Background Protein evolution and protein classification are usually inferred by comparing protein cores in their conserved aligned parts. Structurally aligned protein regions are separated by less conserved loop regions, where sequence and structure locally deviate from each other and do not superimpose well.
Background
Protein evolution and protein classification are usually inferred by comparing protein cores in their conserved aligned parts. Structurally aligned protein regions are separated by less conserved loop regions, where sequence and structure locally deviate from each other and do not superimpose well.Results Our results indicate that even longer protein loops can not be viewed as "random coils" and for the majority of protein families in our test set there exists a linear correlation between the measures of sequence similarity and loop structural similarity. Results suggest that distance matrices derived from the loop (dis)similarity measure may produce in some cases more reliable cluster trees compared to the distance matrices based on the conventional measures of sequence and structural (dis)similarity.
Results
Our results indicate that even longer protein loops can not be viewed as "random coils" and for the majority of protein families in our test set there exists a linear correlation between the measures of sequence similarity and loop structural similarity. Results suggest that distance matrices derived from the loop (dis)similarity measure may produce in some cases more reliable cluster trees compared to the distance matrices based on the conventional measures of sequence and structural (dis)similarity.Conclusions We show that by considering "dissimilar" loop regions rather than only conserved core regions it is possible to improve our understanding of protein evolution.
Conclusions
We show that by considering "dissimilar" loop regions rather than only conserved core regions it is possible to improve our understanding of protein evolution.

Background Globular proteins are considered to be structurally similar if their regular secondary structure elements can be superimposed well and are connected in the same order. The loop regions connecting secondary structures demonstrate less regularity in their conformations even though short loops linking specific secondary structures can be classified into distinct classes [ 1 - 6 ]. The structures and sequences in loop regions may deviate from each other so that they do not superimpose well and as a result loops are very often not aligned by structure-structure or sequence alignment methods. Loops apparently do not contribute much to protein stability but may be quite important for protein specific function and for the interaction with other components of the cell. In our previous work we showed that a measure derived from the loop regions can distinguish homologous from analogous proteins with the same or higher accuracy compared to the conventional measures which are based on comparing proteins in structurally aligned regions only [ 7 ]. Recently it has been observed that structural variation in the core of homologous proteins is linearly correlated with sequence changes [ 8 , 9 ]. As was also shown several years ago, the probability of insertion and deletion events, which occur predominantly in the loop regions, strongly depends on the evolutionary distance between two homologous proteins [ 10 , 11 ]. Based on these observations one might argue that more closely related proteins may exhibit more similarity in the structure of their loop regions compared to distantly related proteins and the structural loop (dis)similarity should correlate with evolutionary distance. To check this hypothesis we performed an analysis of structural variation in the loop regions within different homologous protein families using a recently introduced new measure of loop similarity [ 7 ]. This new measure is based on the concept of the Hausdorff metric, which is used in mathematical topology to define a distance between two point sets of a metric space. It does not require an alignment or one to one correspondence between two point sets. We show that there exists a linear correlation between the average structural change in the loop regions and the evolutionary distance, which allows us to use the loop (dis)similarity measure for inferring the phylogenetic history of homologous protein families.
Background
Globular proteins are considered to be structurally similar if their regular secondary structure elements can be superimposed well and are connected in the same order. The loop regions connecting secondary structures demonstrate less regularity in their conformations even though short loops linking specific secondary structures can be classified into distinct classes [ 1 - 6 ]. The structures and sequences in loop regions may deviate from each other so that they do not superimpose well and as a result loops are very often not aligned by structure-structure or sequence alignment methods. Loops apparently do not contribute much to protein stability but may be quite important for protein specific function and for the interaction with other components of the cell. In our previous work we showed that a measure derived from the loop regions can distinguish homologous from analogous proteins with the same or higher accuracy compared to the conventional measures which are based on comparing proteins in structurally aligned regions only [ 7 ].Recently it has been observed that structural variation in the core of homologous proteins is linearly correlated with sequence changes [ 8 , 9 ]. As was also shown several years ago, the probability of insertion and deletion events, which occur predominantly in the loop regions, strongly depends on the evolutionary distance between two homologous proteins [ 10 , 11 ]. Based on these observations one might argue that more closely related proteins may exhibit more similarity in the structure of their loop regions compared to distantly related proteins and the structural loop (dis)similarity should correlate with evolutionary distance.To check this hypothesis we performed an analysis of structural variation in the loop regions within different homologous protein families using a recently introduced new measure of loop similarity [ 7 ]. This new measure is based on the concept of the Hausdorff metric, which is used in mathematical topology to define a distance between two point sets of a metric space. It does not require an alignment or one to one correspondence between two point sets. We show that there exists a linear correlation between the average structural change in the loop regions and the evolutionary distance, which allows us to use the loop (dis)similarity measure for inferring the phylogenetic history of homologous protein families.Methods Test set To select sets of homologous proteins the Conserved Domain Database (CDD) version 1.62 was taken, which can be accessed at [ 12 ]. The CDD collection of protein domain alignments included curated CDDs [ 13 ] and preprocessed domain families imported from SMART and PFAM, altogether 6222 protein domain families[ 14 ]. Upon import, the sequences from SMART/PFAM alignments with more than 75% identity with known structures were substituted by the most similar structures from the Protein Data Bank [ 15 ]. Each CDD family was decomposed into a set of pairwise structure-structure alignments. Structural alignments were computed by the VAST algorithm [ 16 ] and only those structures which had more than 80% mutual overlap between the VAST alignment footprint and CDD footprint were considered in the analysis. The footprint for a given sequence was defined as a region between the first and the last residues aligned by VAST or CDD. Those families containing short sequence repeats and having average alignment length less than 50 residues were excluded from the test set. The structural pairs within the remaining CDD families were disregarded if at least one of the following conditions held true: - at least one structure in a pair had X-ray resolution of greater than 3.0 Å - the Blast E-value calculated for the VAST alignment exceeded 0.01 - at least one structure in a pair contained a chain discontinuous domain inconsistently aligned between VAST and CDD - at least one structure in a pair contained more than 25% of its nonaligned loops with missing residues. To ensure that protein families span a wide range of sequence similarity, all families were examined and those having less than 30% sequence identity span were not considered in further analysis. The redundancy between protein families was checked by using the procedure implemented in the CDART algorithm [ 17 ] and not more than 2 protein families from the same CDD cluster were retained in the final test set. At the end, the test set comprised 59 CDD families with more than 10 structurally aligned pairs of homologs. This test set covered a wide range of functional and structural classes and the list of test families together with their length, number of protein pairs and correlation coefficients is shown in Table  1 . Table 1 List of the names of 59 test protein families together with their CDD accession names, lengths, number of protein pairs, Pearson correlation coefficients between LHM (AHM) and normalized Blast bitscore. The families are ordered with respect to decreasing quality of LHM correlation. The supplementary table is available at [27]. Family name CDD acc Length #Obs AHM LHM Xylose_isom pfam00259 381 28 -0.99 -0.98 MHC_I pfam00129 175 28 -0.95 -0.96 PTPc smart00194 248 25 -0.92 -0.96 IPT smart00429 97 21 -0.90 -0.94 ZnMc_1 smart00235 137 34 -0.83 -0.94 RNAse_Pc cd00163 99 25 -0.82 -0.94 gpdh_C pfam02800 153 39 -0.72 -0.93 Aamy_C smart00632 81 31 -0.94 -0.90 peroxidase pfam00141 240 48 -0.90 -0.90 copper-bind pfam00127 81 87 -0.84 -0.89 CBM_20 pfam00686 94 15 -0.91 -0.89 RnaseA pfam00074 98 44 -0.48 -0.87 IGv cd00099 105 133 -0.78 -0.86 ADH_zinc_N