The structural plasticity of heparan sulfate NA-domains and hence their role in mediating multivalent interactions is confirmed by high-accuracy 15N-NMR relaxation studies

Considering the biological importance of heparan sulfate (HS) and the significant activity of its highly-sulfated regions (S-domains), the paucity of known functions for the non-sulfated NA-domains is somewhat puzzling. It has been suggested that chain dynamics within the NA-domains are the key to their functional role in HS. In this study, we investigate this hypothesis using state-of-the-art nuclear magnetic resonance (NMR) experiments at multiple frequencies. To resolve the problem of severe overlap in 1H-NMR spectra of repetitive polysaccharides from proteoglycans, we have prepared oligosaccharides with the chemical structure of HS NA-domains containing the 15N nucleus, which has enough chemical shift dispersion to probe the central residues of octasaccharides at atomic resolution using 600 MHz NMR. By performing NMR relaxation experiments at three magnetic-field strengths, high quality data on internal dynamics and rotational diffusion was obtained. Furthermore, translational diffusion could also be measured by NMR using pulse field gradients. These experimental data were used, in concert with molecular dynamics simulations, to provide information on local molecular shape, greatly aiding our relaxation analyses. Our results, which are more accurate than those presented previously, confirm the higher flexibility of the NA-domains as compared with reported data on S-domains. It is proposed that this flexibility has two functional roles. First, it confers a greater area of interaction from the anchoring point on the core protein for the bioactive S-domains. Secondly, it allows multiple interactions along the same HS chain that are dynamically independent of each other.
Introduction
Heparan sulfate (HS), a member of the glycosaminoglycan (GAG) family, is thought to have appeared early in metazoan evolution [1, 2]. It is ubiquitous in vertebrate extracellular matrices as a constituent of proteoglycans and its extensive repertoire of biological roles, via interactions with hundreds of proteins, make it the most intensely studied of the GAGs. In particular, HS has been implicated in several important biological functions that involve binding to anti-thrombin (AT), fibroblast growth factors (FGFs), cytokines and chemokines [3]. Chemically, the HS biopolymer consists of repeating disaccharide units of variably-sulfated uronic acid (d-glucuronic or l-iduronic acid) and glucosamine (N-acetylated or N-sulfated) monosaccharides. It is this variability in chain epimerization and sulfation (see Fig. 1) that appears to be responsible for the specificity of interactions between HS and proteins [4], rather than the basic linkage pattern of the polymer, which is conserved in all forms.
Fig. 1The basic disaccharide repeating unit of heparan sulfate. In the compounds studied here, the number of repeats (n) is 2, 3 and 4, for compounds 1, 2 and 3 respectively. In these compounds, R1 = COCH3 and R2 = R3 = R4 = OH, the uronic acid residue is glucuronic acid and the terminal uronic acid residue is 4–5 unsaturated
In some cases, such as the interaction between anti-thrombin (AT) and a HS pentasaccharide sequence (which includes the rare 3-O-sulfated glucosamine) [5], the local spatial arrangement of sulfo groups is postulated to be the major driver of specificity. Furthermore, the internal and global conformation and dynamics of the chains also play important roles in HS bioactivity [6–8]. These conformational contributions to function are not easy to separate from the others, since the density and arrangement of the opposing sulfo groups may profoundly influence the local dynamics of HS-chains, creating an ambiguity in relating HS structure to function. However, solution-state nuclear magnetic resonance (NMR) studies can be used to resolve this ambiguity by directly measuring molecular conformation and dynamics on the nanosecond time scale with atomic resolution [9, 10].
Numerous NMR studies have been conducted to gain insight into the 3D-structure of HS chains. Much of the early literature was concerned with the conformation and dynamics of the l-iduronate residues [7, 11–13], and not without controversy [14]. From these studies, which used NMR coupling-constants and basic relaxation data, a picture emerged of iduronic acid residues that have high internal flexibility, but their dynamic segmental motions are decoupled from neighboring residues [6]. More recent studies of HS chain dynamics using model-free analysis [15] of NMR relaxation data have attempted to describe the internal and overall motion of the various residues of HS [16–18]. Interpretation of these data has not been without controversy either. Hricovini et al. [18] compared the dynamics of the non-sulfated region of HS (NA-domain) with those from a highly sulfated HS fragment (S-domain) using the model-free analysis and found that the NA-domain was more rigid. This was ascribed to the lack of flexible iduronic acid residues in the NA-domains. However, a more recent publication by Angulo et al. [16] (also using the model-free approach) showed very restricted motion of the internal iduronic acid residues, in contrast to earlier investigations. At the heart of these conflicting results lies: (a) the difficulty in separation of internal motion due to inter-converting ring conformations of the flexible iduronic acid residue and motion due to flexibility of the glycosidic linkages, (b) problems associated with gathering of high quality data for these nearly linear biopolymers, and (c) interpretation of NMR data from polydisperse samples [11]. These difficulties can be overcome by: (a) restricting the analysis to rigid glucosamine rings (which are not epimerized), (b) increasing the NMR chemical shift dispersion using isotopic enrichment, and (c) improved purification protocols.
In protein NMR, isotopic labeling in conjunction with modern heteronuclear filtering experiments (which remove all proton resonances not correlated to a labeled nucleus) are used to overcome issues of signal overlap. In particular, 15N-enrichment is frequently used because the nitrogen atom is highly sensitive to changes in its local environment and consequently has a large range of NMR chemical shifts. The fact that there is often only one nitrogen atom per residue, as opposed to several protons, further adds to spectral simplicity. For dynamic molecular studies, the 15N-nucleus is also desirable since the 1H-15N system within an amide moiety can be considered as an isolated spin-pair for relaxation purposes, simplifying the analysis. Furthermore, 15N has a small negative gyromagnetic ratio, making it more sensitive to dynamics compared to the 13C nucleus (which has a larger and positive gyromagnetic ratio). For example, recent use of 15N-labeling in the GAG hyaluronan has permitted full residue-specific NMR signal assignment and dynamical studies of oligosaccharides of up to eight residues, with unprecedented accuracy [19].
In this study, isotopic 15N-labeling of the glucosamine residues was used to overcome the difficulties associated with previous NMR relaxation analyses of HS dynamics. The polymer used here was the capsular polysaccharide from E. coli K5 bacteria, which is chemically identical to the NA-domains from HS. By growing the bacteria in 15N-enriched media, quantitative isotopic labeling of the polysaccharide can be achieved on the milligram scale, as described previously [20]. The polysaccharide was degraded using K5-lyase enzymatic digestion and separated into pure oligosaccharides using large-scale ion-exchange chromatography to facilitate the study of dynamics at the atomic level. Three 15N-labeled oligosaccharides (see Fig. 1) were purified to homogeneity and used for NMR studies: a tetrasaccharide (1), a hexasaccharide (2) and an octasaccharide (3). 1H-15N NMR relaxation experiments (T1 and heteronuclear nOe experiments) were fitted to both isotropic and anisotropic models of molecular diffusion to assess the validity of assuming isotropic overall motion in these non-sulfated molecules. The libration of the amide group (which contributes to the measured dynamics) was taken into consideration using experimental J-coupling data. Furthermore, diffusion ordered spectroscopy (DOSY) [21, 22] and molecular dynamics (MD) simulations were used to assist in evaluating the models of molecular shape.
The approach presented here can easily be extended to the various modified forms of HS, as these can be produced by chemical and enzymatic modification of the K5 capsular polysaccharide [23]. Our novel findings shed light upon the role of dynamics in HS, and it is intended that these approaches will be adopted more generally, leading to higher quality dynamic data with atomic precision for these most interesting and important biomolecules.
Theory
15N NMR relaxation and rotational diffusion
The dynamic behavior of biomolecules on the sub-nanosecond timescale can be probed by NMR relaxation, by measuring T1- and T2- relaxation rates and the nOe enhancement (η) at multiple magnetic fields [9] and interpreting the results using the Lipari–Szabo model-free approach [15]. For small molecules (as described here) the T1-and T2-relaxation rates are similar for the 1H-15N system, thus in this case only T1 and η are considered [19]. Several (often confusing) treatises of these equations are present in the literature, thus the basic theory is briefly reconsidered here.
Relaxation rates for an isolated N–H spin-pair are related to the spectral density function, J(ω), through Eqs. 1 and 2, where γ is the gyromagnetic ratio, ħ is Planck’s constant over 2π, ω is the relevant nuclear Larmor frequency, ΔσN is the difference between the axial symmetric 15N chemical shift tensor (estimated to be −135 ppm for N-acetylglucosamine see also methods section) and rNH is the N–H inter-nuclear distance (a value of 1.01 Å is used here, see also methods section).
For an isotropic (spherical) model of molecular diffusion the spectral density function is given by Eq. 3, where τ is the average rotational correlation time (radians s−1) and D is the rotational diffusion coefficient. In the case of a symmetric top (cylindrical) model of molecular diffusion the anisotropic spectral density function is given by the Eqs. 4 and 5, where  and  are the correlation time and rotational diffusion coefficient perpendicular to the symmetry axis,  and  are the corresponding variables parallel to the symmetry axis (by this definition ), and α is the angle between the N–H vector and the cylindrical axis of symmetry.
When the internal motion (τe) is much faster than the global rotational correlation time (τM), these two phenomena can be separated using the model free approach of Lipari and Szabo [15]. In their treatment, if it is assumed that molecular tumbling is isotropic, the spectral density function is represented by Eqs. 6 and 7, where S2 is the generalized order parameter. The corresponding anisotropic relationship is shown in Eq. 8, where Janiso(ω) is described by Eq. 4 and the  are given in Eq. 9.
It should be noted that this derivation (resulting in Eq. 8) is consistent with the original proposal of Lipari and Szabo [15] and slightly different to a recent and broadly-used implementation of that theory [24].
Diffusion measurements
The average translational diffusion rate can be measured by pulsed-field-gradient (PFG) NMR [25], often referred to as diffusion ordered spectroscopy (DOSY) [21, 22]. In this experiment, the diffusion coefficients can be estimated by varying the gradient amplitude (g) and fitting the experimental data to the theoretical Stejskal–Tanner Eq. 10 [26].
In Eq. 10, S is the signal amplitude, S0 is the echo amplitude that would have resulted had there been no diffusion, Dt is the translational diffusion coefficient (not to be confused with the rotational diffusion rates described above), δ is the gradient pulse width and Δ′ is the diffusion time corrected for the effects of a finite gradient pulse width.
