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Health and the Little Ice Age in Southeastern Germany and Alpine Austria: Synergies between Stress, Nutritional Deficiencies, and Disease.

The European Little Ice Age (A.D. 1300-1850; LIA) was characterized by decreases in temperature, temperature oscillation, and general climatic instability, resulting in floods, droughts, and other negative circumstances (Brooke 2014; Buntgen and Hellman 2014; Grove 1988; Lamb 1977, 1995; Parker 2013). Research into the intersection of these climate fluctuations and socioeconomic circumstances has shown that the LIA was associated with increased social conflict, economic uncertainty, and epidemic disease in Europe (Behringer 1999; Brazdil et al. 2005; Grove 1988; Lamb 1977; Nicklisch et al. 2017; Pfister and Brazdil 1999; Zhang et al. 2011). Although climate was clearly not constant during the LIA, fluctuations in precipitation and lowered temperatures are thought to have been the principal cause of disrupted crop cycles and ruined harvests, leading to subsistence stress, famine, and epidemics in a range of contexts (Brazdil et al. 2005; Grove 1988; Lamb 1977, 1995). The adverse climate conditions of this time period mainly affected the European populations north of the Alps (Brazdil et al. 2005; Pfister 2005; Pfister and Brazdil 1999), making modern-day Germany an ideal place in which to examine the health outcomes of the LIA.

Bioarchaeology of Climate Change

Climate is defined by the relationship between weather and time (Lamb 1995), and climate fluctuations represent environmental stressors that must be dealt with by a society (Jones et al. 1999; McMichael 2012). This energetic relationship between the environment and humans means that populations can also determine their own vulnerability (Oliver-Smith 2002). The social practices that alter the environment are reflections and constructors of social systems; these practices and their effects on the environment are particular to the type of social system from which they are derived (Oliver-Smith 2002). For example, the deforestation around eleventh-fourteenth century Chaco Canyon and its concomitant resource stress would have exacerbated the effects of any later climatic changes (Jones et al. 1999). Similarly, overhunting caused resource stress in the Southwest Pueblo peoples, negatively affecting their health (Walker et al. 2009), and a restricted resource base and depen-dance on Europe during the beginning years of the LIA probably doomed the Norse colonists in Greenland (McGovern 1980).

The LIA and its simultaneous social, political, and economic instability are represented as having a negative effect on human populations throughout Europe, from Scotland through Switzerland (Grove 1988; Lamb 1995; Pfister and Brazdil 1999). However, vulnerability to climatological events depends not only on the climate event but also on the ability of the society to prepare for and buffer against such episodes (Barlow et al. 1997; Oliver-Smith 1996; Pfister 2005; Pfister and Brazdil 2006). While it is possible for a population to develop cultural responses that allow them to manage cyclical, shorter-term climatic events (Beek-man 2015; Robbins Schug 2011; Stojanowski and Knudson 2011, 2014), longer-term climatic extremes can cause population decline and culture change (Harrod and Martin 2014; McMichael 2012; Robbins Schug et al. 2013). Indeed, large-scale changes are the most complex and most difficult for populations to buffer against (Binford et al. 1997; deMenocal 2001; Jones et al. 1999). The lack or collapse of adaptive strategies in the face of increasing aridity is argued as an influence in the disintegration, disruption, or decline of the classic Maya civilization (deMenocal 2001; Haug et al. 2003; Hodell et al. 2001), the Tiwanaku and their contemporaries in the Andes (Binford et al. 1997; Knudson and Torres-Rouff 2015; Ortloff and Kolata 1993; Tung et al. 2016), the Anasazi (Benson et al. 2007a, 2007b; Billman et al. 2000; Jones et al. 1999; Larson et al. 1996), the Khmer at Angkor Wat (Buckley et al. 2010; Stone 2006), the Indus Valley civilizations (Robbins Schug et al. 2013; Robbins Schug and Blevins 2016), and other complex societies (Beekman 2015; deMenocal 2001; Robbins Schug 2011; Robbins Schug and Goldman 2014; Weiss and Bradley 2001; Weiss et al. 1993).

Whether anthropogenic or not, these environmental changes provoke different responses depending on the cultural context, especially in economic practices and household dynamics. Some populations contend with climate stress by migrating (Beekman 2015; Harrod and Martin 2014; Robbins Schug et al. 2013; Robbins Schug and Goldman 2014), others by changing subsistence strategy and mobility patterns (Robbins Schug 2011; Robbins Schug and Goldman 2014; Stojanowski and Knudson 2011, 2014), and others still by increasing insularity and centering on local adaptive strategies (Harrod and Martin 2014; Knudson and Torres-Rouff 2015; Robbins Schug and Blevins 2016; Robbins Schug et al. 2012). Individuals and populations also have violent responses to climatic shifts (Torres-Rouff and Junqueira 2006; Tung et al. 2016), though this response is less likely than previously thought (Harrod and Martin 2014). Recent bioarchaeological research highlights the multiple ways in which human communities respond to environmental change (Gregoricka 2014, 2016; Knudson et al. 2015; Robbins Schug 2011; Robbins Schug and Blevins 2016; Robbins Schug et al. 2013; Robbins Schug and Goldman 2014; Stojanowski and Knudson 2011, 2014).

Climatic extremes are likely to cause famine in populations that are insufficiently buffered against food shortages (Harrod and Martin 2014). Dietary resource availability has been an avenue of archaeological investigation into climate change in the North Atlantic (Bigelow et al. 2005), the American Southwest (Billman et al. 2000), and the Southern California Coast (Lambert 1993; Lambert and Walker 1991), particularly human economic and subsistence adaptations to late Holocene climate change. Of course, food shortages are not dependent on climate alone but are linked to interactions between the environment and cultural, social, and political contexts (McMichael 2003). Individuals act within the structures of their societies to enact their agency in response to climate change (Harrod and Martin 2014). Not only does a population-level response to climate change affect individual health, but social hierarchies often ensure that the repercussions of food shortages are not equally felt across the population (Harrod and Martin 2014; Robbins Schug and Blevins 2016; Tung et al. 2016). Understanding how local populations contended with the climatic extremes of the peak-LIA requires understanding the influence of resource stress on health in Germany during the LIA.

Climate Change in Medieval and Early Modern Europe

Europe between A.D. 900 and A.D. 1200 (Medieval Climate Anomaly [MCA]) was a period during which sea ice decreased, land productivity increased, and warmer temperatures extended far into the northern latitudes (Lamb 1995). Summer temperatures were 0.7-2.0[degrees]C warmer in England and 1.4[degrees]C warmer in central Europe than twentieth-century temperatures (Lamb 1995). In central Germany, fruit trees flowered along the Rhine in December and January during the warmest parts of the MCA (Pfister et al. 1998), and cultivation boundaries shifted toward a greater intensity of growth (Behringer 2010). Concomitant with this, population was rapidly increasing, as was urbanization (Herlihy 1987; Rosener 1996).

However, the High and Late Middle Ages, particularly the fourteenth century, were not times of unrestrained plenty. Medieval Bavaria was the site of political instabilities in the face of the dissolution of the Carolingian Empire and the rise of the German Holy Roman Empire (Reuter 1991; Wende 2005). Traditional economic systems and labor relations were also being reorganized, especially the relationships between landlords and tenants (Haverkamp 1992; Scott 2005). After the generally warm temperatures of the MCA declined into the fourteenth century, two major mortality crises--the Great Famine and the Black Death--affected the populations of Germany and Austria (Curschmann 1970; Jordan 1996; Lima 2014; Scott 2005). The population of much of Europe declined in the wake of these crises.

The LIA in Germany was a time of social, political, and economic instability. In addition to the social changes that occurred following fourteenth-century mortality crises, populations were vulnerable to famine due to crop failures (Bauernfeind and Woitek 1999; Lamb 1995; Sieglerschmidt 1996). Along with unpredictable subsistence agriculture, disease peaked throughout the LIA (Appleby 1980; Behringer 2010), and populations contended with long- and short-term conflicts, such as the Thirty Years' War, peasant revolts, and witchcraft persecutions (Behringer 1999, 2010; Nicklisch et al. 2017; Turk 1999). In the later LIA the industrialization that characterizes much of nineteenth-century American and British economic development was still on the distant horizon in southern Germany, where rural economies flourished (Ewert 2006; Hochstadt 1981).

The climatic changes of the LIA in Germany led to frequent food shortages and famines (Pfister 1996). Germans in the sixteenth century consumed less meat and contended with more expensive grain than in previous periods, particularly the Middle Ages (Braudel 1981). Grain was a crucial part of European diets, and disruptions to the growing cycle, harvests, or storage would have a significant impact on the standard of living (Braudel 1981). For example, late-sixteenth-century Vogelsberg (ca. 160 km North West of Ochsenfurt) saw a distinct drop in rye yields linked to cold, wet weather and grain shortages even for the upper classes, likely reducing dietary protein intake (Pfister 1996). Additionally, vitamin C deficiencies are linked to cold temperatures (Reynolds 1996) and periods of famine (Brickley and Ives 2008), especially those caused by harsh winters (Crist and Sorg 2014).

Climate data for central Europe and southeastern Germany indicate that the LIA was a climatic phenomenon in this region and that the populations living during this period were subject to its shifts in temperature (Brazdil et al. 2005; Buntgen et al. 2010; Glaser 1992; Lamb 1995; Pfister 1996; Pfister and Brazdil 1999). It is important to note, however, that neither the LIA nor the MCA was continuous in its climatic extremes (Buntgen and Hellman 2014; Ljungqvist 2010a, 2010b). Recent reconstructions of central European climate data indicate a warm period between A.D. 800 and A.D. 1280, with three anomalous cold events (A.D. 1050, A.D. 1170, A.D. 1250), although these reconstructions are hindered by sparse records (Ljungqvist 2010b). Based on tree-ring widths, tenth-century Germany was dry and warm, with frequent periods of extended drought (Lamb 1995). In contrast, the mid-eleventh and mid-twelfth centuries were warm but characterized by more precipitation than in the tenth century (Lamb 1995). Overall, the period before the beginning of the thirteenth century was drier than succeeding centuries, particularly the wet thirteenth and fourteenth centuries (Buntgen et al. 2010). Temperatures shifted again in the fifteenth century, and Germany was relatively cool and dry from 1430 to 1720 (Buntgen et al. 2010). Historical records for the mid-sixteenth to early seventeenth century refer to the frequent and unseasonable rain, snow, frost, and cold (Pfister 1996).

Between 1730 and 1800, Germany again entered a wet period (Buntgen et al. 2010). Across Europe, temperatures became more inconsistent as periods of extended warmth were interspersed by periods of cold relative to twentieth-century temperatures (Lamb 1995). This erratic climate culminated in 1816, the "Year without a Summer," when the eruption of the Tambora volcano caused extreme cold temperatures throughout much of Europe and North America (Lamb 1995). The differing types of climatic changes (temperature, precipitation, unpredictability) that characterize the LIA support an analytical division of this period. There is consensus among paleoclimatologists that the later part of the LIA (post-A.D. 1550, in particular) represents the coldest part of the early modern period. We therefore divide our samples into two comparative periods: the MCA/LIA sample, which dates from approximately A.D. 800 to 1400, and the peak-LIA sample, which dates from approximately 1500 to 1850.

Assessing Health and Stress in Skeletal Remains

Examining skeletal indicators linked to stress, nutritional deficiency, and disease can illuminate multiple ways in which European populations were affected by the LIA. Balanced nutrition is an important aspect of individual health, since development and immune response are dependent upon the availability of essential vitamins and minerals in the diet and through other sources (Akikusa et al. 2003; Brickley and Ives 2008; Jacob and Sotoudeh 2002; Millman and Kates 1990; Roberts and Manchester 2005). Deficiencies in iron, vitamin [B.sub.12], vitamin D, and vitamin C leave specific signatures on bone (Brickley and Ives 2006; Brickley et al. 2007; Ortner and Ericksen 1997; Ortner and Mays 1998; Stuart-Macadam 1989; Walker et al. 2009), and those signatures provide evidence for poor health in the past. There is also a well-documented synergy between malnutrition and disease (Huss-Ashmore et al. 1982; Larsen 2015; Livi-Bacci 1991; Ortner 2003; Scrimshaw 1990). Malnutrition lowers the ability of the immune system to resist an infectious disease, and infectious diseases, depending on type (Livi-Bacci 1991), can restrict the body's ability to absorb essential nutrients (Scrimshaw 1990). Archaeologically, infectious disease can be examined in bones through diagnostic skeletal signatures for particular diseases and through non-specific infection rates (Ortner 2003; Roberts and Manchester 2005). Unfortunately, much infectious disease is invisible to archaeological research, because it must be of significant duration and severity to affect the skeletal tissues (Ortner 2003).

In bioarchaeology there are challenges to defining and applying a concept of health that would allow for a straightforward investigation of nutritional deprivation and disease (Reitsema and McIlvaine 2014; Temple and Goodman 2014). Skeletal indicators are biased toward chronic conditions that afflict an individual for sufficient time to affect bone (Steckel et al. 2002), and they are complicated by selective mortality and differential individual frailty (Steckel et al. 2002; Wilson 2014; Wood et al. 1992). Likewise, analysis of these indicators alone is not sufficient to capture holistic individual health (Temple and Goodman 2014). This requires controlling for age during analysis of skeletal indicators (Klaus 2014; Reitsema and McIlvaine 2014; Wilson 2014; Wright and Yoder 2003), accounting for frailty and selective mortality (DeWitte and Stojanowski 2015; Marklein et al. 2016; Wood et al. 1992), integrating pathophysiology into interpretation of results (Klaus 2014), and recognizing the role of cultural factors in mediating the relationship between indicators (Tanner and Team 2014). Additionally, research on living human populations highlights the problems with using a single indicator (cribra orbitalia, linear enamel hypoplasia, periosteal lesions) to understand health (Piperata et al. 2014; Reitsema and McIlvaine 2014).

To address these issues, we examine multiple indicators of stress, disease, and deprivation relative to the age, sex, and cultural context of the samples. We test several hypotheses to better understand how medieval and early modern period climatic fluctuations affected European populations. First, we test the hypothesis that early childhood developmental stress will increase in the peak-LIA, as reflected by greater linear enamel hypoplasia prevalence and recurrence. Second, we hypothesize that nutritional deficiencies will surge in the peak-LIA, as seen in increased cribra orbitalia and porotic hyperostosis prevalence and severity. Third, we predict that the peak-LIA will have greater prevalence and severity of osteoperiostitis, representing inflammation linked to infectious disease or malnutrition.

Dental Growth Disruption

Stress is the disruption of the body's homeostasis (Selye 1993), and it can leave indicators on the bones and teeth that provide evidence of individual and population-level health, particularly nutrition (Huss-Ashmore et al. 1982; Ortner and Ericksen 1997; Ortner and Mays 1998; Stuart-Macadam 1992; Walker et al. 2009) and disease (Goodman and Martin 2002; Goodman et al. 1988; Klaus 2014; Larsen 2015; Ribot and Roberts 1996). The most reliable stress indicators (in terms of assessment) form during an individual's development (Larsen 2015), reflecting the interruption of human growth processes by physical or psychological stressors (Bateson et al. 2004; Goodman et al. 1980, 1988; Goodman and Martin 2002; Goodman and Rose 1990).

As some of the most frequently examined indicators of health, linear enamel hypoplasia (LEH) represent measures of stress during early childhood (Duray 1996; Goodman and Rose 1990; Guatelli-Steinberg 2001). Enamel hypoplasias form whenever amelogenesis is disrupted and then resumed, representing acute periods of stress during early childhood when the teeth are forming (Armelagos et al. 2009; Enwonwu 1973; Goodman and Armelagos 1988; Goodman et al. 1980; Goodman and Rose 1990; Guatelli-Steinberg et al. 2012; Guatelli-Steinberg and Lukacs 1999; Hillson 1996; Zhou and Corruccini 1998). Because enamel does not remodel, they are enduring indicators of disruption and are associated with early mortality in adulthood (Armelagos et al. 2009).

Nutritional Deficiencies

Bone not only serves a structural function but also acts as a reservoir for essential nutrients and minerals (Larsen 2015; Ortner and Turner-Walker 2003; Parfitt 2003). In the face of a deficit in iron or other key nutrients, the body attempts to accelerate red blood cell production through intensification of marrow production (marrow hypertrophy) and concomitant expansion of the regions of the cranium most responsible for this production (Stuart-Macadam 1987; Wapler et al. 2004). In bone, this marrow expansion results in consistent morphological changes, including thickening of the orbital roof and diploe, as well as porous and frequently bilateral lesions to the roof of the orbit (cribra orbitalia) and cranial vault (porotic hyperostosis) (Stuart-Macadam 1987, 1989). Megaloblastic and iron deficiency anemias resulting from a diet deficient in iron, folic acid, and vitamin [B.sub.12], as well as parasitic infection and chronic illness, are possible explanations for these lesions (Gowland and Western 2012; McIlvaine 2015; Piperata et al. 2014; Stuart-Macadam 1989; Sullivan 2005; Walker et al. 2009; Wapler et al. 2004).

The etiology of cribra orbitalia may be entirely different than that of porotic hyperostosis (Rivera and Lahr 2017; Schultz et al. 2001; Walker et al. 2009; Wapler et al. 2004). Indeed, scurvy is sometimes associated with new bone formation and eventual hypertrophy in the orbits, secondary to sub-periosteal hemorrhage (Akikusa et al. 2003; Brickley and Ives 2008; Ortner and Ericksen 1997; Pimentel 2003). Scurvy, caused by vitamin C deficiency (Cheung et al. 2003; Fain 2005; Jacob and Sotoudeh 2002; Pimentel 2003), manifests skeletally as porosity in the eye orbits and in greater wings of the sphenoid, posterior maxilla, and scapulae of subadults (Brickley and Ives 2006; Ortner and Ericksen 1997). It is often hard to diagnose in adult remains (Brickley and Ives 2008; Brickley et al. 2016): due to this difficulty, data on additional scurvy markers were not collected. Unfortunately, the etiology of cribra orbitalia as anemia or vitamin C deficiency is not easily apparent; new research suggests that cribra orbitalia may be linked to the anemia of chronic disease rather than iron deficiency or megaloblastic anemia (Rivera and Lahr 2017). Thus, these lesions are best viewed as non-specific indicators of nutritional deficiency and stress (Walker et al. 2009).

