Presentation on theme: "The Importance of Numbers: What Large Skeletal Samples Can (and Cannot) Reveal About the Health Status of Earlier Human Population Phillip L. Walker Department."— Presentation transcript:
The Importance of Numbers: What Large Skeletal Samples Can (and Cannot) Reveal About the Health Status of Earlier Human Population Phillip L. Walker Department of Anthropology University of California, Santa Barbara
The history of paleopathology: from small to large numbers Stage I: Case Studies –Dominated almost the end of the 20 th century –“Physician to the dead” approach –century took a descriptive, case study –Emphasis on determining the spatial temporal distribution of diseases. Stage II: Population Studies –Mainly during the last 50 years. –Emphasis on calculating the prevalence of common pathological conditions in cemetery collections – Bioarchaeological approach with an emphasis on cultural and ecological determinants of health status
Goals of Modern Paleopathology Describe the chronology and spatial distribution of health-related conditions in an earlier populations Determine the biocultural interactions that occur as a population responds to its environment, using disease as an index of the success or failure of adaptation Use the prevalence and pattern of disease to shed light on the adaptation of the population Investigate the processes involved in prehistoric the evolution of ancient diseases
What are the limitations of a population-based approach in paleopahtology? How large are the samples that we will need to detect population differences we might reasonably expect to see in the frequency of pathological conditions? How significant are sample biases introduced by age, sex, and preservation differences between samples? What problems are there with pooling samples from different sites to increase sample sizes?
Western Hemisphere and History of Health in Europe Project Sites 893 sites, total n= 142,952
Most archaeological skeletal collections are small!
Cemetery collections from archaeological sites: median =59, mode= 1
Number of skeletons required to detect a statistically significant difference in the proportion of people afflicted with a pathological condition
Cutting up the Pie Makes Things Worse! Testing bioarchaeological hypotheses typically requires subdividing site samples Age Sex Social Status
Sex is a big part of the pie! % 39.8 of burials in the Western Hemisphere sample are younger than 15 years old and thus probably not subject to reliable sex determination.
The real world situation is worse.. Only 41% of the Western Hemisphere sample could be sexed to the level of “probable male” or “probable” female. This means that about 24 burials in a sample with the median size of 59 can be reliably sexed. Assuming a balanced sex ratio, this would mean that within-site sex comparisons would typically involve 12 males and 12 femailes
Age Subadults: 59 x 0.38= 22 Adults: 59 x 0.62= 37
The effects of preservation biases can be significant!
How should frequencies of pathological lesions be measured?
The under-representation of pathological conditions in skeletal samples Many diseases such as tuberculosis only leave lesions on a small proportion of individuals Many lethal injuries leave no skeletal traces Poor preservation of ancient skeletal material means that often subtle signs of disease and traumatic injury will either be unobservable or uninterpretable
A Caveat: variation among contemporaneous populations within a region can be significant
Variations in the bathtub curve Wide differentials in the excess mortality occurring at the youngest and oldest ages Marked differences in the timing of the decline in juvenile mortality or the rise in adult mortality
Could we detect minor variations in the bathtub curve? The adolescent “accident hump” Apparent slowing down of the rate of increase of mortality among the oldest of the old
What are our chances of detecting the “Basic” human mortality pattern? The “bathtub curve” this is a species-wide theme in human mortality Basic features –Excess mortality at the youngest ages of the life span –Rapid decline to a lifetime low at around 10-15 years of age –Accelerating, roughly exponential, rise in mortality at later ages
Conclusions Small sample sizes and preservation biases mean that paleodemographers will ever be able to reconstruct the fine details of any set of mortality rates. At best, we can hope to learn something about the overall level and age pattern of death in the distant past - and perhaps something about the gross differences in material conditions that led to variation in level and age pattern. Paleodemographers will probably never be able to reconstruct the "bumps and squiggles" in ancient mortality patters. Reconstructing the general shape and level of the bathtub curve will be challenging enough.
Statistical Power The probability of rejecting a false statistical null hypothesis. Performing power analysis and sample size estimation is an important aspect of experimental design, because without these calculations, sample size may be too high or too low. If sample size is too low, the experiment will lack the precision to provide reliable answers to the questions it is investigating. If sample size is too large, time and resources will be wasted, often for minimal gain.
Determining Sample Size What kind of statistical test is being performed. Some statistical tests are inherently more powerful than others. Sample size. In general, the larger the sample size, the larger the power. However, generally increasing sample size involves tangible costs, both in time, money, and effort. Consequently, it is important to make sample size "large enough," but not wastefully large. In paleopathological studies increasing sample size is typically impossible The size of experimental effects. If the null hypothesis is wrong by a substantial amount, power will be higher than if it is wrong by a small amount. The level of error in experimental measurements. Measurement error acts like "noise" that can bury the "signal" of real experimental effects. Consequently, anything that enhances the accuracy and consistency of measurement can increase
Bioarchaeologically Interesting Differences Time: how does health status vary through time Space: What regional or intraregional differences are there Age: What is the relationship between age at death and the presence of pathological lesions indicative of specific diseases Sex: how does a person’s sex influence their health status Social Status: How do social stratification and gender roles influence health status.
alpha specifies the significance level of the test; the default is alpha (.05). power(#) is power of the test. Default is power(.90).
A priori sample size estimation Based on the acceptable statistical significance of your outcome measure. Specify the smallest effect you want to detect of the Type I and Type II error rates
Error Types Type 1 error: The chance of accepting the research hypothesis when the null hypothesis is actually true ("false positive"). Type 2 error: The chance of accepting the null hypothesis when the research hypothesis is actually true ("false negative").