Often the relative diffusion coefficient is sought, but when the absolute value is of interest, a more correct value of Dt can be obtained by taking the systematic errors (due to non-uniform field gradients) into account by fitting to a different expression, which compensates for this, Eqs. 11 and 12 [27], where the coefficients cn are probe and pulse-sequence dependent.
The hydrodynamic radius of a molecule can be shown to be related to the translational diffusion coefficient, Dt. This radius, and thus Dt for an isolated polymer in solution, scales (via an exponent, v) with molecular mass (M), Eq. 13. The value of v can be shown to be 3/5 and 1/3 for fully solvated and collapsed polymer chains, respectively [28]. A polymer is fully solvated in a solvent where its interactions with the solvent are favored over self-association—the converse is true for a collapsed polymer. In a so-called Flory or θ solvent, the polymer behaves as a random coil, where v = 1/2 [28].
Results
15N-labeled polymer with a chemical structure identical to the non-sulfated domains of heparan sulfate (HS) was prepared from E. coli. K5 capsular polysaccharide and enzymatically digested. The resulting oligomers were purified and three samples were prepared that had 4, 6 and 8 sugar units (see experimental section for details). 1D 1H-NMR spectra and mass spectrometry (not shown) determined that the oligosaccharides were uniformly 15N-labeled and 1H-15N-HSQC spectra (see Fig. 2) confirmed that the samples contained no major contaminants.
Fig. 215N-1H HSQC spectrum of the tetra-, hexa- and octa-saccharides (from top to bottom) of the NA-domain of heparan sulfate at a proton-resonant frequency of 900 MHz. The abscissa shows 1H chemical shift [ppm]. Labels on the spectrum correspond to amide groups, labeled according to their position within the oligosaccharide, using Greek nomenclature. Variability in the line shapes are due to acquisition and processing of the NMR spectra
Relaxation experiments (1H-15N T1 and heteronuclear nOe) were measured for each of the oligosaccharides (1, 2 and 3) at three magnetic field strengths (14.1, 18.1 and 21.1 T). In each case the 1H-15N HSQC-based T1-relaxation experiments were repeated 12 times with a variable relaxation delay and the resultant peak heights were fitted to exponential curves (relaxation curves for the octasaccharide at 14.1 T are shown in Fig. 3). The heteronuclear nOes were calculated from the ratio of peak heights in 1H-15N spectra, recorded with and without 1H-presaturation.
Fig. 3T1-relaxation curves for an octasaccharide unit from the NA-domain of heparan sulfate at the lowest magnetic field strength used in this study (14.1 T). The resonances from the amide groups of the central residues (ψ and γ) are resolved and have slightly different relaxation times. For the specific assignments see Fig. 2
Table 1 shows the calculated exponential decay constants (T1) and the corresponding nOe ratios (η). The errors in the tabulated T1 values were taken to be twice the exponential curve fitting errors, while the errors in the nOe ratios were estimated by calculating the ratio of the sum of the amplitudes (A) of the two HN signals from the anomeric amide group (α/β) and the same signal in the ω-amide group (clearly resolved in all spectra, see Fig. 2), according to Eq. 14.
Table 1Fitted 1H-15N T1-relaxation data and measured nOe (η) ratios for three lengths (4-, 6- and 8-saccharide units denoted 1, 2 and 3 respectively) of the NA-domain from heparan sulfate at three magnetic-field strengths (14.1, 18.1 and 21.1 T) 14.1T18.1T21.1TηT1[ms]ηT1[ms]ηT1[ms]1α−1.351,790−1.231,625−0.421,526β−1.631,614−1.221,524−0.561,445ω−1.891,255−0.951,183−0.391,1162α−0.961,613−0.851,473−0.281,400β−1.121,456−0.791,368−0.341,310γ−1.07995−0.339420.14889ω−1.171,050−0.38978−0.019393α−0.811,437−0.841,362−0.141,342β−0.991,318−0.751,278−0.331,252γ−0.587550.047970.50803aψ−0.77831−0.178240.40803aω−0.92946−0.189100.25908aHigh resolution spectrum not collected—value estimated from a low-resolution spectrum (see experimental section).
The heteronuclear experiments for the octasaccharide (3) were collected using two different sweep-widths. In the first experiment a larger sweep width was used, where all nuclei were present and unfolded, but in which the γ- and ψ-amide groups could not be resolved. These resonances were resolved using a second, narrower spectral width (not shown). The spectral width of the second experiment was chosen so that aliased peaks did not interfere with the resonances of interest.
Conversion of the resultant relaxation measurements into 3D-molecular information was performed via the model-free method for calculating the spectral density function (as needed by relaxation Eqs. 1 and 2). Due to the potential shape anisotropy of the oligosaccharides, two methods were used to estimate the spectral density functions, one that assumed spherical symmetry and one that incorporated cylindrical symmetry, see Eqs. 6 and 8. Fitting the relaxation data to these equations resulted in order parameters, overall and internal tumbling times, and in the case of anisotropy, a set of angles. It was found that the relaxation data was not sensitive to the internal correlation time (τe) and thus it varied significantly when allowed to change during the fitting process. However, this parameter is of secondary interest, and by fixing the internal correlation time at 30 ps (consistent with molecular dynamics simulations), higher quality and more robust fits to the experimental data could be produced, as noted previously [29]. The uncertainty in this variable is reflected in previous literature data for similar compounds, where τe ranges from a few picoseconds to several hundred picoseconds in similar compounds (15–50 ps for a HS pentasaccharide and 110–290 ps for a HS hexasaccharide) and with large errors (±25–85 ps) [16–19, 30]. The results, given in Table 2, therefore assume a constant internal correlation time of 30 ps and clearly show the expected trend in generalized order parameter (S2), where the amide groups at the central residues are more ordered (higher S2 values) than those at the terminal residues. Also, the overall correlation times increase as the oligosaccharides get longer, as expected a priori.
Table 2Calculated model-free parameters for the tetra-, hexa- and octa-saccharides (1, 2 and 3 respectively) of the NA-domain of heparan sulfate, using a constant internal correlation time of 30 ps, fitted to isotropic and anisotropic models of rotational diffusionModels of rotational diffusionIsotropicS2S2calc1τM [ns]0.57α0.320.46τe [ns]0.03β0.350.59ω0.480.622τM [ns]0.71α0.350.49τe [ns]0.03β0.36 γ0.600.70ω0.550.613τM [ns]0.74α0.32 τe [ns]0.03β0.34 γ0.87 Ψ0.82 ω0.67AnisotropicS2α[°]S2calcαcalc[°]1 [ns]0.35α0.3315.40.4664.2 [ns]0.65β0.3640.10.5962.11.88ω0.5658.60.6263.1τe [ns]0.03 2 [ns]0.57α0.313.80.4962.8 [ns]0.79β0.3649.5 1.39γ0.6258.60.7064.4τe [ns]0.03ω0.6184.70.6163.33 [ns]0.39α0.3975.0  [ns]1.00β0.4166.7 2.59γ0.652.2 τe [ns]0.03Ψ0.6623.2 ω0.5431.1 These values were calculated from experimental data collected at three magnetic-field strengths (14.1, 18.1 and 21.1 T). The S2calc and αcalc values were calculated by averaging data extracted from 50 ns molecular dynamics simulations
Molecular shape was probed using the DOSY experiment, applied to the three samples. The diffusion coefficients were estimated from their respective methyl peaks at 2.03 ppm (Table 3) using the Stejskal–Tanner Eq. 10. The resulting diffusion values were used to calculate the scaling-factor exponent (ν) in Eq. 13, which provides information about the overall molecular shape. Figure 4 shows a plot of log(Dt) against log(M), showing the slope of the fitted line (ν) to be 0.48 .
Fig. 4Plot of log(Dt) vs. log(M) for three different lengths of the NA-domain of heparan sulfate, consisting of 4, 6 and 8 sugar units (Dt is the translational diffusion time and M the weight-averaged molecular mass). The dotted line is the best-fit to a straight line (as described in the theory section, Eq. 13)Table 3Calculated translational diffusion times using the methyl resonance of the various oligomers (tetra-, hexa- and octasaccharides labeled in table as 1, 2 and 3 respectively) of the NA-domain from heparan sulfate at a proton frequency of 400 MHzCompoundD [10−6 cm2/s]13.08 ± 0.000422.55 ± 0.000732.22 ± 0.0007
Molecular dynamics (MD) simulations were performed on the tetra- and hexa-saccharides in water boxes. Figure 5 shows 40 snapshots from the simulation of the hexasaccharide (stripped of ions and water molecules), superimposed on the atoms from the central linkage. The simulation data were analyzed to give theoretical values of both S2 and the angle (α), defined as the angle between the molecular axis and the N–H vector (see Table 2). The calculated S2 values show similar trends to the experimental data, but are generally higher. The α values for both the tetra- and hexa-saccharide show nearly random behavior, consistent with an isotropic model.
Fig. 5Overlay of 40 snapshots from 50 ns of molecular dynamics simulations of the hexasaccharide of unsulfated heparan sulfate. The molecules are superimposed using the atoms in the adjacent linkages. The position of the nitrogen atom (blue sphere) in each structure is highlighted
The coupling constants of 1 and 2 were measured from 1D 1H spectra except for the central residues of 2 where there was overlap in the 1D spectrum. In this case, cross-peaks in the 1H-15N HSQC were fitted to 2D Lorentzian line-shapes using a non-linear fitting scheme (these data are given in Table 4).
Table 43J(H2−HN)-coupling constant (in Hz) of the tetra- and hexa-saccharides (1 and 2 respectively) from the NA-domains of heparan sulfate 3J(α− [H2−HN]) [Hz]3J(β− [H2−HN]) [Hz]3J(γ− [H2−HN]) [Hz]3J(ω− [H2−HN]) [Hz]18.438.559.1428.308.659.149.24All values have an estimated error of ±0.05 Hz.
Discussion
Analysis of the NMR relaxation data
The results clearly show the advantages of 15N-labeling, where even the amide groups in the central residues of an octasaccharide are well resolved (Fig. 2). In addition to the gain resolution, an excellent spread of relaxation data is found due to the use of the 15N-nucleus (Table 1). The average change in nOe enhancement (η) over the different fields is 1 unit, with some of the η-values passing through zero. In addition, much more precise measurements can be made of the longer T1-relaxation times in the 1H-15N system (compared to 1H-13C). The data follows a clear trend and even without further analysis the difference in relaxation properties of the central and terminal residues of the octasaccharide can be appreciated, as well as the difference between the various lengths of the polymer.