Infection and Inflammation

Periostitis (here called osteoperiostitis) is inflammation of the periosteal surface of the bone; its frequency in past populations has been used to gauge population-level differences in stress and health (e.g., Buzon 2006; Hutchinson and Norr 2006; Lambert 1993; Lambert and Walker 1991; Lewis 2002). In particular, osteoperiostitis has been seen as a proxy for nonspecific infectious disease (Ortner 2003). More recently, Weston (2012) has cautioned against definitively linking periosteal reactions to infectious disease processes, noting the complex and varied etiology of osteoperiostitis, including associations with trauma and nutritional deficiencies. Osteoperiostitis can be linked to stress-related inflammatory processes (Klaus 2014), which may become chronic throughout the lifespan (DeWitte 2014). Indeed, osteoperiostitis has been associated with increased mortality and worsened health in skeletal samples from medieval London (DeWitte 2014; Yaussy et al. 2016). Therefore, despite doubts posed by Weston (2012) as to the appropriateness of using periosteal reactions to gauge stress, osteoperiostitis can serve as a non-specific indicator of stress in bioarchaeological research (DeWitte 2014; Klaus 2014; Yaussy et al. 2016).


The hypotheses were tested using four skeletal series from medieval and post-medieval Germany and Austria (Figure 1). All individuals were curated at the Staatssammlung fur Anthropologie und Palaoanatomie Munchen (SAPM). Three sites--Oberammerthal (n = 21), Ochsenfurt (n = 123), and Speinshart (n = 9)--are from the modern southeastern German state of Bavaria and the historic regions known as Franconia and the Upper Palatinate. One site, Hall in Tirol (n = 17), is from the Austrian Alps and would have been a part of Bavaria in the medieval period (Reuter 1991). Through time these regions would change in their affiliation with larger geopolitical powers or in their own concept of regional identity (Reuter 1991). Individuals were allocated to one of the two periods based upon archaeological and curatorial information for each series. Individuals were excluded from the analyses due to ambiguous or contradictory provenience data, as were any bones or teeth with unclear associations. The MCA/LIA sample includes individuals from Oberammerthal (n = 21), Ochsenfurt (n = 13), Hall in Tirol (n = 17), and Speinshart (n = 7). The peak-LIA sample includes individuals from Ochsenfurt (n = 110) and Speinshart (n = 2).

Two samples date solely to the MCA/LIA period and are distinct in terms of geography and time period: Oberammerthal and Hall-in-Tirol. Oberammerthal was an early-high medieval site from northern Bavaria located in a castle that had been destroyed during the political turmoil of the early eleventh century (Ettel 1998, 2001). Oberammerthal, along with other castles built in the ninth century, was likely constructed to safeguard a noble family and the settlements in the surrounding areas (Ettel 2001). Although the castle at Oberammerthal is first mentioned in A.D. 1003 as a castle owned by the Count of Schweinfurt, the [.sup.14]C dating of the site places its earliest construction phase in the early ninth century (Ettel 1998). The graves date to what Ettel calls the "Carolingian/Ottonian" period; given the description of the castle's foundation dating, this should place the graves in A.D. 800 at the earliest. The latest date for the graves is likely A.D. 1002/1003, due to their location in the castle that was subsequently destroyed (Ettel 2001). The graves were found in the foundations of the castle, with all but one in the southwestern section of the castle (Ettel 2001). Brooches dating from the Carolingian period were found with grave 6 and 7, which Ettel argues likely indicates the higher social status of these individuals (Ettel 1999).

Hall in Tirol (Pfarrgasse 9) is a small sample from the Austrian Alps in a region known for its salt mining (Scott 2002) and for being part of transalpine trade routes (Toch 1993). Excavations in 2009 by the Verein Stadtarchaologie Hall in Tirol recovered human remains in close proximity to a church cemetery wall (Zanesco 2009). Archaeological examinations of the Hall in Tirol remains indicate a link to the earliest recorded date for the associated St. Stephen's Church in A.D. 1281, although the cemetery was in use until approximately A.D. 1505, after which interments moved to another city cemetery (Zanesco 2009). The dating of these individuals to the thirteenth to fourteenth centuries and their placement in the MCA/LIA group is based on this finding. Their association with the tumultuous period of the transition between the MCA and LIA and in particular with the years of the Great Famine (1315-1322) (Jordan 1996) and with the demographic and social catastrophe of the Black Death (1348-1351) (Rosener 1996) provide important contextual information when comparing the composite of Hall in Tirol and the other MCA/LIA populations with the peak-LIA.

Ochsenfurt, which contributes the highest proportion of individuals to the overall sample, dates from the seventh to the eighteenth century (Hoppe 2008; Keyser and Stoob 1971), is situated on a major trade route (Keyser and Stoob 1971), and is in a fertile part of north central Bavaria (Keyser and Stoob 1971). The population of Ochsenfurt began to expand in the eighteenth century, with a population close to two thousand individuals by the mid-eighteenth century. The 2007 excavations by the Bayerisches Landesamt fur Denkmalpflege focused on the graves surrounding the church (Hoppe 2008). The major distinguishing factor in the cemetery allowing assignment of the individuals into a particular time period is the presence of stones surrounding some of the graves. These stone-lined graves, the style of which is consistent with other sites from the region that date to the seventh-twelfth centuries, are argued to belong either to the earliest phases of the church in the thirteenth century, or to an earlier cemetery (Hoppe 2008). These stone-lined graves are assigned the MCA/LIA. We assigned the graves in the cemetery without the stone boxes, many of which have inclusions from the sixteenth-eighteenth centuries (Hoppe 2008), to the peak-LIA based on images of the graves taken by archaeologists and the accompanying listing of find numbers and descriptions (Hoppe et al. 2007: unpublished data). This makes the assumption that these graves do not, in fact, date directly from the founding of the church.

Speinshart is a clerical sample from a Premonstratensian monastery situated in north-central Bavaria (Keyser and Stoob 1974; Sandor-Proschold and Sanke 2002). Founded in A.D. 1145 by Adelvolc de Speine-shard, the cloister was a large complex of farm buildings and a baroque-period abbey (Sandor-Proschold and Sanke 2002). Grave Horizon I is associated with the earliest constructions of the church in the mid-twelfth century; Grave Horizon II dates to the period from the thirteenth century to the mid-sixteenth century (Sandor-Proschold and Sanke 2002). Grave Horizon III is associated with the burial of the canons of the monastery in the baroque period between 1682 and 1803 (Sandor-Proschold and Sanke 2002). Individuals from Grave Horizon I and Grave Horizon III are included in the MCA/LIA and peak-LIA samples, respectively. Individuals from Grave Horizon II are excluded from these analyses. All individuals in this context are buried within the cloister and thus are likely community members associated with the monastery itself, rather than those from the surrounding region (O'Sullivan 2013:271).

The four skeletal series were in various states of preservation and had different degrees of certainty in their dates. The choice of these sites is based upon the timing of cemetery use, the location of the sites, and the archaeological documentation available. Most series had been previously examined, at least in part, by researchers and students at the SAPM, with the exception of Ochsenfurt, which was reported for the first time in Williams (2013). The MCA/LIA period is a composite of all four sites, the greatest proportion of which are from Oberammerthal, while the peak-LIA period is 98% composed of Ochsenfurt individuals. Pooling these sites yields a general understanding of the two periods, but these groups are affected by the composition of the site samples in terms of age and sex (Table 1) as well as in terms of social context. For example, Speinshart, as a clerical sample, is biased by the possible high status of the individuals interred within the cloister. However, the Speinshart individuals are only 12.1% of the MCA/LIA and 2% of the peak-LIA sample. A larger effect on the results likely arises from the sites of Oberammerthal and Ochsenfurt. The possible influence of Oberammerthal's context on the MCA/LIA results and the implications of having Ochsenfurt comprise such a large part of the peak-LIA sample are examined in the discussion.


Age and sex

Methods of age and sex estimation, deriving from commonly used standards (Acsadi and Nemeskeri 1970; Brooks and Suchey 1990; Buikstra and Ubelaker 1994; Klales et al. 2012; Krogman and Iscan 1986; Lovejoy et al. 1985; Meindl and Lovejoy 1984; Meindl et al. 1985; Schaefer et al. 2009; Smith 1991; Steckel et al. 2006; Ubelaker 1989; Walker 2005), were employed to complete a sex and age-at-death profile of the overall sample of 170 individuals. Sexes were documented as male, female, or unknown; individuals assessed as possible male and possible female were placed into the male and female categories for analysis. Each individual was also placed into one of seven age categories.

