Concepts of Epidemiology : Integrating the Ideas, Theories, Principles, and Methods of Epidemiology.

Bhopal, Raj S.
Oxford : Oxford University Press, Incorporated, 2016.
1 online resource (481 pages)
3rd ed.

Location Notes Your Loan Policy


Electronic books.
Describes and illustrates epidemiology and its applications to policy making, health service planning, and health promotion. The book emphasises interactive learning, with each chapter including learning objectives, theoretical and numerical exercises, questions and answers, and a summary.
Concepts of Epidemiology
Glossary of selected terms
1 What is epidemiology? The nature, scope, variables, principal measures, and designs of a biological, clinical, social, and ecological science
1.1 The individual and the population
1.2 Defining epidemiology and a statement of its central idea
1.3 Directions in epidemiology and its uses
1.4 Epidemiology as a science, practice, and craft
1.5 The nature of epidemiological variables
1.6 Definition and diagnosis of disease: an illustration of the interdependence of clinical medicine and epidemiology
1.7 The basic tools of epidemiology: two measures of disease frequency and five study designs
1.8 Conclusions and seeking epidemiology's theoretical foundations
Exemplar 1.1
Sample questions
2 The epidemiological concept of population
2.1 The individual, the group, and the population
2.2 Harnessing variety in individual and group-​level disease and risk factor patterns
2.3 Disease patterns as an outcome of individuals living in changing social groups
2.4 Sick populations and sick individuals
2.5 Individual and population-​level epidemiological variables
2.6 Epidemiology and demography: interdependent population sciences
2.7 The dynamic nature of human population and the demographic and epidemiological transitions
2.8 Applications of the epidemiological concept of population
2.9 Conclusion
Exemplar 2.1
Sample questions
3 Variation in disease by time, place, and person: background and a framework for analysis of genetic and environmental effects
3.1 Introduction to variation in disease by time, place, and person
3.2 Reasons for analysing disease variations: environment and genetics.
3.3 Introducing human genetic variation and genetic epidemiology
3.3.1 The human genome
3.3.2 Genomic variation as the potential basis of human disease and population variation
3.3.3 Susceptibility to diseases, chronic diseases, and genetics
3.3.4 Mapping and manipulating the genome
3.3.5 Population level differences in disease and genetics: the cautionary example of race
3.4 Variations and associations: real or artefact?
3.5 Applying the real/​artefact framework
3.6 Disease clustering and clusters in epidemiology
3.7 Applications of observations of disease variation
3.8 Epidemiological theory underpinning or arising from this chapter
3.9 Conclusion
Exemplar 3.1
Sample questions
4 Error, bias, and confounding in epidemiology
4.1 Introduction to error, bias, and confounding in epidemiology
4.2 A classification of error and bias
4.2.1 Bias in the research question, theme, or hypothesis
4.2.2 Choice of population-​selection bias, and the benefits and disbenefits of representativeness
4.2.3 Non-​participation: non-​response bias
4.2.4 Comparing disease patterns and risk factor-​disease outcome relationships in populations which differ (context for confounding)
4.2.5 Measurement errors and natural variations
4.2.6 Regression to the mean
4.2.7 Analysis and interpretation of data
4.2.8 Publication
4.2.9 Judgement and action
4.3 A practical application of the research chronology schema of bias and error
4.4 Conclusion
Exemplar 4.1
Sample questions
5 Cause and effect: the epidemiological approach
5.1 Introduction: causality in science and philosophy and its relevance to epidemiology
5.1.1 Some philosophy
5.2 Epidemiological causal strategy and reasoning: the example of Semmelweis.
5.3 Models of cause in epidemiology
5.3.1 Interplay of host, agent, and environment
5.3.2 Necessary and sufficient cause, proximal and distal cause, and the interacting component causes, models: individuals and populations
5.4 Susceptibility, risk/​effect modification, and interaction
5.5 Causal graphs: introducing the directed acyclic graph
Exemplar 5.1
5.6 Guidelines (sometimes erroneously called criteria) for epidemiological reasoning on cause and effect
5.6.1 Comparison of epidemiological and other guidelines for causal reasoning
Exemplar 5.2
5.6.2 Application of guidelines to associations
5.6.3 Judging the causal basis of the association