The experimentally measured relaxation datasets were interpreted using the model-free approach [15], which relates the measured relaxation values at multiple fields to dynamic conformational properties. In the model-free approximation it is assumed that internal motion (libration) and global motion (diffusion) are uncoupled and on significantly different timescales. Furthermore, it is assumed that molecular dynamics occurs with respect to a rigid diffusing molecular frame. While neither of these assumptions are entirely true for biomolecules, they yield good results if an appropriate model for molecular diffusion is chosen. The two simplest models for diffusion are based on the isotropic sphere and the anisotropic symmetric top (rod or cylindrical shape) [15]. In proteins, for example, the simpler isotropic spherical shape usually suffices, but this model may not be accurate for rod-shaped carbohydrate polymers [18], which are expected to become more anisotropic (rod shaped) with increasing length of the polymer chain. Previous studies on HS analogues have modeled diffusion using an anisotropic model, improving the agreement with experimental data [6, 11, 16–18, 30, 31]. In such a symmetric-top model, the correlation time about the symmetry axis of the rod shape  is separated from that perpendicular to the symmetry axis . Values of the parallel correlation time  tend to be similar in linear carbohydrates (~0.5 ns) and (by definition) are always smaller than the perpendicular correlation time ; the ratio of the two correlation times can be used as a measure of anisotropy. Other parameters in the model-free approach are the internal correlation time (τe), the generalized order parameter (S2) and, in the anisotropic model, an angle (α).
Isotropic or anisotropic model of rotational diffusion?
From the diffusion ordered spectroscopy (DOSY) data in Table 3 and Fig. 4 it is apparent that the overall polymeric shape of the NA-domain of HS is not consistent with a rigid rod. In fact, the slope of the line in Fig. 4 is almost equal to the theoretical value for a random coil (ν = 1/2), which indicates that even the oligosaccharides have significant segmental motion. This is supported by the model-free analysis of the tetra- and hexa-saccharides (using an isotropic model of diffusion), where relatively low values of S2 are found. However, when similar analysis was used to interpret relaxation measurement from the octasaccharide, relatively higher S2-values were found at the central amide groups. In contrast, the corresponding parameters, calculated using the anisotropic model of diffusion, are consistent with a random coil. These data therefore suggest that the tetra- and hexa-saccharides undergo nearly isotropic diffusion, whereas the octasaccharide diffuses anisotropically (all with high internal and segmental motion). It is likely that it is the α-linkages within the oligosaccharides that are responsible for a compact dynamic shape at shorter lengths, which only starts to behave anisotropically as an octasaccharide.
The lower order parameters calculated using an anisotropic diffusional model are due to the introduction of an additional parameter (α) that is the angle between the inter-nuclear N–H vector and the molecular cylindrical-symmetry axis. This parameter adds another dimension of complexity, providing a more accurate interpretation of the experimental data than using isotropic diffusion. For example, in the isotropic diffusional model, if there is a large amplitude motion along an N–H vector that is coincident with the long axis of the molecule, the T1 and η values will be interpreted as low amplitude motion, which will be compensated for by an increase in the overall rotational diffusion. However, in the anisotropic model, the angular dependence of the internal motion with respect to the molecular frame is taken into account, and the data fitted so that the angle (α) is also optimized. Inspection of Table 2 reveals that the amide groups at the central residues in the octasaccharide are indeed coincident with long axis of the molecule (small α), explaining the decrease in order parameter compared to the isotropic model. Further inspection shows that whenever the N–H vector is along the long axis, the anisotropic model predicts more motion and, when the vector is pointing along the short axis (large α), the converse is true.
More importantly, the anisotropic interpretation indicates that the two amide groups in the central residues of the octasaccharide have similar dynamics, as inferred from their S2 values (Table 1), in contrast to the results from the isotropic analysis, where the γ-amide group appears to be more ordered (higher S2) than the more central ψ-amide group. Furthermore, naïve interpretation of the raw experimental relaxation values (see Fig. 3, for example) would agree with the conclusion of the analysis via the isotropic model. However, this initial inspection is likely to be erroneous, because one would logically expect that the amide group in the most central residue (i.e., ψ in Fig. 2) would be more ordered (or at least similarly ordered) than that in a neighboring residue, which is in agreement with the initially counterintuitive results from the anisotropic diffusional model. Therefore, these findings (in addition to the DOSY data) substantiate the use of an anisotropic model in the analysis of octasaccharide dynamics.
Considering now analysis of the shorter oligosaccharides using an anisotropic model of diffusion, it is found that both the parallel  and perpendicular  diffusional rates increase, whereas one would expect only  to increase. This suggests that there is no well-defined anisotropic interpretation and the simpler isotropic model is preferable. Such an observation is further substantiated by molecular dynamics simulations performed for the tetra- and hexa-saccharide (see Fig. 5 and Table 2). In these simulations it was found that the angle α is nearly random for all amides, consistent with an isotropic model.
Microscopic interpretation of S2
NMR scalar coupling constants (J-couplings) [32] can also be used to measure dynamics due to bond librations (via a suitable empirical equation) and the 3J(H2−HN) coupling constant is a direct reporter of amide dynamics in these oligosaccharides. Theoretical calculations have shown that, given the same chemical environment, a decrease in the coupling constant is associated with increased amide librations. From Table 4, it can be seen that in 2 the central γ-amide group has a higher coupling constant compared to that of the terminal ω-amide group. This is contrary to expectation, as the central amide group is regarded to be more rigid. In this case, it appears that the change in chemical environment causes the unexpected observation. This is not expected to be the case when comparing coupling constants measured in 1 and 2 from amides in similar chemical environments, i.e., those at the α-, β- and ω-positions, where a variability of 0.1 Hz is observed in the coupling constants. This can be attributed to slight changes in amide libration of a few degrees (using a previously derived relationship between coupling constants and torsional libration [32]). A similar relationship between dihedral angle and S2 indicated that these angular variabilities would correspond to deviations in S2 of less than 0.01 [15]. Furthermore, the expected differences in amide group librations across individual molecules are less than 5° (using the above mentioned relationship), which would result in small changes in S2 of less than 0.05. Therefore, differences in amide libration are not responsible for the more dramatic change in S2 observed at specific amide positions (Table 2). The major remaining site-specific contributor to S2 in the NA-domains from HS (which contain relatively rigid 4C1-puckered sugar rings) must thus be due to segmental motions induced by linkage librations. The S2-values reported here can therefore be seen as direct reporters of the chain dynamics in these polymers.
Role of dynamics in HS
The S-domains of HS have previously been investigated using relaxation data from C–H vectors and an interpretation based on an anisotropic diffusional model (see Angulo [16] and references therein). The values reported for  of such domains are 1.6, 1.7 and 2.6 ns for penta-, hexa- and hepta-saccharide fragments, respectively. In addition, these fragments show a much higher degree of anisotropy compared to the NA-domain analyzed here. This would suggest that the S-domains are comparatively more rigid, as reflected in the higher S2 values reported: 0.91, 0.91 and 0.93 for central residues of the penta-, hexa- and hepta-saccharide fragments, respectively (cf. ~0.65 for the NA-domains found here).
From the findings presented here it thus appears that the NA-domains of HS are much more dynamic than the S-domains. At the molecular level, the only chemical difference between the NA- and S-domains of HS is sulfation and epimerization, and it has been suggested previously that both share a similar local 3D-chain conformation [33]. Therefore, it is more likely that the major contributor to chain rigidity of S-domains is the repulsion of multiple negative charges, rather than specific cross-residue intramolecular interactions, but this remains to be validated by further experimentation.
From a biological viewpoint this confirms the hypothesis that the primary function of the NA-domains is to give the HS chains flexibility, since the S-domains are significantly stiffer. This would give proteoglycan HS chains the ability to rapidly and effectively explore space around their tethering points on the core protein (Fig. 6). It would also allow them to dynamically adapt their shapes and present themselves effectively to a range of protein targets. In addition, this would permit HS chains to interact in a multivalent fashion, without the dynamics of one interaction greatly affecting others. This may explain the omnipresence of functional S-domains in HS, which are separated by completely unsulfated NA-domains [34]. Supporting evidence is provided by Stringer and Gallagher [8], who found an HS fragment with specifically high affinity for the platelet factor 4 (PF4) tetramer. This HS fragment consisted of two S-domains separated by an NA-domain long enough to allow for interaction of the two S-domains with each side of the PF4 tetramer. This argument is also consistent with the diversity of molecules that HS interacts with and the picture that its primary role is in regulation and control [2, 4, 35].
Fig. 6Schematic diagram illustrating the proposed increase in flexibility due to the NA-domains of heparan sulfate and the concomitant increase in spatial range that can be covered together with the potential for mediating multivalent interactions
Conversely, the current study would suggest that the S-domain rich heparin proteoglycans do not have the local plasticity of HS proteoglycans. Therefore, in order to provide the same effective local concentration of sulfo-groups, heparin may be required at higher concentrations than HS. This is supported by the fact that when mast cells are activated their cytoplasmic granules (where heparin is thought to act as a depository for cationic molecules i.e. histamines, serotonin, etc.) are released into the extracellular matrix resulting in a high concentration of highly active (S-domain rich) heparin molecules [36]. Such action would allow heparin to quickly interact with proteins at the site of release, a response which is important in inflammatory processes, where heparin acts as an anticoagulant [36]. However, as its concentration falls due to diffusion from the point of release its highly potent effects would drop-off rapidly.
Conclusions
Heparan sulfate (HS) NA-domains have been investigated using NMR and computational methods, to determine whether their dynamic properties are consistent with previous hypotheses, suggesting that NA-domains add flexibility to HS. For these nearly-linear biomolecules conventional 1H-NMR methods yield poor spectra with overlapping resonances that are difficult to interpret. We have demonstrated that these difficulties can be overcome by use of 15N-labeling and efficient purification protocols. This approach, which could easily be extended to sulfated domains, allows the dynamical properties of NA-domains of HS to be studied with unprecedented accuracy.
In this study, three lengths of the enzymatically-digested polymer (consisting of 4, 6 and 8 sugar units, respectively) were isolated, purified and high-quality 15N-NMR relaxation data was collected for each at three magnetic-field strengths. The resultant data was analyzed using the model-free approach using both isotropic and anisotropic models of rotational diffusion. To complement these studies, and to ease their interpretation, molecular shape was further probed by measuring translational diffusion (using NMR pulse field gradients) and by performing solvated molecular dynamics simulations. The resultant order parameters (S2) from the model-free analysis reveal a high degree of disorder in the oligosaccharide chains, with S2-values that are similar in the hexa- and octa-saccharide, suggesting that these values represent polymer behavior. Following these analyses, it was determined that the NA-domains of HS behave as random coils and that for shorter oligosaccharides their rotational diffusion can be approximated to be isotropic, whereas for the octasaccharide the results are more consistent with anisotropic diffusion.
The flexibility exhibited by the NA-domains is contrary to literature data on the sulfated S-domains of HS, which appear to be much more rigid [16]. It is proposed that the NA-domains primarily serve a motional role in HS, consistent with reports of the domain structure of HS. It is therefore argued that the motional properties of the NA-domains increase the efficiency of HS in control and regulation. These findings also suggest that heparin does not have this flexibility, a view consistent with the known roles of heparin, which include storage of ionic molecules and involvement in processes requiring a fast, local response.
Methods and materials
Biochemical preparation of oligosaccharides
K5 capsular polysaccharide was prepared from cultures of Escherichia coli Bi8337/41 [O10:K5:H4], originally supplied by Professor Ian S. Roberts. Cells were grown in a starter culture of 50 ml of LB broth, 1 ml of that media was pelleted and resuspended in 500 ml of M9-minimal media containing 1g/l 15N-ammonium chloride (15N>99%) as the only nitrogen source. The polysaccharide purification procedure was similar to that described previously and is summarized here [37]. After 16 h of growth the cells were harvested (5,000 g, 10 min) and washed once with 50 ml of phosphate-buffered saline (pH 7.2). Cell pellets were resuspended and incubated (30 min, 37°C) in 50 ml extraction buffer (50 mM Tris–Cl, 5 mM EDTA, pH 7.3) four times, retaining the supernatants. Polysaccharide was precipitated from the pooled supernatants by addition of sodium cetyl-3-ethyl ammonium bromide to 0.1% (w/v) followed by a 16 h incubation at room temperature. The precipitate was recovered by centrifugation (10,000 g, 20 min), re-suspended in 1 M NaCl, precipitated by addition of ethanol to 80% (v/v) and again recovered by centrifugation. Following dissolution in distilled water (5 ml), the preparation was dialysed against distilled water, centrifuged (100,000 g, 1 h) and the supernatant containing purified polydisperse K5 polysaccharide (20 mg/l) was lyophilized.
Lyophilized 15N-enriched K5 polysaccharide (~30 mg) was dissolved in 5 ml of digest buffer (100 mM Tris–HCl, 100 mM Na-acetate, pH 8.5) and incubated at 37°C for 16 h with 180 U of K5-lyase enzyme (kindly supplied by Professor Ian S. Roberts). The reaction was stopped by incubation in a boiling water bath for 5 min and the precipitated enzyme was removed by centrifugation (13,000 g, 2 min).
Differing lengths of oligosaccharides from the K5-polysaccharide digest were separated using anion exchange FPLC on an ÄKTA system (GE Healthcare). Briefly, digest supernatant was loaded onto a Sepharose-Q HR column (GE Healthcare), equilibrated in Milli-Q H2O at flow rate of 10 ml/min, and eluted with a gradient of 0–100 mM NaCl over 90 min. The eluent was monitored at both 214 and 232 nm. Following lyophilization, each oligosaccharide was desalted using a Bio-Gel P2 column (BioRad) running in 50 mM HN4Ac, and then repeatedly lyophilized to remove residual NH4Ac by sublimation. Purity was confirmed by analytical ion-exchange chromatography using a Mono-Q HR 5/5 (GE Healthcare), NMR and mass spectrometry.
Sample preparation
Samples of oligosaccharides for NMR spectroscopy were prepared from lyophilized material reconstituted in 5% (v/v) D2O, 0.02% (w/v) NaN3, 0.3 mM DSS, pH 6.0; pH 6.0 was chosen to allow amide HN protons to be observed. The final concentration of all samples was calculated to be approximately 5 mM.
NMR spectroscopy
The T1-relaxation and nOe experiments were performed at 24.5°C, using standard pulse sequences [40]. All experiments were repeated at three different magnetic-field strengths: 14.1, 18.1 and 21.1T. The T1-relaxation delays were: 4, 12, 16, 32, 48, 80, 120, 160, 240, 300, 360 and 480 ms, performed in a random order to avoid systematic errors. 1024 complex data points were acquired in the direct (1H) dimension and 18 complex points in the indirect (15N) dimension, using a spectral window of 10 ppm in the direct dimension and 2.2 ppm in the indirect dimension. In addition, a second spectrum was collected for three (for all experiments), which had a narrower spectral width of 0.35 ppm in the indirect dimension.
The data was processed using the NMRPipe processing software [41]. The data was typically zero-filled in the acquisition dimension to 16,384 complex points and 1,024 complex points in the indirect dimension. The acquisition dimension was apodized using an exponential window function (typically with 15 Hz line-broadening) and the indirect dimension was apodized using a Lorentzian to Gaussian apodization function, in which the line broadening and narrowing parameters were adjusted as appropriate for the spectrum. The various peaks were picked using the peak-picking algorithm within the NMRPipe software and the peaks heights were used in all cases. The nonlinLS line shape fitting algorithm within the NMRPipe software was used on a subset of the peaks to validate this approach, and the agreement between the two approaches was found to be excellent (with the zero-filling used here). The J-couplings were measured by finding the difference between peak frequencies, determined also using the nonlinLS algorithm.
The assignment of the NH resonances follow the strategy employed by Blundell et al. [38, 39], applied to the oligosaccharides of hyaluronan. The full assignment of these oligosaccharides will be published elsewhere, the assignment of the resonances of interest, however, is given here. The assignment of the tetrasaccharide is straightforward as there are only three cross-peaks, two of which are weaker than the third. The weaker peaks have intensities equal to 60 and 40% of the intensity of the third peak. Based on their intensities, the weaker peaks were assigned to the anomeric protons (α and β), which are further assigned based on their intensities, since the α-anomer is dominant with preserved ratio in both free GlcNAc [32] and in the terminal GlcNAc of hyaluronan [39] (this assignment is further verified by a NOESY cross-peak between the NH proton and the previously assigned [37] anomeric proton at position 1 of α/β-GlcNAc). The remaining cross-peaks can thus be assigned to the remaining NH atoms in the ω-residue of the tetrasaccharide. In the hexasaccharide all of the cross-peaks present in the tetrasaccharide are observed together with an additional cross-peak. This additional peak is assigned as the more central γ-residue, as it does not experience the same “end effects” (see also references to end effects in Blundell et al. [38, 39]). Finally, the octasaccharide was assigned based on the known assignment of the shorter fragments and the trend towards polymer properties. The cross-peaks for the NHs at α-, β- and ω-positions were nearly identical to those in the hexasaccharide, as these experience the same end-effects and could thus be readily assigned. The position of the NH cross-peaks of the γ- and ψ-residues in the octasaccharide are close to each other. However, one of these resonances has 1H and 15N chemical shifts that are almost identical to γ-residue of the hexasaccharide and is thus assigned as the γ-amide in the octasaccharide. The remaining cross-peak is therefore due to the ψ-amide. These assignments were further verified using the HSQC spectrum of a decasaccharide. In this spectrum, the intensity of the cross-peak corresponding to the ψ-amide increases (not shown), due to convergence to polymer properties and overlap, whereas the intensity of the remaining resonances are unchanged. This tendency towards polymer behaviour was additionally used to verify the assignment of the NH cross-peak in the ψ-residue of the octasaccharide, which experiences the least amount of end-effects.
Model-free NMR relaxation analysis
Computer programs were written in the C-programming language to calculate the relaxation times for an isolated N–H vector, according to the equations presented in the theory section. The internal correlation time at each vector was fixed at 30 ps (as described in the text), with an associated variable S2 at each position and for each molecule a single variable for the isotopic model. For the anisotropic model, the rotational correlation time  and angles between the long axis and each N–H vector (α) had to be additionally considered. Predictions were compared with experimental data using the χ2 function and optimized using a Monte Carlo algorithm [19]. This process was carried out ten times for each oligosaccharide and the mean and standard deviation values for each fitted parameter were reported.
The N–H bond length and the chemical shift anisotropy of the amide nitrogen were calculated by ab initio DFT calculations on N-acetyl-d-glucosamine [32] (while the calculations were performed previously these values have not been reported before). Briefly, the computation of the chemical shift anisotropy was performed using the GIAO method in the Gaussian software at the B3LYP/IGLOO-III level of theory, which resulted in a value of −135 ppm, in close agreement with previously-reported values that used similar methods [42, 43]. Similarly, the N–H bond length was taken from quantum mechanical geometry optimization at B3LYP/631G(d,p) level of theory. The N–H distance was found to range from 1.008 to 1.020 Å, depending on solvation model used (1.008, 1.020 and 1.015 Å for calculations performed in vacuo, with implicit solvent and with explicit solvent, respectively), which is in agreement with similar calculations on proteins [42, 44]. Due to this small variability of the N–H distance, the relaxation calculations were performed using a bond length of both 1.01 and 1.02 Å (the results of the latter calculation are included in supplementary Table 2).
Molecular dynamics simulations
Molecular dynamics (MD) simulations were performed using CHARMm and a force-field suitable for glycosaminoglycans, as described previously [45] using an integration time-step of 2 fs. Briefly, oligosaccharides were built in a low-energy conformation consistent with preliminary NOESY measurements (unpublished results) and placed into rhomboidal dodecahedral water-boxes. The boxes had dimensions of 4.0 and 4.5 nm, containing 1504 and 2141 TIP3P water molecules for the tetrasaccharide and hexsaccharide, respectively. Long-range electrostatic interactions were treated using the particle-mesh Ewald approach and truncated to zero between 0.8 and 1.2 nm. Simulations (using the CPT ensemble) were carried out at 298 K on both α and β-anomers of the tetrasaccharide and on the β-anomer of the hexasaccharide, for a total of 50 ns in each case. Coordinates were stored every 5 ps for later analysis.
The S2 values were calculated as the autocorrelation of N–H vectors extracted from the MD simulation. The N–H vectors were extracted by overlaying all frames from the MD simulation on the same set of atoms. These atoms were the linkage atoms adjacent to each amide (O4 of the same residue GlcNAci to C1 of previous residue GlcAi−1 (d-glucuronic acid) and O4 of next residue GlcNAci+1 to C4 of that residue GlcNAci+1). For the GlcNAc residue at the reducing terminus the corresponding anomeric atoms (O1 and its attached proton) were used instead (as there is no i + 1 residue).
Values for cos(α) were calculated from the simulation by alignment (rotation and translation) of each coordinate frame such that the radius of gyration tensor was diagonal. Each N–H vector was normalized and then projected along the unit vector coincident with the long axis of the aligned molecule, which resulted in estimates for cos(α). These values were arithmetically averaged over the whole simulation.
