CoChat
Sign Up
Cited by 42Historical and modern epidemiology studiesStatistical Methods in EpidemiologyHealth and Medical Education

The DOI matches its Crossref record, with a small difference in title or year.

Essential Epidemiology: An Introduction for Students and Health Professionals

Webb, Penny, Bain, Chris, Page, Andrew·2016·Cambridge University Press eBooks
DOI 10.1017/9781316275948
Open this paper in CoChat — free account

Abstract

Taking a practical approach and supported by global examples from all areas of health, the new edition of this popular and highly commended textbook has been updated to reflect current epidemiological thinking and teaching. Based on feedback from teachers and students, material has been reordered to better suit courses and reflect the underlying logic and purpose of epidemiology.Provides students with a rounded picture of the field by emphasizing the commonalities across different areas of epidemiology, including clinical epidemiology, and highlighting the key role of epidemiology in public healthAvoids complex mathematics by restricting this to optional material, thereby keeping the book accessible to students from non-quantitative backgrounds Integrated and supplementary questions help students to reinforce conceptsA wealth of online material is available at www.cambridge.org/essential_epidemiology, including additional questions, advanced material for key concepts, recommendations for further reading, links to useful websites and slides for teaching, supporting both students and teachers.

Overview

Integrity signals

Registry

Crossref · 10.1017/9781316275948

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • By design, noncomparative studies are severely limited in their power to make causal inferences due to common selection biases and confounding.

    “By design, noncomparative studies are severely limited in their power to make causal inferences due to common selection biases and confounding [43, 44].”

    From Evidence for Human-to-Human Transmission of Hantavirus: A Systematic Review (2022) · cites this paper as [44]

  • The next best study designs fall in the observational design category but also require the use of a control (or comparison) group, such as in a cohort or case-control design.

    “The next best study designs fall in the observational design category but also require the use of a control (or comparison) group, such as in a cohort or case-control design—which are commonly used designs for outbreak investigations [44].”

    From Evidence for Human-to-Human Transmission of Hantavirus: A Systematic Review (2022) · cites this paper as [44]

Citations

Cites

Still being checked.

Cited by

Sorted by citations

Related

Metadata

Authors
Webb, P., Bain, C., & Page, A.
Affiliations
  • QIMR Berghofer Medical Research Institute (AU)
  • Western Sydney University (AU)
ORCID
Venue
Cambridge University Press eBooks
Publisher
Cambridge University Press
Published
2016-11-30
DOI
10.1017/9781316275948
OpenAlex
W4240639671
Cited by
42
References
0
Citations by year
2026: 2 · 2025: 1 · 2024: 1 · 2023: 6 · 2022: 1 · 2021: 8 · 2020: 6 · 2019: 3 · 2018: 5 · 2017: 2
FWCI
2.87
Type
book
Language
la
Open access
closed
Locations
Cambridge University Press eBooks (publishedVersion)
Topics
  • Health Sciences › Medicine › Epidemiology › Historical and modern epidemiology studies
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Health Sciences › Health Professions › General Health Professions › Health and Medical Education
Keywords
epidemiology, clinical epidemiology, public health, epidemiological methods

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review