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Journal of Political Ecology: 
Case Studies in History and Society

 

 

VOLUME 6 (1999)

Human Demography and Disease, by Susan Scott and Christopher Duncan (1998) New York: Cambridge University Press. 354 pp.

 

Reviewed by Robert D Hoppa, Department of Anthropology, University of Manitoba, Winnipeg, Manitoba, Canada

Human Demography and Disease represents what I believe to be a much welcome and much needed addition to the broad field of historical demography. As a reference work or teaching manual for quantitative methods for analysing historical demographic data, the book is excellent. Drawing heavily from their own research on epidemic diseases and time series analysis of parish records, the authors present a series of case studies to illustrate the application, methods, and interpretation of various statistical approaches to understanding the relationships between mortality, disease and environment (e.g. weather, economic conditions etc) over long periods of time. While their case studies are very specific, for the most part they are easily transferable to other kinds of analyses that individuals might be interested in.

This book is not for the total novice, however. In places it becomes extremely technical and requires a solid grasp of the material within the chapters. All the same, its technical depth is what makes the volume most useable as both a reference work for individual researchers and a training manual for courses in quantitative history, historical demography or related topics like population ecology/historical geography or epidemiology.

The first two chapters of the book essentially provide an introduction to the general subject areas and the sources of analysis. Chapter 1 provides an extremely brief and generalized overview of the fundamental relationship between mortality and morbidity in populations, and acts as a backdrop for demonstrating the very broad readership that this book is geared to. Chapter 2 is also an introductory chapter, but here presenting some comments on the principal source of data most readers will be using (parish records) and the principal methodological approach explored throughout the book ö time series analysis. This chapter sets the overall tone for the volume, and in my opinion is the litmus test for readers. If you shy away from the book after reading this brief chapter, this book probably isn't for you. However, I suspect that for many readers like myself, reading Chapter 2 will result in a response along the lines of "Hey, that's what I'd like to be able to do with my data. Let's see how this works."

The next eleven chapters provide illustrations of various aspects of time series analysis for historical demography. While they appear at first glance somewhat modular in structure, this is in fact a strength of the book. Each chapter contains numerous cross-references to either previous or forthcoming chapters, and because each deals with a specific analytical approach, many people will find that they are able to jump to individual chapters that are more directly relevant to their own research questions and the type of analysis they would like to undertake.

Of key importance in this book is the idea of short wavelength versus long wavelength oscillations in historical demographic data. Understanding and interpreting these differences form the basis of Chapters 3 through 8. Chapter 3 provides an introduction to the anatomy of time series analysis using a familiar and relatively standard data type.

In this way, it is an extremely useful chapter for those not familiar with time-series analysis. Specifically, this chapter introduces the concept of intrinsic versus extrinsic mortality cycles. While much of the later chapters increase in complexity, Chapter 3 provides a solid introduction that acts as a foundation on which subsequent chapters build. Chapters 4 and 5 explore one of the real advantages of mathematical epidemiology for historical demography ö the ability to model population processes and then compare model prediction or outcomes to observable outcomes from the data themselves. Chapters 6 through 8 provide a series of illustrations for understanding the impact of short-wave or exogenous mortality cycles (grains prices, wool prices) and their interactions with the general demographic features of a population (fertility rates, infant mortality etc) and long-wave or exogenous cycles associated with epidemic diseases. One of the nice features of this book is again illustrated in this section whereby subsequent chapters continue to build on evidence presented in previous case studies, while at the same time remaining independent enough for use as individual methodological references.

Chapters 9 through 13 present a series of cases studies focused primarily on smallpox, measles and Whooping Cough in London, to illustrate various approaches to modelling disease processes in historical populations. These are probably the most rigorous chapters for the non-specialist as they provide a series of in-depth illustrations of the power of mathematical epidemiology for addressing historical demographic questions. Key issues addressed include inter-epidemic interval, changes in age-specific susceptibility, population structure, the effects of co-dependent variables like temperature or nutrition and even interactions among multiple diseases. The latter is particular relevant as too often analyses model the impact of specific diseases independent of one another. While this adds to computational ease it is an undesirable approach since we know that causes of death do not operate independently of one another.

Chapter 14 presents a large-scale illustration of the analytical techniques presented in the previous 5 chapters. This forms as a lead-in to the final chapter that argues succinctly for the power of time-series methods for meta-analyses in historical demography. Here the authors reiterate that one of the strengths of the techniques presented in this volume is the ability to differentiate endogenous mortality cycles related to broader interactions of demographic structure versus exogenous mortality cycles that can be detected independently, but likely have interactions with other changes socio-economic factors.

Overall, the book is highly recommended to anyone interested in learning more quantitative approaches to disease-mortality-environment interactions in historical demographic data. Useable as both a reference source and a teaching manual, it will serve as an invaluable research tool for historical demographers, quantitative historians, historical geographers, historical epidemiologists, and related specialists.