Diffusion ordered spectroscopy (DOSY) experiments
Diffusion measurements were carried out without spinning on a 400 MHz Varian Inova instrument, using a 5 mm diameter indirect detection probe equipped with a z-gradient coil, allowing gradient pulses up to 30 G cm−1. No sample temperature control was used; experiments were carried out at the probe quiescent temperature in a room air-conditioned to about 20°C. Nominal gradient strengths were calibrated according to manufacturer’s recommendations. The Oneshot pulse sequence [46] was used with a diffusion delay Δ of 0.2 s and a net diffusion-encoding pulse width δ of 2 ms (i.e., each bipolar pulse-pair consisted of two 1 ms gradient pulses) with 10 gradient strengths, ranging from 3.0 to 27.3 G cm−1, chosen to give equal steps in gradient squared. A spectral width of 4,000 Hz and 16,384 complex data points were acquired for each gradient strength. Correction for instrumental imperfections by reference deconvolution [25], using the TSP peak as reference, were performed on the DOSY data using the standard instrument software (Vnmr6.1C). The diffusion coefficients were fitted to Eq. 10 using four coefficients in the power series, where the coefficients for correction of the non-uniform field gradients (0.928, −9.78 × 10−3, −3.83 × 10−4 and 2.51 × 10−5) were obtained as previously described [27].
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supporting Table 1
Experimental and calculated nOe ratios (η), and T1 relaxation data for isotopic models of diffusion. (PDF 24.5 kb)
Supporting Table 2
Calculated model-free parameters for the tetra-, hexa-, and octa sacchrides (1, 2, and 3 respectively) of the NA-domain of heparan sulfate, using a constant internal correlation time of 30 ps, fitted to isotopic and anisotropic models of rotational diffusion (PDF 23.5 kb)

dynamics
nuclear magnetic resonance (nmr)
polysaccharide
proteoglycan
glycosaminoglycan
carbohydrate
heparin
n-acetyl heparosan


Societal Burden of Clinically Anxious Youth Referred for Treatment: A Cost-of-illness Study

A prevalence-based cost-of-illness study using a societal perspective was conducted to investigate the cost-of-illness in clinically anxious youth aged 8–18 in The Netherlands. Discriminant validity of the cost diary used was obtained by comparing costs of families with an anxious child (n = 118) to costs of families from the general population (n = 41). To examine the convergent validity, bottom-up acquired costs derived from cost diaries were compared to top-down acquired costs obtained from national registrations. Bottom-up acquired costs measured by means of cost diaries amounted to €2,748 per family of a clinically referred anxious child per annum. Societal costs of families with clinically anxious children were almost 21 times as high compared to families from the general population. With respect to convergent validity, total health care costs using the bottom-up approach from clinically anxious children were quite comparable to those of top-down data of anxious children, although costs within the subcategories differed considerably. Clinical anxiety disorders in childhood cost the Dutch society more than 20 million euros a year. Based on results of discriminate and convergent validity, the cost diary seems a valid method in establishing cost-of-illness in childhood anxiety disorders.
Anxiety disorders are among the most common childhood mental disorders. Prevalence rates for children with anxiety disorders vary considerably in the international literature from 1.8 (Anderson et al. 1987) to 25% (Kessler et al. 1994). In The Netherlands, a 6-month prevalence rate of 23.5% for anxiety disorders was found in 13- to 18-year-old adolescents. When impairment in daily functioning is taken into consideration, Klein and Pine (2002) estimated the prevalence rate to be between 5 and 10%. A Dutch study revealed that the prevalence rate of severely interfering child anxiety disorders is 9.7% (Verhulst et al. 1997). The yearly prevalence rate of children with a mental disorder seeking treatment in mental health care ranges from 1.6 (Sytema and Koopmans 1998) to 8.1% (Leaf et al. 1996). In The Netherlands, merely 3.5% of 4- to 18-year-old children with a mental disorder had been referred yearly to mental health care for treatment (Verhulst and van der Ende 1997). This latter percentage resembles the 4% of Dutch people with anxiety disorders who are referred for further treatment by their general practitioner (van Wieren et al. 2007).
With respect to costs, the total costs for anxiety disorders in adults were highest of all mental illnesses, namely 46.6 billion dollars, accounting for 31.5% of total expenditures for mental health in 1990 in the USA (Dupont et al. 1996). Greenberg and colleagues (1999) estimated the annual costs of anxiety disorders in adults to be 63.1 billion dollars in the USA in 1998 by extrapolating the costs of 1990. Besides direct costs of treating anxiety disorders, indirect costs due to impaired social functioning, such as financial dependence and unemployment also are found in adults with anxiety disorders (Leon et al. 1995). In The Netherlands, health care costs of anxiety disorders across all ages were 180 million euro in 1999, accounting for 0.5% of the health care budget. More specifically, health care costs due to anxiety disorders in 1- to 24-year-old children and young adults amounted to 21.6 million euros (Polder et al. 2002).
An analysis to measure the magnitude of costs related to illness is called a cost-of-illness study and involves an estimation of the total economic burden which an illness imposes on society. The main objective of a cost-of-illness study is to translate the burden on society into monetary costs. Typically in a cost-of-illness study all costs associated with a particular illness are identified and measured, including health care costs, patient and family costs and costs occurring in other sectors. The results of a cost-of-illness study can be used to gain insight in how much society is spending on a disease, and how much potentially can be saved if effective treatment is offered. Also, it identifies the different cost categories and the size of the contribution of each sector in society. The information coming from cost-of-illness studies can be helpful in setting priorities for health care efficiency research (Ament and Evers 1993; Polder et al. 2002; Rice and Miller 1995).
Essentially there are two approaches to establish the cost-of-illness, namely the bottom-up and the top-down approach. The bottom-up approach acquires data on the patient level by means of registrations or self-report measures. Self-report measures contain retrospective cost questionnaires, retrospective cost interviews or prospective cost diaries (Bruijnzeels et al. 1998a; Sleed et al. 2005). The bottom-up approach enables one to capture resource use that is likely to vary from patient to patient and may not be easily extracted from existing data sources. Multiplying the costs per patient by the national prevalence rate results in the total cost-of-illness for a particular patient group. An advantage of the bottom-up approach is that detailed data can be obtained regarding costs outside the health care sector, such as costs due to productivity losses, out-of-pocket costs, costs of informal care, and costs due to absence from school. A disadvantage of this method may be that the sample of patients used for the cost-of-illness calculations is not representative of the entire patient group, for example due to strict inclusion and exclusion criteria (Oostenbrink et al. 2004; Polder et al. 2002). The top-down approach involves gathering data from existing (national) registrations on total costs of a service category such as hospital care, and dividing these costs by an appropriate unit, such as a patient group using the service category, mostly per year if appropriate. While there are benefits using this approach such as being less resource intensive, this calculation may also prove to be less accurate (Byford et al. 2003). Furthermore, when using a top-down approach it is difficult, if not impossible, to obtain costs outside the health care sector (Oostenbrink et al. 2004; Polder et al. 2002).
Another important distinction in cost-of-illness studies is that costs can be determined from a prevalence-based or incidence-based perspective. In a prevalence-based approach, the costs of a disorder are determined for a fixed period of time, as a result of the prevalence of the disorder (number of cases of a disorder at a specific time). This period is called the base period and most often studies investigate the annual economic burden, thus based on a year (Ament and Evers 1993; Rice and Miller 1995). A disadvantage of the prevalence-based approach is that it may be difficult to isolate potential differences in costs by stage or duration of the disorder (Greenberg et al. 1999). The incidence based approach estimates the lifetime costs of an illness from its onset, thus costs are assigned to the year in which the illness begins (Ament and Evers 1993; Rice and Miller 1995). Incidence based studies can isolate the differences in costs by stage or duration of the disease. However, a disadvantage of using this approach is that follow up research is required.
In this article, a prevalence-based cost-of-illness analysis was performed in 8- to 18-year-old children and adolescents with anxiety disorders, who were referred to treatment. The primary aim of this study was to establish the societal costs of illness in families with a clinically anxious child, using the bottom-up approach by means of a prospective cost diary. A secondary aim of this study was to investigate the discriminative validity of the prospective cost diary by comparing the costs related to anxiety, psychological, physical and other problems in families with a clinically anxious child to the same costs in families from the general population. The third aim of this study was to establish convergent validity by comparing bottom-up acquired health care costs obtained with the cost diaries of the clinically anxious children with top-down acquired health care costs of children with a primary diagnosis of anxiety disorder.
Materials and Methods
Participants
For the cost-of-illness study and the discriminative validity study, participants were clinically anxious children and adolescents from the general population, respectively. The clinically anxious group consisted of 118 families with a child referred for community mental health treatment because of anxiety, and participated in a multi-centre randomized clinical trial comparing family Cognitive Behavioural Therapy (CBT) with individual CBT (Bodden et al. submitted for publication). This study was conducted from March 2002 to November 2005, inclusion ended in March 2004. Inclusion criteria were (1) age 8–18, (2) a primary anxiety disorder (except for obsessive-compulsive and post-traumatic stress disorder), (3) IQ ≥ 80, and (4) at least one parent willing to participate. Children were excluded if suffering from (1) substance abuse, (2) current suicide attempts, (2) psychoses, (3) autism-spectrum disorders, or (4) untreated Attention Deficit Hyperactivity Disorder (ADHD). Children using medication for their anxiety were only included if dosage was kept constant during treatment and follow-up. There were 74 girls (63%) and 44 boys (37%), range 8–17 years (M = 12.4, SD = 2.6). Most were Caucasian (n = 117, 99%) and 53 (45%) attended primary education. The remaining 65 (55%) attended secondary education.
According to the compound diagnosis on the Anxiety Disorders Interview Schedule (ADIS; Silverman and Albano 1996), the mean number of diagnosis per child was 2.9 (SD = 1.7), whereas the mean severity level of the primary diagnosis was 7.1 (SD = 1.0). Furthermore, primary anxiety diagnoses consisted of social phobia (n = 37, 31%), separation anxiety (n = 32, 27%), generalized anxiety disorder (n = 21, 18%), specific phobia (n = 20, 17%), and panic disorder with or without agoraphobia (n = 8, 7%). Of the 118 children, 94 (80%) had one or more comorbid anxiety disorder(s); separation anxiety disorder (n = 20, 17%), social phobia (n = 43, 36%), specific phobia (n = 51, 43%), panic disorder (n = 20, 17%), generalized anxiety disorder (n = 42, 36%), obsessive-compulsive disorder (n = 5, 4%), and post-traumatic stress disorder (n = 7, 6%). Moreover, 29 (25%) had comorbid dysthymic disorder or depression, 9 (8%) ADHD, 2 (2%) conduct disorder, and 1 (1%) oppositional defiant disorder.
The sample from the general population consisted of a group of 41 families who were recruited via advertisements. Children of these families were comparable to children from the clinically anxious group based on gender, χ2 (1) = 19, p > 0.1, age, F (1, 154) = 0.0, p > 0.1, and educational level, F (1, 154) = 2.6, p > 0.1. There were 27 girls (66%) and 14 boys (34%), range 8–17 years (M = 12.5, SD = 2.5). Most were Caucasian (n = 40, 98%) and 16 (39%) attended primary school. The remaining 25 (61%) attended secondary education. According to the compound diagnosis on the ADIS, the prevalence of anxiety disorders in this sample was 12% (n = 5) and 2.4% (n = 1) for ADHD, closely resembling Dutch prevalence of anxiety disorders and ADHD, respectively 9.7% and 2.3% (Verhulst et al. 1997). The primary anxiety diagnoses consisted of social phobia (n = 1, 20%) and specific phobia (n = 4, 80%) with a mean severity level of 4.8 (SD = 0.8). The overrepresentation of simple phobia and the relatively low severity rates of the anxiety disorders in the control group also support the “normality” of the control children.
All children and parents received and signed a written informed consent after receiving oral and written information on the study. Clinically anxious children, their siblings and parents were assessed before treatment and families from the general population were only measured once. Furthermore, all family members had to fill in a battery of questionnaires, including a prospective cost diary.
Anxiety Disorders Interview Schedule
Children’s diagnostic status was assessed using the Dutch ADIS-C/P (Siebelink and Treffers 2001), which is a semi-structured diagnostic interview measuring anxiety disorders and other DSM-IV childhood psychopathology. The severity score consists of impairment in daily functioning per diagnosis and ranges from 0 to 8, 4 indicating a clinical diagnosis. Based on criteria, child and parents ratings are combined in a compound summary. The ADIS-C/P possesses good psychometric properties (Silverman et al. 2001). In our study, the total inter-rater agreement for all ADIS diagnoses (kappa) was averaged and was 0.89 for the ADIS-C and 0.83 for the ADIS-P. The mean inter-rater agreement for the severity score was calculated with correlations and was 0.98 for the ADIS-C and 0.83 for the ADIS-P.
Prospective Cost Diary
A cost diary was used to determine the resources used (Freer 1980; Goossens et al. 2000) in both the cost-of-illness study as well as in the discriminative validity study. Cost dairies were completed prospectively by one of the parents and covered a period of 2 weeks. Families of clinically anxious children received the cost diary 2 weeks before treatment and the sample of families from the general population received the same cost diary during their one time assessment. Each cost diary was presented in a booklet form with instructions and an example diary. The cost diary was designed such that all costs which are potentially relevant to society were captured, including health care costs, patient and family costs and costs in other sectors. Parents recorded the following resource use: visits to psychologist or psychotherapist, visits to the general practitioner, outpatient consultations at the hospital, medication, day treatment, costs of paid house help, children’s day care, after school care, informal care, productivity losses due to absence of work by the parents, loss of household activities or voluntary work, loss of leisure time, absence from school of the child, and out-of-pocket costs. Informal care means that persons near the family, such as relatives, friends, or neighbors, take care of the family and possibly take over some domestic tasks. Out-of-pocket costs are actual expenses made by the family (and are therefore paid ‘out of the pocket’), and include for example transportation costs, parking costs, over-the-counter medication and own monetary contributions for health care services which are not fully reimbursed by health insurance companies. Also, parents recorded the name of the family member for whom the resource use was applicable (child, sibling, mother or father), as well as the reason for the resource use by that family member. For analysis, the reported reasons were grouped into (1) due to anxiety, (2) due to psychological problems, (3) due to physical problems, and (4) due to other reasons.
Unit Prices
Costs were calculated by multiplying the resources used, obtained with the prospective cost diaries, by the unit price of each resource. Costs were calculated at 2003 euros (1€ = 0.087$, July 30th 2003). It should be noted that, whenever possible, unit prices and not transfer payments such as charges, reimbursements or patient bills were used to value health care resource use. Money transfers may bear little relation to actual resource consumption as a result of (local) budgeting agreements and political negotiations (Finkler 1982). For example, in The Netherlands hospitals receive an annual budget. As a consequence, the reimbursement of a day of hospitalization (which is the amount a hospital charges to a health care insurance company) may vary from year to year as it is used as a balancing item for a budget overdrawing. The key point is that the amount that exchanges hands between providers, third-party payers and patients are not necessarily a good measure of resources used. Therefore, for the valuation of resource consumption in health care, mostly actual unit prices are used, which are obtained by performing unit price calculations. Unit prices consist of personnel, material and capacity costs, as well as costs of housing and overhead.
Almost all unit prices were obtained by using published Dutch guideline prices (Oostenbrink et al. 2004). However, medication prices were obtained from the Medication and Aid Information Project database on the Internet (Board of Health Insurances 2005) and were based on the Daily Defined Dosage (DDD), which indicates the mean medication usage per person a day with claw back and value added tax (VAT) (20%). An average unit price was calculated for medication for anxiety problems and medication for psychological problems. As actual unit prices for day treatment and institutionalization were not available, the prices were derived from the average of actual reimbursements (Board Tariff Rates Health Care and Care Authority 2005). It should be noted that the unit price of informal care, loss of leisure time, voluntary work and domestic activities was based on a ‘shadow price’ as stated in the Dutch guideline (Oostenbrink et al. 2004). A key characteristic of informal care, leisure time, voluntary work and domestic activities is that it is unpaid. So, these activities by definition do not have an official unit price, since no actual payments take place. If an official unit price is not available, resource use should be valued against a ‘shadow price’. This means that informal care, loss of leisure time, voluntary work and domestic activities were valued against the wage rate per hour of a housekeeper. To determine the costs associated with absence from school, actual annual tuition, which consists of a contribution by parents and a state-subsidy per child (National Institute of Budget information 2005), was divided by the total annual hours at school according to the standard, resulting in a price per hour of school attendance. As the larger part of school costs is fixed, such as the costs of housing, (teaching) personnel and school materials, a child missing school does not result in actual savings. Therefore, the actual costs per hour of school attendance were used to value an hour of school absence. Productivity costs of the parents due to absence from paid work were calculated by means of the friction cost method, based on a mean added value of the Dutch working population (Oostenbrink et al. 2004). This method only takes into account production losses confined to the period needed (usually 90 days) to replace a sick employee.
Cost-of-illness
The primary aim of this study was to calculate the societal burden of illness of families of clinically anxious youth in The Netherlands. The cost-of-illness study was performed from a societal perspective and included direct health care costs, direct non-health care costs, indirect costs and out-of-pocket costs.
The costs per family over a period of 2 weeks were extrapolated to a period of 1 year (i.e. multiplied by 26) under the assumption that data obtained with the cost diaries were representative for that period (Goossens et al. 2000). Subsequently, the annual costs per family were multiplied by the Dutch prevalence figure for anxiety disorders, which is 9.7% (Verhulst et al. 1997) and the percentage of referred children of 3.5% (Verhulst and van der Ende 1997), which results in a multiplication factor of 0.34%, to calculate the total annual societal cost-of-illness of families of clinically anxious children referred for treatment. It was assumed that the percentage of referred children with a mental disorder (3.5%) could be attributed to anxiety disorders as well. Furthermore, a low and high estimation of the cost-of-illness was calculated. The low multiplication factor of 0.08% was derived by multiplying the low prevalence rate of children with a mental disorder seeking treatment in mental health care of 1.6% with the low prevalence rate of impairment in functioning of 5% (i.e. 1.6 × 5%). The high multiplication factor of 0.81% was derived by taking the high prevalence rate of children with a mental disorder seeking treatment in mental health care of 8.1% and multiply this with the high prevalence rate with impairment in functioning of 10% (i.e. 8.1 × 10%).
Discriminative Validity
To determine the discriminative validity of the prospective cost diary, the societal costs of the 118 families of anxious children were compared to the societal costs of 41 families from the general population. For this purpose, all costs related to the child, irrespective of reason, were taken into account (i.e. psychological, physical or other). For both groups, the costs per family over a period of 2 weeks were extrapolated to a period of 1 year to obtain the annual costs per child for both groups. It was hypothesized that costs for anxiety problems were higher in the clinically anxious group compared to the general population but comparable between the two groups for psychological, physical and other problems.
Convergent Validity
To establish convergent validity, health care costs per child per year obtained with the prospective cost diaries of 118 clinically anxious children were compared with the health care costs of children with a primary diagnosis of anxiety disorder, according to top-down registrations. For this comparison, only health-care costs due to anxiety of the child were considered, because the costs obtained with the top-down registration applied to children with a primary diagnosis of anxiety disorder and only consisted of costs related to the health care sector.
The top-down data were obtained from a generic cost study based on national registrations (Polder et al. 2002). In this study, the total costs of health care in 1999 were assigned to diagnosis groups based on actual delivered care, according to distribution codes which are chosen in such a way that they represent equal units of care. These distribution codes are adjusted to the best available data about the care usage (for more details see Polder et al. 2002). This was done for each of the 21 health care sectors including the mental health care sector. Diagnosis groups were based on the International Statistical Classification of Diseases, injuries and causes of death (ICD-9; World Health Organization 1977). The ICD-9 is divided into 17 chapters, including chapter V, mental and behavioural disorders. Furthermore, a more intricate classification is used, by itemizing the chapters into 96 specific diagnosis groups, including anxiety disorders. All anxiety disorders are taken into account (ICD-9 300.0-3 and 300.5-9) (World Health Organization 1977).
The top-down data could not be compared directly to the group of 8–18 year old children included in the bottom-up cost-of-illness analysis, because costs were presented for the age groups 5- to 9-, 10- to 14- and 15- to 19-year-olds. Therefore, the assumption was made that costs were equally distributed within the age groups, so the corresponding ages (8–18) could be filtered out by averaging the costs and multiplying them by each age year (Polder et al. 2002). Since top-down data were based on costs in 1999, for comparison with the bottom-up cost-of-illness approach, costs were actualized to 2003 euros by using the Consumer Price Index (CPI; Statistics Netherlands 2005).