pfam00107 337 64 -0.93 -0.86 ldh_C pfam02866 143 29 -0.93 -0.86 RIP pfam00161 232 28 -0.87 -0.85 Peptidase_C1 pfam00112 200 55 -0.82 -0.85 ZnMc_2 cd00203 134 23 -0.87 -0.85 PROF cd00148 120 15 -0.90 -0.85 plant_peroxidase cd00314 236 76 -0.90 -0.83 alpha-amylase_C pfam02806 78 39 -0.93 -0.82 sodcu pfam00080 139 15 -0.98 -0.81 fer2_1 cd00207 78 38 -0.86 -0.80 Pept_C1 smart00645 202 90 -0.86 -0.79 ferritin pfam00210 152 19 -0.94 -0.79 ldh pfam00056 135 44 -0.82 -0.78 SH2 pfam00017 86 21 -0.48 -0.78 flavodoxin pfam00258 143 26 -0.88 -0.78 EFh cd00051 57 59 -0.75 -0.77 rhv_1 cd00205 195 71 -0.86 -0.76 LYZ1_1 smart00263 116 67 -0.66 -0.75 aldo_ket_red pfam00248 277 28 -0.93 -0.73 COesterase pfam00135 485 28 -0.80 -0.72 TIG pfam01833 89 39 -0.90 -0.72 fer2_2 pfam00111 69 73 -0.77 -0.70 beta-lactamase pfam00144 264 45 -0.90 -0.70 rhv_2 pfam00073 216 95 -0.86 -0.70 GLECT cd00070 124 28 -0.80 -0.67 globin pfam00042 133 96 -0.74 -0.66 GST_C pfam00043 107 77 -0.77 -0.63 LYZ1_2 cd00119 109 24 -0.43 -0.61 PA2c smart00085 102 210 -0.29 -0.57 lipocalin pfam00061 131 55 -0.62 -0.56 phoslip pfam00068 102 102 -0.21 -0.54 proteasome pfam00227 189 56 -0.80 -0.51 UBCc smart00212 141 45 -0.79 -0.50 Sm smart00651 63 30 -0.54 -0.49 Tryp_SPc smart00020 208 561 -0.55 -0.46 CLECT_1 smart00034 90 35 -0.59 -0.44 crystall pfam00030 81 10 -0.76 -0.41 CLECT_2 cd00037 93 263 -0.45 -0.36 RHO smart00174 173 10 -0.52 -0.36 IGc1 cd00098 88 85 -0.65 -0.32 Tryp_SPc cd00190 211 378 -0.55 -0.31 MHC_II_beta pfam00969 86 32 -0.52 -0.26 ADK pfam00406 174 28 -0.37 -0.19 Rho cd00157 172 66 -0.20 -0.16 Phycobilisome pfam00502 148 15 -0.85 -0.10 ADF smart00102 116 10 -0.85 0.34 Measures of structural and sequence similarity To measure the sequence similarity between homologous proteins from the same family we used a Blast bitscore normalized by the alignment length. Among structure similarity measures used in this paper, two of them, RMSD and alignment-based Hausdorff measure (AHM) were computed by comparing the proteins in structurally aligned regions, while the loop-based Hausdorff measure (LHM) quantified the difference in the loop regions. The root mean squared deviation (RMSD) was calculated using the superposition algorithm due to McLachlan [ 18 ]. The AHM and LHM measures were based on the mathematical concept of Hausdorff distance[ 19 ]. Let  A  = { a 1 ,...,  a m } and  B  = { b 1 ,...,  b n } be finite point sets in a Euclidean space. The Hausdorff distance between the sets  A  and  B  is then defined by: d H  ( A ,  B ) = max {min  j  d ( a 1 ,  b j ),..., min  j  d ( a m ,  b j ), min  i  d ( a i ,  b 1 ),..., min  i  d ( a i ,  b n )}     (1) Here the terms  d ( a i ,  b j ) denote the usual Euclidean distance between the points. In other words, the Hausdorff distance between the sets  A  and  B  is the smallest distance such that every point  a i  ∈  A  is within this distance of some point  b j  ∈  B  and vice versa. Hausdorff distance can be calculated under the assumption that the  C α atoms for both structures are in a common coordinate frame which is defined by the structural alignment between two domains. The Hausdorff measure for loops (LHM) was calculated as an average of Hausdorff distances over all loops in the protein pair, where  n s  is the number of aligned secondary structure elements: The "loop" was defined as a region between two consecutive aligned secondary structure elements and: h i  = 0, if the  i -th loop regions do not have any unaligned residues; h i  =  d H  ( A i ,  B i ), where  A i  contains the set of  C α coordinates of non-aligned residues in the  i -th loop of the first structure in a pair, the last aligned residue from the preceding aligned region and the first aligned residue from the following aligned region. Similarly,  B i  is defined for the second structure in a pair. The sets ( A i ,  B i ) are defined to include two aligned residues so that the measure can be defined even if one of the sets of non-aligned residues is empty. The Hausdorff measure for the structurally aligned regions (AHM) was defined similarly. In this case, instead of the sets that contain the coordinates for the  C α atoms in the loops, we use the coordinates for the  C α atoms in the aligned segments and average over the number of aligned segments. The correlation analysis between the measures of sequence and structural similarity, linear/nonlinear regression analyses and cluster analysis were performed using Splus version 6. Pearson (ρ) and Spearman correlation coefficients were calculated to quantify the accuracy of linear correlation. The P-value under the null hypothesis that the correlation coefficient between two variables is equal to zero has been estimated and those families with the P-values less than 0.01 were considered as having statistically significant correlation. The cluster analysis was done using the complete linkage clustering [ 20 ] where the distance between two clusters was measured as a maximum distance between a point in one cluster and a point in another cluster. The cluster trees based on p-distance and LHM were compared using the Phylip program [ 21 ] by generating 1000 bootstrap alignments from the structural alignments of a protein family and by calculating p-distance based cluster trees from the bootstrap alignments. The bootstrap support for the LHM based tree or different partitions of this tree was calculated by counting how many times the LHM topology occurs among the bootstrap cluster trees.
Methods
Test set To select sets of homologous proteins the Conserved Domain Database (CDD) version 1.62 was taken, which can be accessed at [ 12 ]. The CDD collection of protein domain alignments included curated CDDs [ 13 ] and preprocessed domain families imported from SMART and PFAM, altogether 6222 protein domain families[ 14 ]. Upon import, the sequences from SMART/PFAM alignments with more than 75% identity with known structures were substituted by the most similar structures from the Protein Data Bank [ 15 ]. Each CDD family was decomposed into a set of pairwise structure-structure alignments. Structural alignments were computed by the VAST algorithm [ 16 ] and only those structures which had more than 80% mutual overlap between the VAST alignment footprint and CDD footprint were considered in the analysis. The footprint for a given sequence was defined as a region between the first and the last residues aligned by VAST or CDD. Those families containing short sequence repeats and having average alignment length less than 50 residues were excluded from the test set. The structural pairs within the remaining CDD families were disregarded if at least one of the following conditions held true: - at least one structure in a pair had X-ray resolution of greater than 3.0 Å - the Blast E-value calculated for the VAST alignment exceeded 0.01 - at least one structure in a pair contained a chain discontinuous domain inconsistently aligned between VAST and CDD - at least one structure in a pair contained more than 25% of its nonaligned loops with missing residues. To ensure that protein families span a wide range of sequence similarity, all families were examined and those having less than 30% sequence identity span were not considered in further analysis. The redundancy between protein families was checked by using the procedure implemented in the CDART algorithm [ 17 ] and not more than 2 protein families from the same CDD cluster were retained in the final test set. At the end, the test set comprised 59 CDD families with more than 10 structurally aligned pairs of homologs. This test set covered a wide range of functional and structural classes and the list of test families together with their length, number of protein pairs and correlation coefficients is shown in Table  1 . Table 1 List of the names of 59 test protein families together with their CDD accession names, lengths, number of protein pairs, Pearson correlation coefficients between LHM (AHM) and normalized Blast bitscore. The families are ordered with respect to decreasing quality of LHM correlation. The supplementary table is available at [27]. Family name CDD acc Length #Obs AHM LHM Xylose_isom pfam00259 381 28 -0.99 -0.98 MHC_I pfam00129 175 28 -0.95 -0.96 PTPc smart00194 248 25 -0.92 -0.96 IPT smart00429 97 21 -0.90 -0.94 ZnMc_1 smart00235 137 34 -0.83 -0.94 RNAse_Pc cd00163 99 25 -0.82 -0.94 gpdh_C pfam02800 153 39 -0.72 -0.93 Aamy_C smart00632 81 31 -0.94 -0.90 peroxidase pfam00141 240 48 -0.90 -0.90 copper-bind pfam00127 81 87 -0.84 -0.89 CBM_20 pfam00686 94 15 -0.91 -0.89 RnaseA pfam00074 98 44 -0.48 -0.87 IGv cd00099 105 133 -0.78 -0.86 ADH_zinc_N pfam00107 337 64 -0.93 -0.86 ldh_C pfam02866 143 29 -0.93 -0.86 RIP pfam00161 232 28 -0.87 -0.85 Peptidase_C1 pfam00112 200 55 -0.82 -0.85 ZnMc_2 cd00203 134 23 -0.87 -0.85 PROF cd00148 120 15 -0.90 -0.85 plant_peroxidase cd00314 236 76 -0.90 -0.83 alpha-amylase_C pfam02806 78 39 -0.93 -0.82 sodcu pfam00080 139 15 -0.98 -0.81 fer2_1 cd00207 78 38 -0.86 -0.80 Pept_C1 smart00645 202 90 -0.86 -0.79 ferritin pfam00210 152 19 -0.94 -0.79 ldh pfam00056 135 44 -0.82 -0.78 SH2 pfam00017 86 21 -0.48 -0.78 flavodoxin pfam00258 143 26 -0.88 -0.78 EFh cd00051 57 59 -0.75 -0.77 rhv_1 cd00205 195 71 -0.86 -0.76 LYZ1_1 smart00263 116 67 -0.66 -0.75 aldo_ket_red pfam00248 277 28 -0.93 -0.73 COesterase pfam00135 485 28 -0.80 -0.72 TIG pfam01833 89 39 -0.90 -0.72 fer2_2 pfam00111 69 73 -0.77 -0.70 beta-lactamase pfam00144 264 45 -0.90 -0.70 rhv_2 pfam00073 216 95 -0.86 -0.70 GLECT cd00070 124 28 -0.80 -0.67 globin pfam00042 133 96 -0.74 -0.66 GST_C pfam00043 107 77 -0.77 -0.63 LYZ1_2 cd00119 109 24 -0.43 -0.61 PA2c smart00085 102 210 -0.29 -0.57 lipocalin pfam00061 131 55 -0.62 -0.56 phoslip pfam00068 102 102 -0.21 -0.54 proteasome pfam00227 189 56 -0.80 -0.51 UBCc smart00212 141 45 -0.79 -0.50 Sm smart00651 63 30 -0.54 -0.49 Tryp_SPc smart00020 208 561 -0.55 -0.46 CLECT_1 smart00034 90 35 -0.59 -0.44 crystall pfam00030 81 10 -0.76 -0.41 CLECT_2 cd00037 93 263 -0.45 -0.36 RHO smart00174 173 10 -0.52 -0.36 IGc1 cd00098 88 85 -0.65 -0.32 Tryp_SPc cd00190 211 378 -0.55 -0.31 MHC_II_beta pfam00969 86 32 -0.52 -0.26 ADK pfam00406 174 28 -0.37 -0.19 Rho cd00157 172 66 -0.20 -0.16 Phycobilisome pfam00502 148 15 -0.85 -0.10 ADF smart00102 116 10 -0.85 0.34
