Evaluation of the Serotonergic Genes htr1A, htr1B, htr2A, and slc6A4 in Aggressive Behavior of Golden Retriever Dogs

Aggressive behavior displays a high heritability in our study group of Golden Retriever dogs. Alterations in brain serotonin metabolism have been described in aggressive dogs before. Here, we evaluate whether four genes of the canine serotonergic system, coding for the serotonin receptors 1A, 1B, and 2A, and the serotonin transporter, could play a major role in aggression in Golden Retrievers. We performed mutation screens, linkage analysis, an association study, and a quantitative genetic analysis. There was no systematic difference between the coding DNA sequence of the candidate genes in aggressive and non-aggressive Golden Retrievers. An affecteds-only parametric linkage analysis revealed no strong major locus effect on human-directed aggression related to the candidate genes. An analysis of 41 single nucleotide polymorphisms (SNPs) in the 1 Mb regions flanking the genes in 49 unrelated human-directed aggressive and 49 unrelated non-aggressive dogs did not show association of SNP alleles, genotypes, or haplotypes with aggression at the candidate loci. We completed our analyses with a study of the effect of variation in the candidate genes on a collection of aggression-related phenotypic measures. The effects of the candidate gene haplotypes were estimated using the Restricted Maximum Likelihood method, with the haplotypes included as fixed effects in a linear animal model. We observed no effect of the candidate gene haplotypes on a range of aggression-related phenotypes, thus extending our conclusions to several types of aggressive behavior. We conclude that it is unlikely that these genes play a major role in the variation in aggression in the Golden Retrievers that we studied. Smaller phenotypic effects of these loci could not be ruled out with our sample size.
Introduction
Dogs have been living in close proximity to humans for at least 15,000 years (Clutton-Brock 1995). Behavior has been a strong selective factor in the domestication and breeding of dogs. According to the breed standard, Golden Retriever dogs should have a friendly character (http://www.goldenretrieverclub.nl; link accessed March 2007). However, there are reports of very aggressive Golden Retrievers (Galac and Knol 1997; Heath 1991). We recently described the behavioral phenotype of 110 Golden Retrievers referred to our clinic for aggression problems and 118 Golden Retrievers that were recruited because they were related to one or more of the aggressive dogs (van den Berg et al. 2006). The phenotypes were based on mail questionnaires and on personal interviews with dog owners. In a quantitative genetic study including 325 Golden Retrievers, we found a heritability of 0.8 for the traits of human-directed aggression and dog-directed aggression (Liinamo et al. 2007).
The influence of serotonin (5-hydroxytryptamine, 5-HT) on aggressive behavior has been studied extensively (reviewed by Berman and Coccaro 1998; Gingrich and Hen 2001; Lesch and Merschdorf 2000). There is evidence for a role of the 5-HT system in canine aggression as well. For instance, Reisner and colleagues (1996) reported decreased levels of 5-hydroxyindoleacetic acid (the major metabolite of 5-HT) in cerebrospinal fluid of dominant aggressive dogs. Badino et al. (2004) found modifications of 5-HT receptor concentrations in brains of aggressive dogs. Domestication of silver foxes, which are taxonomically close relatives of dogs, seems to cause modifications in the 5-HT system (see Trut 2001 for a review). The role of 5-HT in canine aggression is further supported by two small clinical studies, where pharmacological or dietary intervention in the 5-HT system was shown to modulate aggressive behavior (DeNapoli et al. 2000; Dodman et al. 1996).
Four genes that code for factors involved in serotonergic neurotransmission are particularly good candidates for the regulation of aggressive behavior: the serotonin receptor genes 1A (htr1A), 1B (htr1B), and 2A (htr2A), and the serotonin transporter gene (slc6A4). Serotonin receptor 1A plays a role in anxiety, stress response, and aggression (Olivier et al. 1995). Htr1A knockout mice show increased anxiety and stress response and an antidepressant-like phenotype (Heisler et al. 1998; Ramboz et al. 1998). In the above-mentioned studies of silver foxes, the researchers observed a lower density of 5-HT1A receptors in the hypothalamus of tame foxes compared to their wild counterparts (Popova et al. 1991). Many studies have suggested involvement of htr1B in the etiology of mental disorders. For instance, Huang et al. (2003) and Sanders et al. (2002) reported an association between one of the polymorphisms in the human HTR1B gene and alcoholism, suicidality, and obsessive-compulsive disorder. Knockout mice lacking htr1B display increased aggression (Saudou et al. 1994). A mutation in the human HTR2A gene is associated with altered 5-HT binding, which has been implicated in schizophrenia, suicidal behavior, impaired impulse control, and aggression history (Abdolmaleky et al. 2004; Bjork et al. 2002; Khait et al. 2005). Peremans and colleagues (2003) found an increased binding index of serotonin 2A receptors in cortical brain regions of impulsive aggressive dogs. A polymorphism in the promoter region of SLC6A4 influences serotonin transporter density in the brain and is associated with mental disorders in humans (Anguelova et al. 2003; Hariri et al. 2002; Lesch et al. 1996). Slc6A4 knockout mice show reduced aggression (Holmes et al. 2003).
In this paper, we test the hypothesis that there is a strong effect of variation in these genes on the variation in aggression in Golden Retrievers. We performed mutation screens of the coding DNA sequence in unrelated aggressive Golden Retrievers. In addition, we used linkage analysis to determine the likelihood of the presence of a strong aggression locus in or close to the genes in several dog families. Third, we used 50 unrelated aggressive Golden Retrievers and 50 unrelated non-aggressive Golden Retrievers to search for association of alleles of 41 SNPs flanking the candidate genes with the trait of human-directed aggression. To complete our analyses, we evaluated the effects of variation in the genes on a range of aggression-related phenotypes using the same models as in Liinamo et al. (2007), extended to include the effects of the most common candidate gene haplotypes.
Materials and methods
Animals, DNA isolation, and phenotyping
We have collected behavioral information of 328 privately owned Golden Retrievers. This group includes 162 dogs that were referred to our clinic because of their aggressive behavior (“probands”) and 166 relatives of 36 probands. DNA samples were available for 281 of these dogs. In addition, we collected DNA of a cohort of random privately owned Goldens that were born between July 2002 and February 2003. No phenotypes were available for these random dogs. Genomic DNA was isolated from whole blood leucocytes using a standard protocol (Miller et al.1988). For each type of analysis (mutation analysis, linkage analysis, association analysis, and quantitative genetic analysis) we selected a study group that was suited for the study design. The study groups are described in the Supplementary Information I.
We have collected various quantitative measures of aggressiveness for the dogs (van den Berg et al. 2003a, 2006). In the linkage and association analysis we focused on one of these measures: the dog owners impression on human-directed aggression. Owner impressions were collected in a personal interview. We asked the owners if their dog was aggressive towards humans and the status of the dog was coded in three classes: non-aggressive (score 1), threatens (score 2), or bites (score 3). We focused on human-directed aggression because the majority of the probands were referred to our clinic for human-directed aggressive behavior. Owner impressions were available for all dogs and the quantitative genetic analyses showed that the heritability of this trait was high in our population of dogs (Liinamo et al. 2007).
Mutation screening
We analyzed the coding DNA sequence (CDS) of the four candidate genes in seven (htr1A and htr1B) or eight (htr2A and slc6A4) probands. The CDS were amplified and sequenced using overlapping primer pairs as described previously (van den Berg et al. 2004, 2005). Possible functional effects of polymorphisms were predicted with POLYPHEN (http://www.genetics.bwh.harvard.edu/cgi-bin/pph/polyphen.cgi). Effects of polymorphisms close to splice sites were predicted with three splice prediction programs: NetGene2 (Brunak et al. 1991), Splice Prediction by Neural Network (Reese et al. 1997), and SpliceSiteFinder (Shapiro and Senapathy 1987).
Linkage analysis
We used nine families for linkage analysis (Figs. S1–S9 of Supplementary Information I). DNA samples were available for 31 affected and 65 unaffected dogs from these families. We converted the owner impression about human-directed aggression into a dichotomous variable for the linkage analysis (see Supplementary Information I). We have described polymorphic markers for the candidate genes before (van den Berg et al. 2003b, 2004, 2005). We selected three microsatellite markers and seven single nucleotide polymorphisms for linkage analysis (see Table 1). Microsatellite markers were genotyped after PCR on an ABI 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA). PCR conditions were described by van den Berg et al. (2004, 2005). GENESCAN 3.7 software was used for genotype assessment. Single nucleotide polymorphism genotyping was performed by DNA sequencing of PCR products on the ABI 3100 Genetic Analyzer (van den Berg et al. 2004, 2005). The DNA sequence chromatograms were inspected using LASERGENE software (DNASTAR, Inc., Madison, WI USA). We combined several markers into haplotypes for the genes htr1B, htr2A, and slc6A4. For dogs that were heterozygous for multiple markers, we deduced the haplotypes from the data of relatives. If this was not possible, we assigned the most frequently observed possible haplotypes to these dogs.
Table 1Markers used for linkage analysisGeneaType of markerbPosition of markercAlleles observed (allele frequency)dHaplotypes observed (haplotype frequency)dhtr1A(CA)n(UU160O12)*7370297 (0.5)303 (0.5)–htr1BA/C SNPe157A (0.58)C (0.42)143-A-G-T-G (0.20)143-C-A-T-G (0.42)143-A-G-C-C (0.06)139-A-G-C-C (0.24)139-A-G-T-G (0.05)147-A-G-T-G (0.03)
G/A SNP246G (0.58)A (0.42)T/C SNP955T (0.69)C (0.31)G/C SNP1146G (0.69)C (0.31)(GA)n(UU18L8)−68395139 (0.29)143 (0.68)147 (0.03)htr2AC/T SNPIVS 2-10C (0.85)T (0.15)128-C (0.07)130-C (0.41)132-C (0.37)132-T (0.15)(CA)n(UUHTR2AEX2)IVS2 + 1439128 (0.07)130 (0.41)132 (0.52)slc6A4C/T SNP411C (0.75)T (0.25)C-G (0.75)T-A (0.25)G/A SNPIVS9-12G (0.75)A (0.25)ahtr1A, htr1B, htr2A = respectively serotonin receptor 1A, 1B, and 2A gene; slc6A4 = serotonin transporter genebSNP = single nucleotide polymorphism. Names of microsatellite markers have been included in bracketscPosition refers to the coding sequence of the canine gene. We used the nomenclature recommended by den Dunnen and Antonarakis (2001): the A of the ATG start codon is designated number 1, the nucleotide 5′ to this A is numbered −1, and the nucleotide 3′ of the translation termination codon is *1. Positions in introns refer to the nearest exon. The nomenclature of the introns is based on the human gene structure. IVS = intervening sequencedAllele and haplotype frequencies were determined in a group of 27 (htr1A and htr2A), 31 (htr1B), or 26 (slc6A4) parentseThis polymorphism is nonsynonymous
We performed a parametric affecteds-only linkage analysis to determine whether the candidate gene haplotypes were linked to aggressive behavior in the Golden Retriever families. Marker haplotype frequencies were determined in a group of 27 (htr1A and htr2A), 31 (htr1B), or 26 (slc6A4) parent dogs (see Supplementary Information I). The mode of inheritance of the aggressive phenotype in our families is unclear. We therefore analyzed the data under both autosomal dominant and autosomal recessive models. The penetrance of the genotype at risk was set at 0.01. In this way, affected dogs are assumed to have the risk allele and the software calculated likelihood that aggressive dogs share alleles by descent from a common ancestor. Unaffected dogs with the genotype at risk have no effect on the outcome of the calculation. We assumed that there were no phenocopies in the families and we assumed genetic homogeneity because all probands were related to each other within a limited number of generations (not shown). The frequency of the aggression allele was set at 0.1 to allow for multiple transmitting ancestors in the pedigrees. SUPERLINK software was used to calculate two-point logarithm of the odds (LOD) scores (Fishelson and Geiger 2002, 2004).
In order to estimate the power of the pedigrees, we calculated the maximum obtainable LOD scores. Affected individuals were assigned haplotypes 2/2 in these calculations; unaffected parents were assigned haplotypes 1/2; and other unaffected individuals were assigned haplotypes 1/1. We assigned haplotypes 0/0 (unknown) to dogs from which we did not have a DNA sample. We assumed that there were four alleles of the hypothetical marker with equal allele frequencies.
Association study
To test for a more complex genetic effect of variants of the candidate genes, we performed an association study. Fifty aggressive Golden Retrievers were selected from our database. The main selection criteria were high estimated breeding values for human-directed aggression and as little interrelationship among the cases as possible. Non-aggressive dogs were selected for low estimated breeding values for both human- and dog-directed aggression. We avoided an excess of relationship within either the case or the control group. The non-aggressive group was completed with 25 dogs from the random group. A more detailed description of cases and controls is provided in Supplementary Information I.
We genotyped a total number of 60,073 SNPs in these 100 Golden Retrievers using customized Affymetrix Genotyping Arrays. The SNPs were selected for the chip using a scoring system that optimized the SNPs accounting for low repeat content, low likelihood of SNPs in the assay probe sequences and their distribution over the genome as a whole (Lindblad-Toh 2007; personal communication). Twenty 32-mer probes interrogated each locus with genotyping calls made using the algorithm BRLMM (http://www.affymetrix.com) which analyses intensities for sets of probes that interrogate both forward and reverse sequences with perfect match and mismatched probes. Dogs with call rates of lower than 50% were discarded from the analysis. The total set of SNPs was filtered for genotype call probability, heterozygosity rate, and call rate across a large set of dogs, reducing the dataset to 26,625 SNPs. From this set, only SNPs within 1 Mb of the four candidate genes were used for the analysis described in this paper. There were 29 SNPs within 1 Mb of htr1A, 20 within 1 Mb of htr1B, 25 within 1 Mb of htr2A, and 20 within 1 Mb of slc6A4. From these SNPs, we selected 43 SNPs with a minor allele frequency >0.05 and call rates of >0.75.
We used Haploview software version 4.0 (Barrett et al. 2005) for the analysis of the presence of Hardy-Weinberg equilibrium (HWE), the local association analysis, and the calculation of pairwise linkage disequilibrium (LD) between the SNPs. A HWE P-value cutoff of 0.001 was used. We used Bonferonni correction to account for multiple testing in the association analysis. Genotype frequencies in cases and controls were compared with Chi square tests using SPSS software. Two tailed Fisher’s exact tests were used when the number of expected cases was less than 5 in more than 20% of the categories. Haplotype blocks were formed using three methods in Haploview (confidence intervals, four gamete rule, and solid spine of LD). For all possible combinations we performed 10,000 permutations to obtain empirical P values for haplotype association tests. We used the genetic power calculator prepared by Purcell to estimate the power of the association analysis (http://www.pngu.mgh.harvard.edu/~purcell/gpc/). The following assumptions were made: high-risk allele frequency = 0.1; prevalence = 0.01. The mean pairwise D′ between the SNPs flanking a candidate gene was used as an estimate of the local D′ in these estimations. Calculations were performed for two different genotype relative risks: 2 (genotype relative risk Aa = 2; genotype relative risk AA = 4) and 5 (genotype relative risk Aa = 5; genotype relative risk AA = 10).
Quantitative genetic analysis
In addition to the owner impression on human-directed aggression and dog-directed aggression, we collected a variety of other aggression-related behavioral measures using the canine behavioral assessment and research questionnaire (CBARQ; Hsu and Serpell 2003). As described in Liinamo et al. (2007), these measures were of three types: original CBARQ items (27 items on the aggressiveness of the dog in various everyday situations), shortened CBARQ scores (scores based on questions that addressed stranger-directed, owner-directed, and strange dog-directed aggression), and CBARQ factors (scores based on questions about stranger-directed, owner-directed, and strange dog-directed, and familiar dog-directed aggression). For further explanation of the measures and the difference between shortened CBARQ scores and CBARQ factors, see Liinamo et al. (2007).
The effects of the haplotypes of the serotonergic genes on the different aggression measures were estimated with Restricted Maximum Likelihood (REML) method (Patterson and Thompson 1971), using univariate analyses and an animal model with the VCE4.2.4 software (Groeneveld 1997). The analyses were an extension of the analyses outlined in Liinamo et al. (2007), using similar linear animal model methodology, but this time also including the haplotype classes of the dogs for the four studied loci as additional fixed effects in the model. For instance, the linear animal model that was assumed in the analyses for owner impression traits was: where yijklmnois the observed value for the owner impression score for animal o;μ the general mean in the population; sexithe fixed effect of the reproductive status (i = 1–4, with 1 = intact male, 2 = castrated male, 3 = intact female, and 4 = castrated female); agej the fixed effect of the age j (j = 1–11, with 1 = 0.5–1 year old, 2 = 1–2 years old,…,10 = 9–10 years old, and 11 = over 10 years old); htr1ak , htr1bl, htr2am, and slc6a4n the fixed effects of the respective haplotype classes, aothe random additive genetic effect (i.e., polygenic breeding value) of the animal o, and eo the random residual effect related to the animal o. The age and reproductive status of the dogs had been recorded at the same time as the owner impressions. The haplotype classes were formed so that the most common haplotypes formed separate classes, the very rare haplotypes were all combined in one class, and the unknown haplotypes were classified as a separate class (see Table 4).
Results
Mutation screening of the coding DNA sequence
The coding DNA sequence of each candidate gene was scanned for mutations in seven (htr1A and htr1B) or eight (htr2A and slc6A4) aggressive Golden Retrievers. Analysis of the CDS in non-aggressive Golden Retrievers has been described by van den Berg et al. (2004; 2005). There was no variation in the CDS of htr1A and htr2A in the Golden Retrievers. We observed five SNPs in the CDS of htr1B and one SNP in the CDS of slc6A4. The allele distribution of these SNPs in the two groups of Golden Retrievers did not indicate a role in aggressive behavior. In conclusion, there seems to be no systematic difference between the CDS of the candidate genes in aggressive and non-aggressive Golden Retrievers.
Linkage analysis
We observed two alleles for htr1A marker UU160O12 (Table 1). The four SNPs in htr1B were fully in LD (D′ = 1). The SNPs displayed six haplotypes in the Goldens, three of which were rare (frequency <0.1). We detected four haplotypes of htr2A in the Golden Retrievers. The two SNPs in slc6A4 were fully in LD and formed two haplotypes in the dogs. In the nine families that we used for linkage analysis, haplotypes were deduced with certainty in 86% of the dogs for htr1B, 100% for htr2A, and 87% for slc6A4. We calculated the maximum achievable LOD score using hypothetical genotypes. The maximum LOD score generated by our pedigrees was 2.8 at recombination fraction θ = 0 assuming a dominant mode of inheritance (Table 2). Under a recessive model, the maximum LOD score was 5.3 at θ = 0. The families are therefore theoretically powerful enough to prove linkage under a recessive model and powerful enough to provide a good indication of the presence of linkage under a dominant model. There was no significant linkage of any of the candidate genes with the aggressive phenotype (Table 2). LOD scores varied from −1.0 to +0.26 assuming dominant inheritance and from −2.3 to −0.30 assuming recessive inheritance. The highest LOD scores were obtained for htr1A (+0.26 under a dominant model and −0.30 under a recessive model).
Table 2Results from the ODDS (LOD) scoresaGeneAutosomal dominant Autosomal recessivehtr1A0.26−0.30htr1B−0.72 −2.3htr2A−1.0 −2.1slc6A40.030−1.2Maximum2.85.3aLOD scores were calculated with the following assumptions: frequency of the “aggression allele” = 0.1; penetrance of the “aggression allele” = 0.01; θ = 0. Marker haplotype frequencies were deduced from a group of parents
Association study
Two dogs (one case and one control) were discarded from the association analysis because they had call rates lower than 50%. Mean call rates in the other 98 dogs were 92% for SNPs flanking htr1A, 94% for htr1B, 93% for htr2A, and 93% for slc6A4. There were 43 SNPs with a minor allele frequency >0.05 and call rates of >0.75 that occurred within 1 Mb of the candidate genes. The genotype frequencies of BICF2P1093362 for htr1B and BICF2P969902 for slc6A4 deviated from HWE in control dogs (P = 4.42E-14 and P = 5.242E-12, respectively). All but one dog in both case and control group had heterozygous genotypes for these SNPs. We concluded that the data for these two SNPs was artefactual and they were excluded from further analyses. The observed genotype frequencies of the other 41 SNPs were in HWE in controls (P values are listed in Table S2 in Supplementary Information II). The final SNP set used for the association analysis consisted of 12 SNPs flanking htr1A, 11 flanking htr1B, 8 flanking htr2A, and 10 flanking slc6A4 (Table 3).
Table 3Single nucleotide polymorphisms (SNPs) used for the association study, their allele frequencies in 49 aggressive cases and 49 control dogs, and results of chi-square tests for comparisons of case and control allele frequenciesSNP nameaChromosomal locationChromosomal position (Mb)bMinor allele frequency in controlsCorresponding allele frequency in casesχ2P-valueBICF2P546848251.800.290.300.0440.83BICF2P1051894251.960.0730.0281.60.21BICF2P1398268252.010.330.330.0110.92BICF2S23127755252.140.230.210.0780.78BICF2P1200391252.220.300.330.210.64BICF2P590055252.350.240.270.190.66BICF2S22939125252.440.480.530.540.46BICF2S23215863252.480.300.310.0250.87BICF2P25993252.730.490.450.190.66BICF2S23442706252.760.190.281.80.18Htr1A252.88–52.88––––BICF2P519607253.220.270.240.170.68BICF2P1341930253.620.490.540.520.47BICF2P11592411241.110.120.130.0120.91BICF2P5551301241.590.130.212.10.15BICF2S233262291241.630.240.321.30.25Htr1B1241.65–41.66–––BICF2P6703311241.800.150.232.20.14BICF2S231537601241.840.0390.123.20.073BICF2P14265221242.290.120.120.0130.91BICF2P275711242.340.120.100.210.65BICF2S234440661242.390.120.110.0320.86TIGRP2P164447_rs88059861242.510.0380.123.60.059BICF2P8121531242.570.0330.124.80.029BICF2P8554021242.620.0210.126.90.0086BICF2G630315581226.3830.100.140.830.36BICF2G630315746226.6110.100.140.450.50BICF2P1168502226.9730.100.110.00200.96Htr2A227.395–7.453––––BICF2P164280227.5090.430.542.00.16BICF2G630316047227.7090.440.531.70.19BICF2S23125159227.8660.190.210.0470.83BICF2S23661838228.2070.310.360.590.44BICF2S22954191228.4700.230.270.350.55BICF2P813837946.930.390.301.50.22BICF2S23018060947.060.330.370.220.64BICF2S23551918947.460.340.380.240.62Slc6A4947.55–47.57–BICF2S23325050947.640.110.150.530.47BICF2S23124809947.790.180.220.380.54BICF2P950384947.880.110.120.0320.86BICF2S245135948.020.270.250.0740.79BICF2S2347312948.090.220.280.840.36BICF2S23154457948.130.310.241.20.28BICF2S23141984948.260.360.360.00700.93ahtr1A, htr1B, htr2A = respectively serotonin receptor 1A, 1B, and 2A gene; slc6A4 = serotonin transporter gene. The genes are included in the table to show their position relative to the SNPsbSNP positions are based on the second version of the dog genome assembly, released in May 2005 (CanFam2.0) as can be viewed on
http://www.broad.mit.edu/ftp/pub/papers/dog_genome/snps_canfam2/Positions of the genes are based on the second version of the dog genome assembly (CanFam2.0) as displayed in NCBI Map Viewer for Canis familiarishttp://www.ncbi.nlm.nih.gov/mapview/map_search.cgi?taxid=9615
The power of our association analysis depends on the local extent of LD. The mean r2 between the SNPs flanking htr1A was 0.36 and the mean D′ between these SNPs was 0.89. Mean r2 values were 0.23, 0.25, and 0.21 for htr1B, htr2A, and slc6A4, respectively. Mean D′ values were 0.80, 0.72, and 0.88 for htr1B, htr2A, and slc6A4, respectively. When the marker allele frequency is 0.1, the power to detect a variant with a relative risk of 5 with 49 cases and 49 controls would be 0.91 for htr1A, 0.85 for htr1B, 0.78 for htr2A, and 0.90 for slc6A4. Additional power estimations are provided in Fig. S10 in Supplementary Information II.