Statistical Analysis
Statistical analysis could only be performed on the cost-of-illness study and on the discriminative validity study, since top-down data were not available on child level. Missing items in the cost diaries were handled with the Missing Value Analysis of SPSS based on the regression models using available data of the group mean, the available data of the individual and the pattern of change of the group. To investigate whether data were normally distributed, a Kolmogorov–Smirnov test was performed. Due to highly skewed cost distributions, bootstrap simulations were conducted in order to get insight in significant differences on subtotal and total costs between families of clinically anxious children and families from the general population and the uncertainty surrounding these costs. The bootstrap method estimates the sampling distribution of a statistic through 1,000 simulations, based on sampling with replacement from the original data (Briggs, Wonderling and Mooney 1997). Bootstrap methods are increasingly being used for analysis of cost data, as traditional parametric and nonparametric statistical methods to analyse the difference in mean costs between groups may be inappropriate when data are skewed (Barber and Thompson 2000; Desgagné et al. 1998).
Results
Cost-of-illness
All resources used and costs made related to the anxiety of the child by families with a clinically anxious child were summed and averaged for 2 weeks and 1 year (Table 1). The mean costs per family over 2 weeks were extrapolated to a year and amounted to €2748 per family with a clinically anxious child. Costs were not normally distributed, Kolmogorov–Smirnov Z = 4.5, p < 0.01, and skewed to the left, indicating that a minority of families (n = 25, 21%) reported costs related to the anxiety of the child. A logistic regression was performed using families with and without costs as the dependent variable and age, gender, severity and number of diagnoses as covariates within an enter method. When comparing families who reported costs to families who reported no costs, binary logistic regression revealed only one main effect for age, corrected for gender, severity and number of diagnoses, Wald (1) = 11.4, p < 0.01, O.R. = 1.4. No main effect was found for gender, severity or number of diagnoses, Wald (1)s < 0.2, ps > 0.1. This indicates that in families who reported costs related to the child’s anxiety, the child was older compared to families who reported no costs, irrespective of gender, severity and number of diagnoses. The main contributor to costs were costs related to institutionalized treatment (26% of total costs), day treatment (23% of total costs), productivity loss of the parents (23% of total costs) and school absence (17% of total costs).
Table 1Mean total resource use, subtotal and total societal costs due to anxiety reasons using a bottom-up approach (n = 118) Costs per unitTotal resource use (2 weeks)Total costs (2 weeks)Total costs per child (2 weeks)Total costs per child a year  SumSumM (SD)M (SD)Direct health care costsPsychologist124/contact10.000.000.000.00Psychiatrist76.00/contact10.000.000.000.00G.P practice20.20/contact13.0060.600.51 (4.14)13.35 (108)G.P. telephone10.10/contact11.0010.100.09 (0.93)2.23 (57.98)Anxiety medication0.72/ddd214.2510.260.09 (0.50)2.26 (13.00)Pharmacists fee6.45/med.114.2591.910.78 (4.50)20.28 (117)Institutionalized treatment164/day520.003,28627.85 (213)724 (5,537)Day treatment95.87/day530.002,87624.37 (152)634 (3,940) Subtotal  6,33553.69 (261)1,396 (6,773) Bootstrapped subtotal   52.78 (24.05)1,371 (583)Direct non-health care costsPaid house keeper12.70/h12.0025.440.22 (2.34)5.61 (60.84)Informal care8.30/h132.002692.28 (19.04)59.23 (495) Subtotal  2942.50 (19.16)64.84 (498) Bootstrapped subtotal   2.43 (1.76)68.15 (47.59)Indirect costsPaid work34.98/h183.292,88824.48 (154)636 (4003)Unpaid work8.30/h14.0033.600.28 (3.09)7.40 (80.34)Loss of leisure time8.30/h143.003613.06 (16.47)79.59 (428)Household work8.30/h16.5054.600.46 (4.03)12.03 (105)School absence4.37/h34742,07017.55 (57.89)456.20 (1,505) Subtotal  5,40845.83 (198)1,191.64 (5,135) Bootstrapped subtotal   46.12 (18.22)1,193.64 (473)Out-of-pocket costsTransportation0.16/km112620.200.17 (1.41)4.42 (36.66)Own contribution alternative treatment  3502.97 (32.22)77.12 (838)Own contribution medication not prescribed  63.300.54 (5.83)13.95 (152) Subtotal  4343.67 (38.22)95.52 (994) Bootstrapped subtotal   3.86 (36.48)97.52 (94.08)Total costs  12,471106 (340)2,748 (8,841)Bootstrapped total costs   104 (32.45)2,749 (815)1Source of volume price: Oostenbrink et al. 2004, 2Gip databank, 3Nibud, 4Cost diary, 5Actual costs
The total number of Dutch children aged 8–18 in 2003 was computed using data of the Statistics Netherlands (2005), and resulted in 2,175,382 children. Multiplying this figure by 0.34% resulted in 7,385 Dutch children with an anxiety disorder that interferes with daily functioning and who are referred to treatment. Hence, the annual societal cost-of-illness accumulated to €2,748 × 7,385 = €20,293,958 for families of clinically anxious children referred for treatment in the Netherlands. The same calculation was performed using the low and high multiplication factors. This resulted in a low estimate of the cost-of-illness of €4,783,839 and a high estimate of €48,436,370.
Discriminative Validity
The mean total annual societal costs related to anxiety, psychological, physical and other reasons for families with a clinically anxious child and families from the general population are presented in Table 2. In the clinically anxious group, costs due to anxiety reasons were highest (89% of total costs), followed by costs due to physical reasons (6% of total costs), costs due to other reasons (3% of total costs) and psychological reasons (3% of total costs). The total annual costs of families with clinically anxious children due to anxiety problems, psychological problems, physical problems and other amounted to a mean of €3,084 (SD = 8,945) per child.
Table 2Subtotal and total costs for clinically anxious children and children from the general population per year using a bottom-up approach (n = 118) Anxiety reasonPsychological reasonPhysical reasonOther reasonTotal a year Cl. anx.Gen.Cl. anx.Gen.Cl. anx.Gen.Cl. anx.Gen.Cl. anx.Gen.Direct health care costsPsychologist/psychiatrist0.000.000.000.000.000.000.000.000.000.00G.P practice13.350.004.450.0013.350.000.000.0028.860.00G.P. telephone2.230.002.230.004.450.000.000.008.840.00Hospital visit0.000.000.000.0034.3798.8017.190.0051.4898.80Emergence visit hospital0.000.000.000.0030.630.000.000.0030.680.00Medication2.260.002.290.006.804.680.220.0011.704.68Pharmacist fee20.250.0018.480.0029.8420.541.420.0069.9420.54Day treatment6340.000.000.000.000.000.000.006340.00Institutionalized treatment7240.000.000.000.000.000.000.007240.00 Subtotal1,3960.0027.440.0011912418.830.001,559124 Bootstrapped subtotal        1,521124Direct non-health care costsHouse keeper5.610.000.000.000.000.008.410.0014.040.00Informal care59.230.000.000.000.000.0014.810.0074.100.00 Subtotal64.830.000.000.000.000.0023.210.0088.050.00 Bootstrapped subtotal        87.210.00Indirect costsPaid work6360.000.000.000.000.007.670.006440.00Unpaid work7.400.000.000.000.000.000.000.007.280.00Leisure time loss79.590.000.000.000.000.000.000.0079.560.00Household work12.030.000.000.000.000.000.000.0011.960.00School absence4560.0051.010.0048.600.0015.880.005720.00 Subtotal1,1920.0051.010.0048.600.0023.550.001,3150.00 Bootstrapped subtotal        1,3050.00Out-of-pocket costsTransportation4.450.000.760.003.680.000.850.009.620.00Alcohol0.000.000.000.000.000.0011.130.2911.180.29Caffeine0.000.000.000.000.500.000.780.001.300.00Tobacco0.000.000.000.000.000.007.400.007.280.00Own expenses91.070.000.007.291.3816.490.000.0092.5623.66 Subtotal95.520.000.767.295.5516.4920.160.2912223.92 Bootstrapped subtotal        12523.89Total costs2,7480.0079.217.2917414185.750.293,084148Bootstrapped total2,7900.0078.447.2317314286.990.323,070147Incremental costs (95% CI)−2,790 (−4,530 to −1,336)−71 (−205 to 0)−31 (−199 to 131)−87 (−152 to −33)−2,923 (−4,505 to −1,470)
Costs of families from the general population were highest for physical reasons (95% of total costs). A small proportion of costs was assigned to psychological reasons (5% of total costs) and other reasons (0.2% of total costs). Despite the fact that five children in the control group met criteria for an anxiety disorder, families reported no costs for anxiety reasons. The total annual costs obtained from families of the general population amounted to a mean of €148 (SD = 523) per family.
Costs in both groups were not normally distributed (Kolmogorov–Smirnov Z > 1.6, p < 0.05). The bootstrapped results indicated that total societal costs were significantly higher in families with clinically anxious children compared to families from the general population. Subdividing the costs into reason showed significant higher costs related to anxiety and other reasons in families with a clinically anxious child compared to families from the general population. Costs related to psychological reasons were borderline significantly higher for the families with a clinically anxious child (see Table 2).
Convergent Validity
The total health care costs of 8- to 18-year-old children with a primary diagnosis of anxiety disorder amounted to €8,018,000 in 1999 using the top-down approach, which augmented to €9,075,613 in 2003 when corrected with CPI. The total number of anxious 8- to 18-year-old children referred for treatment in 2003 was 7,385 (i.e. 2,175,382 × 0.34%). Dividing the total health care costs due to anxiety in children by this number resulted in €1,229 per anxious child that was referred (see Table 3).
Table 3Direct health care costs, anxiety based per child a year based on the Bottom-up and top-down approach Bottom-up costsTop-down costsA day of hospitalization0.00203Day treatment0.001.84Polyclinic visit0.0042.51Categorical hospital0.0032.71Remaining0.007.60 Subtotal hospital care0.00287General practitioner15.5858.27Paramedical care0.001.23 Subtotal Primary care92.7059.50Pharmaceutical help36.4967.50Mental health care1,358785Management and care insurances0.0029.92Total costs1,4101,229
Direct health care costs derived from the bottom-up method were used to compare with the top-down approach and amounted to €1,410 per referred anxious child (see Table 3). Total health care costs for clinically anxious children resulted in €10,412,187 with a low estimate of €2,453,541 and a high estimate of €24,842,036. Although the overall difference in total health care costs between the bottom-up and top-down approach per anxious child per year were about €180 (top-down 13% lower than bottom-up), the differences in costs per health care sector were considerably higher. For instance, costs of medical hospital care were €287 higher using the top-down approach (23% of total costs) compared to costs obtained with the bottom-up approach (0% of total costs). On the other hand, mental health care costs were €573 higher (top-down 42% lower than bottom-up) using the bottom-up approach (96% of total costs) compared to the costs obtained with the top-down approach (64% of total costs).