Growth disruption

Linear enamel hypoplasias (LEH) on all anterior dentition were assessed using the score categories from the Global History of Health Project data-collection protocol (Steckel et al. 2006). The crown surface of each tooth was examined macroscopically and, where possible, tactilely by running the fingernail over the surface and recording any detectable depressions in the enamel. LEH were recorded per anterior tooth as absent, as a single defect, or as two or more defects. LEH were reconfirmed during data entry via any available close-up photographs. Where ambiguity was present in photographs or photographs were unavailable, coded data sheets were given primacy.

LEH were analyzed by calculating prevalence, requiring the presence of at least one anterior tooth with a defect to count as present for the individual, regardless of the number of observable anterior teeth. LEH absence was recorded when an individual lacked defects on any extant anterior teeth, regardless of how many teeth were available for observation. Multiple defects on a single tooth, here referred to as "multiple episodes," reflect multiple periods of growth disruption. Unlike simple prevalence, these are analyzed using an individual's maximum LEH score for any anterior tooth.

The percentage of the enamel surface present and visible was estimated to control for observable crown height, an important parameter in detecting intra- and interpopulation variation (Hodges and Wilkinson 1990; King et al. 2005). When the cemento-enamel junction (CEJ) was visible, the crown was measured in the midline of the tooth from the CEJ to the tip. Any part of the crown surface obscured by calculus, wear, or weathering (chipping) was subtracted from the CEJ-to-tip value. To avoid potential intra- and interobserver error differences (some crown heights were measured at a separate date and by another observer), total visible enamel was rounded to the nearest whole number. Six individuals with missing crown height measurements were excluded from the LEH analysis.

Crown height was significantly different (t = -3.234, df = 83, p = 0.002) between the MCA/LIA ([bar.x] = 41.54, s = 26.94, n = 35) and the peak-LIA ([bar.x] = 62.3, s = 30.55, n = 50), due to over 20 mm more visible enamel per individual in the peak-LIA. Applying weights for individual visible enamel would mean reconstructing the crown heights of each tooth (Guatelli-Steinberg et al. 2007, 2012), accounting for dental calculus and damage, and then determining the proportion of crown height present. Instead, the entire data set was pooled and summary statistics (mean and standard deviation) were calculated ([bar.x] = 53.75, s = 30.72, n = 85). Individuals with crown heights above 85 mm and below 23 mm, one standard deviation from the mean, were removed from the analysis of LEH in order to control for both extreme dearth and excess of visible enamel. The resulting data set (Figure 2) no longer shows statistically different average crown heights between the two periods (t = 0.639, df = 50, p = 0.526), which now differ by less than 5 mm (MCA/LIA: [bar.x] = 54.95, s = 17.16, n = 22; peak-LIA: [bar.x] = 51.87, s = 17.26, n = 30).

Nutritional deficiencies

The eye orbits and cranial vaults were examined for evidence of healed and unhealed porosities indicative of cribra orbitalia and porotic hyperostosis in the field and then reexamined via photographs to reduce intraobserver error. Any evidence of these conditions was coded according to the Global History of Health protocol (Steckel et al. 2006), which uses a three-point ordinal scale for cribra orbitalia where "1" represents the absence of the condition, "2" is a grouping of foramina less than 1 [cm.sup.2], and "3" is a grouping of foramina greater than 1 [cm.sup.2]. Parietals are also scored in a similar three point scale, where "2" is "slight pitting or severe parietal porosity" and "3" is a "gross parietal lesion with excessive enlargement of bone" (Steckel et al. 2006: 13). Both orbits and both parietals were scored separately, and intermediate scores were assigned in the field. Individuals whose orbits could not be reassessed were excluded from the final analyses of cribra orbitalia. (1) Photographs were not available for all parietal bones; to be conservative, intermediate scores were ultimately rounded to the lower category. The maximum score for the right or left side of the cranium was used as the primary variable of analysis. Additionally, the percentage of the orbits (from photographs) and parietal bones (from data sheets) observable was estimated (in 5% increments) and used as a weight variable in statistical tests to account for differential preservation between groups. Results for cribra orbitalia and porotic hyperostosis are discussed in terms of severity and prevalence of porosity.


The long bones in the four skeletal series were examined for osteoperiostitis. Percentage of bone observable was estimated from data sheets (in 5% increments) and calculated into a weight variable used in statistical tests. Osteoperiostitis was recorded according to the Global History of Health Project protocol (Steckel et al. 2006), using intermediate scores (i.e. 2.5, 4.5) to account for variation in disease expression between the broader categories (Table 2). Healed and active lesions are pooled for these analyses. Assessment of osteoperiostitis prevalence required any lower limb bone to show evidence of new bone formation and focused on scores of 3-6, with scores of 1-2 counted as absent. The maximum composite score for the lower limb was calculated and used to address severity of infection. This is meant to be more discriminating in catching disease processes rather than minor insults or physiological perturbations that can cause remodeling on the long bones, especially since analysis of single bones can overestimate the rate of morbidity (Weston 2008, 2012).

Analytical methods

The interpretation of lesions in skeletal samples requires attention to several details. Lesions must be examined within the context of age-at-death composition and the possible effect that age and frailty might have on the likelihood of morbidity and mortality (Glencross and Sawchuk 2003; Milner et al. 2008; Saunders and Hoppa 1993; Usher 2000; Vaupel et al. 1979; Wilson 2014; Wood et al. 2002). To do this, the periods are subdivided into their respective sex and age-at-death samples in order to account for bias in terms of age- or sex-selective conditions. These comparisons between the age and sex subsets of the MCA/LIA and peak-LIA should help address some of Wood et al.'s (1992) concerns about selective mortality (Klaus 2014; Wright and Yoder 2003), while also accounting for the age-related nature of many pathological conditions. Likewise, age- and sex-specific odds ratios are computed to avoid losing the information in their pattern (Klaus 2014; Waldron 1994). The "common odds ratio" is also calculated, as it accounts for age specific prevalence but does not require a point-by-point comparison (Waldron 1994).

The statistical tests applied to test the hypotheses included the chi-square and G-statistic for nominal and ordinal data, and t-tests and ANOVAs for metric data. While statistical significance is set at the standard 0.05, we also highlight results in which the p-values are between 0.051 and 0.15, recognizing that the likelihood of making a Type I error increases in conjunction with the p-value. Statistical tests could not be completed for all comparisons due to small sample sizes. Attempts were made both during data collection in Munich and during later data entry and analysis to reduce intraobserver error: skeletons examined early during data collection were reexamined at the end of data collection (with the exception of the Hall in Tirol skeletons, which were returned to Austria before reanalysis); (2) database accuracy was double-checked following data entry, and lesion presence (LEH, cribra orbitalia) was reassessed with photographic documentation, as is discussed in the methodological descriptions above.


Age and sex

Both periods show a nearly equal proportion of males and females, an age distribution that peaks in the middle adult (35-49 years) age category, and a minority of subadults (Table 3). Statistical tests (chi-square, Kolmogorov-Smirnov, p > 0.15) show no significant differences between the MCA/LIA and the peak-LIA in age or sex distributions. Indeed, when both sex and age are examined by period, the only difference in this pattern is seen with MCA/LIA females, who have a greater proportion of young adults than middle or older adults. The lack of significant differences between the periods does indicate that they are comparable in terms of underlying age and sex structure.

Individual health indicators by period

Linear enamel hypoplasias were extremely common; all but one individual (peak-LIA) had a defect on at least one anterior tooth (Table 4). While prevalence distinctions were not statistically significant, the frequency of multiple-episode LEH was (Table 5), with a greater proportion of multiple episodes in the peak-LIA (likelihood ratio = 7.403, df = 2, p = 0.025). Age structure comparisons showed a consistent pattern in which a higher proportion of peak-LIA individuals had multiple episodes of LEH. This pattern was most clearly seen in young and middle adults, and in young adult females, though nowhere did it reach statistical significance.

There is more cribra orbitalia in the MCA/LIA than in the peak-LIA, a consistent pattern through most age categories and for both males and females (Table 4). However, the discrepancy between the periods is less extreme in females than in males. Likewise, severe porosity is more prevalent in the MCA/LIA, while the frequency of light porosity is not as distinct between the two periods; this is statistically significant for middle adult males (likelihood ratio = 6.051, df = 2, p = 0.049) (Table 6). Porotic hyperostosis also tends to be more prevalent in the MCA/LIA as compared to the peak-LIA (Table 4). Severity of porotic hyperostosis is more complex, with higher rates of severe porosity in the MCA/LIA, but slightly higher rates of light porosity in the peak-LIA (Table 6). None of the porotic hyperostosis differences are significant, especially when differential preservation between the samples is accounted for. Overall, cribra orbitalia is more prevalent in both the MCA/LIA and peak-LIA than porotic hyperostosis.

Prevalence of osteoperiostitis of the lower limb increases into the peak-LIA (Table 4). Although this is not a significant difference, the pattern holds consistently by sex and across ages. Mean maximum scores ([chi square] = 9.718, df = 3, p = 0.021) indicate that severe and moderate lower limb osteoperiostitis is more common in the peak-LIA (Table 5); in contrast, mild osteoperiostitis is more frequent in the MCA/LIA. Though these patterns tend to remain consistent across age and sex subsets, the differences are not significant when differential preservation is accounted for.