The allele frequencies of the SNPs did not differ significantly between cases and controls after correction for multiple testing (Table 3). Genotype frequencies also did not display significant differences between cases and controls (see Table S2 in Supplementary Information II). We also analyzed the association of haplotypes with the phenotype. No significant associations were found (data not shown). In conclusion, there seemed to be no association between alleles, genotypes or haplotypes of SNPs that flank the candidate genes and human-directed aggression in the Golden Retrievers.
Quantitative genetic analysis
We completed our analyses with a study of the effect of variation in the candidate genes on a collection of aggression-related phenotypic measures. The haplotype effects were studied on owner impressions on human- and dog-directed aggression, the original CBARQ items related to stranger- and owner-directed aggression, the shortened CBARQ scores and the CBARQ factors. The haplotypes did not have a significant effect on any of the studied measures, i.e. the heritability estimates of the measures remained similar to the results presented in Liinamo et al. (2007) in spite of incorporation of the haplotypes in the mixed model. The results for owner impressions on human- and dog-directed aggression, which are the most reliable estimates due to the largest number of observations, are presented in Table 4. In conclusion, the large genetic variability between the dogs could not be explained by the serotonergic genes studied in this paper.
Table 4The effects of the studied genotypes (htr1A) or haplotypes (htr1B, htr2A, and slc6A4) on owner impressions of human-directed aggression and dog-directed aggression in 320 dogs Genotype or haplotype classaNumber of animalsEffect relative to class ‘unknown’Human-directed aggressionDog-directed aggressionhtr1AUnknown440.000.00297/297740.0280.11297/3031110.240.054297/305150.0120.089303/303620.180.085303/30510−0.11−0.067Other4−0.046−0.17htr1BUnknown1660.000.00143-A-G-T-G/143-A-G-T-G20−0.730.28143-A-G-T-G /143-C-A-T-G8−0.63−0.19143-A-G-T-G /139-A-G-C-C190.110.19143-C-A-T-G/143-C-A-T-G41−0.170.13143-C-A-T-G /143-A-G-C-C10−0.0210.059143-C-A-T-G /139-A-G-C-C300.00060.32139-A-G-C-C/139-A-G-C-C90.240.11Other170.150.36htr2AUnknown1140.000.00132-C/132-C40−0.15−0.19132-C/130-C60−0.27−0.023132-C/132-T28−0.220.073132-C/128-C15−0.38−0.43130-C/130-C21−0.33−0.072130-C/132-T220.10−0.19130-C/128-C9−0.52−0.29Other110.33−0.036slc6A4Unknown1030.000.00C-G/C-G1160.025−0.23C-G/T-A940.14−0.23Other70.079−0.10None of the effects was significantaNote that there are additional alleles compared to Table 1 as a result of the larger study group. The htr1A class “other” contains genotypes 295/297 (n = 2 dogs) and 305/305 (n = 1 dog). For htr1B, the class “other” contains haplotypes 143-A-G-T-G/143-A-G-C-C (n = 3 dogs), 143-A-G-T-G/139-A-G-T-G (n = 1 dog), 143-A-G-T-G/147-A-G-T-G (n = 2 dogs), 143-A-G-C-C /139-A-G-C-C (n = 3 dogs), 143-A-G-C-C/147-A-G-T-G (n = 1 dog), 139-A-G-C-C /139-A-G-T-G (n = 3 dogs), and 143-C-A-T-G/139-A-G-T-G (n = 4 dogs). For htr2A, “other” contains haplotypes 0/130-C (n = 2 dogs), 0/132-T (n = 1 dog), 132-T /132-T (n = 6 dogs), and 132-T/128-C (n = 2 dogs). For slc6A4, “other” contains C-G/T-G (n = 1 dog) and T-A/T-A (n = 6 dogs). Unknown genotypes and haplotypes are the result of failure of genotyping or of the absence of a DNA sample (n = 44 dogs)
Discussion
We collected behavioral information and DNA samples of 281 dogs over a period of 10 years. Dogs were selected from this collection to evaluate four genes involved in serotonin metabolism by four methods: DNA sequence analysis of the coding region of the genes, genetic linkage analysis, genetic association analysis, and quantitative genetic analysis. The results indicate that it is unlikely that there is a major locus effect of one of the genes on aggression in the Golden Retrievers that we studied.
The genetic study of the variation of aggression in Golden Retrievers is a promising tool to identify the molecular systems involved in aggression. The relative ease to find disease loci in the dog genome compared to the human genome is the result of the population structure of dog breeds. Within a breed, Lindblad-Toh and colleagues (2005) observed a limited number of common haplotypes per genomic region. In addition, LD in dog breeds extends over at least 50-fold greater distances than in human populations. These characteristics make the dog highly suited for molecular genetic studies of complex traits (Sutter and Ostrander 2004).
We did not detect mutations in the CDS of the genes specific for aggressive dogs. All SNPs except A157C in htr1B were synonymous. This A157C variation, resulting in an isoleucine/leucine polymorphism of amino acid 53, was predicted to be functionally insignificant by POLYPHEN. The genes htr2A and slc6A4 contain SNPs close to splice sites (at position IVS2-10 and IVS9-12, respectively) that could theoretically affect splicing. However, the polymorphisms did not have a large effect on splice site prediction by three software programs. We performed mutation screening in a limited number of dogs and it is possible that we have missed rare alterations in the genes. Apart from this limitation, we conclude that there is no common variant acting on protein structure that contributes to the variation in aggression in our Golden Retriever sample.
We used linkage analysis to evaluate the likelihood that there is a major aggression-influencing variant in the chromosomal regions surrounding the coding exons. The affecteds only parameters that we used in the calculations are a simplification with the assumption that all affected dogs of a family have the genotype at risk but unaffected dogs can have any genotype at the aggression locus. A LOD score of 3 is usually considered as evidence for linkage, whereas LOD scores below −2 exclude the gene. A power calculation with simulated genotypes was not feasible in this study due to the complexity of some pedigrees with multiple loops. Instead, we calculated the maximum achievable LOD scores to get an impression of the power embodied in the pedigrees. We assumed full informativeness of the markers in these calculations, but in reality, we expect the markers to have limited informativeness. Realistic obtainable LOD scores would then be lower than the maximum values that we presented in Table 2. These LOD scores are too low to obtain significant results, but they provide a means to set the obtained results into perspective. The LOD scores for htr1B and htr2A were low compared to the maximum obtainable scores. A major role of these genes is unlikely. The results for htr1A and slc6A4 are less conclusive. This is probably the result of the poor informativeness of the markers. For both genes, we observed only two alleles or haplotypes with high frequencies. Typing of additional markers might help to definitively exclude the genes. However, in the light of the observed low level of variation it is unlikely that htr1A and slc6A4 have a strong effect on aggression in the Golden Retriever families.
Our linkage analysis does not account for genetic heterogeneity or phenocopies. We have thus only tested for a very strong major locus effect. In reality, the aggressiveness in the Golden Retrievers may be more complex. We therefore used a third study design to investigate the candidate genes: association analysis. For this analysis, we used data from a large-scale genotyping project in 100 Golden Retrievers. Our power calculations demonstrate that this sample size is expected to be sufficient to detect variants that confer a high relative risk for a range of marker allele frequencies. From the total set of 60,073 SNP genotypes, we selected 41 SNPs that flank the candidate genes. We found no association between alleles, genotypes or haplotypes of these SNPs flanking the candidate genes and human-directed aggression of the Golden Retrievers.
We focused on human-directed aggression in the affecteds-only linkage analysis and the association study. In our quantitative genetic analysis, we studied additional types of aggression. There is no consensus in the literature on how aggression should be subdivided (Houpt and Willis 2001; Jacobs et al. 2003; Serpell and Jagoe 1995). There are indications that various types of aggression have a distinct genetic basis. For instance, selection of rats and silver foxes for reduced fear-induced aggression towards humans did not change predatory or inter-male aggression (Naumenko et al. 1989; Popova et al. 1993). This suggests that molecular genetic studies of aggressive behavior should focus on specific classes of aggression. However, reduced aggressiveness towards man in the rats and foxes was accompanied by reduced fear of novelties and irritable aggression, indicating that there is overlap between classes. As long as the genetic roots of aggressive behavior are poorly understood, it will remain impossible to design a classification that reflects the genetic basis.
In conclusion, none of the four methods of analysis provided evidence for a strong effect of variants of the candidate genes on aggression in the Golden Retrievers that we studied. These results seem to contradict reports of the involvement of the candidate genes in the regulation of aggressive behavior. However, the study designs that we used are not powerful enough to detect variants of small effect. We can therefore not rule out the possibility that variation in the candidate genes has a smaller genetic effect on aggression. In addition, our results cannot be construed as evidence against a major role for these genes in aggression in other dog breeds. Possibly, other genes in the serotonin pathway play a role. With the completion of the dog genome project, genome-wide association studies have become feasible in dogs (Lindblad-Toh et al. 2005). This opens the opportunity for finding genes that have not been associated with aggression up to date. Such studies are in progress.
Electronic supplementary material
Below is the link to the electronic supplementary material.
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aggression
dog
serotonin
linkage
association
candidate gene


Application of Respondent Driven Sampling to Collect Baseline Data on FSWs and MSM for HIV Risk Reduction Interventions in Two Urban Centres in Papua New Guinea

The need to obtain unbiased information among hard–to-reach and hidden populations for behavioural and biological surveillance, epidemiological studies, and intervention program evaluations has led researchers to search for a suitable sampling method. One method that has been tested among IDU and MSM recently is respondent-driven sampling (RDS). We used RDS to conduct a behavioural survey among FSWs and MSM in two urban centres in Papua New Guinea (PNG). In this paper we present the lessons learned implementing RDS in a developing country setting. We also present comparisons of RDSAT-adjusted versus unadjusted crude estimates of some key socio-demographic indicators as well as comparisons between the estimates from RDS and a hypothetical time–location sample (TLS). Overall, the use of RDS among the MSM and FSWs in PNG had numerous advantages in terms of collecting a required sample size in a short time period, minimizing costs and maximising security for staff and respondents. Although there were a few problems these were easily remedied and we would recommend RDS for other similar studies in PNG and other developing countries.
Introduction
Papua New Guinea is currently experiencing a major generalized HIV epidemic driven by high rates of unprotected sex, multiple sexual partners, early age of sexual debut, and high rates of STIs. Sexual violence and aggression is also widespread.1 PNG's HIV surveillance system has limited capacity, and valid scientific information is needed to guide community prevention efforts.2
Female sex workers (FSWs) and men who have sex with men (MSM) have been identified as groups at high–risk for HIV throughout the world. There have been few published studies about FSWs in PNG.3,4 No previous quantitative research has been conducted with MSM in PNG, but qualitative research and unpublished reports indicate that MSM behaviour is prevalent in some areas of the country.5 Moreover, numerous anthropological studies indicate that many tribes in PNG have practised male–male sexual behaviour in traditional rites and rituals.6–8
The major challenge in studying HIV–risk behaviours and developing prevention efforts among high risk populations is gathering information from non–biased samples.9 Members of population groups such as men who have sex with men (MSM), injecting drug users (IDU) and female sex workers (FSWs) are involved in stigmatized or illegal behaviour, leading individuals to hide their identity and to be reluctant to participate in research studies. No sampling frame exists for these groups, and the fact that creating one may be difficult and very costly makes them hard to sample with traditional probability–based sampling methods.
Respondent driven sampling (RDS) is a relatively new adaptation of chain–referral sampling, where subsequent respondents are recruited by previous respondents through their network of acquaintances.9–11 It has several features that overcome the limitations of other sampling methods that allow it to provide unbiased and representative population–based estimates. RDS has therefore been recommended as an alternative sampling approach for “hidden” populations that do not congregate in identifiable or accessible locations.12–14 Because RDS relies on participants from the target population to recruit subsequent respondents, it has the advantages that it requires less detailed formative research, is less costly, can be completed in a shorter amount of time, and has greater external validity than other methods.
This paper presents lessons learned from applying RDS in a study to collect baseline behavioural data for HIV prevention programming among two high risk populations in two urban centres in Papua New Guinea. The research among FSWs in Goroka and Port Moresby and MSM in Port Moresby was linked to the Poro Sapot Project (PSP), which is an HIV prevention initiative of Save the Children in PNG (SCiPNG). The Papua New Guinea Institute of Medical Research (PNGIMR) partnered with SCiPNG and Family Health International (FHI) to conduct the research.
Reasons for Choosing RDS
The first phase of the research project was a qualitative evaluation of the two target populations. Qualitative research methods included field observations, focus group discussions, in–depth interviews, and key informant interviews. In addition, a literature review of research studies on these populations in PNG was carried out.
Our formative research showed that there was a relatively large and socially well connected population of MSM in Port Moresby, the capital and largest city of PNG. Although there were a number of commercial and public spaces where MSM met and socialised, none of them catered to a primarily MSM clientele. There were no formal community–based or social organizations of gay or homosexual men. Due to social stigma and discrimination, the vast majority of MSM kept their sexual orientation hidden from their family, friends and co–workers. The lack of MSM–identified sites and the hidden nature of homosexuality in PNG meant that it would be difficult to use time–location sampling (TLS) as the recruitment methodology.13,14
The decision to use RDS for the survey of FSWs came about following formative assessments in both locations. FSWs in PNG were found to be very mobile. Although a few illegal brothels did exist, only a small number of women worked in those locations. Most FSWs frequented formal establishments such as disco bars, hotels, motels, and guest houses in search of potential clients. In most locations FSWs mixed with the general population in a way that would have made it difficult to differentiate sex workers from other females.
In addition, security for the research staff and participants was a major concern. MSM informants had complained of physical and verbal abuse due to social stigmatization. The crime rate in Port Moresby was very high and had steadily deteriorated in the time period before the research was conducted. Since most MSM and FSWs went out at night to socialise or meet clients, it would have been difficult to ensure the security of research staff members if they had to contact potential respondents in public areas. RDS provided a safer alternative in this regard because respondents were not actively recruited by study interviewers but were instead referred by their peers to be interviewed at a safe study facility during a convenient time of the day.
Research Methods
The field work in Port Moresby began in February 2005 and was completed at the beginning of April 2005. Data collection in Goroka began in April 2005 and was completed in 2 weeks. The interviewers were all FSWs and MSM who received 1 and 1/2 weeks of intensive training prior to the starting of field work.
The research teams at each site were comprised of a coupon manager, a research assistant and a group of peer interviewers. In Port Moresby we trained and used six MSM interviewers and four FSWs interviewers. In Goroka we trained and used five FSWs interviewers.
The Coupon Management System
A coupon management system was developed in Microsoft FoxPro 2.6 and was used to track the relationships between the recruiters and their recruits. The same program was used to record biometric measurements (circumference of both wrists and length of both forearms), which were used to check for and prevent duplication of recruitment. The latter was done through the calculation of the index of differences between the candidate and all of the other biometric measures already entered.15
Recruitment of Seeds and Study Participants
The FSWs seeds for the RDS sample were drawn from distinct geographic areas within Port Moresby and Goroka. The areas chosen in each location were the coverage areas that were mapped out and targeted for the intervention programs. Therefore eight seeds were chosen who lived and worked in five different areas of the city of Port Moresby, and six seeds were chosen from six different areas in Goroka. These areas and the number of seeds drawn for Port Moresby and Goroka are shown in Tables 1 and 2, respectively.
Table 1Socio–demographic characteristics of FSWs in Port Moresby SeedsSample(n=8)Percent(n=245)Crude (%)RDSAT–adjusted  (%)Absol. diff  (%)Age16–19112.54819.625.05.420–24112.53815.518.63.125–29112.55422.022.70.730+562.510542.933.59.4Marital statusEver married675.018575.566.98.8Currently married112.55823.717.26.5Currently married  cohabiting with  spouse00.072.93.91.0Education levelNo formal education00.05823.726.67.9Primary337.512149.447.51.9Secondary562.56124.922.22.7Other (eg. vocational)00.052.03.21.2Region of birthHighlands225.011346.142.73.4Southern562.511647.351.23.9Others112.5166.55.90.6Place of residenceTown/Koki area225.06225.318.46.9Boroko area112.510040.823.617.27–mile/airport225.0156.14.61.5Gordons/Hohola225.0197.88.81.0Waigani/Gerehu112.54618.843.224.4Others00.031.21.11.2Table 2Socio–demographic characteristics of FSWs in Goroka SeedsSample(n=6)Percent(n=249)Crude (%)RDSAT–adjusted  (%)Absol. diff  (%)Age16–19116.74518.116.61.520–24116.78032.135.33.225–29116.76224.923.91.030+350.06224.924.00.9Marital statusEver married350.016465.965.80.1Currently married00.02710.812.41.6Currently married  cohabiting with  spouse00.000.00.00.0Education levelNo formal education233.38734.935.80.9Primary233.310843.443.20.2Secondary233.35321.322.00.7Other (eg. vocational)00.020.81.00.2Region of birthHighlands6100.023895.696.40.8Momase00.083.22.70.5Others00.031.20.80.4Place of residenceSeigu/fish Wara116.77329.331.21.9Asariyufa116.76224.926.31.4North Goroka116.73514.111.13.0Kama116.7124.82.62.2Lofi/Faniyufa116.7239.27.12.1Waterise116.73212.915.52.6Others00.0124.85.70.9
In some areas of Port Moresby two seeds were chosen: one seed was a younger FSWs while the other was an older one. Formative research had shown that the younger and older FSWs in some locations did not mix socially and therefore could be considered as separate populations. In other areas only one seed was used as in these areas the younger and older FSWs mixed together socially.
The eligibility criteria for the selection of FSWs seeds as well as their subsequent recruits were defined as “a female over the age of 16 who has exchanged sex for money or other goods and services in the last 12 months and is not drunk at the time of the interview.”
For the MSM study in Port Moresby we also attempted to recruit a diverse set of seeds based on self–identified sexual orientation and region of origin.
Seeds were recruited through SCiPNG, which had recently initiated a peer education program with MSM. The peer educators in the project also worked as interviewers for the research. These men tended to recruit MSM seeds from their own social group. As a result, eight of the ten seeds used were self–identified as homosexual, and seven of the ten were from the southern region, which was the site of the research. As we will report later, the biases inherent within the initial group of MSM seeds were not present in the final sample population, in line with RDS theory.
The eligibility criteria for the selection of the MSM seeds as well as their subsequent recruits were defined as “a man over the age of 16 who has had sex with another man in the last 12 months and is not drunk at the time of the interview.”
In none of the three study groups was it found necessary to add additional seeds during the course of the study.
Incentives
Seeds and their recruits were offered monetary incentives as well as gift bags containing pamphlets, posters, free condoms and lubricant. Participants in Port Moresby were given 20 PNG Kina (K20, equal to U.S.$6.00) for successfully participating in the study and K10 (U.S.$3.00) each for every successfully recruited peer. The FSWs in Goroka were given K10 (U.S.$3.00) for participating and K5 (U.S.$1.50) for every successful recruitment made. The amount for incentive was less in Goroka because of the lower cost of living there compared to Port Moresby.
Interviews and Setting
As part of the RDS field work in Port Moresby, the research team rented two private properties situated in secluded locations of the city. The FSWs study in Goroka was also carried out at a private property in a residential part of town. Data was collected by trained interviewers who were MSM and FSWs themselves and was directly supervised by the principal investigators. All interviews took place in private rooms with only the interviewer and subject present. The secure nature of the locations used also allowed the research staff to exclude non–participants from the sites while recruitment and interviews were taking place.
Participants gave witnessed verbal consent to be part of the study. Interviews were conducted using a structured questionnaire. Information obtained included demographics, sexual behaviour, drug and alcohol use, knowledge and use of male and female condoms, knowledge and attitudes towards HIV/AIDS and STI, history and treatment seeking behaviour of STI, experience with stigma and discrimination, and exposure to intervention programs. The MSM interviews included information about their sexual orientation. Additional information was collected that was specifically required for RDS methodology: personal network size, relationships to recruiters, and the number of recruitment refusals encountered.
Sample Sizes
A total of 245 and 249 FSWs were interviewed in Port Moresby and Goroka, respectively. For the MSM study in Port Moresby, a total of 225 MSM participants were interviewed.
A total of 471 and 621 coupons were given out to 157 and 207 FSWs in Port Moresby and Goroka, respectively, while 603 coupons were given out to 201 MSM. Participants in all sites were told in advance that recruitment would stop when the desired sample size was reached. Participants who had lost a coupon were not given any additional ones.
Failure to meet the eligibility criteria resulted in 15 females (four from Goroka) and 35 males being refused participation in the study. Half of the women who were excluded in Port Moresby were under 16 years of age, while the other half were excluded because they reported that they had never sold sex. The four FSWs that were excluded in Goroka were all under 16 years of age. The bulk of the men that were excluded from the MSM study (33) did not meet the criteria of having had sex with another man in the previous 12 months, while two others were excluded for trying to repeat the interview.
Data Analysis Methods
Data was double entered using Microsoft FoxPro 2.6 and analysed using Stata 8.0 (Stata Corporation, Texas, USA), RDS Analysis Tool (RDSAT, Cornell University, NY, USA) and EpiInfo 6.04 (CDC, USA and WHO, Geneva). Adjusted analysis was carried out using RDSAT on single categorical variables whereas bivariate and multivariate analysis was done using EpiInfo and Stata. RDSAT adjusts the analysis of population estimates by taking into consideration the links between the recruiters and their recruits as well as their reported personal network sizes.11,12 A detailed description of the methods of calculation is beyond the scope of this paper.
Some Key Results
Of the 157 and 207 FSWs in Port Moresby and Goroka that were given coupons, 56.7 and 42.0%, respectively, recruited one or more of their peers into the study. For the MSM study, 57.2% of the 201 participants that were given coupons recruited one or more peers. Two questions were asked with regards to the number of coupons given out and the number of refusals encountered by each recruiter to determine refusal rates. However, the research assistants did not consistently record the answers to these questions. Nevertheless, the refusal rates appeared to be very low as indicated by the fact that 74.2 and 85.1% of the FSWs recruiters who successfully recruited other participants in Port Moresby and Goroka returned all three coupons. Of the successful MSM recruiters, 52.2% of them had all three coupons returned, 35.6% had two coupons returned and 12.2% had one coupon returned. Half of the MSM seeds (five) did not recruit any peers while three FSWs seeds (two from Port Moresby) did not recruit any peer.
It took a median of 4 days (Range=1–12) and 3.5 days (Range=1–10) for a coupon to be returned by FSWs in Port Moresby and Goroka, respectively. MSM returned the coupon at a median of 3 days (range=1–18). For both the FSWs and MSM, however, recruitment occurred at a much faster rate than had been anticipated and recruits presented to the study locations faster than they could be interviewed. It became necessary to slow down recruitment by scheduling interviews each day on a first–come first–served basis. Those that could not be interviewed the day they first presented were scheduled for the next day. Eventually the recruiters were told to bring their recruits in on specified dates in order to control the flow of new subjects to the study sites. The fast rate of recruitment for the three subpopulations enabled the sample sizes for FSWs in Port Moresby and Goroka to be reached in 17 and 20 days, respectively, while the sample size for MSM was reached in 22 days.
The main sociodemographic characteristics of the seeds and the overall samples for the three studies are presented in Tables 1, 2 and 3. Comparisons between the RDSAT–adjusted and crude analyses are given as well.
Table 3Socio–demographic characteristics of MSM in Port Moresby SeedsSample(n=10)Percent(n=225)Crude (%)RDSAT–adjusted  (%)Absol. diff  (%)Age16–19220.04921.820.81.020–24110.08136.038.12.125–29660.06026.727.71.030+110.03515.613.22.4Education levelNo formal education00.0125.36.71.4Primary00.08738.749.510.8Secondary660.06930.740.49.7Secondary440.0156.73.13.6Employment statusUnemployed220.012054.052.02.0Employed330.02913.011.02.0Self–employed550.06931.035.04.0Students00.042.02.00.0Region of birthHighlands220.03314.723.99.2Southern—gulf only110.011450.754.13.4Southern—others660.04821.310.710.6New Guinea islands00.0104.44.80.4Momase110.0208.96.32.6Sexual orientationHeterosexual110.02912.910.02.9Gay/homosexual880.06528.923.05.9Bisexual110.013158.267.08.8Paid by man for sexYes990.016975.170.74.4No110.05624.929.24.3
The results for the FSWs study in Port Moresby show that, while the seeds where roughly comparable to the overall sample, there were still some major differences with regard to educational status and region of birth.
For the FSWs study in Goroka, there was less variation between the seeds and final sample (Table 2), although the seeds tended to be older than overall sample.
