancer mortality, these values were 1.27 (95% CI: 1.16-1.39), 1.28 (95% CI: 1.17-1.40), and 1.11 (95% CI: 1.01-1.21). <b>Conclusions:</b> When evaluating measures of absolute risk, differences between standard and competing-risks-adjusted methods were small in absolute terms, but large in relative terms. For the investigation of epidemiological relationships using relative hazards models, standard survival analysis methods produced near-identical risk estimates to the CSH competing risks methods for the clinical associations evaluated in this Thesis. For the evaluation of risk prediction using relative hazards models, CSH models produced consistently higher risk estimates than SDH models, and their use may lead to over-estimation of the predictive effect of albuminuria on either outcome. Where outcomes are less common (like cancer) CSH models provide poor estimates of risk prediction, and SDH models should be used. This research demonstrates that differences can be present between risk estimates derived using CSH and SDH methods, and that the two are not necessarily interchangeable. Moreover, such differences may be present in other clinical areas.

This thesis examines the changing patterns of mortality in Scotland, with particular emphasis on whether there are widening mortality inequalities among small areas in Scotland. The annual number of deaths in Scotland has decreased steadily since the 1950s, yet mortality rates in Scotland are amongst the highest in Europe for many causes. Furthermore, mortality from some causes, such as suicide, has been increasing over time, particularly among young adults. Evidence suggests that inequalities in mortality have widened over time in Scotland, despite substantial investment in policies aimed at reducing inequalities. Therefore, it is important to seek geographical clues that might help explain what causes these high mortality rates. The changing patterns in Scottish mortality between 1980 and 2001 were examined for small areas, created by the author, known as Consistent Areas Through Time (CATTs). These areas have the same boundaries for each census, so that direct comparisons over time are possible. In this study, CATTs have been used to investigate three aspects of the mortality gap in Scotland. First, the widening mortality gaps between 1980-1982 and 1999-2001 are examined for the total population and for premature mortality (<65 years). Second, the influence that geographic scale and deprivation have on the relationship between population change and premature mortality are assessed. Third, suicide inequalities are examined for the younger (15-44 years), older (45+) and total population, using mortality ratios and statistical modelling. The research found that inequalities in premature mortality (< 65) have widened for all causes of death studied, particularly for suicide. The negative association between mortality and population change was affected by geographic scale, but this relationship could not be fully explained by deprivation. Small area analyses found that the Highlands and Islands had higher suicide rates than elsewhere in Scotland for males, but not females, when social variables were controlled for.

No / There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable.