Modifications of the Fortelius and Solounias (2000) me- sowear method involve assigning each individual tooth a number based upon cusp shape and cusp relief according to two methods.

Teeth that show high relief and sharp cusps are then assigned a value of 0, those with high relief and round cusps are assigned a value of 1, those with low relief and rounded cusps are assigned a value of 2, and those with low or negative relief and blunted cusps are assigned a value of 3.

Mesowear scores of individuals are then averaged to produce a species average (Rivals & Semprebon, 2006; Rivals et al., 2007).


For the second mesowear scoring method of Kaiser, Brasch, Castell, Schulz, and Clauss (2009), the cusps are categorized in the same way as Fortelius and Solounias (2000) except that they add an additional mesowear category, low relief and sharp cusps.

Teeth that show high relief and sharp cusps are assigned a value of 0, those with high relief and round cusps are assigned a value of 1, those with low relief and sharp cusps are assigned a value of 2, those with low relief and rounded cusps are assigned a value of 3, and those with low or negative relief and blunted cusps are assigned a value of 4 (Kaiser et al., 2009).


The "low- magnification" microwear (LMM) method was devel- oped by Solounias and Semprebon (2002) and uses light stereomi- croscopy (rather than SEM) to examine microwear at comparatively low magnification, thus allowing for large numbers of samples to be processed relatively quickly.

The original LMM method involves vi- sualizing tooth casts under a standard light stereomicroscope at 35× magnification utilizing oblique lighting and evaluating the number of pits and scratches on the enamel surface in a 0.4 × 0.4 mm area.

Some newer implementations have increased the magnification to 70× (Townsend & Croft, 2008), but we have not included these data in our analyses for reasons of comparability.

Photographic methods have been introduced (Fraser et al., 2009; Merceron et al., 2005) but have not been applied widely enough for inclusion.


The DMTA method was developed by Ungar et al. ( 1991) and uses a white- light confocal profile to generate and quantify a 3D point cloud of the surface being scanned (Scott et al., 2006).

For each analysis, the profiler scans at 100× magnification four ad- jacent fields of 104 × 138 μm 2 each, spaced laterally by 0.18 μm and with a vertical resolution >0.05 μm in order to be compara- ble to SEM- based microwear (Ungar, 2015).

However, attempts to compare DMTA photosimulations to other types of microwear have produced mixed results (DeSantis et al., 2013).


Using scale- sensitive fractal analysis (SSFA), quantified length, area, and vol- ume scale measurements are calculated, which are the values used for comparison and analysis across and between taxa (Articus, Brown, & Wilhelm, 2001; Brown & Siegmann, 2001; Pedreschi, Aguilera, & Brown, 2000).

While SSFA produces over a dozen textural variables (Scott et al., 2006), only three are consistently reported in the literature.


The length- scale variable, anisotropy (epLsar), represents how similarly directionally aligned features of similar length are to one another, with higher anisotropy indi- cating more aligned features (Ungar et al., 1991).

Consumption of tough foods (e.g., grass, muscle) may yield more anisotropic sur- faces (Scott et al., 2012).


The area- scale variable, area- scale frac- tal complexity (Asfc), is a measure of how surface area changes as a function of scale, where the resultant Asfc value is the curve of the steepest part of the slope (Scott et al., 2006).

Higher Asfc val- ues indicate a more "pitted" surface thought to be caused by the consumption of brittle foods (e.g., nuts, bones) (Scott et al., 2005; Ungar, Scott, et al., 2007; Ungar, Merceron, et al., 2007; Ungar et al., 2003).


The volume scale variable, textural fill volume (Tfv), is measured by filling the scanned area with course (10 μm) and fine (2 μm) cuboids.

The course volume is then subtracted from the FRASER Et Al.

fine volume, yielding only the volume of the textural features and canceling out any overall shape or curve of the broader scanned area (Scott et al., 2006).

Higher Tfv is associated with chewing of harder food items (e.g., chitin and calcite shells, grit) (Scott et al., 2012).


Additional textural features not often reported in the liter- ature are described in Scott et al. ( 2006).


To summarize the multivariate DMTA data, we performed a prin- cipal component analysis (PCA) on epLsar, Asfc, and Tfv and extracted the species scores on the first and second principal components for further analysis.


We also extracted mammal dietary information from the EltonTraits 1.0 database (Wilman et al., 2014).

The EltonTraits diet data were combined into six categories: the percentage of the diet composed of (1) invertebrates, (2) vertebrates, (3) fruits, (4) nectar, (5) seeds, and (6) other plant materials (e.g., leaves and stems).

Because the data must sum to 100% and are thus inap- propriate for PCA, we transformed the data into z- scores (Pineda- Munoz, Lazagabaster, Alroy, & Evans, 2017).

We calculated the z- scores as the difference between individual values and the group mean divided by the standard deviation.

We extracted the species scores on the first and second principal components for further analysis.


We used the phylogeny of Faurby and Svenning (2015) for all of the analyses of phylogenetic signal.

The number of species included in each analysis of phylogenetic signal is shown in Table 1.


We used two measures of phylogenetic signal: (1) phylogenetic heritability or H 2 sensu (Lynch, 1991) and (2) Pagel's λ (Freckleton et al., 2002; Pagel, 1992, 1999).

We do not use p- values because the traditional cutoffs between significant and nonsignificant (.05 or .001) are arbitrary and thus uninformative for our analyses.


H 2 is a measure of the phylogenetic heritability of a trait because it is an estimate of the degree to which phylogenetically related species can be used to predict the phenotype of each other (Lynch, 1991).

H 2 is calculated as: where σ 2 α is the additive component of the trait variance (the phy- logenetic effect or component that is transmitted to descendants) and σ 2 T is the estimated total variance of the trait mean (the sum of the additive variance and residual variance).


We calculated H 2 using a phylogenetic generalized linear mixed- effects modeling in the MCMCglmm R package (Hadfield, 2010).

We included phylogeny as a random effect and used an inverse Wishart prior for the vari- ances and a normal prior for the fixed effects.

The parameters for the inverse Wishart are V and ν, the variance and degree of belief for V, respectively.


For all models, we used an uninformative prior with the following parameters: V = 10 −6 , ν = −1.

MCMCglmm uses a normal distribution for the fixed effects, which we considered ap- propriate.

We ran the MCMC for 2,600,000 iterations with a burn- in of 600,000 iterations.

We subsampled the MCMC at an interval of 2,000 iterations.


In R, H 2 was then calculated by dividing the poste- rior distribution of covariance matrices for the random effect (phylo- genetic effect) by the sum of the covariance matrices for the random and fixed (tooth wear) effects.

We report the mean and upper and lower 95% confidence limits.


We also estimated Pagel's (1999) λ using fitContinous in the Geiger R package (Harmon, Weir, Brock, Glor, & Challenger, 2008).

Pagel's λ is a branch- length transformation parameter that is itera- tively estimated using maximum likelihood (ML) from a set of tip data and a phylogenetic topology.

When λ = 0, all tips of the phylogeny are equal to the root- tip distance, and thus, the topology is equiva- lent to a star phylogeny.

As all tip branch lengths are the same, a ML value of 0 for λ indicates a situation in which there is no relationship between tip data and branch lengths in the tree.

When λ = 1, the internal branches of the phylogenetic tree are left alone.

Therefore, a ML value of λ = 1 applies to tip data that are directly inversely re- lated to the branch lengths connecting tip taxa.

This is equivalent to a Brownian motion model of character evolution along the phyloge- netic topology.


To demonstrate the impact of using phylogenetic comparative methods on discriminability among species with differing die- tary niches, we used phylogenetic discriminant function analysis (PDFA) and quadratic discriminant function analysis (QDFA).

We chose to analyze the LMM dataset for this purpose because mi- crowear is comparable among a large set of taxa (unlike mes- owear, which can be quantified only for selenodont artiodactyls and lophodont perissodactyls).

Dietary categorization was based on the Elton Traits 1.0 database (Wilman et al., 2014).

Carnivores were defined as species relying primarily on vertebrates and in- vertebrates (including insectivores).

We combined vertebrate and invertebrate carnivores primarily because very few species spe- cialize exclusively on one or the other, excepting mammalian hy- percarnivores; and we wanted to avoid arbitrary cutoffs as much as possible.

Herbivores were defined as species relying primarily on plants (including fruits).

Finally, omnivores were defined as spe- cies relying on any mix of vertebrates, invertebrates, and plants.


We recognize that mammal dietary data can be classified in a va- riety of ways but employment of a different classification system would not alter our prediction that nonphylogenetic methods overestimate discriminability among dietary guilds.


Quadratic discriminant function analysis is used for discrimina- tion of groups, in this case dietary guilds, based on a multivariate dataset (the training dataset).

QDFA identifies the combination of variables (quadratic functions) that achieves maximum separa- tion among the defined groups in the training dataset (Hammer & Harper, 2006).

QDFA differs from linear DFA in allowing for unequal variance-covariance matrices among classes (i.e., dietary guilds).

Classification is made by assigning observations to the class with the closest centroid using the Mahalanobis distance.

The utility of the resulting set of discriminant functions is assessed by the rate at which observations are correctly grouped into their original class.

A high rate of correct classification means that species with unknown diets can be classified with a reasonable degree of confidence.


Phylogenetic discriminant function analysis relies on an approach called flexible discriminant analysis, which takes advantage of the fact that linear discriminant analysis (LDA) is equivalent to canonical correlation analysis and thus reduces to a multiresponse linear re- gression (Hastie, Tibshirani, & Buja, 1994).

When implementing FDA, portions of the LDA calculation are replaced by least squares regres- sion (Hastie et al., 1994), thus also allowing for the removal of phy- logenetic bias (Motani & Schmitz, 2011).

Phylogenetic comparative methods such as phylogenetic generalized least squares regression remove phylogenetic bias through a modification of variance-cova- riance matrices (Grafen, 1989), as is done during PDFA.

We imple- mented PDFA as described in detail by Motani & Schmitz, 2011.


The first (PC1) and second (PC2) principal components of mammal diet explain a cumulative 50.8% of the variance in the data.

PC1 most strongly delineates insectivores and carnivores from species that feed primarily on plant materials.

PC2 reflects separation of the frugivores and nectar specialists from other species (Figure S1).

Diet PC1 and PC2 show very high values of λ and phylogenetic heritability or H 2 , which suggest a high level of dietary PNC among the taxa examined (Table 1; Figures 1, 2a-b).


Likewise, mesowear (both scoring methods) shows very high val- ues for H 2 and λ (Table 1; Figures S2-S3).

LMM stands out as having the lowest values for both heritability and phylogenetic signal as es- timated by λ (Table 1; Figure S4).


PC1 and PC2 of dental microwear textures (DMTA) explain a cumulative 79.2% of the data variance.

PC1 reflects separation of species with high anisotropy (epLsar) and high- scale fractal complex- ity (Asfc) as well as textural fill volume (Tfv).

PC2 largely reflects the separation of species with high textural fill volume from the others (Figure S5).

Similar to diet and mesowear, DMTA PC1 and PC2 show very high values of H 2 and λ (Table 1; Figures S6-S7).


Among the categories of carnivore, herbivore, and omnivore, quadratic discriminant analysis misclassified ~34.5% of species, thus correctly classifying ~65.5% of mammal species for which we compiled microwear data.

In contrast, PDFA incorrectly classified ~73.6% of species, thus correctly classifying only ~26.4% of species in the dataset.

The high rate of misclassification using PDFA results largely from misclassification of carnivorous and herbivorous spe- cies as omnivorous.


Tooth wear is known to reflect everything from the lifetime diet to the last few meals of individual animals and is therefore thought to vary on relatively short timescales (Davis & Pineda- Munoz, 2016; Teaford & Oyen, 1989); tooth wear shows change throughout the lifetime of individuals as well as variation among individuals, and dif- ferent populations within species (DeSantis et al., 2009; Fortelius et al., 2002; Rivals & Semprebon, 2006; Scott et al., 2005).


For these F I G U R E 1 Analytical tooth wear methods show high phylogenetic signal as measured using heritability (H 2 ) and Pagel's λ.

DMTA stands for dental microwear texture analysis.

PC1 and PC2 refer to the first and second principal components, respectively FRASER Et Al. reasons, tooth wear is often considered to be taxon or phylogeny free, as it is thought to reflect feeding behavior under the implicit assumption that this behavior is highly labile (Blomberg, Garland, & Ives, 2003).


Despite the assumption of lability, one of the most common applications of analytical tooth wear methods is for the in- ference of average species diets (Fortelius & Solounias, 2000; Fraser & Theodor, 2011; Semprebon et al., 2004; Solounias & Semprebon, 2002), an approach that has been applied with considerable F I G U R E 2 Dietary preference shows phylogenetic clustering that corresponds to the major clades of mammals.

Phylogenetic trait maps of the first (a) and second (b) principal components of mammal diet.

Darker colors indicate positive values on the principal component axis while warm colors indicate negative values FRASER Et Al. success (Donohue et al., 2013; Fortelius & Solounias, 2000; Fraser & Theodor, 2011; Haupt et al., 2013; Hedberg & DeSantis, 2017; Semprebon et al., 2004; Solounias & Semprebon, 2002).


We show herein that mammalian diets, summarized at the species- level, show high phylogenetic signal (Table 1; Figure 2a-b) and that mammals may show some degree of trophic PNC (although it is outside the scope of this paper to definitively test for PNC in mammal diets; Münkemüller, Boucher, Thuiller, & Lavergne, 2015).


If tooth wear proxies are reliable reflections of average spe- cies' diets, as many studies suggest they are (Donohue et al., 2013; Fortelius & Solounias, 2000; Fraser & Theodor, 2011; Haupt et al., 2013; Hedberg & DeSantis, 2017; Semprebon et al., 2004; Solounias & Semprebon, 2002), then logic would dictate that they must show a similar degree of phylogenetic signal.

We demonstrate that all of the analytical tooth wear data analyzed herein show strong phylo- genetic signal (Table 1; Figure 1); mean H 2 and Pagel's λ are consis- tently high for mesowear and microwear.

Microwear is thought to be acquired during an animal's last few meals, suggesting a greater de- gree of lability, but we find similar degrees of phylogenetic signal for mesowear and dental microwear textures (DMTA; Table 1; Figure 1).


LMM shows the lowest phylogenetic signal, although we still con- sider it to be high (Table 1; Figure 1).

The difference in phylogenetic signal between LMM and DMTA might reflect (1) the coarser scale at which LMM is quantified, (2) observer bias (i.e., high variability among the studies we included), or (3) that LMM is less reflective of enamel microstructure, which is likely to be phylogenetically inher- ited.

Regardless, as with diet (Table 1; Price et al., 2012), the ways in which mammal teeth wear appear to be conserved among closely related taxa and slow to change on evolutionary timescales.

The re- sult is the overestimation of discriminability among mammal species with diverse diets.

In the case of LMM, we find a ~40% difference in correct dietary classification between quadratic and PDFA.


We suggest that our finding of high dietary and tooth wear phylo- genetic signal among mammals reflects evolutionary conservation of functional traits related to feeding; that is, the traits that determine the foods that individuals can effectively exploit are phylogeneti- cally conserved, these traits constrain the average diets of species, and, in turn, the ways in which the teeth wear during feeding.


Mammal diets are constrained by their feeding morphology (i.e., the form and function of the feeding apparatus) (Mendoza, Janis, & Palmqvist, 2002; Perez- Barberia & Gordon, 1999).

A classic ex- ample of dietary constraint occurs among members of the genus Equus.

Modern equids have continuously erupting, hypsodont (i.e., high crowned) cheek teeth (premolars and molars) that require con- stant wear, which is acquired through the introduction of exogenous grit into the mouth during grazing (Jardine, Janis, Sahney, & Benton, 2012).

In the absence of sufficient wear, horses develop a deadly condition referred to as wave mouth, when the molars fail to wear at equal rates (Damuth & Janis, 2011; Janis & Fortelius, 1988).

As a result, over their lifetimes, individuals are constrained into a grazing lifestyle (but note that they can exploit other resources for short periods of time).

Due to their gritty diet, molars of adult Equus tend to have flat mesowear profiles and microwear dominated by abrasive features (i.e., scratches) (Fortelius & Solounias, 2000; Solounias & Semprebon, 2002).


The dentition of xenarthrans may be similarly constraining.

The earliest xenarthrans were likely insectivores, and, like many other insectivorous mammals, evolved a simplified dentition or became completely edentulous (e.g., aardvarks, pangolins) (Gaudin & Croft, 2015; Vizcaíno, 2009).

Within Pilosa, anteaters continue to persist on an insectivorous diet and are completely edentulous, whereas modern sloths have evolved into exclusively arboreal folivores (McNab, 1979).

Having lost the ability to produce enamel, sloths instead rely on teeth composed of hypermineralized ever- growing dentin (sometimes defined as "orthodentine"), which, like equids, also require consistent grinding against each other, food, and exog- enous particulates to prevent overgrowth (Kalthoff, 2011; Ungar, Teaford, Glander, & Pastor, 1995).

Although dentin is softer than enamel and thus exhibits wear in noncomparable ways (Hirschfeld, 1985; MacFadden, DeSantis, Labs Hochstein, & Kamenov, 2010), microwear studies have shown that xenarthran tooth wear is indica- tive of diet when compared within xenarthra (Haupt et al., 2013).

We suggest that dietary constraints, such as are apparent among Equus species and sloths, are responsible for the high phylogenetic signal of mesowear and microwear.


There are a number of ways that tooth morphology influences and constrains the diets of individuals.

Mammalian teeth vary widely in both form and function (Lucas, 2004; Reilly, McBrayer, & White, 2001; Wall & Smith, 2001).

Teeth of different shapes are evolved to perform specific functions such as cutting, grinding, slicing, or pul- verizing (Lucas, 2004).

For example, grinding teeth are often charac- terized by numerous flat blades of enamel oriented perpendicular to the chewing stroke (Kaiser, Fickel, Streich, Hummel, & Clauss, 2010), while pulverizing teeth are shaped and function like a mortar and pestle (Lucas, 1979).

Different tooth shapes have evolved to increase the effectiveness with which mammals orally process different food types.

For example, carnivoran carnassial teeth perform slicing, al- lowing effective mastication of flesh, while lophodont equid molars perform grinding and thus effective processing of grasses (Lucas, 2004).

While the evolution of specific tooth shapes improves mas- tication of particular foods, it can also limit the degree to which individuals can exploit other foods.

For example, monocot leaves are characterized by numerous sclerenchymatous bundle sheaths and have thick cell walls (Wright & Illius, 1995).

Significant grind- ing is required to exploit the nutrition in monocot cells.

Carnivorans, particularly hypercarnivores (>70% meat in diet) such as felids (Van Valkenburgh, 2007), have reduced dentition and lack teeth with grinding surfaces (Solé & Ladevèze, 2017).

Therefore, the shearing function of hypercarnivore carnassial teeth is insufficient for pro- cessing tough monocot leaves.

Although less specialized carnivores can and do eat grasses, due, in part, to inefficient mastication, they are precluded from relying on monocots as their sole source of nu- trition (considerations of gut morphology and excessive tooth wear notwithstanding).

As such, through limiting the foods individual mammals can effectively orally process, tooth shape can limit the average diet of a species (Pineda- Munoz et al., 2017).

Furthermore, FRASER Et Al. it is well established that tooth shape is phylogenetically conserved (Kangas, Evans, Thesleff, & Jernvall, 2004).

We therefore expect members of the same species and of closely related species with similar tooth morphology to display similar dietary preferences (as shown in Table 1 and Figure 2a-b) and thus characteristics of their tooth wear.


Teeth of certain shapes may also influence wear through act- ing as guides during the chewing stroke (Kaiser et al., 2010; von Koenigswald, Anders, Engels, Schultz, & Kullmer, 2013).

In mam- mal species with complex and high- relief tooth cusps, the occlusal pathway is altered as the antagonistic teeth occlude (Maier, 1984).

Tooth guidance is apparent as both attritional wear facets and stria- tions or microwear oriented in the direction of tooth movement (von Koenigswald et al., 2013).

Tooth guidance is less apparent among species with low- relief cusps in which tooth occlusion is controlled primarily by the activity of the jaw- closing muscles (Kaiser et al., 2010).


The wear experienced by flatter teeth tends to be dominated by abrasive wear rather than attritional wear.

Therefore, to some degree, guidance of the teeth by their antagonists or lack thereof during mastication influences the presence and distribution of cer- tain types of wear.


Given that tooth shape is phylogenetically con- served (Kangas et al., 2004), the degree of tooth guidance and, thus, the types of wear apparent on the teeth are expected to be similarly phylogenetically conserved.


Tooth wear is also influenced by the orientation of the chewing stroke, as it is determined by the organization of the chewing mus- cles (Fraser & Rybczynski, 2014; Lucas, 2004).

The chewing stroke is divided into two phases, which correspond broadly to jaw closing and tooth incursion (phase I) and jaw opening and tooth excursion (phase II) (Greaves, 1980; Hiiemae, 2000; Janis, 1979; Rensberger, 1973; Wall & Smith, 2001).

The angle of tooth incursion is influenced by the arrangement of the three primary jaw- closing muscles, the masseter, pterygoideus, and temporalis.

Grazing mammals, for exam- ple, emphasize transversely oriented shredding and shearing during chewing, in part because the origination surface for the superficial masseter is extended anterior to the zygomatic arch (Mendoza et al., 2002; Spencer, 1995).

The result is the re- orientation of the mas- seteric force arm from caudal to more rostral, allowing more mus- cular force to be applied at the molars in a buccolingual direction (DeMar & Barghusen, 1972; Greaves, 1991; Herring & Herring, 1974; Herring, Rafferty, Liu, & Marshall, 2001; Smith, 1993; Williams, Vinyard, Wall, & Hylander, 2007).

Similarly, three- dimensional re- constructions of carnivoran tooth occlusion show nonplanar attri- tional contact; the amount of lateral movement of the teeth during mastication is correlated with tooth number and dental complexity (Evans & Fortelius, 2008), both characters known to be highly phylo- genetically conserved.

As above, species with transversely oriented chewing strokes tend to show abrasive wear features.

Thus, the ori- entation of the chewing stroke is also an influential factor in how the teeth wear, particularly in species with limited tooth guidance.


Although there is likely some individual variation, it is unlikely that the orientation of the chewing muscles (areas of insertion and orig- ination) is widely variable within a species or among closely related species that have inherited similar diets from their most recent com- mon ancestor.


In summation, we suggest that both mesowear and microwear show high phylogenetic signal because the feeding apparatus of mammals is an evolutionary and functional module.

Characteristics of the jaws, teeth, and chewing stroke evolve in concert to enable the exploitation of new food types (Fraser & Rybczynski, 2014).

As a functional module, the different components of the feeding appa- ratus (e.g., jaws, teeth) must evolve and change together to main- tain function or produce a change in function such as a transition from one feeding type to another (Raia, Carotenuto, Meloro, Piras, & Pushkina, 2010).


For example, modern Equus have evolved high crowned cheek teeth as a means of increasing the lifespan of the teeth in the face of increased abrasive wear.

But high crowned teeth require considerable space in the jaw.

Modern Equus has therefore also evolved a robust and dorsoventrally wide mandible as well as posteriorly displaced orbits (Macfadden, 2000).


A transition from brachydont to hypsodont could not have occurred in the absence of compensatory changes to the jaws.

Furthermore, a shift to graz- ing on tough grasses that demand significant grinding required an associated shift in the orientation of the chewing stroke, the orien- tation of the enamel bands of the cheek teeth, and flattening of the tooth profile to enable a highly transverse chewing stroke (Fraser & Rybczynski, 2014).

In turn, the ways in which Equus teeth experience wear are altered, trading attritional for abrasive wear and thus higher relief cusps for low relief.


In a similar vein, hypercarnivores such as felids require powerful jaw- closing muscles for capturing and dispatching prey (Valkenburgh & Ruff, 1987).

As a result, the temporalis muscle, a jaw- closing mus- cle with a slightly posteriorly directed line of action, is comparatively large (Wall & Smith, 2001).

The sagittal crest of the skull is similarly enlarged to provide a larger surface of origination for the tempora- lis.

The result is a comparatively vertically oriented chewing stroke evolved for killing prey and cutting flesh (Valkenburgh & Ruff, 1987).

The teeth of felids also show complex relief, enhanced tooth guid- ance during chewing, and prevalence of shearing- type wear (Ungar, 2010).

As in Equus, transitioning into a carnivorous niche has pro- duced a suite of changes in the felid feeding apparatus.

Slow changes in the function of the feeding apparatus and mammal diet on evolu- tionary timescales have thus combined to produce detectible phy- logenetic signal in tooth wear proxies thought only to reflect much shorter timescales (i.e., the lifetime of individuals or last few meals).


Our finding of high phylogenetic signal for mesowear and micro- wear does not imply that the only information conveyed by tooth wear is phylogenetic; tooth wear is not necessarily taxonomy or phylogeny in disguise.

The utility of mesowear and microwear for delineating individual and temporal differences in tooth wear is clear (Louys, Ditchfield, Meloro, Elton, & Bishop, 2012; Scott et al., 2005).


Our interpretation is that the tooth wear of individuals from a particular species can vary within some range that is constrained FRASER Et Al.

by the evolutionary morphology of their feeding apparatus.

As such, changes through time or among individuals can be detected but these changes will fall within the range set by morphology and thus the ways in which the feeding apparatus functions.


In some cases, experimental diets produce changes in tooth wear but also induce wear features expected given the chewing mechanics of the species (Hoffman et al., 2015).

Similarly, comparison of dentin wear among distantly related taxa with different dental morphologies suggests there is little basis for comparison (Haupt et al., 2013).


We therefore do not intend to recommend the disuse of microwear and mesowear for understanding average species diets or more detailed aspects of their biology. But what do our findings mean in practice?


We do not suggest that the species, population, and individual- level tooth wear differences reported in previous studies reflect phenomena other than chewing mechanics and diet.

Our argu- ment is a statistical one.

Phylogenetic nonindependence can be a considerable problem if a comparison of distantly related taxa is desired (e.g., a comparison of Equus to Bos) and p- values or ex- ploratory methods such as discriminant function analysis are used to test for significant differences among trophic groups (Barr & Scott, 2014).

We will not elaborate here because there is a vast literature on phylogenetic nonindependence.

We highlight that compensations for phylogenetic nonindependence are rarely applied in tooth wear studies (Fraser et al., 2014; Mihlbachler & Solounias, 2006), despite the widespread use of phylogenetic comparative methods in ecology and paleontology (Blomberg & Garland, 2002; Losos, 2008; Miles & Dunham, 1993).

We there- fore recommend their application in studies of tooth wear wher- ever possible.

For modern mammals, nearly complete phylogenies are widely available (Faurby & Svenning, 2015; Fritz & Purvis, 2010) using the taxonomy of both Wilson and Reeder (2005) and the International Union for Conservation of Nature, respectively.


For fossil mammals, the number of phylogenies is increasing, cla- distic and otherwise (Fraser et al., 2015), and in the absence of phylogenetic information, taxonomy can also be used (Soul & Friedman, 2015).

There are numerous phylogenetic comparative methods that can be put to good use by scientists using tooth wear including, but not limited to, phylogenetically independent contrasts, phylogenetic generalized least squares, phylogenetic principal components analysis, and PDFA.

All of these analyses are now available in the free software platform R in packages such as ape, geiger, and caper (Harmon et al., 2008; Orme et al., 2011; Paradis, 2006).


• Mammal diets and tooth wear show significant phylogenetic sig- nal when using Pagel's λ and phylogenetic heritability (H 2 ).


• We suggest that phylogenetic conservation of functional traits re- lated to feeding explains phylogenetic signal in mammal diet and tooth wear.


• The ways in which teeth wear are determined, in part, by di- etary constraints placed on animals by their morphology and the influence of morphology on the ways in which the teeth occlude.


• Tooth wear is not merely phylogeny in disguise; it can vary among individuals and time periods, but the degree of variation is con- strained by morphology and thus phylogeny.


• Application of common, well-documented phylogenetic compara- tive methods can circumvent statistical issues related to noninde- pendence among tooth wear data points due to PNC.


• Restriction of tooth wear comparisons to closely related species with similar dental morphologies will reduce the need for phylo- genetic comparative methods but limit within-guild comparison of distantly related taxa.


Dear Editor, A new study by Fraser, Haupt, and Barr (2018) urges the use of phylogenetic comparative methods, whenever possible, in analyses of mammalian tooth wear.

We are concerned about this for two reasons.

First, this recommendation may mislead the research community into thinking that phylogenetic signal is an artifact of some sort rather than a fundamental outcome of the evolutionary process.

Secondly, this recommendation may set a precedent for editors and reviewers to enforce phylogenetic adjustment where it may unnecessarily weaken or even directionally alter the results, shifting the emphasis of analysis from common patterns manifested by large clades to rare cases.


Fraser et al. ( 2018) take phylogenetic niche conservatism as a starting point for their study, implying that species are constrained by ancestral traits and therefore have a “reduced dietary ability over evolutionary time.” That is plausible, as per our hypothetical example above.

However, what is misleading is the follow-up statement that “dietary inferences from tooth wear are biased by phylogenetic relatedness,” inferred from the fact that “tooth wear dietary proxies show strong phylogenetic signal” [1].

Methods of determining diet from tooth wear, as well as any other analytical methods of determining diet such as stable isotopes or fecal analyses, do not assume or require diet to be independent of phylogenetic affinity.

What they assume is that within the domain of validity of a method, each diet leaves a detectable trace (i.e. it wears teeth in a distinct way or leaves a distinct composition of isotopes), no matter which taxon the tooth comes from.


This gives these proxies the flexibility to not only monitor similarities in diet when they might be expected (e.g. between certain distantly related taxa), but to document differences in diet when they might not necessarily be expected based on tooth morphology alone.

Differences between closely related taxa have been captured, for instance, in bovids [2], cervids [3], ungulates [4], feliforms [5], canids [6], primates [7], and macropodids [8].

Indeed, many bioarchaeological studies have demonstrated distinctive and predictable diet-related differences in both gross dental wear and microwear within a single species, Homo sapiens [9].

The Equus and Bos example, pointed out by Fraser et al. ( 2018) as a case of possible problem with phylogenetic non-independence, in fact shows the opposite: Distantly related taxa, with convergently similar diet, have very similar results from all dental wear proxies [10].

Further, experimental studies have demonstrated different microwear attribute values in the same species when fed foods with different textural properties and/or grit loads [11].

In many cases, the diets of extinct taxa are also vastly different from those of extant taxa, despite being members of the same family [12].

Function-driven methods, such as mesowear or microwear, therefore give us the chance to recognize both similarities and differences in paleobiology that might not be detectable via traditional morphological or taxic techniques.


The same sort of thing can be said for stable isotope techniques where, for instance, experimental manipulations of diet result in changes to the isotopic composition of sampled tissues [13] and where, for example, carbon isotope values for bovids from the early hominin sites in South Africa indicate that 25% of specimens would be misclassified (in terms of diet) if based on taxonomic affiliation alone [14].

The fact that diet is not independent of phylogeny is one of the fundamental outcomes of the evolutionary process, since a descendant species is never created de novo but builds upon inheritance from its immediate ancestor.

“ Species of the same genus have usually, though by no means invariably, some similarity in habits and constitution, and always in structure” [15], this is a phylogenetic signal.

Phylogenetic niche conservatism, however, results when closely related species are more similar ecologically than would be expected based on their phylogenetic relationships [16].

Therefore, the phylogenetic signal itself, as tested by Fraser et al. ( 2018), would not indicate evolutionary constraints; however, its residual as compared to random divergence could indicate it [17].

Multiple studies (reviewed by Losos, 2008) have found that phylogenetic niche conservatism is by no means omnipresent, while phylogenetic signal is the norm.


And even if lineages remain conserved within their diets and habitats, it does not follow that functional relationships between (dental) traits and diets become decoupled.

If teeth of the same kind remain useful for the same diets, like carnassials for eating meat, inference of diets via dental proxies would not be biased.

An analogy can be made to an X-ray machine used at airports to detect the presence of sharp objects.

Suppose a family arrives that have jointly prepared for a picnic, and therefore, all have knives in their pockets.

If the machine correctly detects all the knives, it would yield the equivalent of a phylogenetic signal in its outputs.

This does not mean that the method by which the machine detects knives is biased, or needs to be adjusted. And if we adjust the machine in order to decrease phylogenetic signal, in this situation, the result will be undoubtedly worse; the machine will either make false alarms or miss real detections (Type I and Type II errors).

Tooth wear is a function of behaviors occurring in life.

It is fundamentally different from genetically mediated dietary proxies, such as occlusal morphology, so its analysis should not be constrained by the use of phylogenetic comparative methods.


We do not mean to imply that phylogenetic methods are wrong as such. They have their meaningful uses but their relevance depends on the research question rather than the source of the data.

Yet a statement received by some of us from a recent reviewer echoes a worrying trend to enforce phylogenetic adjustments everywhere where data of phylogenetic origin are used: “Although I agree with the authors’ rationale for not wanting to use PGLS1, in this day and age it is simply not acceptable to NOT do the analyses using PGLS.

” We argued that this was unnecessary, and the editors were in agreement.

Analysis of phylogenetically related data is not trivial, and there is no solution that fits all purposes (see Westoby, Leishman, and Lord (1995), Losos (2008), Revell (2010) for comprehensive discussions).

We strongly disagree with the recommendation of Fraser et al (2018) to use phylogenetic comparative methods in studies of mammalian tooth wear whenever possible.

Phylogenetic regression is not by itself a better or more advanced analysis method than ordinary regression.

The choice of the methods and interpretations of results depends on the authors of analyses and the research questions they tackle.


In our recent study, we describe strong phylogenetic signal in tooth wear dietary proxies.

We believe that phylogenetic signal is detectable in tooth wear because the feeding apparatus acts as an evolutionary and functional module [1].

Due to the significant functional role of the feeding apparatus, changes in one component impact the other components of the module [2].

The result is a difference in chewing stroke, the ways in which the teeth contact each other, and, thus, the ways which the teeth wear.

We further argue that such modularity of the chewing apparatus results in certain functional and thus evolutionary constraints, potentially leading to phylogenetic niche conservatism (PNC) [3].

The end result is that tooth wear (like diet) shows strong phylogenetic signal.

In fact, our finding seems unsurprising given that much of the methodological practice in the application of analytical tooth wear methods acknowledges this phylogenetic signal [4].

We subsequently make a recommendation that, when possible and necessary, appropriate phylogenetic comparative methods (PCMs) be applied, a recommendation that has been made time and time again in the ecology and evolution literature [5].


DeSantis et al. ( 2018), however, express concern that (a) our study will cause editors to unnecessarily enforce the use of phylogenetic comparative methods during review and (b) that our study will mislead researchers into thinking phylogenetic signal is merely an artifact, rather than a result of the evolutionary process.

In our recent study, we do not suggest that tests for phylogenetic signal and PCMs be applied when they are unnecessary.

For example, studies focused on a small number of species within the same genus are unlikely to require such statistical corrections [6].

We also recommend authors be familiar with the limitations of tests for phylogenetic signal when their sample of species is small [7].

We strongly assert that the source of phylogenetic signal in diet and thus tooth wear is the evolutionary process of descent with modification from a common ancestor [8].

However, there is a complex relationship among evolutionary process, rate, and phylogenetic signal [9].

The implication of the DeSantis et al. ’s argument, however, is that if phylogenetic signal results from the evolutionary process, there is no need to correct for it statistically.

We disagree strongly with this implication.

Even if the phylogenetic signal in dietary proxies such as mesowear and microwear is "meaningful" in the sense that it is related to diet, this does not change the statistical difficulties introduced by phylogenetic nonindependence [10].

Furthermore, in no way does the presence of phylogenetic signal, nor suggesting that PCMs be used when necessary, imply that dietary proxies are unreliable indicators of diet.


Suppose we are using hypsodonty (presence of high crowned teeth) as a metric for inferring grazing behavior among mammals [11].

If we sample a number of mammals and use a discriminant function analysis to estimate the strength of association between hypsodonty and feeding behavior, we would find that hypsodonty is a very good indicator of a grazing lifestyle.

However, the gelada baboon, an open-area feeder whose diet is dominated by grasses, would be misclassified by the discriminant function, based on the absence of hypsodont cheek teeth [12].

Thus, the strength of the association between hypsodonty and grazing is over-estimated in this scenario.

Hypsodonty is an honest indicator of grazing, but it is constrained to certain phylogenetic groups, and there exist other ways to be a grazer that do not require hypsodonty.

In a further hypothetical example used by DeSantis et al. ( 2018), families on their way to the airport tend to be either "knife carrying" or "knifeless." We extend this example to suggest that, if the material from which the knives are made shows "phylogenetic signal," airport security might overestimate the strength of the link between beeping magnetometer alarm and the presence of sharp cutting tools.

While the beeping alarm is an honest signal of the presence of a knife, there are other equivalent knife-wielders that are not detectable by this method.

In our recent study, we use a phylogenetic discriminant function analysis to demonstrate the result of such overconfidence arising from phylogenetic signal on our ability to correctly classify mammals by diet using tooth wear [13].


DeSantis et al. ( 2018) further suggest that (a) convergence of diets among distantly related mammals and (b) intraspecific dietary differences negate the need to correct for phylogenetic signal in tooth wear.

We consider the following example.

Hypsodonty has evolved numerous times within Mammalia but; for our purposes, let us consider only artiodactyls and perissodactyls.

All extant equids are hypsodont.

Some clades of artiodactyls possess similarly high-crowned teeth such as members of the tribe Bovini and family Antilocapridae.

The presence of high-crowned teeth is notably nonrandomly distributed throughout Artiodactyla and Perissodactyla [14].

Hypsodont members of these clades have inherited their high-crowned teeth from a common ancestor.

In fact, as far as we are aware, hypsodonty never reverses; hypsodont taxa never return to a low-crowned condition.

So, we would attain significant estimates of phylogenetic signal for hypsodonty even though it has arisen multiple times via different developmental and genetic mechanisms.

Convergent evolution therefore does not negate the need for phylogenetic comparative methods.


Dietary change through time might indicate weak or absent PNC.

However, as acknowledged by DeSantis et al. ( 2018), PNC, and phylogenetic signal are not the same phenomenon [15].

Significant phylogenetic signal can result both from a Brownian Motion model of evolution, wherein a trait evolves via a random walk, as well as PNC, a phenomenon wherein closely related species are more similar than is expected given a Brownian Motion model of trait evolution [16].

Therefore, significant phylogenetic signal can occur in the absence of PNC.

The fact that intraspecific shifts in diet occur among mammals therefore has no bearing on whether dietary phylogenetic signal exists within Mammalia or whether it needs to be corrected for.

The tooth wear of members of a species can and do vary through time but we suggest that they vary within some range that is determined by functional constraints of the feeding apparatus [17].

This manifests as dietary shifts, say from a less gritty diet to a more gritty diet, but should rarely manifest as an intraspecific shift from herbivory to carnivory, for example [18].

We further agree with DeSantis et al. ( 2018) that experimental diets can and do change the properties of mammal tooth wear [19].

However, our study refers only to the natural diet of mammals.

Feeding a lion rocks would most assuredly change how the teeth wear but has no bearing on whether the natural diets of mammals show phylogenetic signal and indicates nothing about how common PNC is among mammals.


Finally, DeSantis et al. ( 2018) rightly indicate that most PCMs assume that the residuals of y ~ x from an ordinary least squares regression are statistically independent [20].

In cases where the x and y variables show no autocorrelation but the residuals of y do, ordinary least squares regression performs poorly [21].

Herein, we demonstrate that when both x and y show high phylogenetic signal, the λ estimates for the residuals of y consistently indicate high phylogenetic signal [22].

Therefore, justified in recommending the use of PCMs.

Furthermore, Revell (2010) recommends fitting a phylogenetic generalized least squares model using maximum likelihood to simultaneously estimate λ and the regression coefficients.

If λ is zero, PGLS converges on the ordinary least squares solution.

Therefore, the application of a PCM, specifically PGLS, from the outset should not mislead researchers.


In conclusion, we agree with DeSantis et al. ( 2018) that phylogenetic comparative methods need not be applied when their use is unnecessary or unjustified such as during a study of intraspecific dietary variation.

However, we note that application of phylogenetic comparative methods (most notably phylogenetic generalized least squares) when phylogenetic signal is low will not lead researchers astray [23].

Our recent study thoroughly describes the fact that phylogenetic signal can arise from descent with modification and PNC.

We thus also agree with DeSantis et al. ( 2018) that phylogenetic signal in the context of tooth wear not be viewed as a mere inconvenient artifact. But we disagree with their implication that PCMs are therefore unnecessary.

If authors are interested in interpreting p-values from comparisons of species from disparate clades, they should have an understanding of the structure of their data as it pertains to phylogenetic signal.


Based on all the findings about the possible treatment in the pre- clinical phase of RA, reliable biomarkers are needed to identify patients who are on the trajectory to develop RA.

Once a panel of biomarkers has been found and carefully validated, popula- tion screening for evidence-based, effective treatment is a con- ceivable approach in the efforts to prevent RA.

In this context, recently, the combination of ACPAs, RF and anticarbamylated peptide (CarP) autoantibodies 5 has been shown to provide a very high odds ratio for RA.

6 Unfortunately, RF and anti-CarP autoantibody data were not available for the population described by van Zanten et al. 1


As the Lifelines Study is a prospective longitudinal cohort study with a targeted 30-year follow-up, it might be possible to gain follow-up information on this study population.

It will be interesting to see which partici- pants will eventually develop RA.

In addition, it would be valu- able to test the stored serum samples for as many biomarkers as possible in order to identify patients who develop RA in a certain period of time.

Lastly, combining biomarker data with clinical parameters might result in sufficient power to precisely predict the development of RA.

7 Several studies have now repeatedly shown that ACPAs and other biomarkers (eg, autoantibodies, inflammatory proteins, cytokines and microRNA) can antedate the development of RA by many years.

7 8 Although these data are intriguing, it would be more valuable to have biomarkers that are able to provide insights into the evolution of RA within the next 6-12 months, corresponding to the so-called window of opportunity.

9 10 When it comes to population screening, health economics becomes an important factor in the equation.

Since the global healthcare expenditures are constantly increasing and reaching non-sustainable thresholds in many jurisdictions, health eco- nomic studies of direct and indirect costs will be needed to dem- onstrate that investments in screening for pre-RA and early interventions or therapies are not outweighed by poor clinical outcomes.

Based on remarkable healthcare expenditures on the management of RA, there is a significant opportunity to achieve meaningful savings.

2 11 12


The design of the different RA prevention trials is rather diverse in terms of the treatment used and the inclusion criteria for the individuals at risk to be treated.

2 Therapeutic agents range from hydroxychloroquine to biologics (eg, tumour necrosis factor- alpha inhibitors).

Different treatments surely require also differ- ent timings in the preclinical phase of the disease.

Lastly, infor- mation as to who will develop RA in the distant future might be more useful in case preventive drugs (such as vaccines) become available.


Anticitrullinated protein/peptide antibodies (ACPAs), in addition to rheumatoid factor (RF), represent a serological hallmark in the diagnosis of rheumatoid arthritis (RA).

In this context and with interest I read the recent article by van Zanten et al [1] summarising the results from the Dutch ‘Lifelines Study’, a large population-based study of 40,132 individuals.

In this setting and using an adjusted cut-off for the ACPA assay, the prevalence of ACPAs was 1.0% and associated older age, female gender, smoking, joint complaints, RA and first-degree relatives with rheumatism.


The early identification of patients in the preclinical phase of RA is of high importance as it became evident during the last decade that early intervention can prevent joint damage in patients with RA.

Consequently, several ongoing studies are focused on the prevention of RA based on the treatment of individuals at high risk to develop RA [2].

All these prevention trials leverage the concept of the ‘window of opportunity’ to prevent or delay the clinical ravages and attending healthcare expenditures associated with RA.

Although a recent trial was unable to prevent the onset of RA (PRAIRI, unpublished data), the study clearly demonstrated that the treatment in the preclinical phase of RA postponed the development of the disease.

Most studies aim to treat patients as early as possible, an approach which seems intuitive.

However, it is unclear if treatment too early in the clinical course also leads to failure of response.

Most conventional approaches to the prevention of RA are based on the concept of restoring the lost tolerance of the immune system.


PRECLINICAL RA
The risk for the development of RA depends on many factors, which can be divided into two main categories: the modifiable (eg, smoking, behaviour) and the constant risk factors (eg, genetics) [3][4].

The preclinical phase of RA may be initiated by modification of the risk profile (eg, smoking) and is characterised by break of tolerance as part of the autoimmune processes, leading to increasing joint inflammation and eventually tissue damage and significant morbidity.

The development of RA has primarily been linked to factors that affect the gastrointestinal, the respiratory and reticuloendothelial systems [4].

An additional challenge to identify the right time for treatment is the patient-specific rate of the evolution from preclinical to clinical RA.

In some patients, the preclinical phase can take several years, whereas in other patients, this conversion happens in a shorter period of time.

Factors contributing, to defining or accelerating this transition are not fully understood.


BIOMARKERS
Based on all the findings about the possible treatment in the preclinical phase of RA, reliable biomarkers are needed to identify patients who are on the trajectory to develop RA.

Once a panel of biomarkers has been found and carefully validated, population screening for evidence-based, effective treatment is a conceivable approach in the efforts to prevent RA.

In this context, recently, the combination of ACPAs, RF and anticarbamylated peptide (CarP) autoantibodies [5] has been shown to provide a very high odds ratio for RA [6].

Unfortunately, RF and anti-CarP autoantibody data were not available for the population described by van Zanten et al [1].

As the Lifelines Study is a prospective longitudinal cohort study with a targeted 30-year follow-up, it might be possible to gain follow-up information on this study population.

It will be interesting to see which participants will eventually develop RA.

In addition, it would be valuable to test the stored serum samples for as many biomarkers as possible in order to identify patients who develop RA in a certain period of time.

Lastly, combining biomarker data with clinical parameters might result in sufficient power to precisely predict the development of RA [7].


Several studies have now repeatedly shown that ACPAs and other biomarkers (eg, autoantibodies, inflammatory proteins, cytokines and microRNA) can antedate the development of RA by many years [7][8].

Although these data are intriguing, it would be more valuable to have biomarkers that are able to provide insights into the evolution of RA within the next 6–12 months, corresponding to the so-called window of opportunity [9][10]. When it comes to population screening, health economics becomes an important factor in the equation.

Since the global healthcare expenditures are constantly increasing and reaching non-sustainable thresholds in many jurisdictions, health economic studies of direct and indirect costs will be needed to demonstrate that investments in screening for pre-RA and early interventions or therapies are not outweighed by poor clinical outcomes.

Based on remarkable healthcare expenditures on the management of RA, there is a significant opportunity to achieve meaningful savings [2][11][12].


CLINICAL PREVENTION TRIALS
The design of the different RA prevention trials is rather diverse in terms of the treatment used and the inclusion criteria for the individuals at risk to be treated [2].

Therapeutic agents range from hydroxychloroquine to biologics (eg, tumour necrosis factor-alpha inhibitors).

Different treatments surely require also different timings in the preclinical phase of the disease.

Lastly, information as to who will develop RA in the distant future might be more useful in case preventive drugs (such as vaccines) become available.


We would like to thank Michael Mahler for his letter, ‘Population-based screening for anticitrullinated protein antibodies (ACPA): A step in the pathway to the prevention of rheumatoid arthritis?’ In his letter, Dr Mahler makes a plea to analyse and define reliable biomarkers to identify subjects who are on the trajectory to develop rheumatoid arthritis (RA).

We agree with this notion of Dr Mahler as current efforts are increasingly focusing on the possibility to install early preclinical treatment to prevent progression to RA.


For example, several randomised placebo-controlled trials (RCTs) have been initiated recently.

These clinical trials will investigate the therapeutic potential of agents such as rituximab (PRAIRI study: NTR No. 1969), abatacept (APIPPRA study: ISRCTN No. 46017566 and ARIAA study: EudraCT No. 2014-000555-93), hydroxychloroquine (StopRA trial: NCT No. 02603146) and methotrexate (TREAT EARLIER: NTR No. 4853) in individuals at risk of RA.

A study evaluating the immunomodulatory effect of atorvastatin in patients with seropositive arthralgia has also been initiated (STAPRA study: NTR No. 22389).


Our publication contributes to these and other efforts as it describes the presence of a prominent biomarker for RA, ACPAs, in a large population-based study and thereby aids to the development of predictive algorithms identifying individuals at risk for RA development.

As outlined in this publication, we determined the prevalence of ACPA positivity and its association with known RA risk factors in 40,136 participants from the Lifelines cohort [2].

Lifelines is a large ongoing prospective population-based cohort study and biobank to investigate the interaction between environmental and genetic factors in the development of chronic diseases [3].

The standardised protocol includes physical examination, extensive questionnaires, and biobanking of serum, plasma, urine and DNA.


Participants will be followed up according to a fixed protocol for at least 30 years [4].

Within the existing infrastructure, additional collection of data and additional studies in already biobanked and future samples can be performed.

For example, the composition of the microbiome is currently being analysed in over 900 subjects participating in the Lifelines deep cohort [5], eventually facilitating the contribution of the microbiome composition to the risk to develop RA.

Likewise, genetics such as whole genome sequencing including human leukocyte antigen (sub-) typing can be performed, thereby allowing the analyses of the contribution of the microbiome in the susceptible genetic background to the development of autoimmunity [6].

Other described predictive markers like the acute-phase reactants erythrocyte sedimentation rate and C reactive protein and additional markers can be incorporated in the development of prediction models.


Indeed, recent insight into the specificity of the combined presence of ACPA, rheumatoid factor and anticarbamylated protein antibodies provides interesting possibilities in further narrowing down persons at risk [7][8].

Overall, Lifelines will give us the unique opportunity to gain follow-up information on our study population.

With funding, it will be possible to measure additional biomarkers as indicated above.

Therefore, this cohort is well suited for subsequent analyses on other biomarkers and risk factors, as also advocated by Dr Mahler.


Our future goal is to build a prediction model that can distinguish participants at high risk from those at low risk to develop RA.

Such a model is relevant because both animal data and clinical data suggest that prevention of RA may be possible and early treatment aiming at drug-free remission is possible [9][10].

The performance of RCTs on preventive treatment strategies in individuals at risk for RA is challenging.

The development of reliable assays and predictive algorithms to define a population with high enough at risk is crucial to allow the design of effective preventive therapy aiming to restore immune homeostasis with a great specificity, low toxicity and long-term effectiveness.


Underreporting of violent events has been defined as failure of victimized employees to report these events to their employers, the police, or other officials (Findorff, McGovern, Wall, & Gerberich, 2005).

Underreporting hinders violence prevention efforts in two ways.

First, underreporting results in an underestimation of the true extent of the problem, thus indicating less of a need for prevention of possible negative effects than may actually be warranted (Bensley et al., 1997).

Second, without knowledge of the full spectrum of violent events to which workers are exposed, prevention efforts can only be designed to affect limited aspects of the problem (Arnetz, 1998; Arnetz, Aranyos, Ager, & Upfal, 2011a).

In health care, various reasons for underreporting WPV have included lack of injury or time lost, time-consuming incident reporting procedures (Arnetz, 1998; Gates, 2004; Lanza & Campbell, 1991), lack of supervisory or coworker support, fear of reprisal or blame (Gates, 2004; Sato, Wakabayashi, Kiyoshi-Teo, & Fukahori, 2013), belief that reporting will not lead to any positive changes (Gates, 2004; Kvas & Seljak 2014), and the common perception among health care workers that violence is simply "part of the job" (Gates, 2004; Lanza & Campbell, 1991; Lanza, Schmidt, McMillan, Demaio, & Forester, 2011).

Varying definitions of violence among employees and within organizations (Arnetz, 1998; Sato et al., 2013) can also affect reporting behavior.


Physical assaults by patients, relatively common in emergency (Gacki-Smith et al., 2009; Gates, Ross, & McQueen, 2006; Taylor & Rew, 2011), psychiatric (Lanza et al., 2011), and geriatric (Leonard, Tinetti, Allore, & Drickamer, 2006; Zeller et al., 2009) departments, may not be reported if staff perceive that the aggressive behavior was unintentional, that is, related to the patient's illness (Hesketh et al., 2003; Lanza & Campbell, 1991; Sato et al., 2013).

Health care workers may also be reluctant to report non-physical violence from patients or co-workers because the behavior incurs no injury and may be organizationally sensitive, especially when it concerns the misuse of power by the perpetrator, such as when an employee is bullied by a supervisor (Ferns & Meerabeau, 2009; Findorff et al., 2005).


The magnitude of WPV underreporting among health care workers is difficult to quantify.

Questionnaires have been used to measure reporting behavior among workers (Ferns & Meerabeau, 2009; Findorff et al., 2005; Sato et al., 2013).

These studies have found a high prevalence of underreporting of both physical and non-physical violence, with all results based solely on employee self-report.

Earlier research compared different forms of incident reporting (Arnetz, 1998; Bensley et al., 1997; Grenade & Macdonald, 1995) and also found high rates of underreporting.

In a 12-month period, employees at 47 health care workplaces recorded 684 violent events on incident reporting forms developed for the research study; based on self-report, only 147 incidents (21%) were also filed as official work injury reports (Arnetz, 1998).


Bensley and colleagues (1997) compared assault injury rates among staff in a psychiatric hospital using compensation claims, hospital incident reports, and questionnaire data.

Rates differed widely: 13.8, 35, and 415 injuries per 100 employees per year were reported based on compensation claims, incident reports, and self-report, respectively.

That study used the hospital ward as the unit of analysis because questionnaires were anonymous and it was not possible to link individual responses to individual workers' compensation or work injury reports.

Grenade and Macdonald (1995) found student nurses' underreporting of physical assault using both documented incidents and a self-report questionnaire.

However, that study only compared results via the two methods without linking the two data sets.


To the researchers' knowledge, no study to date has linked individual questionnaire responses about WPV exposure and reporting behavior with actual incident documentation.

Using individual workers as the unit of analysis could quantify the magnitude of violence underreporting and identify specific worker characteristics associated with underreporting.


The current study compared individual questionnaire responses with actual documentation of WPV events in a cohort of hospital employees.

The aim of the study was to increase understanding of underreporting by investigating differences between self-report and actual documentation practices, and explore characteristics and reporting patterns of health care workers who underreport.

Based on previous research (Arnetz, 1998; Bensley et al., 1997; Grenade & Macdonald, 1995), it was hypothesized that reports of violence via questionnaire would exceed the number of actual documented incidents.

Furthermore, it was expected that reporting would be highest among hospital employees who were injured as a result of a WPV incident (Findorff et al., 2005; Sato et al., 2013) and among those working on psychiatric and emergency units (Arnetz, Hamblin, Ager, Aranyos, Upfal, et al., 2014).


The study was conducted in an American hospital system comprised of seven hospitals and approximately 15,000 employees.

The hospital system maintains a centralized electronic database of employee-reported occupational accidents and incidents, including needlestick injuries, slips, trips and falls, and violent events.

Incident reports are documented by employees via any hospital system computer.

The current study used a subset of the database that included only WPV data.

Hospital employees are encouraged to report all types of WPV, both physical and non-physical, including incidents perpetrated by patients or visitors, Workplace Health Saf.

Author manuscript; available in PMC 2016 August 31. known as Type II violence, and those perpetrated by other employees, Type III violence (Injury Prevention Research Center [IPRC], 2001).

The violence database was linked to the human resource database that provides information on employee age, gender, job category, date of hire, employment status, and paid productive hours (PPH).

This linkage enabled the calculation of standardized rates of violence per 100 full-time equivalents (FTEs) per year, thus providing the hospital system with comparison rates of violence occurrence across hospitals, work units, and over time.


This population-based surveillance and reporting system and WPV database have been described previously (Arnetz et al., 2011a; Arnetz, Aranyos, Ager, & Upfal, 2011b).

Hospital system policy required employees to document all WPV incidents, both with and without resulting injury, via the electronic reporting system or to a supervisor (Arnetz, Hamblin, Ager, Aranyos, Essenmacher, et al., 2014).

The current study was limited to employees on 42 hospital units (N = 2,010) across the hospital system.

Based on analysis of rates of WPV from a 30-month period (January 2010-June 2012), these units were identified as being at increased risk for violence (Arnetz, Hamblin, Ager, Aranyos, Upfal, et al., 2014).


The questionnaire developed for the study measured employees' experience with violence and aggression at work during the past year.

Socio-demographic/background items included age, gender, place (hospital) of employment, job category, supervisor status (yes/no), length of employment in the health care field, and length of employment within the hospital system.

Violence was defined as acts or threats of physical or verbal aggression.

Employees were asked whether they had been a target of violence or aggression at work during the past year.

Response alternatives were "No, never," "Yes, once or twice," and "Yes, several times" (Arnetz & Arnetz, 2001).

Violence-related injury: Employees were also asked whether they had sustained any physical injury as a result of a violent incident (No, none; Yes, mild injury; Yes, serious injury) and whether they had lost time from work as a result of a violent event (No, Yes).

Four items concerned reporting of violent incidents and asked whether employees were familiar with the centralized system for reporting incidents of WPV (No, Yes); whether they had reported a violent incident via the system during the past year (No, never; Yes, once or twice; Yes, several times); if employees had not reported a WPV incident in the electronic system, they were asked to provide a reason for not doing so; and whether they had reported WPV another way: to a supervisor, via the Compliance Hotline, a toll-free number that an employee may call to report any type of work-related issue anonymously, or by some other means.


Underreporting was defined as the percentage of employees who self-reported experiencing a WPV event but did not report any events into the electronic system.

Current hospital system policy mandates employees report any known incidents of violence through the electronic system or to a supervisor.

Supervisors must record all reported incidents through the electronic system within 24 hours from the end of the shift.

Thus, incidents reported by employees to their supervisors are theoretically entered into the system, either by the employee or the supervisor.


In the spring of 2013, employees assigned to all 42 hospital units (N = 2,010) were asked by the researchers to participate in a questionnaire survey regarding their exposure to WPV and knowledge of the WPV reporting system.

Questionnaires were mailed home to employees along with a postage-paid return envelope and a cover letter.

The letter described the purpose of the study and informed employees that participation was voluntary and questionnaire responses were anonymous.

Employees gave their consent to participate in the study by completing and returning the questionnaire.

Each employee responding to the questionnaire received a US$10 gift card by mail.

The cover letter explained that each questionnaire was coded with an identification number that enabled the research team to identify respondents from a master list.

Once the questionnaires had been returned and the gift cards had been mailed out, the list linking identification numbers with respondent names and addresses was destroyed.


Approximately 2 weeks after the first mailing, reminders and questionnaires were re-sent to non-respondents.

Approval for this study was granted by the Institutional Review Board at the university and the Research Review Council of the hospital system.


Using the pseudo identification numbers on the questionnaire survey, individual questionnaire responses were compared with actual events entered into the electronic reporting system in the previous 12 months.

Questionnaire data were linked to the database by a hospital system data analyst; the research team only had access to de-identified data from the hospital system database.


Chi-square analysis was used to compare questionnaire respondents with non-respondents in regard to gender, age group, employment status (full-time, part-time, and contingent), job category, job tenure, and documentation of WPV incidents via the electronic system.

Underreporters, that is, employees who reported WPV through this study but did not document any incidents in the electronic system, were compared with reporters, that is, employees who both self-reported an incident through this study and documented incidents in the system, using chi-square statistics.

In a final step, forward stepwise logistic regression was used to determine factors associated with reporting.

The dependent variable, reporting (yes/no), was calculated as having reported exposure to WPV on the questionnaire and also reported an incident in the electronic system.

An additional logistic regression was conducted to assess factors associated with reporting a violent event to a supervisor.

Independent variables in both regressions included employee age, gender, type of work unit, job tenure, employment status, violence-related injury, and lost work time due to a violent event.


A total of 446 employees responded to the questionnaire (response rate 22%).

More than 80% of respondents (n = 364) were female and 35% (n = 157) were 50 years of age or older.

The majority of respondents (n = 269, 60%) were nursing staff, part-time employees (n = 194, 44%) and worked in either acute care nursing units (n = 157, 35%) or emergency departments (n = 120, 27%).

Respondents differed significantly from non-respondents with Workplace Health Saf.

Author manuscript; available in PMC 2016 August 31.

regard to age (60.1% respondents vs. 49.1% non-respondents ≥ 40 years, p < .001), job tenure (17.5% respondents vs. 11.6% non-respondents worked ≥ 20 years in the hospital system, p < .01), and job category (5.2% patient care associates among respondents vs. 12.6% among non-respondents, p < .001).

Respondents did not differ significantly from non- respondents with regard to documentation of a WPV incident in the electronic system in the past year (8.3% respondents vs. 6.5% non-respondents, p = .19).


Figure 1 presents a flowchart summarizing survey responses and hospital system documentation of violent workplace incidents.

Of the 446 employees who responded to the questionnaire, 275 (62%) reported experiencing at least one WPV event in the previous year; of these, 243 did not document an incident in the database, representing an overall rate of underreporting of 88%.

Surprisingly, 63 of the 275 who self-reported violent events (23%) also reported having documented at least one incident via the electronic reporting system, but only 12 (4%) actually did so.

Among the remaining 212 employees who stated in the questionnaire that they did not document a violent incident electronically, 20 (9%) actually did so.

Thus, in reality, only 32 of the 275 employees (12%) who self-reported a violent event had formally documented the incident via the electronic reporting system.


Table 1 summarizes and compares characteristics of employees who documented violent events in the electronic reporting system ("reporters," n = 32) with employees who did not ("underreporters," n = 243).

No significant differences were found between the two groups with regard to gender, age, or length of employment.

Reporters included a significantly greater proportion of full-time employees than underreporters, and no contingent workers were found among reporters (p < .05).

Fifty percent of reporters (n = 16) had been injured as a result of a WPV incident, compared with 11.5% (n = 28) among underreporters (p < .001); 25% of reporters (n = 8) had lost time from work due to a WPV incident compared with 4.5% (n = 11) among underreporters (p < .001).


Of the 275 questionnaire respondents who said they had experienced a WPV incident, 45% (n = 125) reported the violence using an alternative method (Table 2).

Of the 32 who did report via the electronic system, 14 (43.8%) also reported in some other way.

Among the 243 underreporters, who did not report an incident via the electronic system, 111 (45.7%) did report using an alternative method.

Reporting to a supervisor was the most common alternative method; slightly less than 80% of both reporters and underreporters reported these incidents to their supervisors.


Table 3 summarizes the reasons for not reporting a WPV incident as described by the 212 questionnaire respondents who stated they had experienced a violent event, but had not reported it.

As indicated in Figure 1, 20 of the 212 individuals actually had documented an incident in the electronic system; thus, Table 3 compares results by reporters and underreporters.

The most common reasons for not reporting were that the individual had not been a target of/witness to a violent event (29.9%) and did not believe that reporting leads to change (28.4%).

The only significant difference between reporters and underreporters Workplace Health Saf.

Author manuscript; available in PMC 2016 August 31. concerned being unsure of how to report; surprisingly, this was more common among reporters (20%) than underreporters (5.4%) although only 14 respondents in total had selected this alternative.

Overall, 43 respondents had noted "other" reasons for not reporting including fear of retaliation (n = 13); the incident was reported by someone else (n = 6); the incident was patient-related (psychological or cognitive impairment) and was included in either patient notes or discussed among the health care team (n = 6).


Results of the logistic regression that examined factors associated with reporting incidents of violence in the electronic system are summarized in Table 4.

Employees had a higher likelihood of reporting via the electronic system if they incurred a physical injury as the result of the violent incident (OR = 6.22) or if an incident resulted in time away from work (OR = 3.56).

No other demographic or work-related factors were significant.


Factors associated with reporting WPV to a supervisor are summarized in Table 5.

Having worked less than 5 years (OR = 0.42) and working as security staff (OR = 0.22) were both significantly associated with lower likelihood of reporting WPV to a supervisor.


The aims of this study were (a) to compare the self-report of WPV via the questionnaire with actual documentation of violent incidents in a cohort of hospital employees, and (b) to examine the individual and job characteristics and reporting patterns of "reporters" versus "underreporters." As hypothesized, a greater proportion of questionnaire respondents (62%) self-reported an incident of WPV in the previous 12 months, compared with 12% who actually documented the incident via the electronic reporting system.

This finding is consistent with previous research (Ferns & Meerabeau, 2009; Findorff et al., 2005; Sato et al., 2013) based solely on self-report.


Findorff and colleagues (2005) conducted a study in a single health care organization and found that less than 60% reported physical violence and less than 50% reported non-physical violence to their employers; most reports were oral and not otherwise documented.

Ferns and Meerabeau (2009) reported that 45% of nursing students experienced verbal abuse during their clinical training and the majority (63%) stated they had reported the incident.

However, only four incidents were documented in writing.

Sato and colleagues (2013) found that more than 30% of nurses reported experiencing patient aggressive behavior in the previous month, but 70% did not report the incident.


The second hypothesis was that reporting would be highest among hospital employees who were injured, and among those working on psychiatric or emergency units; this was partially supported.

A significantly greater proportion of reporters had been injured or lost time from work as a result of a violent event, compared with underreporters; these two factors were also significantly associated with a higher likelihood of reporting through the electronic system.

This finding may in part be explained by hospital system policy, which states that employees who have been injured on the job and seek care at Occupational Health Services must document the incident in the electronic system.

These findings support previous research that found a higher likelihood of reporting when the symptoms or impact of the violence were more severe (Findorff et al., 2005; Sato et al., 2013).

Both studies were based Workplace Health Saf.

Author manuscript; available in PMC 2016 August 31.

solely on self-report.


Results of the current study did not support the hypothesis that psychiatric and emergency department employees are more likely to report violence compared with employees on other units.

Comparisons by type of unit (Table 1) revealed that a larger proportion of psychiatric employees were reporters (18.8%) than underreporters (6.2%), but emergency department employees had a larger proportion of underreporters (30.9%) than reporters (28.1%).

However, the researchers did not find a higher likelihood of reporting through the electronic system in any one type of work unit.


Only 32 of the 275 employees (12%) who self-reported violence in the workplace had formally documented the incident via the electronic reporting system.

This finding suggests an overall underreporting rate of 88%.

An earlier study of 47 health care workplaces based entirely on worker self-report found a similar underreporting rate of 79% (Arnetz, 1998).


However, a closer look at the questionnaire data in the current study revealed that more than 45% of the 275 who self-reported violence in the past year (n = 125) used other methods to report the incident.

Those individuals reported the incident verbally to their supervisors (79.2%), via the Compliance Hotline (15.2%), or other means (18.4%).

Although a small number (n = 14, 11.2%) of personnel had also reported through the electronic system, the majority (n = 111) had not.


The only factors significantly associated with informal incident reporting to a supervisor were short job tenure (< 5 years) and working as security staff; both were associated with lower likelihood of reporting.

Sato and colleagues (2013) found that nurses with less work experience were less likely to report aggressive behavior.


Combining all of the alternative methods with electronic documentation, the rate of underreporting dropped to 48%; 143 reporters (32 electronic reporters and 111reporters by other means) leaves 132 of 275 exposed who did not report.

Although this is an improvement over 88%, almost half of the incidents were not reported.


This finding has implications for this and similar health care organizations.

The hospital system in this study has worked for the past decade to establish a centralized electronic system for reporting WPV (Arnetz et al., 2011a, 2011b), and human resource policy mandates reporting of known incidents of violence.

Although employees who verbally report incidents to their supervisors may be fulfilling their responsibility to report, these informal reports may not always be available to upper management for policy decisions.


This is the first study to examine underreporting of WPV among health care workers by linking and comparing individual questionnaire responses (self-report) regarding WPV experiences with actual incident documentation.

However, a number of limitations should be considered.


First, the study was conducted in a single hospital system in one geographic area of the United States, and results may not be generalized to hospitals in other areas.


Second, the response rate on the questionnaire was low (22%).

However, the researchers compared characteristics of respondents with those of non-respondents and found few significant differences.

Moreover, all further analyses were limited to only the cohort of respondents who self-reported WPV (n = 275).


Third, the questionnaire items related to experience of violence were retrospective (past year), and recall bias may have affected the results.

This bias was especially evident in that 63 of the 275 who self-reported violent events (23%) also self-reported having documented at least one incident via the electronic reporting system, when, in fact, only 12 (4%) actually had done so.

It may be that several of these individuals had indeed reported incidents in the electronic system earlier, but not in the past year.

Conversely, of the 212 employees who self-reported that they did not record a violent incident electronically, 20 (9%) actually had.


Finally, the main aim of this study was to better understand the magnitude of underreporting of WPV generally.

Analyses did not examine the type of violence experienced or reported.

As suggested in earlier research (Findorff et al., 2005), it is likely that underreporting of non-physical violence is greater than that of physical violence.


The lack of agreement between employees' survey responses and actual report practices may be due to lack of injury, recall bias, or a lack of motivation to use the central electronic reporting system to report violent events.

Underreporting is a hindrance to determining the actual extent of WPV toward health care workers.

Understanding the magnitude of underreporting and the characteristics of health care workers who are more likely to underreport may provide hospitals with a more accurate estimate of WPV and determine where to focus education, training, and prevention efforts.


The recent study by Arnetz et al. [ 1] concluded that hospital employees underreported incidents of workplace violence mainly because non-victims (witnesses) tended to report incidents far less often than victims.

However, this conclusion is not well supported by the data in their study because of unclear definitions of violence, insufficient separation of the participants into the two groups of victims of violence and witnesses of violence, and plausible alternative explanations for the findings, as explained below.


Of the 446 hospital workers surveyed, a flowchart in Figure 1 in the above study indicates that 62% of them (n = 275) answered “Yes, I was a target of violence within the last year.” Of these 275 respondents, the same flowchart shows that 212 of them answered that “I did not report the violence” in the survey.

However, when asked about reasons for not reporting incidents of violence (Table 3), 30% of the 212 respondents answered that they were neither a target nor witnesses to violence.

The contradicting answers indicate a low reliability for the participants’ determination of whether they were a target of workplace violence.

This problem can be addressed by giving the participants of the survey an option to indicate the nature of the violence experienced and their role (e.g., target, witness).


The low reliability regarding workplace violence may have been due to an unclear definition of violence.

Although the flowchart in Figure 1 documents whether a participant had been a target of violence, the authors state in the “Methods” section that the question was whether the participant had been a target of either violence or aggression with no definition provided for aggression.

The two forms of this question would be expected to produce different responses.

Furthermore, the “Results” section reported that the question was whether the participant had experienced a violent incident, presumably including the witnessing of an incident without being a target.

The confusing definitions of violence could explain the low reliability of the responses, but this is not the only possible explanation for the low reliability of the responses to the same question at two different places in the survey.

Another possible explanation is faulty recall due to the lengthy recall period of 1 year.

Recall periods for injuries or similar events, such as hospital stays, have been shown to be unreliable if more than 3 months prior [2][3].


The study reported that 88% of hospital workers who experienced workplace violence failed to report the incident using the official electronic system, and thus concluded that this finding indicated a need to take action.

The estimate of 88% is likely exaggerated for the following reasons.


First, one incident could have been witnessed by multiple employees but reported only once by one person in the electronic reporting system.

If an employee knows that someone else reported it, they may decide that this is sufficient.

This explanation is supported by the study conclusion that employees tended not to report incidents if they were not the victims of violence themselves.

By the methods used in the study, this explanation would inflate the numbers of employees who failed to report an incident.


Second, the study hospital system only encouraged workers to report via the electronic system, but does not require it. If it is only encouraged, workers cannot be expected to report anything or everything.

It is probably unreasonable to conclude that a failure-to-report problem exists when reporting is not mandated.

This explanation also would be expected to inflate the failure-to-report percentage.


The study by Arnetz et al. has many strengths including an adequate sample size, use of multiple sites within a hospital system, appropriate statistical analysis, clear discussion of workplace violence literature, and a focus on a critical occupational health and safety problem.

Despite these strengths, due to the low response reliability of the key questions and evidence of alternative explanations for the findings, the study conclusion, that hospital employees drastically underreport workplace violence, is not well supported.

Instead, an alternative conclusion, that workplace violence reporting may be acceptable, but the value of the electronic reporting system is low among hospital employees, particularly nurses, is proposed.


We are grateful for the thoughtful comments on our recent article, “Underreporting of Workplace Violence: Comparison of Self-Report and Actual Documentation of Hospital Incidents” [2].

The writer raises issues that are well worthy of discussion.

However, on a number of points, there has been some misunderstanding that we hope this response will clarify.


Conclusion.

The writer states that our article concluded “ . . .

that hospital employees under-reported incidents of workplace violence mainly because non-victims (witnesses) tended to report incidents far less often than victims” [4].

This was not the main conclusion of our article and is factually incorrect.

A closer look at Table 3 (p. 206) reveals no statistically significant difference between reporters and under-reporters with regard to being a target or witness of violence.

Our main conclusions in this article were (a) the majority of hospital workers who experienced violence at work (88%) did not record the incident in the hospital system’s electronic database, (b) nearly half of hospital workers who experienced violence (45%) did report the incident to their supervisors, (c) hospital workers who were injured in a violent incident and/or lost time from work due to a violent incident were significantly more likely to report using the electronic system (Table 4), and (d) workers with less than 5 years job tenure and security staff were significantly less likely to report a violent incident to supervisors (Table 5).


Unclear definitions of violence.

Our survey did, in fact, provide the following general definition: “In this survey, ‘violence’ includes acts or threats of physical or verbal aggression.” We then asked, “Have you been a target of violence or aggression at work during the past year?” Thus, we clearly delineated between physical violence and verbal aggression.


We believed that the juxtaposition of “physical” and “verbal” would suffice as indication to the questionnaire respondents that we were using a broad definition of “violence.” Moreover, in the item asking “What type(s) of violence/aggression did you experience?” the first response alternative was “Verbal aggression (shouting, swearing).

” The writer posits that “the two forms of this question would be expected to produce different responses,” but in truth, we could not be sure that responses would differ.


Workplace violence is subjective; what may be perceived by one worker as “violence” may be perceived by another as lower-level aggression [1].

We recognize that perception may influence reporting, which is why we specifically combined physical violence and aggression in a single question.


Low reliability for the participants’ determination of whether they were a target of workplace violence.

The question on whether the individual had been a target of violence or aggression during the past year was followed by two additional questions: “Who was violent or aggressive towards you?” and “What type(s) of violence/aggression did you experience?” Both items had “I wasn’t a target of violence” as a possible response alternative.

However, questions regarding why employees did not report a violent incident used the response alternative, “I wasn’t a target or witness of violence.

” We believe that this may be the source of the writer’s confusion.


In other words, when asking about experience of violence at work, we asked only whether employees had themselves been the target of violence.

When asking why they had not reported an incident, we included the option that they had not even witnessed such an incident.

The reason the questions were structured this way has to do with hospital system policy, as indicated on page 202 of our article.

Hospital system policy actually mandates that employees report any known incidents of violence, either through the electronic system or to a supervisor.

Policy does not specify that an employee must be a target of violence to report the violent episode; rather, any “known incident of violence” should be reported.


In previous research on this hospital system, we found that incident reporting by third parties not directly involved in the violent incident does occur [3].


Another possible explanation is faulty recall due to the lengthy recall period of 1 year.

We agree with the writer that recall bias is a possible confounder of this study, a point we raised ourselves in the “Strengths and Limitations” section, page 208.

As justification, the survey, part of a large, randomized controlled intervention study, was administered pre-intervention and 1-year post-intervention.

Thus, asking about experience with workplace violence over the course of the previous 12-month period was a deliberate effort on our part to compare self-report with documentation of incidents over the same period of time.


The estimate of 88% is likely exaggerated.

The writer contends that “an incident could have been witnessed by multiple employees but reported only once by one person in the electronic reporting system.

If an employee knows that someone else reported it, they may decide that this is sufficient” [4].

There is nothing in our data to support this statement.

The writer further posits that the statement is supported “by the study conclusion that employees tended not to report incidents if they were not the victims of violence themselves.” As indicated in the point above regarding low reliability, this is a misunderstanding that we hope we have now clarified.


It is probably unreasonable to conclude a failure-to-report problem exists when reporting is not mandated.

This explanation would also be expected to inflate the failure-to-report percentage.

As clarified in the low-reliability point above, reporting acts of known violence is, in fact, mandated by the hospital system.

The writer is correct in that reporting via the electronic system is encouraged but not required.

However, the mandate states that an employee should report either via the system or directly to one’s supervisor.

As we point out on page 202, unit supervisors also have a mandate to report all violent events of which they are notified into the electronic system within 24 hours from the end of the shift during which they received the notification.

Thus, theoretically, incidents should ultimately be entered into the electronic database.

Our study indicates that electronic database reporting occurs in only 12% of cases.


. . . the value of the electronic reporting system is low among hospital employees, particularly nurses.

We can agree that the electronic reporting system is underused for reporting incidents of workplace violence, and among nurses, there were more under-reporters (62.6%) than reporters (40.6%, Table 1).

However, other, more far-reaching implications of this study’s findings exist.

As we stated on page 208, although employees who report verbally to their supervisors may be fulfilling their responsibility for reporting, the informal reports may not be reaching upper management for policy decisions.

A hospital system or other health care organization can only develop prevention strategies based on available data.

By implementing the electronic system, this particular hospital system has made a concerted effort to facilitate the employee reporting process.

Our data indicate that the system is underused.


The purpose of our article was to increase understanding of underreporting by investigating differences between self-report and actual documentation practices, and exploring the characteristics and reporting patterns of health care workers who underreport.

To the best of our knowledge, our study is the first to accomplish these aims by linking documentation behavior and self-report to individuals via de-identified ID numbers.

Hospital employees in this study did drastically underreport workplace violence, both electronically and by other, less formal means.

Our article identified certain worker characteristics associated with both electronic and informal reporting of violent incidents.

It is our hope that knowledge of this phenomenon will help occupational health nurses and health care organizations improve incident reporting, as such data are the first step toward preventing workplace violence.


With the recent detection of gravitational waves originated by two ∼ 30M black holes [1] the idea that Primordial Black Holes (PBHs) might form a considerable fraction of the dark matter [2-4] has regained momentum [5-7] (see Ref.

[ 8] for a recent review).

While it is still unclear if PBHs can contribute to the totality of the dark matter [9-11], there are various hints indicating that their abundance might be comparable to that of dark matter [12] and that they could provide the seeds for the cosmic structures [13].


While PBHs can be generated at the QCD epoch [14] (for a recent analysis see Ref.

[ 15]) or during phase transitions [16], a popular mechanism for the formation of PBHs is within the so-called "spiky" scenario in which PBHs are originated from the enhancement of the curvature power spectrum arXiv:1801.09415v2 [astro-ph.

CO] 12 Mar 2018 below a certain length scale due to spiky features [17-19].

Such an enhancement can be produced either within single-field models of inflation, see for instance Refs.

[ 20-22] for some recent work, or through a spectator field which does not contribute significantly to the total curvature perturbation on large observable scales (which is ultimately responsible for the anisotropies in the cosmic microwave background anisotropies), but whose unique role is precisely to enhance the power spectrum at small scales, see for example Refs. [ 23-26].

This spectator field can be even the Higgs of the Standard Model [27, 28].