The most striking differences between the initially selected seeds and the final sample were found for the MSM study in Port Moresby (Table 3). The final sample was younger, less educated, and more often unemployed. In addition, while the majority of the seeds (80%) were gay identified, less than a third of the final sample self–identified as homosexual and the majority of the final sample self–identified as bisexual.
For all three studies (Tables 1, 2 and 3), a common pattern in the findings that emerged with regard to comparisons of the characteristics of seeds and final samples, was that the overall sample was of lower socioeconomic status (especially for the MSM study). This suggests that RDS is able to penetrate “hidden” populations that are often very different from the initially selected seeds.
In terms of the comparisons between the RDSAT–adjusted and crude analysis in Tables 1, 2 and 3, the greatest variations between the two analyses (as measured by the percent of absolute difference) were found for FSWs in Port Moresby. This was especially true with regard to place of residence, where up to 24.4% differences were found for the ones that reside in one part of the city (Table 1). For FSWs in Goroka, considerably less variation was found with absolute differences ranging from 0 to 3.2% (Table 2). For the MSM study, some considerable differences were also seen and absolute differences ranged from 0 to 10.8%.
The results presented in Tables 4 and 5 were an attempt to understand the potential differences in respondent characteristics that would have existed between RDS and TLS, had this latter sampling method been used instead of RDS. The hypothetical TLS group for this analysis included FSWs and MSM respondents who reported that they ‘always’ looked for and met their sexual partners in visible locations that would have been likely included in a TLS sampling frame (i.e., nightclubs, bars, hotels, guesthouses and identifiable public venues). The results shown in Table 4 indicate that the RDS samples for FSWs in both Port Moresby and Goroka were younger and less educated than the hypothetical TLS group. While reported condom use with clients was similar between the RDS and TLS groups, reported condom use with non–paying partners was substantially lower among the RDS group. On the other hand, forced sex, knowledge, and reported STI symptoms were higher in the TLS group. Analysis of the key program exposure variable, contacted by peer educator, also revealed that program exposure was substantially lower among the RDS sample than hypothetical TLS sample. The analysis of the MSM study population in Port Moresby (Table 5) showed striking differences in almost all variables examined. In comparison to the hypothetical TLS group, the RDS group was older, less educated, more likely to be unemployed, more likely to identify their sexual orientation as heterosexual or bisexual and less likely to use condoms for partnerships examined. This group was also less likely to use lubricant during anal sex and less likely to have been exposed to HIV prevention programming. On the other hand, the hypothetical TLS group was more likely to report forced sex, incidents of discrimination and symptoms of STIs.
Table 4RDS versus hypothesized TLS estimates of key indicators for FSWsKey indicatorsPort Moresby (%)Goroka (%)RDSa (n=235)TLS (n=103)RDSa (n=227)TLS (n=165)Age16–2443.636.951.949.725+56.263.147.950.3Education levelNo formal education26.621.435.830.1Primary47.553.343.244.6Secondary22.224.322.024.1Used condom with client at last sex86.088.279.077.7Used condom consistently with clientc62.661.231.331.3Used condom with non–paying partner  at last sex48.375.743.062.0Used condom consistently with  non–paying partnerc33.548.716.026.0Forced to have sexb58.466.766.570.5Thinks mosquitos can transmit HIV37.150.030.736.1Had vaginal dischargeb43.253.428.637.4Had sore in/around vaginab20.524.312.119.4Never contacted by peerb educator24.418.540.939.6aRDSAT adjustedbIn the last 12 monthscIn the last 4 weeksTable 5RDS versus hypothesized TLS estimates of key indicators for MSM
It is important to note that the observed differences between the two groups in Tables 4 and 5 would have been even more pronounced if the comparisons had been made between the mutually exclusive categories—the hypothetical TLS group and the RDS group restricted to respondents who would likely not have been sampled using TLS.
Discussion of the Lessons Learned
Sampling procedures should be capable of reaching all members of the population or subpopulation under surveillance in order to produce unbiased estimates of trends in HIV behavioural risks.12 In this respect, RDS was chosen to study the three subpopulations of interest in PNG after carefully considering its advantages and disadvantages for each target group. Many factors prompted us to opt for RDS instead of other sampling methods. For MSM, the main reasons why other sampling methods would not be adequate were that most of them were hidden due to social stigma and that no public venues existed to use as a sampling frame for TLS. For the FSWs, creation of a sampling frame for random sampling or TLS would be difficult and costly given their very high mobility and the illegal nature of sex work in PNG.
For all three target groups in both locations, security for both the subjects and the research staff was a major concern. This is an issue specific to PNG, but may also be an important issue in other developing countries with unstable economic and political situations. The ability to conduct interviews in a private and secure location during the daytime and have potential subjects come directly to the study location after being recruited by previous respondents was a major advantage of RDS in this study. In PNG, it would have been difficult to have ensured the safety of the research staff if they had to go out into the field at night time or may have required hiring security guards at considerable expense.
RDS has been described by those studying more the difficult sub–population of illicit drug users as a flexible and robust method that can produce a sample representative of the heterogeneity of the target population.16 We had a similar experience in implementing RDS among FSWs and MSM in a developing country. Although our original seeds were not as diverse as we intended them to be, a comparison of the seeds versus the final sample (Tables 1, 2 and 3) shows that by using RDS, the characteristics of the final samples were often quite different from the initially selected seeds. Particularly striking was our finding that the overall samples in all three subpopulations were mainly lower socio–economic status. A comparison of the RDSAT–adjusted versus the crude estimates of the various socio–demographic characteristics (Tables 1, 2 and 3) showed mostly little variation.
The rapid rate of recruitment among all subpopulations suggests that the dual incentive system did work exceptionally well. We observed that the majority of the recruiters accompanied their recruits to the interview site. The reason for this was most likely that the recruiters wanted to collect their recruitment incentives immediately, but it also ensured that many of the recruited persons did in fact show up at the study sites in a relatively short amount of time.
Not one FSWs or MSM subject used the phone number printed on the coupon to make an appointment. Telephone charges are relatively expensive, and mobile phones are not widespread among the general population in PNG. Therefore, it may have been easier and less expensive for recruits to simply show up at the research site than to have called ahead for an appointment. Thus the use of a telephone call to make appointments may not be necessary in this country in any future application of RDS for these or similar populations.
The results of the comparisons between the RDS and hypothetical TLS samples (Tables 4 and 5) suggest that RDS allowed for greater penetration and representation of more vulnerable segments of the FSWs and MSM target populations. The use of dual financial incentives associated with RDS is also believed to have been important for reaching less educated and unemployed segments of the FSWs and MSM populations because they were more likely to find this a motivating factor for participation. During the RDS field work, it was observed anecdotally that the more economically desperate participants with ample disposable time were more likely to try to recruit individuals who did not meet the study eligibility criteria.
Careful planning, staff training, and preparation for the field work enabled us to handle the onslaught of recruits and ensure the smooth implementation of RDS among FSWs and MSM in Papua New Guinea. The procedures checklist13 and practicing with the coupon management system were particularly important in preparing for the research. We agree with the view expressed by others that RDS is relatively easy to implement and less costly in that it did not require exhaustive mapping exercise to construct sampling frames.2,16 In our experience, the dual incentive system of RDS helped to reduce non–response rates and fuelled recruitment.13
Finally, in this study we relied on others to help select the original seeds. Some of the seeds failed to recruit any additional subjects. Half of the MSM seeds recommended to us simply did not recruit any other MSM, indicating that the recruitment of initial seeds in this study was not very effective. Problems were also encountered with potential recruits who misrepresented themselves in attempts to participate in the study in order to earn money. This was especially true in the MSM study, where many men who were not really MSM tried to participate. Similarly, a number of underage girls were refused entry into the study. This problem most likely resulted from the dual incentive system, in which a participant could potentially earn up to U.S. $15.00, a significant sum in a poor country like PNG. However, the screening and interview process in place was sufficient to track and preclude repeat participation or recruitment of individuals who did not meet the inclusion criteria.
For this study among FSWs and MSM in PNG, using RDS as the sampling methodology had numerous advantages in terms of collecting the required sample size in a short time period, minimizing study costs, and resulting in representative estimates of the target populations. Although there were a few problems in implementing a new research method in a resource–poor setting, these were easily remedied, and we would recommend RDS for similar studies in PNG and other developing nations.