Test set
To select sets of homologous proteins the Conserved Domain Database (CDD) version 1.62 was taken, which can be accessed at [ 12 ]. The CDD collection of protein domain alignments included curated CDDs [ 13 ] and preprocessed domain families imported from SMART and PFAM, altogether 6222 protein domain families[ 14 ]. Upon import, the sequences from SMART/PFAM alignments with more than 75% identity with known structures were substituted by the most similar structures from the Protein Data Bank [ 15 ].Each CDD family was decomposed into a set of pairwise structure-structure alignments. Structural alignments were computed by the VAST algorithm [ 16 ] and only those structures which had more than 80% mutual overlap between the VAST alignment footprint and CDD footprint were considered in the analysis. The footprint for a given sequence was defined as a region between the first and the last residues aligned by VAST or CDD. Those families containing short sequence repeats and having average alignment length less than 50 residues were excluded from the test set. The structural pairs within the remaining CDD families were disregarded if at least one of the following conditions held true:- at least one structure in a pair had X-ray resolution of greater than 3.0 Å- the Blast E-value calculated for the VAST alignment exceeded 0.01- at least one structure in a pair contained a chain discontinuous domain inconsistently aligned between VAST and CDD- at least one structure in a pair contained more than 25% of its nonaligned loops with missing residues.To ensure that protein families span a wide range of sequence similarity, all families were examined and those having less than 30% sequence identity span were not considered in further analysis. The redundancy between protein families was checked by using the procedure implemented in the CDART algorithm [ 17 ] and not more than 2 protein families from the same CDD cluster were retained in the final test set. At the end, the test set comprised 59 CDD families with more than 10 structurally aligned pairs of homologs. This test set covered a wide range of functional and structural classes and the list of test families together with their length, number of protein pairs and correlation coefficients is shown in Table  1 .List of the names of 59 test protein families together with their CDD accession names, lengths, number of protein pairs, Pearson correlation coefficients between LHM (AHM) and normalized Blast bitscore. The families are ordered with respect to decreasing quality of LHM correlation. The supplementary table is available at [27].Family nameFamily nameCDD accCDD accLengthLength#Obs#ObsAHMAHMLHMLHMMeasures of structural and sequence similarity To measure the sequence similarity between homologous proteins from the same family we used a Blast bitscore normalized by the alignment length. Among structure similarity measures used in this paper, two of them, RMSD and alignment-based Hausdorff measure (AHM) were computed by comparing the proteins in structurally aligned regions, while the loop-based Hausdorff measure (LHM) quantified the difference in the loop regions. The root mean squared deviation (RMSD) was calculated using the superposition algorithm due to McLachlan [ 18 ]. The AHM and LHM measures were based on the mathematical concept of Hausdorff distance[ 19 ]. Let  A  = { a 1 ,...,  a m } and  B  = { b 1 ,...,  b n } be finite point sets in a Euclidean space. The Hausdorff distance between the sets  A  and  B  is then defined by: d H  ( A ,  B ) = max {min  j  d ( a 1 ,  b j ),..., min  j  d ( a m ,  b j ), min  i  d ( a i ,  b 1 ),..., min  i  d ( a i ,  b n )}     (1) Here the terms  d ( a i ,  b j ) denote the usual Euclidean distance between the points. In other words, the Hausdorff distance between the sets  A  and  B  is the smallest distance such that every point  a i  ∈  A  is within this distance of some point  b j  ∈  B  and vice versa. Hausdorff distance can be calculated under the assumption that the  C α atoms for both structures are in a common coordinate frame which is defined by the structural alignment between two domains. The Hausdorff measure for loops (LHM) was calculated as an average of Hausdorff distances over all loops in the protein pair, where  n s  is the number of aligned secondary structure elements: The "loop" was defined as a region between two consecutive aligned secondary structure elements and: h i  = 0, if the  i -th loop regions do not have any unaligned residues; h i  =  d H  ( A i ,  B i ), where  A i  contains the set of  C α coordinates of non-aligned residues in the  i -th loop of the first structure in a pair, the last aligned residue from the preceding aligned region and the first aligned residue from the following aligned region. Similarly,  B i  is defined for the second structure in a pair. The sets ( A i ,  B i ) are defined to include two aligned residues so that the measure can be defined even if one of the sets of non-aligned residues is empty. The Hausdorff measure for the structurally aligned regions (AHM) was defined similarly. In this case, instead of the sets that contain the coordinates for the  C α atoms in the loops, we use the coordinates for the  C α atoms in the aligned segments and average over the number of aligned segments. The correlation analysis between the measures of sequence and structural similarity, linear/nonlinear regression analyses and cluster analysis were performed using Splus version 6. Pearson (ρ) and Spearman correlation coefficients were calculated to quantify the accuracy of linear correlation. The P-value under the null hypothesis that the correlation coefficient between two variables is equal to zero has been estimated and those families with the P-values less than 0.01 were considered as having statistically significant correlation. The cluster analysis was done using the complete linkage clustering [ 20 ] where the distance between two clusters was measured as a maximum distance between a point in one cluster and a point in another cluster. The cluster trees based on p-distance and LHM were compared using the Phylip program [ 21 ] by generating 1000 bootstrap alignments from the structural alignments of a protein family and by calculating p-distance based cluster trees from the bootstrap alignments. The bootstrap support for the LHM based tree or different partitions of this tree was calculated by counting how many times the LHM topology occurs among the bootstrap cluster trees.
Measures of structural and sequence similarity
To measure the sequence similarity between homologous proteins from the same family we used a Blast bitscore normalized by the alignment length. Among structure similarity measures used in this paper, two of them, RMSD and alignment-based Hausdorff measure (AHM) were computed by comparing the proteins in structurally aligned regions, while the loop-based Hausdorff measure (LHM) quantified the difference in the loop regions.The root mean squared deviation (RMSD) was calculated using the superposition algorithm due to McLachlan [ 18 ]. The AHM and LHM measures were based on the mathematical concept of Hausdorff distance[ 19 ]. Let  A  = { a 1 ,...,  a m } and  B  = { b 1 ,...,  b n } be finite point sets in a Euclidean space. The Hausdorff distance between the sets  A  and  B  is then defined by:AaamBbbnABd H  ( A ,  B ) = max {min  j  d ( a 1 ,  b j ),..., min  j  d ( a m ,  b j ), min  i  d ( a i ,  b 1 ),..., min  i  d ( a i ,  b n )}     (1)dHABjdabjjdambjidaibidaibnHere the terms  d ( a i ,  b j ) denote the usual Euclidean distance between the points. In other words, the Hausdorff distance between the sets  A  and  B  is the smallest distance such that every point  a i  ∈  A  is within this distance of some point  b j  ∈  B  and vice versa. Hausdorff distance can be calculated under the assumption that the  C α atoms for both structures are in a common coordinate frame which is defined by the structural alignment between two domains. The Hausdorff measure for loops (LHM) was calculated as an average of Hausdorff distances over all loops in the protein pair, where  n s  is the number of aligned secondary structure elements:daibjABaiAbjBCnsThe "loop" was defined as a region between two consecutive aligned secondary structure elements and:h i  = 0, if the  i -th loop regions do not have any unaligned residues;hiih i  =  d H  ( A i ,  B i ), where  A i  contains the set of  C α coordinates of non-aligned residues in the  i -th loop of the first structure in a pair, the last aligned residue from the preceding aligned region and the first aligned residue from the following aligned region. Similarly,  B i  is defined for the second structure in a pair. The sets ( A i ,  B i ) are defined to include two aligned residues so that the measure can be defined even if one of the sets of non-aligned residues is empty. The Hausdorff measure for the structurally aligned regions (AHM) was defined similarly. In this case, instead of the sets that contain the coordinates for the  C α atoms in the loops, we use the coordinates for the  C α atoms in the aligned segments and average over the number of aligned segments.hidHAiBiAiCiBiAiBiCCThe correlation analysis between the measures of sequence and structural similarity, linear/nonlinear regression analyses and cluster analysis were performed using Splus version 6. Pearson (ρ) and Spearman correlation coefficients were calculated to quantify the accuracy of linear correlation. The P-value under the null hypothesis that the correlation coefficient between two variables is equal to zero has been estimated and those families with the P-values less than 0.01 were considered as having statistically significant correlation. The cluster analysis was done using the complete linkage clustering [ 20 ] where the distance between two clusters was measured as a maximum distance between a point in one cluster and a point in another cluster. The cluster trees based on p-distance and LHM were compared using the Phylip program [ 21 ] by generating 1000 bootstrap alignments from the structural alignments of a protein family and by calculating p-distance based cluster trees from the bootstrap alignments. The bootstrap support for the LHM based tree or different partitions of this tree was calculated by counting how many times the LHM topology occurs among the bootstrap cluster trees.Results and discussion Tables   1  and  2  show the accuracy of correlation obtained between the various measures of structural similarity (RMSD, AHM and LHM). As can be seen from these tables, the correlation quantified by the Pearson correlation coefficient is quite high for most of the families and half of the families have coefficients between -0.76 and -0.81 depending on the structural similarity measure used (Spearman rank correlation coefficients were shown to be very close to those reported in Tables  1  and  2 ). This result is consistent with the studies of Wood and Pearson who showed on a smaller test set of 35 protein families that half of them have correlation coefficients greater than 0.878 [ 8 ]. In their case the sequence-structure correlation was quantified, however, by using only the measures based on the structurally aligned regions of the proteins. The dependence of structural similarity on sequence similarity in some cases can be more accurately described by the nonlinear regression model taking into account higher order quadratic terms. To quantify how much the nonlinear terms improve the data fitting, we use the ratio of squared correlation coefficient for linear ( ) and nonlinear ( ) models ( ). In the overall test set only 12 families have  r 2  – ratio smaller than 0.9 (with LHM used as a structural similarity measure) indicating that for these cases adding the non-linear term improves the performance of modeling by about 10%. As was shown previously, the evolutionary relatedness between proteins can be successfully gauged from the comparison of their loop regions [ 7 ]. Indeed, Table  2  and Figure  1  show that within the families of homologous proteins, the structural changes in loops are strongly coupled with evolutionary distance, which in the first approximation can be estimated using normalized Blast score. The structural-sequence dependence in loop regions for 71% of our protein families can be well described by a linear model and for 88% of protein families the linear correlation coefficients are found to be statistically significant. Comparing different measures of structural similarity one can see that AHM performs somewhat better than other quantities yielding 90% of families with statistically significant linear correlation coefficients (with P-value < 0.01) and 80% of families with  r 2  > 0.9. Table 2 Table shows the median of Pearson correlation coefficients, fraction of families with statistically significant correlation (P-value less than 0.01) and the fraction of families with the ratio  r 2  higher than 0.9 for each measure of structural similarity used in the study. Median correlation coefficient % families with significant correlation % families with r 2  > 0.9 RMSD -0.81 90 71 AHM -0.82 90 80 LHM -0.76 88 71 Figure 1 Hausdorff measure (in Angstroms) for loop (LHM) and aligned (AHM) regions is plotted versus the normalized Blast bitscore for three families: Pancreatic ribonucleases (RnaseA), Ig-like plexins/transcription factors (IPT) and Trypsin-like serine proteases (Tryp_SPc). Solid line shows the linear regression fit of the data. However, not all families exhibit such good correlation. One example of a protein family showing particularly low LHM correlation is the family of Actin depolymerisation factor/cofilin-like domains (ADF). The sequence-structure correlation for loop regions of this family is not statistically significant (the Pearson correlation coefficient is close to zero) whereas the sequence-structure correlation for the protein core is very high (ρ = -0.85 with AHM). Indeed, different proteins of this family show distinctly different loop conformations and evolutionary analysis of ADF family argued that the insertions present in the vertebrate ADF/cofilins (and not present in non-vertebrate cofilins) might be important for nuclear function of mammalian cofilins [ 22 ]. Therefore, in this case the structural heterogeneity of loop regions can be explained by the acquisition of a new distinct function by some members of this family. For some families, for example, Trypsin-like serine protease (Tryp_SPc), neither LHM (ρ = -0.31) nor AHM (ρ = -0.55) similarity measures exhibit a good sequence-structure correlation (Figure  1(c) ). Among families with particularly high LHM correlation are the families of Xylose isomerase (Xylose_isom), Class I Histocompatibility antigen (domains alpha 1 and 2, MHC_I), Protein tyrosine phosphatase (PTPc) and others. Figure  1  shows two families with high sequence-structure correlation using the LHM measure: Ig-like plexins (IPT) and Ribonucleases A (RnaseA). The IPT family is characterized by high sequence-structure correlation for both core (ρ AHM  = -0.90) and loop regions (ρ LHM  = -0.94). On the other hand, the protein core structure of the RnaseA family changes very little with sequence whereas the loop structure gradually diverges as sequence becomes more and more dissimilar (ρ AHM  = -0.48, ρ LHM  = -0.87). To understand whether significant sequence-structure correlation for loop regions has an underlying biological meaning, we performed a cluster analysis of proteins from two diverse families, Ribonuclease A (RnaseA), and SH2 domain (SH2, ρ AHM  = -0.48, ρ LHM  = -0.78), using different measures of sequence and structural similarity. Figure  2  depicts the cluster trees constructed using distance/similarity matrices which were based on the fraction of non-identical residues (p-distance), RMSD and LHM for these two families. Figure 2 Complete linkage cluster tree produced using fraction of non-identical residues (p-distance), RMSD (Å), and LHM (Å) is plotted between proteins from Pancreatic ribonuclease family (RnaseA). Five major groups of RnaseA family according to Rosenberg et al [23] are: eosinophil ribonucleases (ER), pancreatic ribonucleases (PR), angiogenins (ANG), Rana ribonucleases (RR) and ribonuclease 4 (R4). The maximum parsimony tree described by Rosenberg et al [23] is given in the Phylip format: (RR, ((ANG, R4), (PR, ER))). The RnaseA family represents a very interesting example to study as it is characterized by considerably different catalytic efficiency and substrate preferences among family members and the different aspects of its activity is not well understood. Although cysteines that form disulfide bonds, catalytic histidines and lysine residues are mostly structurally and sequence conserved, there is a great variability in sequence between other regions of RnaseA proteins [ 23 , 24 ]. We compared the obtained cluster trees (Figure  2 ) with the maximum-parsimony phylogenetic tree derived by Rosenberg et al [ 23 ], the Phylip format of this tree is given in the captions of Figure  2 . As shown in this figure, the RMSD-based tree divides pancreatic ribonucleases (PR) into two groups and puts together two very different proteins: angiogenin (ANG) and  Rana  ribonuclease (RR) although angiogenin has a very weak enzymatic activity and is a tumor-growth promoter while  Rana  ribonuclease P-30 has ribonuclease activity and antitumor effects. In contrast to the RMSD cluster tree, distance matrices based on the loop (dis)similarity measure correctly cluster the representatives of the five major groups of the Ribonuclease family as per Rosenberg et al [ 23 ]. Although the topology of the p-distance based cluster tree is somewhat different from the topology of the LHM based tree (with bootstrap support less than 0.001), it also produces a biologically meaningful clustering as judged from Rosenberg et al [ 23 ]. SH2 domains represent phosphor-tyrosyl peptide binding modules which are found in many signaling proteins. The specificity of phosphate interaction with a protein has been attributed to the hydrophobic pocket which is mostly formed by two loop regions [ 25 ]. Our analysis shows that indeed the loop regions have a much higher accuracy in clustering of functional subfamilies of SH2 domains. Comparing our cluster trees with the classification of Songyang et al [ 26 ] and cluster trees of SH2 phosphotyrosyl binding sites [ 25 ] we can see from Figure  3  that p-distance based and RMSD based distance matrices cluster correctly two representatives of the "1A" subfamily (vsrc, hck), but separate proteins from subfamily "1B" (csk, csk, syk) and "4" (shptp2 and shc). In contrast, these subfamilies ("1B" and "4" [ 26 ]) are very well supported by the cluster tree which is based on the LHM measure. The bootstrap calculations (see Methods) show that the LHM based topology is supported by the p-distance based clustering algorithm at less than the 0.001 level. Different partitions of this tree are supported at higher but still non-significant levels, namely 0.11 for the "1B" subfamily (csk, csk, syk) and 0.01 for the subfamily "4" (shptp2 and shc). This in turn indicates that the two cluster trees can be considered statistically different. Figure 3 Complete linkage cluster tree produced using fraction of non-identical residues (p-distance), RMSD (Å), and LHM (Å) is plotted between proteins from SH2 family (SH2). The classifications of SH2 domains according to [25, 26] are given in the parentheses: syk (1B, B), shptp2 (4, C), vsrc (1A, A), hck (1A, A), csk (1B, B), P85a (3, D) and shc (4,).
Results and discussion
Tables   1  and  2  show the accuracy of correlation obtained between the various measures of structural similarity (RMSD, AHM and LHM). As can be seen from these tables, the correlation quantified by the Pearson correlation coefficient is quite high for most of the families and half of the families have coefficients between -0.76 and -0.81 depending on the structural similarity measure used (Spearman rank correlation coefficients were shown to be very close to those reported in Tables  1  and  2 ). This result is consistent with the studies of Wood and Pearson who showed on a smaller test set of 35 protein families that half of them have correlation coefficients greater than 0.878 [ 8 ]. In their case the sequence-structure correlation was quantified, however, by using only the measures based on the structurally aligned regions of the proteins.The dependence of structural similarity on sequence similarity in some cases can be more accurately described by the nonlinear regression model taking into account higher order quadratic terms. To quantify how much the nonlinear terms improve the data fitting, we use the ratio of squared correlation coefficient for linear ( ) and nonlinear ( ) models ( ). In the overall test set only 12 families have  r 2  – ratio smaller than 0.9 (with LHM used as a structural similarity measure) indicating that for these cases adding the non-linear term improves the performance of modeling by about 10%.rAs was shown previously, the evolutionary relatedness between proteins can be successfully gauged from the comparison of their loop regions [ 7 ]. Indeed, Table  2  and Figure  1  show that within the families of homologous proteins, the structural changes in loops are strongly coupled with evolutionary distance, which in the first approximation can be estimated using normalized Blast score. The structural-sequence dependence in loop regions for 71% of our protein families can be well described by a linear model and for 88% of protein families the linear correlation coefficients are found to be statistically significant. Comparing different measures of structural similarity one can see that AHM performs somewhat better than other quantities yielding 90% of families with statistically significant linear correlation coefficients (with P-value < 0.01) and 80% of families with  r 2  > 0.9.rTable shows the median of Pearson correlation coefficients, fraction of families with statistically significant correlation (P-value less than 0.01) and the fraction of families with the ratio  r 2  higher than 0.9 for each measure of structural similarity used in the study.rMedian correlation coefficientMedian correlation coefficient% families with significant correlation% families with significant correlation% families with r 2  > 0.9% families with r 2  > 0.9RMSDRMSDAHMAHMLHMLHMHowever, not all families exhibit such good correlation. One example of a protein family showing particularly low LHM correlation is the family of Actin depolymerisation factor/cofilin-like domains (ADF). The sequence-structure correlation for loop regions of this family is not statistically significant (the Pearson correlation coefficient is close to zero) whereas the sequence-structure correlation for the protein core is very high (ρ = -0.85 with AHM). Indeed, different proteins of this family show distinctly different loop conformations and evolutionary analysis of ADF family argued that the insertions present in the vertebrate ADF/cofilins (and not present in non-vertebrate cofilins) might be important for nuclear function of mammalian cofilins [ 22 ]. Therefore, in this case the structural heterogeneity of loop regions can be explained by the acquisition of a new distinct function by some members of this family. For some families, for example, Trypsin-like serine protease (Tryp_SPc), neither LHM (ρ = -0.31) nor AHM (ρ = -0.55) similarity measures exhibit a good sequence-structure correlation (Figure  1(c) ).Among families with particularly high LHM correlation are the families of Xylose isomerase (Xylose_isom), Class I Histocompatibility antigen (domains alpha 1 and 2, MHC_I), Protein tyrosine phosphatase (PTPc) and others. Figure  1  shows two families with high sequence-structure correlation using the LHM measure: Ig-like plexins (IPT) and Ribonucleases A (RnaseA). The IPT family is characterized by high sequence-structure correlation for both core (ρ AHM  = -0.90) and loop regions (ρ LHM  = -0.94). On the other hand, the protein core structure of the RnaseA family changes very little with sequence whereas the loop structure gradually diverges as sequence becomes more and more dissimilar (ρ AHM  = -0.48, ρ LHM  = -0.87).To understand whether significant sequence-structure correlation for loop regions has an underlying biological meaning, we performed a cluster analysis of proteins from two diverse families, Ribonuclease A (RnaseA), and SH2 domain (SH2, ρ AHM  = -0.48, ρ LHM  = -0.78), using different measures of sequence and structural similarity. Figure  2  depicts the cluster trees constructed using distance/similarity matrices which were based on the fraction of non-identical residues (p-distance), RMSD and LHM for these two families.The RnaseA family represents a very interesting example to study as it is characterized by considerably different catalytic efficiency and substrate preferences among family members and the different aspects of its activity is not well understood. Although cysteines that form disulfide bonds, catalytic histidines and lysine residues are mostly structurally and sequence conserved, there is a great variability in sequence between other regions of RnaseA proteins [ 23 , 24 ]. We compared the obtained cluster trees (Figure  2 ) with the maximum-parsimony phylogenetic tree derived by Rosenberg et al [ 23 ], the Phylip format of this tree is given in the captions of Figure  2 . As shown in this figure, the RMSD-based tree divides pancreatic ribonucleases (PR) into two groups and puts together two very different proteins: angiogenin (ANG) and  Rana  ribonuclease (RR) although angiogenin has a very weak enzymatic activity and is a tumor-growth promoter while  Rana  ribonuclease P-30 has ribonuclease activity and antitumor effects. In contrast to the RMSD cluster tree, distance matrices based on the loop (dis)similarity measure correctly cluster the representatives of the five major groups of the Ribonuclease family as per Rosenberg et al [ 23 ]. Although the topology of the p-distance based cluster tree is somewhat different from the topology of the LHM based tree (with bootstrap support less than 0.001), it also produces a biologically meaningful clustering as judged from Rosenberg et al [ 23 ].RanaRanaSH2 domains represent phosphor-tyrosyl peptide binding modules which are found in many signaling proteins. The specificity of phosphate interaction with a protein has been attributed to the hydrophobic pocket which is mostly formed by two loop regions [ 25 ]. Our analysis shows that indeed the loop regions have a much higher accuracy in clustering of functional subfamilies of SH2 domains. Comparing our cluster trees with the classification of Songyang et al [ 26 ] and cluster trees of SH2 phosphotyrosyl binding sites [ 25 ] we can see from Figure  3  that p-distance based and RMSD based distance matrices cluster correctly two representatives of the "1A" subfamily (vsrc, hck), but separate proteins from subfamily "1B" (csk, csk, syk) and "4" (shptp2 and shc). In contrast, these subfamilies ("1B" and "4" [ 26 ]) are very well supported by the cluster tree which is based on the LHM measure. The bootstrap calculations (see Methods) show that the LHM based topology is supported by the p-distance based clustering algorithm at less than the 0.001 level. Different partitions of this tree are supported at higher but still non-significant levels, namely 0.11 for the "1B" subfamily (csk, csk, syk) and 0.01 for the subfamily "4" (shptp2 and shc). This in turn indicates that the two cluster trees can be considered statistically different.Conclusions Here we have presented an analysis of how the structure of protein loops changes in evolution as homologous proteins diverge from each other. We showed that for the majority of protein families there exists a statistically significant linear correlation between measures of sequence similarity and average loop structural similarity. This in turn suggests that loops change in evolution via a stepwise insertion or deletion process and clearly one can not portray even longer loop regions as "irregular conformations" or "random coils". Indeed, our results imply that, in general, loops are under constant evolutionary constraints which, apparently, are weaker than those for a protein core but still strong enough to preserve the loop overall structure. Since loops do not contribute much to the protein core stability, these constraints predominantly arise from the importance of loops in interacting with ligands, other proteins and cells, as well as a possible role of loops in protein folding. Modeling of insertion and deletion events in evolution poses a lot of difficulties and protein evolution is usually reconstructed based only on the aligned regions of proteins. We demonstrated that loop regions which usually correspond to the non-aligned protein regions can be very important in inferring the phylogenetic history of a protein family. Moreover, it was shown, that sometimes sequence and structure similarity measures comparing proteins in their core are not sensitive enough to detect subtle (dis)similarities between the subfamilies. Loop-based measures which emphasize the dissimilarities between different protein members can shed light on the evolutionary relationships between homologous proteins.
Conclusions
Here we have presented an analysis of how the structure of protein loops changes in evolution as homologous proteins diverge from each other. We showed that for the majority of protein families there exists a statistically significant linear correlation between measures of sequence similarity and average loop structural similarity. This in turn suggests that loops change in evolution via a stepwise insertion or deletion process and clearly one can not portray even longer loop regions as "irregular conformations" or "random coils". Indeed, our results imply that, in general, loops are under constant evolutionary constraints which, apparently, are weaker than those for a protein core but still strong enough to preserve the loop overall structure. Since loops do not contribute much to the protein core stability, these constraints predominantly arise from the importance of loops in interacting with ligands, other proteins and cells, as well as a possible role of loops in protein folding.Modeling of insertion and deletion events in evolution poses a lot of difficulties and protein evolution is usually reconstructed based only on the aligned regions of proteins. We demonstrated that loop regions which usually correspond to the non-aligned protein regions can be very important in inferring the phylogenetic history of a protein family. Moreover, it was shown, that sometimes sequence and structure similarity measures comparing proteins in their core are not sensitive enough to detect subtle (dis)similarities between the subfamilies. Loop-based measures which emphasize the dissimilarities between different protein members can shed light on the evolutionary relationships between homologous proteins.Authors' contributions AP and TM contributed equally to this paper.
Authors' contributions
AP and TM contributed equally to this paper.


The evidence on early childhood strongly suggests the need to shift child health policy from the current focus on social welfare to a socio-ecologically based approach. This paper reviews three governing instruments, exhortation, expenditure and regulation, that have been used by governments in Australia and discusses the relative effectiveness of these approaches in shifting the child health policy paradigm.

The evidence for healthy public policy for children There can be no keener revelation of a society's soul than the way it treats its children [ 1 ]. Research evidence has demonstrated that the experiences of early childhood can have a profound lifelong impact on a child's health, wellbeing and competence [ 2 ]. The importance of the early years of life in influencing future outcomes, such as crime, obesity, heart disease, mental health problems and poor school outcomes has been identified and highlighted [ 3 ]. While there are many factors found to influence rising crime rates, various researchers have identified children with manifested behaviour disorders in early childhood [ 4 ], academic difficulties and non-engagement in schooling [ 5 ], and the quality of neighbourhood supervision and support as contributing factors to criminal behaviour [ 6 ]. Education, literacy and other social determinants of health can influence the coping skills of children, which provide the basis of learning, behaviour and health throughout life [ 7 ]. Poverty, whether measured in absolute or relative terms, has a negative effect on children's health [ 8 , 9 ]. In particular, poverty is associated with developmental delay, poor school achievement and employment futures, behaviour problems, increased incidence of chronic illness, visual and hearing defects and dental problems [ 10 ]. Parental poverty and exposure to unhealthy environments (eg smoking; low levels of literacy; nutrition; emotional support) reduce a child's life chances. Studies in neurobiology, neurodevelopment and early intervention show that the time period from conception to school age is a critically important time for brain development, setting the scene for prevention of some of the identified adverse outcomes through early identification and intervention [ 11 ]. Consistent with the increasing evidence, many governments have identified support in early childhood as a lifelong determinant of health, wellbeing and competence, as a matter for policy development, initiating actions to ensure comprehensive child development strategies for their societies. This approach requires a whole of government response, integrating health, welfare, education and other relevant parts of government. The evidence suggests that healthy public policy for infants, children and their parents is dependent on understanding of the socio-ecological factors supported by integrated, multidisciplinary and intersectoral policy and programs. In Canada, Britain and the United States, targeted interventions in the antenatal period, infancy and childhood, including parenting skills programs, are recognized for their potential to support healthier families. A socio-ecological model of health is increasingly perceived to be the most appropriate approach for the early years of life agenda. Consistent with this approach, the United Kingdom program  'Sure Start' , has been positively reviewed by the UK Audit Office and is considered by many to be the standard for the whole of government approach [ 12 ]. In addition, a recently released ten year plan is attempting to significantly change the way in which children are treated throughout UK systems [ 13 ]. The Canadian experience is widely quoted as best practice [ 2 ], with both federal and provincial investment in early childhood (See, for example [ 14 - 16 ]). In the US, during the Presidents' Summit for America's Future held in April 1997, Presidents Bill Clinton, George Bush, Jimmy Carter and Gerald Ford and First Lady Nancy Reagan stressed the importance of early childhood, calling the nation to action. American policy in this area has built upon influential reports that have led to investment in early childhood in most states [ 12 ]. The adoption of healthy public policy for children based on this socio-ecological framework has been inconsistent throughout Australia. In an attempt to explore these inconsistencies, this paper reviews the use of three governing instruments, that is exhortation, expenditure and regulation, by national and state governments in Australia. Governing instruments are the major mechanisms governments use to seek compliance, support and implementation of public policy. Governing instruments range from minimum coercion by exhortation, through expenditure, taxation, regulation, to maximum coercion through public ownership [ 17 ]. The following sections describe the impact of the use of exhortation, expenditure and regulation on the implementation of healthy child policy.
The evidence for healthy public policy for children
There can be no keener revelation of a society's soul than the way it treats its children [ 1 ].Research evidence has demonstrated that the experiences of early childhood can have a profound lifelong impact on a child's health, wellbeing and competence [ 2 ]. The importance of the early years of life in influencing future outcomes, such as crime, obesity, heart disease, mental health problems and poor school outcomes has been identified and highlighted [ 3 ]. While there are many factors found to influence rising crime rates, various researchers have identified children with manifested behaviour disorders in early childhood [ 4 ], academic difficulties and non-engagement in schooling [ 5 ], and the quality of neighbourhood supervision and support as contributing factors to criminal behaviour [ 6 ].Education, literacy and other social determinants of health can influence the coping skills of children, which provide the basis of learning, behaviour and health throughout life [ 7 ]. Poverty, whether measured in absolute or relative terms, has a negative effect on children's health [ 8 , 9 ]. In particular, poverty is associated with developmental delay, poor school achievement and employment futures, behaviour problems, increased incidence of chronic illness, visual and hearing defects and dental problems [ 10 ]. Parental poverty and exposure to unhealthy environments (eg smoking; low levels of literacy; nutrition; emotional support) reduce a child's life chances. Studies in neurobiology, neurodevelopment and early intervention show that the time period from conception to school age is a critically important time for brain development, setting the scene for prevention of some of the identified adverse outcomes through early identification and intervention [ 11 ].Consistent with the increasing evidence, many governments have identified support in early childhood as a lifelong determinant of health, wellbeing and competence, as a matter for policy development, initiating actions to ensure comprehensive child development strategies for their societies. This approach requires a whole of government response, integrating health, welfare, education and other relevant parts of government. The evidence suggests that healthy public policy for infants, children and their parents is dependent on understanding of the socio-ecological factors supported by integrated, multidisciplinary and intersectoral policy and programs.In Canada, Britain and the United States, targeted interventions in the antenatal period, infancy and childhood, including parenting skills programs, are recognized for their potential to support healthier families. A socio-ecological model of health is increasingly perceived to be the most appropriate approach for the early years of life agenda. Consistent with this approach, the United Kingdom program  'Sure Start' , has been positively reviewed by the UK Audit Office and is considered by many to be the standard for the whole of government approach [ 12 ]. In addition, a recently released ten year plan is attempting to significantly change the way in which children are treated throughout UK systems [ 13 ].'Sure Start'The Canadian experience is widely quoted as best practice [ 2 ], with both federal and provincial investment in early childhood (See, for example [ 14 - 16 ]). In the US, during the Presidents' Summit for America's Future held in April 1997, Presidents Bill Clinton, George Bush, Jimmy Carter and Gerald Ford and First Lady Nancy Reagan stressed the importance of early childhood, calling the nation to action. American policy in this area has built upon influential reports that have led to investment in early childhood in most states [ 12 ].The adoption of healthy public policy for children based on this socio-ecological framework has been inconsistent throughout Australia. In an attempt to explore these inconsistencies, this paper reviews the use of three governing instruments, that is exhortation, expenditure and regulation, by national and state governments in Australia. Governing instruments are the major mechanisms governments use to seek compliance, support and implementation of public policy. Governing instruments range from minimum coercion by exhortation, through expenditure, taxation, regulation, to maximum coercion through public ownership [ 17 ]. The following sections describe the impact of the use of exhortation, expenditure and regulation on the implementation of healthy child policy.Consensus building – exhortation as the national instrument of choice During the 1990s the Australian Government identified the health of children and young people as a key policy area, with a series of policy documents: •  The National Health Goals and Targets for Australian Children and Youth  (1992) •  The National Health Policy for Children and Young People  (1995) and associated  Implementation Plan  (1996) •  The National Health Policy for Young Australians  (1997). These documents provided broad national goals for children and young people: • Reducing preventable premature mortality • Reducing the impact of disability • Reducing the incidence of vaccine preventable disease • Reducing the impact of conditions occurring in adulthood with their origins or early manifestation in childhood or adolescence • Enhancing family and social functioning Although the evidence supporting a broader definition of child health was strong, the focus of these National Health Goals and Targets remained heavily focused towards surveillance and the reduction of injury and illness, perhaps reflecting a comfort with current and past approaches. To date there has been little evidence of an integrated, multidisciplinary approach to child health at the national level. The 2003/04 federal budget did not provide the broad whole of government approach recommended for child health, with only a few targeted interventions, such as the National Meningococcal C Campaign, and a much greater focus on the health needs of the aging population. In 2003, the Australian of the year, Fiona Stanley suggested that the social and economic policies of the Government were not effective in tackling the issues associated with ensuring healthy children and young people [ 18 ]. The platform for a paradigm shift was established in 2001 with the appointment of the Minister for Children and Youth Affairs and the subsequent statement in 2002 of the intent to develop a National Agenda for Early Childhood. The consultation paper  Towards the Development of a National Agenda for Early Childhood  signaled a changing paradigm, with a whole of child and life course approach addressing promotion, prevention and early intervention for all children. The last years have seen the creation of ever more advisory groups, partnerships and inquiries with a mandate to influence child health policy. The Child and Youth Health Intergovernmental Partnership (under the auspice of the Australian Health Ministers' Advisory Council) was convened in December 2001 to develop a national child public health strategy and advise on the National Agenda for Early Childhood. Their draft strategy framework  Better Child Public Health: A Strategic Approach to Building Capacity – A National Action Plan 2004–2007  has been developed and is being used in consultation and capacity building initiatives. In October 2002 the Minister for Children and Youth Affairs referred an inquiry into improving children's health and well being to the Standing Committee on Family and Community Affairs. The Australian Council for Children and Parenting (ACCAP), an advisory body to the Minister for Children and Youth Affairs, was granted a two year term from July 2003, with a focus on strategic advice in the areas of early childhood intervention and prevention, parenting and child protection, foster care and emerging early childhood initiatives, including advising about the continuing development of the National Agenda for Early Childhood. As described above, the policy approach at the national level has focused almost entirely on exhortation, the least coercive instrument, where support and compliance are sought voluntarily through persuasion and discussion. In comparison with other countries, such as Britain and Canada, the lack of a common and shared understanding of the socio-ecologic approach and its implications has made it difficult to show any significant advances in this area. In fact, the recent demise of the Child Health Unit within the Australian Government Department of Health suggests less focus on child health. Nationally, child health has not been heavily addressed through other policy instruments, such as expenditure, taxation, or regulation, although more recently the Commonwealth Government Department of Family and Community Services (DFaCS) established the  'Communities for Children ' initiative as part of the Stronger Families and Communities Strategy.  Communities for Children  will directly fund 35 Australian communities between $1 and 4 million over four years to support parents, neighbourhoods and the wider community to give children the healthy start they need [ 19 ]. Importantly, there was little evidence of a community development or even a consultative approach in the implementation of this program, with the perception that  Communities for Children  has not been set up to respond to the greatest need. It has been suggested that system change can be accomplished by motivating institutions, systems and actors to move in common directions and develop structures that sustain these efforts over time [ 20 ]. This requires a high level of trust among the participants, such that they eventually share common goals and voluntarily seek to achieve common ends. Success in using exhortation as a policy instrument requires that information not only flow from government, but also to it [ 21 ]. The strong use of exhortation at the national level may be seen as the only way to encourage change, given the shared responsibility for child welfare among the various levels of government in Australia. Yet it is precisely this divided accountability and responsibility that has been identified 'as the greatest barrier to the reform of children's services' [[ 22 ] pg. 980]. The use of exhortation may be successful at motivating common approaches but will be much less effective at ensuring the sustaining structures are developed. This is apparent in the existing committee structures, which still operate from within the government structures and are thus unable to cross the 'silos' to promote the needed whole-of-government approach. To be effective in changing the paradigm in this area, the exhortation process will require back up by more coercive governing instruments.
Consensus building – exhortation as the national instrument of choice
During the 1990s the Australian Government identified the health of children and young people as a key policy area, with a series of policy documents:•  The National Health Goals and Targets for Australian Children and Youth  (1992)The National Health Goals and Targets for Australian Children and Youth•  The National Health Policy for Children and Young People  (1995) and associated  Implementation Plan  (1996)The National Health Policy for Children and Young PeopleImplementation Plan•  The National Health Policy for Young Australians  (1997).The National Health Policy for Young AustraliansThese documents provided broad national goals for children and young people:• Reducing preventable premature mortality• Reducing the impact of disability• Reducing the incidence of vaccine preventable disease• Reducing the impact of conditions occurring in adulthood with their origins or early manifestation in childhood or adolescence• Enhancing family and social functioningAlthough the evidence supporting a broader definition of child health was strong, the focus of these National Health Goals and Targets remained heavily focused towards surveillance and the reduction of injury and illness, perhaps reflecting a comfort with current and past approaches.To date there has been little evidence of an integrated, multidisciplinary approach to child health at the national level. The 2003/04 federal budget did not provide the broad whole of government approach recommended for child health, with only a few targeted interventions, such as the National Meningococcal C Campaign, and a much greater focus on the health needs of the aging population. In 2003, the Australian of the year, Fiona Stanley suggested that the social and economic policies of the Government were not effective in tackling the issues associated with ensuring healthy children and young people [ 18 ].The platform for a paradigm shift was established in 2001 with the appointment of the Minister for Children and Youth Affairs and the subsequent statement in 2002 of the intent to develop a National Agenda for Early Childhood. The consultation paper  Towards the Development of a National Agenda for Early Childhood  signaled a changing paradigm, with a whole of child and life course approach addressing promotion, prevention and early intervention for all children.Towards the Development of a National Agenda for Early ChildhoodThe last years have seen the creation of ever more advisory groups, partnerships and inquiries with a mandate to influence child health policy. The Child and Youth Health Intergovernmental Partnership (under the auspice of the Australian Health Ministers' Advisory Council) was convened in December 2001 to develop a national child public health strategy and advise on the National Agenda for Early Childhood. Their draft strategy framework  Better Child Public Health: A Strategic Approach to Building Capacity – A National Action Plan 2004–2007  has been developed and is being used in consultation and capacity building initiatives. In October 2002 the Minister for Children and Youth Affairs referred an inquiry into improving children's health and well being to the Standing Committee on Family and Community Affairs. The Australian Council for Children and Parenting (ACCAP), an advisory body to the Minister for Children and Youth Affairs, was granted a two year term from July 2003, with a focus on strategic advice in the areas of early childhood intervention and prevention, parenting and child protection, foster care and emerging early childhood initiatives, including advising about the continuing development of the National Agenda for Early Childhood.Better Child Public Health: A Strategic Approach to Building Capacity – A National Action Plan 2004–2007As described above, the policy approach at the national level has focused almost entirely on exhortation, the least coercive instrument, where support and compliance are sought voluntarily through persuasion and discussion. In comparison with other countries, such as Britain and Canada, the lack of a common and shared understanding of the socio-ecologic approach and its implications has made it difficult to show any significant advances in this area. In fact, the recent demise of the Child Health Unit within the Australian Government Department of Health suggests less focus on child health.Nationally, child health has not been heavily addressed through other policy instruments, such as expenditure, taxation, or regulation, although more recently the Commonwealth Government Department of Family and Community Services (DFaCS) established the  'Communities for Children ' initiative as part of the Stronger Families and Communities Strategy.  Communities for Children  will directly fund 35 Australian communities between $1 and 4 million over four years to support parents, neighbourhoods and the wider community to give children the healthy start they need [ 19 ]. Importantly, there was little evidence of a community development or even a consultative approach in the implementation of this program, with the perception that  Communities for Children  has not been set up to respond to the greatest need.'Communities for ChildrenCommunities for ChildrenCommunities for ChildrenIt has been suggested that system change can be accomplished by motivating institutions, systems and actors to move in common directions and develop structures that sustain these efforts over time [ 20 ]. This requires a high level of trust among the participants, such that they eventually share common goals and voluntarily seek to achieve common ends. Success in using exhortation as a policy instrument requires that information not only flow from government, but also to it [ 21 ]. The strong use of exhortation at the national level may be seen as the only way to encourage change, given the shared responsibility for child welfare among the various levels of government in Australia. Yet it is precisely this divided accountability and responsibility that has been identified 'as the greatest barrier to the reform of children's services' [[ 22 ] pg. 980].The use of exhortation may be successful at motivating common approaches but will be much less effective at ensuring the sustaining structures are developed. This is apparent in the existing committee structures, which still operate from within the government structures and are thus unable to cross the 'silos' to promote the needed whole-of-government approach. To be effective in changing the paradigm in this area, the exhortation process will require back up by more coercive governing instruments.Conflicting expenditures – potential for uncertain outcomes in Victoria In comparison with other Australian states, Victoria has been slow to provide visible translation of the socio-ecological model of health for children and young people in a coordinated and systematic way to state policy. A recent review of Victorian paediatric services suggested that Victoria needed to establish a child and young people focus ensuring appropriate mechanisms to plan, coordinate and monitor across government departments and service providers [ 23 ]. It was suggested that a structure was required to coordinate child health among of the various portfolios in the Victorian Department of Human Services – Health, Housing, Welfare, and Disability – as well as among the broader Government departments, contributing to the whole of government approach required for early childhood intervention programs. The lack of coordinated focus on child health in Victoria is perhaps the result of a lingering policy focus on health surveillance. Despite the increasing evidence that surveillance and screening programs have limited effectiveness in child health [ 24 , 25 ], it is only recently that Victoria has increased the focus on the social determinants of health [ 26 , 27 ]. Most recently, Victoria has committed to the  'Best Start'  program and will pilot it as demonstration projects in 10 communities across the State with an investment of $7.6 million.  Best Start  is auspiced by the Departments of Human Services and Education and Training and is focused on reducing the impact of disadvantage (from any cause) and enhancing the life chances of all children by strengthening the universal preventative system [ 28 ]. The aims of  Best Start  are multi-level including the social, emotional and physical well-being of children, capacity building of parents and carers and communities to assist them to become more child friendly, while focusing on specific interventions for socially disadvantaged families [ 29 ]. The demonstration projects are required to follow a prescribed implementation and evaluation process attempting to measure what works, under what circumstances and for whom, to ultimately improve services elsewhere in the State. Five approved demonstration sites with a total of $7.6 million are ensuring a 'brighter future' for the children of Frankston, Hume, Shepparton, Whittlesea and Yarra Ranges, while the rest of the State's children wait in the dark. Despite the intentions of  Best Start , existing government funding and reporting in the area of maternal and child health is still largely focused on surveillance [ 26 ]. The Victorian approach to policy implementation in the area of early childhood support is focused predominantly on expenditure. Public expenditure is moderately coercive, with distribution of government funds to achieve particular policy objectives. But the small expenditure allocated to 'healthy' child policy that is limited to identified demonstration sites with expectations that the program will be shown to be effective before statewide mainstream implementation is overshadowed by a much larger expenditure pool that is not focused on the socio-ecological model. While  Best Start  signals intent to change the child health policy paradigm, the incentives established through the broader expenditure pool suggest, for the moment, maintenance of the status quo in Victoria.
