[How good is HBA1AC at predicting diabetic condition: is this a public education nightmare?]

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ACKNOWLEDGEMENT

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

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ABSTRACT

Type 2 diabetes represents a condition significantly associated with increased cardiovascular mortality. The aims of the study are: (i) to estimate the cumulative incidence function for cause-specific mortality using Cox and Aalen model; (ii) to describe how the prediction of cardiovascular or other causes mortality changes for patients with different pattern of covariates; (iii) to show if different statistical methods may give different results. Cox and Aalen additive regression model through the Markov chain approach, are used to estimate the cause-specific hazard for cardiovascular or other causes mortality in a cohort of 2865 type 2 diabetic patients without insulin treatment. The models are compared in the estimation of the risk of death for patients of different severity. For younger patients with a better covariates profile, the Cumulative Incidence Function estimated by Cox and Aalen model was almost the same; for patients with the worst covariates profile, models gave different results: at the end of follow-up cardiovascular mortality rate estimated by Cox and Aalen model was 0.26 [95% confidence interval (CI) = 0.21-0.31] and 0.14 (95% CI = 0.09-0.18). Standard Cox and Aalen model capture the risk process for patients equally well with average profiles of co-morbidities. The Aalen model, in addition, is shown to be better at identifying cause-specific risk of death for patients with more severe clinical profiles. This result is relevant in the development of analytic tools for research and resource management within diabetes care.

TABLE OF CONTENTS

ACKNOWLEDGEMENT2

DECLARATION3

ABSTRACT4

CHAPTER 1: INTRODUCTION6

CHAPTER 2: LITERATURE REVIEW8

Diabetese8

Characteristics9

Complications11

Social, Economic, and Personal Costs13

CHAPTER 3: METHODOLOGY18

CHAPTER 4: DISCUSSION AND ANALYSIS20

CHAPTER 5: CONCLUSION32

REFERENCES34

CHAPTER 1: INTRODUCTION

Type 2 diabetic patients show a greater risk of mortality, especially from heart disease and stroke than non-diabetic subjects. An Italian study estimated that the risk of death from cardiovascular disease (CVD) in Italian diabetic patients is about 40% higher than in non-diabetic populations (Marrero, Anderson and Funnell, 2007).

Mechanisms and factors underlying this phenomenon have been extensively studied [3-8] and would be of great relevance for secondary prevention. Nevertheless, the determinants of the excess mortality have not yet been completely disentangled given the high number of risk factors interacting with the outcomes, the several competing causes of death, including those due to co-morbidities, each of them possibly depending on a set of possible overlapping covariates.

When studying co-morbidities as cause of death within diabetic populations, appropriate restriction or stratification according to medical intervention and treatment should be implemented, in order to investigate risks factors of mortality in a homogeneous ...