Comparison of health indicators

A comparison of the odds ratios and common odds ratios for four indicators of health (Table 7) underlines the findings above. That is, osteoperiostitis is more frequent in the peak-LIA, while cribra orbitalia and porotic hyperostosis are more frequent, to varying degrees, in the MCA/LIA.

There is a consistent difference between males and females in how these indicators change from the MCA/LIA to the peak-LIA. The effects of the MCA/LIA and peak-LIA are more clearly seen in males, while differences between the two periods are not as distinct for females. The odds ratios for the four skeletal indicators bear this out (Table 8). The male odds ratios are more extreme (i.e., more distant from 1) than female odds ratios in all the weighted samples where the ratio could be calculated. Males have comparatively more osteoperiostitis, less cribra orbitalia, and less porotic hyperostosis in the peak-LIA than females.

Age and age/sex-specific prevalence is more difficult to assess, since small sample sizes often obscured the calculation of the odds ratio (Table 9). The difference between the MCA/LIA and peak-LIA in lower limb osteoperiostitis increases with age. Trends that are consistent across age categories are often reversed or lessened in older adults. Males tend to preserve the overall rates of increased osteoperiostitis in the peak-LIA and little difference between the MCA/LIA and peak-LIA in porotic hyperostosis (Table 10). Females, however, are much less consistent with the overall rates of each pathological condition, showing opposite patterns in cribra orbitalia, porotic hyperostosis, and osteoperiostitis depending on the age category (Table 11).


Early childhood environments

Linear enamel hypoplasias are enduring indicators of childhood stress which represent the environment during dental crown development (Goodman and Martin 2002; Hillson 2008; Larsen 2015). The overall prevalence of LEH is not significant between the MCA/LIA and peak-LIA, likely because enamel defects are very common in both the MCA and the LIA, indicating a poor environment during early growth and development for the individuals living during those periods. High rates of LEH have often been linked to weaning stress (Corruccini et al. 1985; Cunha et al. 2004; Katzenberg et al. 1996; Miszkiewicz 2012; Moggi-Cecchi et al. 1994), as the transition from nutrient-rich breast milk to weanling foods brings about the risk of decreased nutrient intake and increased disease (Katzenberg et al. 1996; Moggi-Cecchi et al. 1994). However, since the association of weaning and LEH is a complex one (Blakey et al. 1994; Katzenberg et al. 1996; Larsen 2015), it is unlikely that weaning alone is responsible for the LEH results. Peak-LIA individuals are more likely have multiple defects per tooth than MCA/LIA individuals, especially in middle adults. This increase, along with increases in osteoperiostitis, may indicate a general degradation of the early childhood environment and increased exposure to pathogens in the peak-LIA.

Malaria in the MCA/LIA

The higher prevalence of cribra orbitalia and porotic hyperostosis in the MCA/LIA may be related not to nutritional deficiencies but rather to disease, especially malaria. Malaria is clinically linked to anemia, which along with cerebral malaria "is a leading cause of morbidity and mortality in malaria" (Chang and Stevenson 2004:1502). Malarial infections, particularly those associated with Plasmodium vivax, can cause both hemolysis (the destruction of red blood cells) and dyserythropoiesis (inadequate production of red blood cells) (Anstey et al. 2009; Gowland and Western 2012). Studies of cribra orbitalia crude prevalence rates in relation to the patterning of malaria zones in Britain found links between the pathological condition and the environment (Gowland and Western 2012), and historians have noted the disappearance of endemic malaria in Renaissance Europe (Behringer 2010).

However, there is evidence that malaria was a significant contributor to mortality in LIA England and that the spread of the disease is more linked to living in marshy estuarine areas and to a confluence of human and environment interactions rather than temperature alone (Reiter 2000). The malarial vector is particularly susceptible to fluctuations in precipitation, and both droughts that increase standing water and deluges create ideal conditions to breed the mosquitoes that spread the disease (Hales et al. 2003). Future research should examine the geography and ecology of southern Germany to model areas of high malarial risk in order to better assess this explanation for differences between the MCA/LIA and peak-LIA.

Selective mortality and immune response

Interpretations of the differences between the MCA/LIA and the peak-LIA must also account for the effects of selective mortality and frailty. The environmental conditions of the MCA/LIA, especially the mortality crises of the fourteenth century, may have acted as selective pressures to reduce the genetic contributions of individuals with frail immune systems into the peak-LIA population. Thus, the populations of the LIA, even in relatively worse environmental conditions, may have been more immunologically competent than their MCA/LIA predecessors. Indeed, a similar phenomenon may have occurred in London, England, in the wake of the Black Death, where the plague acted to remove more frail individuals resulting in a population with increased skeletal lesions linked to chronic inflammation, reflecting hardier immune systems, than those killed before the epidemic (DeWitte 2014). There is evidence for this type of selection in Black Death victims who grew up during the Great Famine and who show no difference in stature compared to individuals who did not mature during the famine (DeWitte and Hughes-Morey 2012).

Selective mortality and frailty might also explain the apparent contradiction between increased nutritional deficiencies in the MCA/LIA and increased inflammation/disease in the peak-LIA. Most cribra orbitalia and porotic hyperostosis in the samples are healed lesions in adults. These indicators provide a record of nutritional stressors during childhood (Stuart-Macadam 1985) but are biased by selective mortality, as only survivors of the early insults live to adulthood (Wood et al. 1992). The types of malnutrition (anemia, vitamin C deficiency) represented by cribra orbitalia could make these individuals less resistant to infectious diseases even as adults, causing these individuals to die before osteoperiostitis can form on the skeleton. This would suggest that the increase in osteoperiostitis into the peak-LIA actually represents an improvement in childhood health, such that individuals survive long enough as adults to develop osteological responses when confronted with stressors (see Yaussy et al. 2016). However, the addition of the LEH data, which show that individuals in the peak-LIA suffered from significantly more episodes of stress during enamel formation than the MCA/LIA, indicates that the peak-LIA early childhood environment was not ideal.

Regional contexts and health effects

An important factor to consider in discussing these results is the nature of the samples: they span multiple periods which share similar climatic features but do not represent a single consistent environment (Figure 3). It has been suggested that Europeans were already stressed by high population levels at the beginning of the fourteenth century when the mortality crises of Great Famine and Black Death strike (Jordan 1996). Some of the populations included in the MCA/LIA sample date to this period of transition between the warmth of the Medieval Climate Anomaly and the cold of the peak-LIA (Brooke 2014). The fourteenth century in particular is characterized by its variability and by events like the Great Famine and the Black Death, which had significant effects on the nutrition and health of the populations of Europe (Campbell 2011). This certainly could explain nutritional and stress indicators, with the exception of osteoperiostitis, that show poor health in the MCA/LIA as compared to the peak-LIA.

The period before the fourteenth century was not always one having sufficient resources. The increased cultivation of marginal lands in the areas north of the Alps during the eleventh, twelfth, and thirteenth centuries did not always yield positive results (Haverkamp 1992). Harvests were more tenuous and weather-contingent, and damage by inclement weather or conflict would force locals to eat their seed stock (Haverkamp 1992). Indeed, transport systems at this stage were not such that locals could rely on imported food in the case of emergencies, meaning short-term food shortages could evolve into longer-term famines (Haverkamp 1992).

As Oberammerthal is the largest site sample in the MCA/LIA, the characteristics of this early, fortified site influence the results for this group. For example, there is archaeozoological evidence indicating that pigs, cattle, sheep, goats, chickens, and geese were a part of the diet of the castle inhabitants at Oberammerthal (MCA/LIA), occasionally supplemented by horse meat, perhaps during the siege of the castle by Heinrich II (Ettel 2001). While the natural diet of the inhabitants would therefore have been full of nutrients like vitamin [B.sub.12] (Walker et al. 2009), their need to resort to eating non-preferred animals like horses during periods of conflict indicates that there was significant resource stress during the MCA/LIA, in this case related to frequent regional conflicts (Ettel 2001; Reuter 1991).

The results may also reflect an increased ability to mitigate the effects of the peak-LIA on population health in Franconia. The peak-LIA sample is dominated by Ochsenfurt, a small city on the Main River. A wide-scale study by Zhang et al. (2011) notes that LIA climatic disasters do not seem to result in health crises in contexts with high carrying capacities, such as humid tropical countries, populations with trade economies, and countries with low population density and plentiful arable land. Similarly, the importation of grain to mitigate famine conditions was an important aspect of the welfare measures that allowed some European communities to avoid catastrophic mortality during mid-eighteenth-century climatic extremes (Post 1984). Indeed, famines themselves may not result from climate change alone; it is the interaction of local communities to climate-related food shortages that cause famine disasters (Slavin 2016). Lower Franconia around Ochsenfurt is a fertile area long associated with farming not just for subsistence but for trade (Keyser and Stoob 1971). In addition to importing grain, potatoes are extensively adopted as a dietary staple in peak-LIA Germany (Braudel 1981); as potatoes are an excellent source of vitamin C, prevalence of scurvy might have dropped, explaining the drop in cribra orbitalia (Brickley and Ives 2008). This trade, however, may have opened up the community to the possibilities of infection through the introduction of new pathogens via visitors to the community (Dobyns 1992, 1993; Merbs 1992; Roberts and Manchester 2005). Thus, while being able to buffer against the nutritional crises of the peak-LIA, the population of Ochsenfurt was exposed to pathogens.

Other important variables to consider are the social, economic, and political changes in these periods that may overwhelm specific climatic effects. The synergistic relationships between sociocultural dynamics and human health have been recognized by many scholars within biological anthropology (Fuentes 2009; Saunders and Hoppa 1993; Schell 1997; Sorensen et al. 2009), and especially within bioarchaeology (Buikstra and Beck 2006; Larsen 2015; Steckel and Rose 2002). Indeed, biological anthropologists should consider questions of sociocultural relevance when examining the biological data, asking questions about the consequences of non-specific indicators of stress on an important household member's work capacity, and the effect on the local community (Goodman et al. 1988).


Overall, the results indicate a complex picture of the health of individuals in medieval and early modern central Europe. Rates of osteoperiostitis are high in the peak-LIA, and multiple-episode enamel hypoplasias, which are permanent markers of stress during early life, also increase. This result shows a pattern of periodic, acute stress events in the peak-LIA. However, indicators of anemia and vitamin C deficiency are higher in the MCA/LIA. This may point toward a medieval environment with endemic malaria and resource stress, and a LIA where local populations can buffer against vitamin C deficiency and anemia.

These results cannot speak to the effects of the whole of the LIA across all of Europe, or even across all of southeastern Germany. Rather, they represent a snapshot of the lives of individuals in these communities during periods of general climatic stress and general environmental abundance. One hundred and seventy individuals from Franconia and the Alps are but a small subset of the populations living during this time, and thus these conclusions need to be expanded upon by additional research into skeletal series dating to the medieval and postmedieval periods in this region. For example, the site of Altenerding, originally thought to date to the peak-LIA (Williams 2013), is undergoing additional scientific analysis that will likely place it in the medieval period and provide more information about the MCA/LIA. Additionally, pooling the four sites to create two larger comparative samples also means that it is problematic to estimate fertility and mortality for these groups as though they represent distinct, bounded populations (Chamberlain 2006). The addition of larger cemetery samples for which demographic indicators can be assessed is a necessary component of future research.

While it is clear from historical research that the LIA was a time of great suffering in terms of food shortages, disease, and conflict, these results suggest that some populations were able to adjust to negative circumstances without showing elevated levels of nutritional deprivation as compared to earlier populations. These findings suggest that, for this setting, the Medieval Climate Anomaly was not universally optimal, and the peak-LIA was not a widespread disaster. This may reflect regional buffering systems that were resilient to climate change or the effects of selection during the MCA/LIA to create a population with more robust immune systems. Overall, then, this study provides a cautionary tale, namely, that the labels used for broad periods of time and the resulting implications of those labels must be contextualized. Moreover, concordant with the conclusions of other researchers (Myers and Patz 2009), we must understand the varying responses of people to climate change, the variable nature of climate systems, and the interactions of those two variables to create disparate realities of life.


This research was funded by Deutscher Akademischer Austausch Dienst, The Ohio State University Alumni Grant for Graduate Research, and Sigma Xi. The authors wish to thank George McGlynn for collecting crown height data, supporting the research in Munich, and providing helpful comments on this manuscript. We would like to acknowledge the previous work done on the skeletal series by O. Rohrer-Ertl (Speinshart), P. Schroter (Oberammerthal), and S. Anders and G. McGlynn (Hall in Tirol). We are also grateful to the Global History of Health Project, The Staatssammlung fur Anthropologie und Palaeoanatomie, John Brooke, Jeffrey Cohen, Debbie Guatelli-Steinberg, Paul Sciulli, Samuel Stout, Richard Steckel, Phillip Walker, Steven Naber, Alison Beach, Gisela Grupe, Michaela Harbeck, Nadja Hoke, Heiner Schwartzberg, Anja Staskewicz, Peter Schroter, Sandy Reh, Jurgen Koch, Raphael Stengel, Kurt Alt, Marc Miltz, and Bernd Paffgen. We thank the anonymous reviewers for their very helpful comments and suggestions. Their supportive and detailed reviews added considerably to the quality of the science behind the study and the readability of the text.

Note: This paper is derived from a doctoral thesis from The Ohio State University by L. Williams, which can be found here: Research has changed based on improved dating information on one of the original sites analyzed, which has been excluded from these data. The reanalysis of this research was presented at the 2017 Paleopathology Association meetings in New Orleans, Louisiana.

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(1.) This resulted in the removal of two individuals (female, middle adult, score 2; male, middle adult, score 1) from Hall in Tirol (MCA/LIA) and one individual (female, middle adult, score 1) from Ochsenfurt (peak-LIA) from the final analysis.

(2.) As data for Hall in Tirol were collected early, there was tendency to overestimate the score of porotic hyperostosis. One individual (middle adult, female) was excluded from analysis, as the Global History of Health Project Score of 3 could not be reconfirmed via photographs.

Leslie Lea Williams (a*) and Clark Spencer Larsen (b)

(a) Department of Anthropology, Beloit College, Beloit, WI 53511, USA

(b) Department of Anthropology, The Ohio State University, Columbus, OH 43210, USA

(*) Correspondence to: Leslie Lea Williams, Department of Anthropology, Beloit College, Beloit, WI 53511, USA


Received 13 June 2017

Revised 23 October 2017

Accepted 25 October 2017

DOI: 10.5744/bi.2017.0012
Table 1. Age and sex distribution by site, with count and column
percent. Subadult and adult categories are for individuals who could
not be assigned to more specific groups.

                      Oberammerthal  Ochsenfurt  Hall in Tirol

Male                    8 (57.1%)    32 (49.2%)    6 (66.7%)
Female                  6 (42.9%)    33 (50.8%)    3 (33.3%)
Child (0-10)            4 (21.1%)      9 (9.8%)    3 (21.4%)
Adolescent (11-19)       0 (0.0%)    16 (17.4%)    2 (14.3%)
Young adult (20-34)     8 (42.1%)    20 (21.7%)    2 (14.3%)
Middle adult (35-49)    6 (31.6%)    28 (30.4%)    5 (35.7%)
Older adult (50+)        1 (5.3%)    19 (20.7%)    2 (14.3%)
Adult                  17 (81.0%)    96 (78.0%)   11 (64.7%)
Subadult                4 (19.0%)    27 (22.0%)    6 (35.3%)


Male                  2 (40.0%)
Female                3 (60.0%)
Child (0-10)          0 (0.0%)
Adolescent (11-19)    0 (0.0%)
Young adult (20-34)   0 (0.0%)
Middle adult (35-49)  1 (33.3%)
Older adult (50+)     2 (66.7%)
Adult                 9 (100.0%)
Subadult              0 (0.0%)

Table 2. Global History of Health Project Standards for scoring of
osteoperiostitis. These scoring categories were used, but intermediate
scores were added to account for morphologies between these scores.
Descriptions from Steckel et al. (2006:30). Reclassification into
maximum osteoperiostitis categories is also indicated.

Score  Description                                 Category

1      No osteoperiostitis present                 None
2      Markedly accentuated longitudinal           None
3      Slight, discrete patch(es) of reactive      Mild
       bone involving less than one-quarter
       of the long bone surface
4      Moderate involvement of the                 Moderate
       periosteum, but less than one-half
       of the long bone surface
5      Extensive periosteal reaction involving     Severe
       over half of the diaphysis, with
       cortical expansion, pronounced
6      Osteomyelitis (infection involving          Severe
       most of the diaphysis with cloacae)
7      Osteoperiostitis associated with a          Not examined in
       fracture                                    this study;
                                                   classified as a
                                                   score of 1

Table 3. Age distribution by period, with count and column percent.
Subadult and adult categories are for individuals who could not be
assigned to more specific groups.

                      MCA/LIA     Peak-LIA

Child (0-10)           7 (12.1%)    9 (8.0%)
Adolescent (11-19)     3 (5.2%)    15 (13.4%)
Young adult (20-34)   13 (22.4%)   17 (15.2%)
Middle adult (35-49)  16 (27.6%)   24 (21.4%)
Older adult (50+)      7 (12.1%)   17 (15.2%)
Subadult (0-19)        1 (1.7%)     2 (1.8%)
Adult                 11 (19.0%)   28 (25.0%)
Total                 58 (100%)   112 (100%)

Table 4. Prevalence for four pathological conditions by sex, with count
and row percent.

                  Linear Enamel            Cribra Orbitalia
                  Hypoplasia Prevalence    Prevalence

                  Absent      Present      Absent     Present

Male    MCA/LIA    0 (0.0%)    5 (100%)    1 (11.1%)   8 (88.9%)
        Peak-LIA   0 (0.0%)   12 (100.0%)  5 (27.8%)  13 (72.2%)

Female  MCA/LIA    0 (0.0%)   10 (100%)    4 (33.3%)   8 (66.7%)
        Peak-LIA   1 (10.0%)   9 (90.0%)   5 (35.7%)   9 (64.3%)

                  Porotic Hyperpstosis       Lower Limb
                  Prevalence                 Osteoperiostitis
                  Absent          Present    Absent      Present

Male    MCA/LIA   (**) 6 (54.5%)  5 (45.5%)   6 (31.6%)   13 (68.4%)
        Peak-LIA      17 (81.0%)  4 (19.0%)   4 (14.3%)   24 (85.7%)

Female  MCA/LIA        9 (81.8%)  2 (18.2%)   8 (57.1%)    6 (42.9%)
        Peak-LIA      14 (82.4%)  3 (17.6%)  10 (38.5%)   16 (61.5%)

Chi-square and likelihood ratio: (**) P-value between 0.15 and 0.051.

Table 5. Frequency of linear enamel hypoplasia and lower limb maximum
osteoperiostitis, by age, sex, and period (count and row percent).
Unweighted scores.

                                     Maximum LEH
                                                            Two or
                                               One LEH      More LEH
                                               on at Least  on at Least
                                     No LEH    One Tooth    One Tooth

Male    Young adult   MCA/LIA        0 (0.0%)   1 (100.0%)   0 (0.0%)
          (20-34)     Peak-LIA       0 (0.0%)   1 (33.3%)    2 (66.7%)
        Middle adult  MCA/LIA        0 (0.0%)   1 (33.3%)    2 (66.7%)
          (35-49)     Peak-LIA       0 (0.0%)   0 (0.0%)     2 (100%)
        Older adult   MCA/LIA        0 (0.0%)   0 (0.0%)     0 (0.0%)
           (50+)      Peak-LIA       0 (0.0%)   2 (40.0%)    3 (60.0%)

Female  Young adult   (**) MCA/LIA   0 (0.0%)   2 (66.7%)    1 (33.3%)
          (20-34)     Peak-LIA       0 (0.0%)   0 (0.0%)     2 (100%)
        Middle adult  MCA/LIA        0 (0.0%)   1 (33.3%)    2 (66.7%)
          (35-49)     Peak-LIA       0 (0.0%)   0 (0.0%)     2 (100.0%)
        Older adult   MCA/LIA        0 (0.0%)   0 (0.0%)     2 (100.0%)
           (50+)      Peak-LIA       1 (20.0%)  1 (20.0%)    3 (60%)
        Child (0-10)  MCA/LIA        0 (0.0%)   0 (0.0%)     0 (0.0%)
                      Peak-LIA       0 (0.0%)   0 (0.0%)     1 (100.0%)
         Adolescent   MCA/LIA        0 (0.0%)   0 (0.0%)     1 (100.0%)
          (11-19)     Peak-LIA       0 (0.0%)   0 (0.0%)     2 (100.0%)
        Young adult   (**) MCA/LIA   0 (0.0%)   4 (20.0%)    1 (20.0%)
          (20-34)     Peak-LIA       0 (0.0%)   2 (28.6%)    5 (71.4%)
        Middle adult  (**) MCA/LIA   0 (0.0%)   3 (42.9%)    4 (57.1%)
          (35-49)     Peak-LIA       0 (0.0%)   0 (0.0%)     4 (100.0%)
        Older adult   MCA/LIA        0 (0.0%)   0 (0.0%)     2 (100.0%)
           (50+)      Peak-LIA       1 (10.0%)  3 (30.0%)    6 (60.0%)
        Male          MCA/LIA        0 (0.0%)   2 (40.0%)    3 (60.0%)
                      Peak-LIA       0 (0.0%)   3 (25.0%)    9 (75.0%)
        Female        MCA/LIA        0 (0.0%)   4 (40.0%)    6 (60.0%)
                      Peak-LIA       1 (10.0%)  1 (10.0%)    8 (80.0%)
        Overall       (*) MCA/LIA    0 (0.0%)  11 (50.0%)   11 (50.0%)
                      Peak-LIA       1 (3.3%)   5 (16.7%)   24 (80.0%)

                      Lower Limb Maximum Osteoperiostitis

                      No                Mild
                      Osteoperiostitis  Osteoperiostitis

Male    Young adult         2 (40.0%)    3 (60.0%)
          (20-34)           1 (12.5%)    3 (37.5%)
        Middle adult        4 (44.4%)    3 (33.3%)
          (35-49)           1 (10.0%)    4 (40.0%)
        Older adult    (**) 0 (0.0%)     3 (100.0%)
           (50+)            1 (16.7%)    1 (16.7%)

Female  Young adult         3 (60.0%)    1 (20.0%)
          (20-34)           3 (60.0%)    1 (20.0%)
        Middle adult        4 (80.0%)    1 (20.0%)
          (35-49)           4 (44.4%)    1 (11.1%)
        Older adult         0 (0.0%)     2 (66.7%)
           (50+)            3 (37.5%)    2 (25.0%)
        Child (0-10)        1 (25.0%)    2 (50.0%)
                            2 (22.2%)    4 (44.4%)
         Adolescent         1 (100.0%)     0 (0.0%)
          (11-19)           5 (35.7%)    6 (42.9%)
        Young adult         5 (41.7%)    6 (50.0%)
          (20-34)           4 (26.7%)    5 (33.3%)
        Middle adult  (**) 10 (62.5%)    4 (25.0%)
          (35-49)           7 (30.4%)    6 (26.1%)
        Older adult     (*) 0 (0.0%)     5 (83.3%)
           (50+)            4 (23.5%)    4 (23.5%)
        Male            (*) 6 (31.6%)   10 (52.6%)
                            4 (14.3%)   10 (35.7%)
        Female              8 (57.1%)    4 (28.6%)
                           10 (38.5%)    6 (23.1%)
        Overall        (*) 19 (43.2%)   19 (43.2%)
                           30 (28.8%)   35 (33.7%)

                      Lower Limb Maximum Osteoperiostitis

                      Moderate          Severe
                      Osteoperiostitis  Osteoperiostitis

Male    Young adult    0 (0.0%)          0 (0.0%)
          (20-34)      3 (37.5%)         1 (12.5%)
        Middle adult   2 (22.2%)         0 (0.0%)
          (35-49)      3 (30.0%)         2 (20.0%)
        Older adult    0 (0.0%)          0 (0.0%)
           (50+)       2 (33.3%)         2 (33.3%)

Female  Young adult    1 (20.0%)         0 (0.0%)
          (20-34)      1 (20.0%)         0 (0.0%)
        Middle adult   0 (0.0%)          0 (0.0%)
          (35-49)      2 (22.2%)         2 (22.2%)
        Older adult    1 (33.3%)         0 (0.0%)
           (50+)       3 (37.5%)         0 (0.0%)
        Child (0-10)   1 (25.0%)         0 (0.0%)
                       2 (22.2%)         1 (11.1%)
         Adolescent    0 (0.0%)          0 (0.0%)
          (11-19)      2 (14.3%)         1 (7.1%)
        Young adult    1 (8.3%)          0 (0.0%)
          (20-34)      5 (33.3%)         1 (6.7%)
        Middle adult   2 (12.5%)         0 (0.0%)
          (35-49)      6 (26.1%)         4 (17.4%)
        Older adult    1 (16.7%)         0 (0.0%)
           (50+)       6 (35.3%)         3 (17.6%)
        Male           3 (15.8%)         0 (0.0%)
                       9 (32.1%)         5 (17.9%)
        Female         2 (14.3%)         0 (0.0%)
                       8 (30.8%)         2 (7.7%)
        Overall        6 (13.6%)         0 (0.0%)
                      28 (26.9%)        11 (10.6%)

Chi-square and likelihood ratio: (*) Significant difference at the 0.05
level. (**) P-value between 0.15 and 0.051.

Table 6. Frequency of cribra orbitalia and porotic hyperostosis maximum
score, by age, sex, and period (count and row percent). Unweighted

                                            Cribra Orbitalia
                                            Maximum Score
                                            No Cribra    Mild Cribra
                                            Orbitalia    Orbitalia

Male    Young adult (20-34)   MCA/LIA        0 0.0%       1 (100.0%)
                              Peak-LIA       1 (20.0%)    4 (80.0%)
        Middle adult (35-49)  (*) MCA/LIA    0 0.0%       5 (83.3%)
                              Peak-LIA       3 (50.0%)    3 (50.0%)
        Older adult (50+)     (**) MCA/LIA   1 (100.0%)      0 0.0%
                              Peak-LIA       1 (20.0%)    4 (80.0%)
Female  Young adult (20-34)   MCA/LIA        2 (40.0%)    3 (60.0%)
                              Peak-LIA       3 (75.0%)    1 (25.0%)
        Middle adult (35-49)  MCA/LIA        0 0.0%       2 (66.7%)
                              Peak-LIA       1 (25.0%)    3 (75.0%)
        Older adult (50+)     MCA/LIA        1 (33.3%)    2 (66.7%)
                              Peak-LIA       1 (25.0%)    3 (75.0%)
        Child (0-10)          MCA/LIA        0 (0.0%)     2 (50.0%)
                              Peak-LIA       0 (0.0%)     1 (100.0%)
        Adolescent (11-19)    MCA/LIA        0 (0.0%)     1 (100.0%)
                              Peak-LIA       3 (60.0%)    1 (20.0%)
        Young adult (20-34)   MCA/LIA        2 (28.6%)    5 (71.4%)
                              Peak-LIA       5 (50.0%)    5 (50.0%)
        Middle adult (35-49)  (**) MCA/LIA   1 (9.1%)     8 (72.7%)
                              Peak-LIA       4 (40.0%)    6 (60.0%)
        Older adult (50+)     MCA/LIA        2 (50.0%)    2 (50.0%)
                              Peak-LIA       3 (27.3%)    8 (72.7%)
        Male                  MCA/LIA        1 (11.1%)    7 (77.8%)
                              Peak-LIA       5 (27.8%)   12 (66.7%)
        Female                MCA/LIA        4 (33.3%)    7 (58.3%)
                              Peak-LIA       5 (35.7%)    9 (64.3%)
        Overall               MCA/LIA        7 (22.6%)   20 (64.5%)
                              Peak-LIA      18 (37.5%)   28 (58.3%)