Conflicting expenditures – potential for uncertain outcomes in Victoria
In comparison with other Australian states, Victoria has been slow to provide visible translation of the socio-ecological model of health for children and young people in a coordinated and systematic way to state policy. A recent review of Victorian paediatric services suggested that Victoria needed to establish a child and young people focus ensuring appropriate mechanisms to plan, coordinate and monitor across government departments and service providers [ 23 ]. It was suggested that a structure was required to coordinate child health among of the various portfolios in the Victorian Department of Human Services – Health, Housing, Welfare, and Disability – as well as among the broader Government departments, contributing to the whole of government approach required for early childhood intervention programs.The lack of coordinated focus on child health in Victoria is perhaps the result of a lingering policy focus on health surveillance. Despite the increasing evidence that surveillance and screening programs have limited effectiveness in child health [ 24 , 25 ], it is only recently that Victoria has increased the focus on the social determinants of health [ 26 , 27 ]. Most recently, Victoria has committed to the  'Best Start'  program and will pilot it as demonstration projects in 10 communities across the State with an investment of $7.6 million.  Best Start  is auspiced by the Departments of Human Services and Education and Training and is focused on reducing the impact of disadvantage (from any cause) and enhancing the life chances of all children by strengthening the universal preventative system [ 28 ]. The aims of  Best Start  are multi-level including the social, emotional and physical well-being of children, capacity building of parents and carers and communities to assist them to become more child friendly, while focusing on specific interventions for socially disadvantaged families [ 29 ]. The demonstration projects are required to follow a prescribed implementation and evaluation process attempting to measure what works, under what circumstances and for whom, to ultimately improve services elsewhere in the State. Five approved demonstration sites with a total of $7.6 million are ensuring a 'brighter future' for the children of Frankston, Hume, Shepparton, Whittlesea and Yarra Ranges, while the rest of the State's children wait in the dark.'Best Start'Best StartBest StartDespite the intentions of  Best Start , existing government funding and reporting in the area of maternal and child health is still largely focused on surveillance [ 26 ]. The Victorian approach to policy implementation in the area of early childhood support is focused predominantly on expenditure. Public expenditure is moderately coercive, with distribution of government funds to achieve particular policy objectives. But the small expenditure allocated to 'healthy' child policy that is limited to identified demonstration sites with expectations that the program will be shown to be effective before statewide mainstream implementation is overshadowed by a much larger expenditure pool that is not focused on the socio-ecological model. While  Best Start  signals intent to change the child health policy paradigm, the incentives established through the broader expenditure pool suggest, for the moment, maintenance of the status quo in Victoria.Best StartBest StartGuidelines – will enforcement back the regulatory approach in NSW? In New South Wales the  'Families First'  initiative targets families with children 0 to 8 years, with the aim of helping parents give their children a good start in life. Demonstrating the commitment to a whole of government approach, the Office of Children and Young People (OCYP), located within The Cabinet Office, reporting directly to the Premier, has played a lead role in the development and implementation of the  Families First  strategy. This evidence-based approach is delivered jointly by five NSW government agencies – Area Health Services, Community Services, Education and Training, Housing and Disability, Ageing and Home Care in partnership with parents, community organisations and local government. NSW Health supports 'the ongoing development of partnerships at policy, planning and service delivery levels to enable improved co-ordination and intersectoral collaboration in the delivery of child health services' [[ 30 ] pg. 44]. NSW has also successfully translated much of the evidence into coordinated service planning and delivery at the regional level. Paediatric networks, associated with the Area Health Services, were established in 1997 and today provide designated primary, secondary and tertiary level services for families with children aged 0 to 5 years [ 31 ]. In addition, the NSW Commission for Children and Young People focuses on increasing the participation of children and young people in decision making that affects their lives, promoting the safety and welfare of children and young people, and strengthening the important relationships in the lives of children and young people and improving their well-being [ 32 ]. The implementation of  Families First  has been guided by a series of policy and practice guidelines. Recently, an independent review of  Families First  implementation within three regions, (Orana Far West, Illawarra and South West Sydney) found that the system changes required to build and strengthen service networks for families needed more than agreement and goodwill, with considerable effort to develop structures and processes that sustain interagency collaboration [ 33 ]. This resulted in a further guide to implementing sustainable and effective child and family service networks. Regulation involves the imposition of requirements to meet specific obligations. Often regulation is seen to exist within legislation outlining strict rules of behaviour. However, in health policy guidelines are considered effective means of imposing regulation, recognising the inherent uncertainty in safe practice in health care [ 17 ]. The implementation of 'healthy' child policy in NSW suggests a strong focus on the socio-ecological approach supported by the research evidence. This is apparent in the whole of government approach with leadership from the Premier's Office, backed by regulation to effect the necessary changes in the delivery system. However, it is yet to be seen whether the policy will be supported with the necessary resources for compliance. While regulation involves shifting costs of compliance from government to other participants, enforcement and monitoring can be expensive and difficult [ 21 ]. Without adequate enforcement the potential for inequality and inequity in access to the proposed service model is high.
Guidelines – will enforcement back the regulatory approach in NSW?
In New South Wales the  'Families First'  initiative targets families with children 0 to 8 years, with the aim of helping parents give their children a good start in life. Demonstrating the commitment to a whole of government approach, the Office of Children and Young People (OCYP), located within The Cabinet Office, reporting directly to the Premier, has played a lead role in the development and implementation of the  Families First  strategy. This evidence-based approach is delivered jointly by five NSW government agencies – Area Health Services, Community Services, Education and Training, Housing and Disability, Ageing and Home Care in partnership with parents, community organisations and local government. NSW Health supports 'the ongoing development of partnerships at policy, planning and service delivery levels to enable improved co-ordination and intersectoral collaboration in the delivery of child health services' [[ 30 ] pg. 44].'Families First'Families FirstNSW has also successfully translated much of the evidence into coordinated service planning and delivery at the regional level. Paediatric networks, associated with the Area Health Services, were established in 1997 and today provide designated primary, secondary and tertiary level services for families with children aged 0 to 5 years [ 31 ]. In addition, the NSW Commission for Children and Young People focuses on increasing the participation of children and young people in decision making that affects their lives, promoting the safety and welfare of children and young people, and strengthening the important relationships in the lives of children and young people and improving their well-being [ 32 ].The implementation of  Families First  has been guided by a series of policy and practice guidelines. Recently, an independent review of  Families First  implementation within three regions, (Orana Far West, Illawarra and South West Sydney) found that the system changes required to build and strengthen service networks for families needed more than agreement and goodwill, with considerable effort to develop structures and processes that sustain interagency collaboration [ 33 ]. This resulted in a further guide to implementing sustainable and effective child and family service networks.Families FirstFamilies FirstRegulation involves the imposition of requirements to meet specific obligations. Often regulation is seen to exist within legislation outlining strict rules of behaviour. However, in health policy guidelines are considered effective means of imposing regulation, recognising the inherent uncertainty in safe practice in health care [ 17 ]. The implementation of 'healthy' child policy in NSW suggests a strong focus on the socio-ecological approach supported by the research evidence. This is apparent in the whole of government approach with leadership from the Premier's Office, backed by regulation to effect the necessary changes in the delivery system.However, it is yet to be seen whether the policy will be supported with the necessary resources for compliance. While regulation involves shifting costs of compliance from government to other participants, enforcement and monitoring can be expensive and difficult [ 21 ]. Without adequate enforcement the potential for inequality and inequity in access to the proposed service model is high.Conclusions A variety of instruments have been used by government to change the child health policy paradigm from that focused on social welfare to 'healthy' public policy predicated on a socio-ecological foundation. NSW has chosen regulation to implement a child health policy framework that is built upon the international evidence of the effectiveness of integrated, multidisciplinary and intersectoral policy and programs. A little slower to change paradigms, Victoria has established demonstration programs through targeted expenditure, without an overarching whole-of-government child health policy framework. Nationally, there is a move to change the paradigm to a broader definition of child health almost exclusively through exhortation. Instrument choice is influenced by a variety of factors. The use of exhortation by the Commonwealth Government is a relatively risk-free easy approach, which can counteract the divided accountabilities among federal and state governments in the area of health and social services. Exhortation is easy to implement; it is the least coercive and relies on voluntary goodwill. While it may be successful in building a common understanding, and even this is debatable, an independent review of the  Families First  implementation found that the system changes required to build and strengthen service networks for families needed more than agreement and goodwill to develop the necessary structures and processes – a suggestion that without other approaches to structural reform, exhortation is unlikely to be successful. The expenditure policy of Victoria illustrates an approach that is compromised by the lack of an underlying agreed evidence-based policy framework. This lack of coordinated government approach is reflected in conflicting expenditure policy in this area, with the potential to confound outcomes. While the regulatory approach of NSW suggests bold steps to change the paradigm, in fact, because regulation is not subjected to the same level of scrutiny of other instruments, such as expenditure, and even exhortation [ 17 ], it is a deceptively simple mechanism to implement policy [ 21 ]. The strength of the government intent to change the paradigm will only become apparent with visible enforcing of the service delivery directions. The evidence for a new policy paradigm is strong. But the use of these different policy instruments underscores the lack of shared understanding and policy agenda. Oberklaid suggests that while there are similarities in the rhetoric throughout Australia, there has been relatively little investment in child health [ 12 ]. The change in the child health public policy paradigm will only be successful when the governing instrument or combination of instruments induces the appropriate public and private behaviour. Perhaps we should be thankful that child health policy is on the agenda, and even without a strong, coordinated approach built on the evidence, one would agree that 'these developments in early childhood services demonstrate the translation of research evidence into policy and practice, even if the implementation may be flawed, belated or under-resourced' [[ 5 ] pg.15].
Conclusions
A variety of instruments have been used by government to change the child health policy paradigm from that focused on social welfare to 'healthy' public policy predicated on a socio-ecological foundation. NSW has chosen regulation to implement a child health policy framework that is built upon the international evidence of the effectiveness of integrated, multidisciplinary and intersectoral policy and programs. A little slower to change paradigms, Victoria has established demonstration programs through targeted expenditure, without an overarching whole-of-government child health policy framework. Nationally, there is a move to change the paradigm to a broader definition of child health almost exclusively through exhortation.Instrument choice is influenced by a variety of factors. The use of exhortation by the Commonwealth Government is a relatively risk-free easy approach, which can counteract the divided accountabilities among federal and state governments in the area of health and social services. Exhortation is easy to implement; it is the least coercive and relies on voluntary goodwill. While it may be successful in building a common understanding, and even this is debatable, an independent review of the  Families First  implementation found that the system changes required to build and strengthen service networks for families needed more than agreement and goodwill to develop the necessary structures and processes – a suggestion that without other approaches to structural reform, exhortation is unlikely to be successful.Families FirstThe expenditure policy of Victoria illustrates an approach that is compromised by the lack of an underlying agreed evidence-based policy framework. This lack of coordinated government approach is reflected in conflicting expenditure policy in this area, with the potential to confound outcomes.While the regulatory approach of NSW suggests bold steps to change the paradigm, in fact, because regulation is not subjected to the same level of scrutiny of other instruments, such as expenditure, and even exhortation [ 17 ], it is a deceptively simple mechanism to implement policy [ 21 ]. The strength of the government intent to change the paradigm will only become apparent with visible enforcing of the service delivery directions.The evidence for a new policy paradigm is strong. But the use of these different policy instruments underscores the lack of shared understanding and policy agenda. Oberklaid suggests that while there are similarities in the rhetoric throughout Australia, there has been relatively little investment in child health [ 12 ]. The change in the child health public policy paradigm will only be successful when the governing instrument or combination of instruments induces the appropriate public and private behaviour. Perhaps we should be thankful that child health policy is on the agenda, and even without a strong, coordinated approach built on the evidence, one would agree that 'these developments in early childhood services demonstrate the translation of research evidence into policy and practice, even if the implementation may be flawed, belated or under-resourced' [[ 5 ] pg.15].Competing interests The authors declare that they have no competing interests.
Competing interests
The authors declare that they have no competing interests.