Discussion
To the authors’ knowledge, this is the first cost-of-illness study in clinically anxious children referred for treatment. The aim of this study was threefold. The first aim was to measure the societal costs of illness in clinically anxious children and their families, using a bottom-up approach by means of a prospective cost diary. The second aim was to investigate the discriminative validity of the prospective cost diary by comparing the costs of families with a clinically anxious child to the costs of families from the general population. The third aim of the study was to establish convergent validity by comparing bottom-up acquired health care costs of the clinically anxious children with top-down acquired health care costs of children with a primary diagnosis of anxiety.
With respect to results regarding cost-of-illness, bottom-up acquired total societal costs for families of clinically anxious children referred for treatment amounted to more than €20 million per year in the Netherlands with a low estimation of 4.8 and a high estimation of 48 million euros, which is a quite broad range. Costs amounted to €2,748 per family with a clinically anxious child per year. These costs seem relatively low compared to costs of other childhood psychopathology. Knapp et al. (1999) conducted a pilot study on ten children aged 4–10 with conduct disorders. Cost amounted to €22,272 per family per year, which is about eight times as high as the costs per family with a clinically anxious child in our study. However, a more elaborate study (n = 80) on 3- to 8-year-old children with anti-social behaviour (Romeo et al. 2006), reported lower mean annual total costs, namely €8781 per child, still 3.2 times higher than those of anxious children. In another study on 11 children with autism spectrum disorders, societal costs were €51,844 per child per year (Järbrink et al. 2003), which is 19 times higher than the costs per anxious child. It should be noted that the prevalence rate for anxiety is almost four times as high as conduct disorder (2.6%, Verhulst et al. 1997) and 39 to 44 times higher than the prevalence rate for autism spectrum disorders (0.22–0.25%, van der Gaag et al. 1996). Consequently, the relative costs for society of childhood anxiety disorders, combining absolute costs with prevalence, are similar to conduct disorders and higher than those of autism spectrum disorders.
In line with Knapp et al. (1999), Romeo et al. (2006) and Järbrink et al. (2003) a large part of the costs in our study can be attributed to the parents’ productivity loss due to anxiety disorders of the child (23% of total costs). Knapp et al. (1999) found that per family with a child with conduct disorder, the annual costs due to lost employment were €5,683 (30% of the total costs). Costs to the family related to extra time spend on household tasks, the need for repairs and time off work due to the anti-social behaviour of the child were €4,637, which is 78% of the total costs. Järbrink et al. (2003) showed that more than 50% of the total costs of children with an autism spectrum disorder were attributable to parents and costs related to income loss were €17,405 (34% of total costs). Our study also found high costs due to school absenteeism (17% of total costs). School absence can have long-term economic consequences such as decreased job opportunities or even unemployment. Taken together, both productivity costs of parents and school absence costs of children seem very important and should be considered in future costing studies. Furthermore, logistic regression analysis revealed that in families with costs related to the child’s anxiety, the child was older compared to families with no costs, irrespective of gender, severity and number of diagnoses. This is in line with the finding that top-down health care costs related to anxiety disorders and health care costs in general increase with age (Polder et al. 2002). Perhaps older children are more prone to react against their parents and are more independent, thereby reducing the influence of parents (Hudson et al. 2002). It can be speculated that less influence from the parents may lead to more school absenteeism among older children. Indeed, 39% of the children aged 18–24 reported school absenteeism in the last 2 months compared to 13% of the children aged 12–17 (Statistics Netherlands 2005). Also, the older child may be more aware of the anxiety complaints, possibly increasing the costs of mental health care. Future research should investigate this issue more profoundly.
An additional finding of this study concerns the difference in measuring costs in children with anxiety disorders compared to cost-of-illness studies in adults with anxiety disorders. The main difference of measuring costs in children with anxiety disorders, as other mental disorders in children, is that anxiety in the child does not only affect the child, but also the child’s family. Therefore, the costs concerning the child’s anxiety should also be measured at the family level. Although many would argue that the costs of illness for adults spill over to the family as well, studies on costs of anxiety disorders in adults usually do not include family members, especially not their children. So, in future research it may be interesting to assess the costs of adult anxiety disorders on a family level as well. For example, they might be more protective of their children and therefore increase their children’s health care consumption or more often let their child miss a day from school. Moreover, in adult cost-of-illness studies, productivity losses due to absence of paid work are measured. In children, these kind of productivity losses do not occur. However, alternatives such as costs due to absence from school should be taken into account, since school participation can be regarded as a productive activity, and (regular) absence from school may serve as a proxy for future productivity losses.
With respect to the results regarding discriminative validity, total costs of families with clinically anxious children using a bottom-up approach were almost 21 times higher than costs of families from the general population. In line with the hypothesis, the difference in costs between families of clinically anxious children and families from the general population can largely be subscribed to the significant difference in anxiety-related costs, showing good discriminative validity of the cost diary on anxiety related costs. However, significant differences were also found on costs due to other reasons. In the prospective cost diary, the reasons for resource use were recorded based on subjective judgments by the parents. However, since anxiety is an internalizing disorder, parents may not always have been able to interpret children’s symptoms correctly. Therefore, they may have attributed costs mistakenly to non-anxiety reasons. For example, parents regularly reported a baby-sitter for a child old enough to stay on its own, if the child would not have been anxious. Another item that was reported as not being related to anxiety was extra travel expenses for the separation anxious child, who otherwise could have stayed at home.
With respect to convergent validity, total direct health care costs related to anxiety were 13% lower using a top-down approach than using a bottom-up approach, which seems quite comparable. However, subdividing the costs into several cost categories showed that costs were distributed differently among the cost categories. For example, bottom-up acquired mental health care costs were 58% higher than top-down acquired mental health care costs. Furthermore, 23% of the total costs using a top-down approach were attributed to hospital care, whereas no hospital care costs were found using the bottom-up approach. Hence, convergent validity on total direct health care costs were quite comparable, while the specific cost categories differed between bottom-up and the top-down approach. These findings suggest that children, who are not yet referred for mental health care, might consume more medical hospital care compared to children who are referred for mental health care. Therefore, from a cost-of-illness perspective, it would also be interesting to investigate costs of families with children with anxiety disorders who are not referred for mental health treatment, using the cost dairy developed for this study. It might well be that these families have more medical health care costs, as the comparison with top-down acquired data suggests.
This study has some limitations, which should be addressed. The first limitation is that the low and high multiplication factor used in this study caused a broad range of the societal costs of illness. This can be explained by the broad range of internationally reported prevalence rates and percentages of children seeking treatment due to differences in measurements, populations, and criteria that need to be fulfilled. As a Dutch population was investigated, the most appropriate prevalence rate is 9.7% (Verhulst et al. 1997), which was obtained through the use of a reliable structured diagnostic interview. The same is true for the 3.5% of children with mental disorders seeking treatment (Verhulst and van der Ende 1997). Besides, 4% of the Dutch people with anxiety disorders are referred for further treatment by their general practitioner (van Wieren et al. 2007). Therefore, the prevalence and referral rates used in this study seem reliable.
The second limitation of the current study is that it can be argued that 2-week cost diaries filled in by parents are not representative enough to reliably assess 1-year bottom-up costs due to the limited time period. However, Goossens et al. (2000) have found that keeping a diary in a limited period namely 1 week every month, 2 weeks every 2 months or an entire year resulted in similar outcomes in fibromyalgia and pain patients. Another study of Bruijnzeels et al. (1998b) found that there was a substantial agreement between a 3-week cost diary, filled in by parents, and medical records concerning general practitioner visits of children. Moreover, the inclusion period of this study lasted 2 years in which the cost diaries were filled out, therefore possible seasonal influences are ruled out. However, it is likely that service use patterns may vary over the year and do not follow a stable trajectory. So, for some families the 2-weeks period for collecting costs may have led to an underestimation of the total annual costs, whereas for other families this may have led to an overestimation of the total annual costs. Nevertheless, as the mean annual costs are based on cost data from each of these families, it is expected that possible over- and underestimations of annual costs are balanced.
The third, and most serious, limitation of this study is that although total health care costs obtained with the top-down and bottom-up method seemed quite comparable, the sample of children with a primary diagnosis of anxiety disorder used in the top-down study by Polder et al. (2002) might not be fully comparable to the clinically anxious children from our bottom-up approach in at least three respects. First, the children in the bottom-up sample were clinically anxious children with severe anxiety disorders. The mean number of anxiety disorders was 3 and the mean severity level 7, indicating severe interference with daily functioning. The children with a primary diagnosis of anxiety disorder in the top-down sample might suffer from different levels of anxiety disorders; some may have minor anxiety problems, while others may have severe anxiety problems. This kind of information was not available using the top-down data. Second, while the clinically anxious children in the bottom-up sample were awaiting CBT in a community mental health care setting, the anxious children in the top-down sample might be in different types and/or phases of treatment. Again, this kind of information was not available using the top-down data. Third, the clinically anxious children in the bottom-up sample were diagnosed using a universally used semi-structured interview, the ADIS which is based on DSM-IV criteria (American Psychiatric Association 1994). In the top-down sample, children were diagnosed by the doctor in attendance, who attributed the anxiety diagnosis to the patient based on the ICD-9 code, without a structured interview. Due to the classification method, the top-down estimation is possibly less reliable than the bottom-up cost estimation. Therefore, results of the comparison between top-down and bottom-up costs should be interpreted with caution.
In conclusion, this cost-of-illness study shows that the societal costs of families with a clinically anxious child who seek treatment amount to more than 20 million euros a year in the Dutch population, and were 21 times higher than in families of the general population. An effective treatment could decrease these costs on the long term. A recent cost-effectiveness study on family CBT versus individual CBT showed that societal costs in families with a clinically anxious child increased during treatment (due to total costs of 12 sessions CBT a €1,612) but decreased directly after treatment to baseline level. Even more, during the 1-year follow-up societal costs dropped below the costs at baseline (Bodden et al. submitted for publication). Since anxiety disorders tend to last into adulthood (Newman et al. 1996), societal costs are likely to be higher at the long term. This results in anxiety disorders accounting for the highest costs of all mental illnesses in adulthood in the USA (Dupont et al. 1996). Hence, societal costs in adults with anxiety disorders may be saved by providing effective treatments to children with clinical anxiety disorders.

cost-of-illness
cost diary
children
anxiety disorders