                                              Porotic Hyperostosis
                                              Maximum Score
                               Severe Cribra  No Porotic
                               Orbitalia      Hyperostosis

Male    Young adult (20-34)    0 0.0%         (**) 0 0.0%
                               0 0.0%              4 (80.0%)
        Middle adult (35-49)   1 (16.7%)           4 (57.1%)
                               0 0.0%              5 (62.5%)
        Older adult (50+)      0 0.0%              2 (100.0%)
                               0 0.0%              6 (100.0%)
Female  Young adult (20-34)    0 0.0%              3 (60.0%)
                               0 0.0%              4 (80.0%)
        Middle adult (35-49)   1 (33.3%)           3 (100.0%)
                               0 0.0%              3 (75.0%)
        Older adult (50+)      0 0.0%              2 (100.0%)
                               0 0.0%              4 (80.0%)
        Child (0-10)           2 (50.0%)           4 (100.0%)
                               0 0.0%              3 (60.0%)
        Adolescent (11-19)     0 0.0%              0 0.0%
                               1 (20.0%)           2 (40.0%)
        Young adult (20-34)    0 0.0%         (**) 4 (50.0%)
                               0 0.0%             10 (83.3%)
        Middle adult (35-49)   2 (18.2%)           9 (75.0%)
                               0 0.0%              9 (69.2%)
        Older adult (50+)      0 0.0%              4 (100.0%)
                               0 0.0%             13 (92.9%)
        Male                   1 (11.1%)           6 (54.5%)
                               1 (5.6%)           17 (81.0%)
        Female                 1 (8.3%)            9 (81.8%)
                               0 (0.0%)           14 (82.4%)
        Overall                4 (12.9%)          25 (71.4%)
                               2 (4.2%)           47 (75.8%)

                              Mild Porotic  Severe Porotic
                              Hyperostosis  Hyperostosis

Male    Young adult (20-34)    0 0.0%       1 (100.0%)
                               1 (20.0%)    0 0.0%
        Middle adult (35-49)   1 (14.3%)    2 (28.6%)
                               2 (25.0%)    1 (12.5%)
        Older adult (50+)      0 0.0%       0 0.0%)
                               0 0.0%       0 0.0%
Female  Young adult (20-34)    2 (40.0%)    0 0.0%
                               1 (20.0%)    0 0.0%
        Middle adult (35-49)   0 0.0%       0 0.0%
                               1 (25.0%)    0 0.0%
        Older adult (50+)      0 0.0%       0 0.0%
                               1 (20.0%)    0 0.0%
        Child (0-10)           0 0.0%       0 0.0%
                               1 (20.0%)    1 (20.0%)
        Adolescent (11-19)     1 (100.0%)   0 0.0%
                               3 (60.0%)    0 0.0%
        Young adult (20-34)    2 (25.0%)    2 (25.0%)
                               2 (16.7%)    0 0.0%
        Middle adult (35-49)   1 (8.3%)     2 (16.7%)
                               3 (23.1%)    1 (7.7%)
        Older adult (50+)      0 0.0%)      0 0.0%
                               1 (7.1%)     0 0.0%
        Male                   2 (18.2%)    3 (27.3%)
                               3 (14.3%)    1 (4.8%)
        Female                 2 (18.2%)    0 0.0%
                               3 (17.6%)    0 0.0%
        Overall                6 (17.1%)    4 (11.4%)
                              13 (21.0%)    2 (3.2%)

Chi-square and likelihood ratio: (*) Significant difference at the 0.05
level. (**) P-value between 0.15 and 0.051.

Table 7. Odds ratio and common odds ratio, controlling for age, for
four skeletal indicators of health, weighted and unweighted

                                        Odds Ratio
                                        Unweighted  Weighted

Lower limb osteoperiostitis prevalence  1.875 (**)  1.567
LEH prevalence                             --         n/a
Cribra orbitalia prevalence             0.486       0.386 (**)
Porotic hyperostosis prevalence         0.798       0.709

                                        Common Odds Ratio
                                        Unweighted  Weighted

Lower limb osteoperiostitis prevalence  2.011 (**)   1.359
LEH prevalence                             --          n/a
Cribra orbitalia prevalence             0.454        0.518
Porotic hyperostosis prevalence         0.865        1.156

(**) P-value between 0.15 and 0.051.

Table 8. Odds ratio between periods for four skeletal indicators of
health by sex, weighted and unweighted

                                        Males           Females

Lower limb osteoperiostitis prevalence  2.769 (U)       2.133 (U)
                                        2.550 (W)       1.800 (W)
LEH prevalence                             --              --
Cribra orbitalia prevalence             0.325 (U)       0.900 (U)
                                        0.458 (W)       0.857 (W)
Porotic hyperostosis prevalence         0.282 (U) (**)  0.964 (U)
                                        0.341 (W)       1.500 (W)

(**) P-value between 0.15 and 0.051.
U = Unweighted Sample
W = Weighted Sample

Table 9. Odds ratio between periods for four skeletal indicators of
health by age, weighted and unweighted.

                                        Child           Adolescent

Lower limb osteoperiostitis prevalence  1.167 (U)       -- (U)
                                           -- (W)       -- (W) (**)
LEH prevalence                             --           --
Cribra orbitalia prevalence                -- (U)       -- (U)
                                           -- (W)       -- (W)
Porotic hyperostosis prevalence            -- (U) (**)  -- (U)
                                           -- (W)       -- (W)

                                        Young Adult     Middle Adult

Lower limb osteoperiostitis prevalence  1.964 (U)       3.810 (U) (*)
                                        1.400 (W)       2.200 (W)
LEH prevalence                            --               --
Cribra orbitalia prevalence             0.400 (U)       0.150 (U) (**)
                                        0.667 (W)          -- (W) (*)
Porotic hyperostosis prevalence         0.200 (U) (**)  1.333 (U)
                                        0.222 (W)       3.000 (W)

                                        Older Adult

Lower limb osteoperiostitis prevalence     -- (U) (**)
                                           -- (W)
LEH prevalence                             --
Cribra orbitalia prevalence             2.667 (U)
                                        3.000 (W)
Porotic hyperostosis prevalence            -- (U)
                                           -- (W)

(*) Significant difference at the 0.05 level.
(**) P-value between 0.15 and 0.051.
U = Unweighted Sample
W = Weighted Sample

Table 10. Odds ratio between periods for four skeletal indicators of
health by sex and age, males, weighted and unweighted

Males                                   Young Adult     Middle Adult

Lower limb osteoperiostitis Prevalence  4.667 (U)       7.200 (U) (**)
                                        2.500 (W)          -- (W) (**)
LEH prevalence                             --              --
Cribra orbitalia prevalence                -- (U)          -- (U) (*)
                                           -- (W)          -- (W) (*)
Porotic hyperostosis prevalence            -- (U) (**)  0.800 (U)
                                           -- (W) (**)  1.500 (W)

Males                                   Older Adult

Lower limb osteoperiostitis Prevalence    -- (W)
                                          -- (U)
LEH prevalence                            --
Cribra orbitalia prevalence               -- (U) (**)
                                          -- (W) (**)
Porotic hyperostosis prevalence           -- (U)
                                          -- (W)

(*) Significant difference at the 0.05 level.
(**) P-value between 0.15 and 0.051
U = Unweighted Sample
W = Weighted Sample

Table 11 Odds ratio for four skeletal indicators of health by sex and
age, females, weighted and unweighted

Females                                 Young Adult  Middle Adult

Lower limb osteoperiostitis prevalence   1.000 (U)    5.000 (U)
                                         1.000 (W)    2.000 (W)
LEH prevalence                              --           --
Cribra orbitalia prevalence              0.222 (U)       -- (U)
                                         0.333 (W)       -- (W)
Porotic hyperostosis prevalence          0.375 (U)       -- (U)
                                         0.500 (W)

Females                                 Older Adult

Lower limb osteoperiostitis prevalence     -- (U) (**)
                                           -- (W)
LEH prevalence                             --
Cribra orbitalia prevalence             1.500 (U)
                                        1.000 (W)
Porotic hyperostosis prevalence            -- (U)

(**) P-value between 0.15 and 0.051
U = Unweighted Sample
W = Weighted Sample
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Author:Williams, Leslie Lea; Larsen, Clark Spencer
Publication:Bioarchaeology International
Date:Sep 22, 2017
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