Lisa M. Sullivan - Biostatistics For Population Health: A Primer

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Library of Congress Cataloging-in-Publication Data
Names: Sullivan, Lisa M. (Lisa Marie), 1961- author.
Title: Biostatistics for population health: a primer / Lisa Sullivan.
Description: Burlington, MA: Jones & Bartlett Learning, [2021] | Includes bibliographical references and index. | Summary: This short format primer will provide foundational coverage of biostatistical concepts and applications for health professionals. It will use examples that are relevant for clinical and health professionals specifically Provided by publisher.
Identifiers: LCCN 2019051160 | ISBN 9781284194265 (paperback)
Subjects: MESH: Biostatisticsmethods | Population Healthstatistics & numerical data | Statistics as Topic
Classification: LCC QH323.5 | NLM WA 950 | DDC 570.1/5195dc23
LC record available at https://lccn.loc.gov/2019051160

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MicroStockHub/iStock/Getty Images Plus/Getty Images; Zenza Flarini/Shutterstock

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I am grateful for the support of so many people. First, many thanks to Dr. Richard Riegelman for his unending enthusiasm, vision and valuable feedback. Thank you to Kevin Green, Nina Huttemann, Ryan Sullivan and Kim Dukes for their careful review and suggestions for improvement. I am forever thankful to so many extraordinary students and colleagues with whom I have the privilege to work at the Boston University School of Public Health. And finally, thank you to my family and friends who support and inspire me every day.

MicroStockHub/iStock/Getty Images Plus/Getty Images; Zenza Flarini/Shutterstock

Improving the health of populations takes coordinated efforts that address individual, social, environmental, economic, and political factors. Data are key in improving population health. We need data to describe the burden of disease, quantify associations between risk factors and outcomes, shine a light on inequities in health outcomes, and evaluate whether treatments and interventions are effective in reducing disease and promoting health. But to be useful, data must be relevant, carefully collected, managed, analyzed, and interpreted.

In this primer, we review approaches and techniques in applied biostatistics that are regularly used by health professionals to turn data into knowledge for action. We illustrate computations using simple, but realistic, examples and paying special attention to appropriate interpretation of biostatistical results.

The primer is organized into three units:

• 1 Summarizing data for decision making

• 2 Associations between two variables

• 3 Multivariable analysis

In , we discuss techniques for summarizing data for decision making. We begin by defining different types of variables (also called data elements) and how they are measured. We then discuss popular methods for summarizing different types of variables. We define and contrast rates, proportions, and ratios and how they are used in population health studies. We conclude with a brief discussion of graphical displays of data, which are particularly powerful in translating biostatistical results to both scientific and lay audiences.

In , we discuss associations between two variables. In many biostatistical applications, we are interested in the association between a risk factor and an outcome. Is a particular behavior associated with a better health outcome? Does a particular policy adversely affect health outcomes? Are individuals with a particular genetic makeup more susceptible to disease? Is a new treatment safe and effective?

We begin by reviewing key concepts in probability as these underpin the techniques we use to quantify associations. We then discuss screening and diagnostic tests and, more specifically, how to assess the performance of screening tests for improving health. We then move on to statistical inference, where we apply biostatistical techniques to make inferences about populations based on the analysis of samples. We focus on appropriate interpretation of statistical results, recognizing uncertainty and avoiding overstating the results, which often occur in the translation of statistical findings. We discuss confidence interval estimates and procedures for hypothesis testing in detail.