5.6.4 Interpretation of data, paradigms, study design, and causal guidelines
5.7 Epidemiological theory illustrated by this chapter
5.8 Conclusion
Sample questions
6 Interrelated concepts in the epidemiology of disease: natural history and incubation period, time trends in populations, spectrum, iceberg, and screening
6.1 Natural history of disease, the incubation period, and acute/​chronic diseases
6.2 Population trends in disease
6.3 Spectrum of disease: a clinical concept fundamental to epidemiology
6.4 The unmeasured burden of disease: the metaphors of the iceberg and the pyramid
6.5 Screening: early diagnosis of disease or disease precursors
6.5.1 Introduction: definition, purposes, and ethics
6.5.2 Choosing what to screen for: the lasting legacy of criteria of Wilson and Jungner
6.5.3 Sensitivity, specificity, and predictive powers of screening tests
6.5.4 Setting the cut-​off point for a positive screening test: introducing the relation between sensitivity and specificity and the ROC curve.
6.5.5 Distributions of the outcomes we are screening for-​explaining the relations between sensitivity and specificity
6.5.6 Some problems in screening: the example of blood pressure, with potential solutions
6.5.7 Evaluating a screening programme: biases and options
6.6 Applications of the concepts of natural history, spectrum, population pattern, iceberg/​pyramid of disease, and screening
6.7 Epidemiological theory: symbiosis with clinical medicine and social sciences
6.8 Conclusion
Exemplar 6.1
Sample questions
7 The concept of risk and fundamental measures of disease frequency: incidence and prevalence
7.1 Introduction: risks, risk factors, and causes
7.2 Quantifying disease frequency, risk factors, and their relationships: issues of terminology
7.3 The concepts of person-​time incidence rate (incidence rate) and cumulative incidence rate (or proportion)
7.4 Numerator: defining, diagnosing, and coding disease accurately and the challenge of using others' data
7.5 Denominator: defining the population at risk and the problem of inaccurate data
7.6 Prevalence and prevalence proportion (but still often called rate)
7.7 Relationship of incidence and prevalence
7.8 Choice of incidence or prevalence measures
7.9 Presenting rates: overall (actual or commonly crude) and specific
7.10 Conclusion
Exemplar 7.1
Sample questions
8 Summarizing, presenting, and interpreting epidemiological data
8.1 Introduction
8.2 Proportional morbidity or mortality ratio
8.3 Adjusted rates: direct standardization and the calculation of the SMR (indirect standardization)
8.3.1 Direct standardization
8.3.2 Indirect standardization: calculation of the SMR
8.4 Relative measures: relative risk
8.5 The odds ratio.
8.6 Measurements to assess the impact of a risk factor in at-​risk groups and whole populations: attributable risk and related measures
8.6.1 Attributable risk: estimating benefits of changing exposure in the at-​risk group
8.6.2 Population-​attributable risk: estimating the benefits of reducing exposure in the population as a whole
8.7 Presentation and interpretation of epidemiological data in applied settings: health needs assessment
8.8 Avoidable morbidity and mortality and life years lost
8.9 Comparison of age-​specific and summary measures of health status: perceptions and interpretation
8.10 Disability-​adjusted life years and quality-​adjusted life years
8.11 Number needed to treat or to prevent, and the number of events prevented in your population
8.12 Describing the health status of a population
8.13 The relationship between absolute and relative risks: a conundrum for public health action on inequalities, inequities, and disparities in health
8.14 The construction and development of health-​status indicators
8.15 Handling continuous data using correlation and regression: contributions to description of health status and causal thinking
8.16 Conclusion
Exemplar 8.1
Sample questions
9 Epidemiological study designs and principles of data analysis: a conceptually integrated suite of methods and techniques
9.1 Introduction: interdependence of study design in epidemiology, and the importance of the base population
9.2 Classifications of study design: five general approaches
9.3 Case series: clinical and population-​based register and administrative system studies
9.3.1 Overview
9.3.2 Design and development of a case series
9.3.3 Analysis of case series.
9.3.4 Unique merits of and insights from case series: large-​scale studies, and ecological and atomistic fallacies.
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Bhopal, Raj S. Concepts of Epidemiology