sampling
papua new guinea
respondent-driven sampling
female sex workers
men who have sex with men


Better Survival in Patients with Esophageal Cancer After Surgical Treatment in University Hospitals: A Plea for Performance by Surgical Oncologists

Background In primary esophageal cancer, studies have frequently focused on surgical patients in an effort to link outcome to hospital- or surgeon-related experience, with operative mortality used as the main outcome measure. Many studies have found an inverse relationship between operative mortality and hospital volume and surgical expertise. This study aims to assess the influence of surgeon-related expertise and hospital volume on the relative survival of operated esophageal cancer patients.
With an incidence of 6.3 per 100,000 (European standardized rate) in the period 1994–1998, esophageal cancer ranked 13th among malignancies in men and 12th in women in the Netherlands. Incidence increased in both male and female patients, with an estimated annual percentage change of 3.1% and 2.0%, respectively. The mortality for men and women was, respectively, 9.4 per 100,000 and 3.1 per 100,000 in 2004, indicating that the prognosis of patients with esophageal cancer remained poor in this period (http://www.kankerregistratie.nl). The only curative option for esophageal cancer is surgery, which implies that improving the outcome of surgery is the best means of reducing mortality.1
Esophageal cancer is one of the most challenging pathological conditions confronting the surgeon. It therefore seems reasonable to assume that concentration of esophageal surgery could improve outcome. Several studies have shown that various characteristics, including surgeon subspecialty certification, hospital setting, and the number of procedures performed, are associated with practice variation, complication rates, and even outcome.2–4 Evidence of improved outcome associated with specialist care exists for breast cancer,2,4 ovarian cancer,3,5 and malignant teratoma.6 However, there is no evidence of comparable quality for esophageal cancers. Several studies on esophageal cancer have focused on surgical patients in an effort to link outcome to experience, either on the part of the institution or the surgeon, by using postoperative mortality as the main outcome measure.7–9 A study on esophagectomies performed in England during the late 1980s found no independent association between operative mortality and hospital surgical volume,10 whereas two North American studies did demonstrate a lower mortality rate for esophagectomy when high-volume and low-volume hospitals were compared.11,12 Various smaller studies also found lower operative mortality rates among high-volume surgeons.13,14
Few studies, if any, have attempted to relate esophageal cancer patient survival to surgeon expertise or hospital volume. To remedy this, in this study, we assessed the effect of surgeon-related expertise and hospital volume on the relative survival of operated esophageal cancer patients. We compared data from university, teaching nonuniversity, and nonteaching hospitals.
PATIENTS AND METHODS
Patients
All patients diagnosed with a primary invasive esophageal cancer in the region of the Comprehensive Cancer Centre North-Netherlands (CCCN) between January 1994 and January 2002 were eligible for entry onto the study. Patients with a history of cancer other than nonmelanoma skin cancer were excluded. The patients were selected through the population-based Regional Cancer Registry of the CCCN, which covers the northern part of the Netherlands, a mainly rural area with a population of approximately 2.1 million. The area is served by 17 community hospitals, 3 of which are teaching hospitals and 1 of which is a university hospital; the hospitals include four radiotherapy departments and seven pathology laboratories.
Data Collection by the Regional Cancer Registry
PALGA, a Dutch nationwide network and registry of histopathology and cytopathology, regularly submits reports of all diagnosed malignancies to the cancer registry. The national hospital discharge data bank, which receives discharge diagnoses of admitted patients from all hospitals, completes case ascertainment. The cancer registry has no access to death certificates. After notification, trained registry personnel collect data on diagnosis and staging from the medical records, including pathology and radiology reports, in the hospitals. The cancer registry collected all data regarding the diagnosis and staging, but collected no data on specific surgical treatment in this patient population before 2002. The data collection occurs at least 4 months after diagnosis to comprehensively document all aspects of preoperative staging. All patients are staged according to the tumor, node, metastasis system (TNM) system for esophageal cancer in use during that period.15,16
In the Netherlands, the population registries of the municipality contain information on the vital status of their inhabitants. Vital status was established either through information derived from the patient’s medical records or through linkage of cancer registry data with information from the population registries of the municipality within the registry areas or through linkage with the national death registry of the Central Bureau of Genealogy. The regional cancer registry of the CCCN checked vital status by active record linkage with municipal population registries in 2002–2003 and 2005 and with the national death registry of the Central Bureau of Genealogy in 2004.
Guidelines for Staging and Treatment
By establishing multidisciplinary teams and cancer networks, the CCCN strives to improve the quality of cancer care. Within the CCCN area tumor working groups, comprising delegated specialists representing all regional hospitals, that have been developing and revising guidelines on diagnosis and treatment. The regional guidelines for esophageal cancer were based on the international TNM classification according to the International Union Against Cancer in use at that time.
Statistics and Definitions of Variables
The χ2 test was used to compare the distribution over the patient population for categorical variables. For continuous variables, analysis of variance was used. Relative survival analysis was performed to estimate the effect of university, teaching nonuniversity, and nonteaching hospitals on the prognosis of operated patients with esophageal cancer. The a priori hypothesis was that patient volume and hospital expertise would increase from nonteaching to university teaching hospitals. Survival time was calculated from the date of diagnosis and ended at the date of death, including perioperative death, or the date of most recent linkage with the municipal population registries and/or national death registry. The overall survival probability was estimated by the Kaplan-Meier method.
The expected survival probability was calculated by using age-, sex-, and period-matched mortality rates that were based on life expectancy tables in the Netherlands (http://www.statline.cbs.nl/StatWeb/) and was estimated by the Ederer 2 method.17 The cumulative relative survival (the ratio of the overall survival to the expected survival) was estimated by Stata (version 8.0) software and the strs function. The relative survival, which estimates the net esophageal cancer survival in the hypothetical situation that esophageal cancer is the only possible cause of death, has been shown to be a good estimator of disease-specific survival in the absence of information on the cause of death or in case information on the cause of death is inaccurate.
The excess mortality rate was calculated by subtracting the expected number of deaths, estimated from the expected survival probability, from the observed number of deaths in a subgroup or stratum and dividing the resulting excess number of deaths by the number of accumulated person-years, taking censoring into account. The relative excess risks (RER) of death were estimated as the ratio of excess mortality rates. RERs were estimated by a multivariate generalized linear model with a Poisson error structure, which was based on collapsed relative survival data, by using exact survival times.18 By use of this model, the effect of the type of hospital (university, teaching nonuniversity, and nonteaching) was studied, adjusting for the effect of various covariables on the excess mortality experienced by our cohort.
Variables included in the final model were age (<50, 50–59, 60–69, >70), stage based on collapsed TNM data (stage 1,2a, 2b, 3/4, unknown), hospital volume (<20 patients operated, ≥20 patients operated), frequency of referral (high referral [>33.3%], low referral [≤33.3%]), and time since diagnosis (1-year intervals). The pathological stage was used whenever possible; in the absence of information about the pathological stage, the clinical stage was used.
RESULTS
TABLE 1.
Characteristics of operated and nonoperated patients with esophageal cancer diagnosed 1994–2002
Characteristic
Total
Operated
Nonoperated
P value
N
%
n
%
n
%
Sex
.465
Male
796
69.1
152
71.4
644
68.6
Female
353
30.9
61
28.6
292
31.2
Histology
<.001
Squamous cell carcinoma
415
36.1
62
29.1
353
37.7
Adenocarcinoma
592
51.6
140
65.7
453
48.4
Other
141
12.3
11
5.2
130
13.9
Tumor location
<.001
Upper thoracic
82
7.1
3
1.4
79
8.4
Middle thoracic
210
18.3
36
16.9
174
18.6
Lower thoracic
770
67.0
169
79.3
601
64.2
Overlapping and unspecified
87
7.6
5
2.3
82
8.8
Age at diagnosis (y)
<.001
<50
86
7.5
24
11.3
62
6.6
50–59
221
19.2
67
31.5
154
16.5
60–69
319
27.8
80
37.6
239
25.5
70+
523
45.5
42
19.7
481
51.4
Stage
<.001
1
52
4.5
32
15.0
20
2.1
2A
174
15.1
65
30.5
109
11.7
2B
69
6.0
26
12.2
43
4.6
3
207
18.0
74
34.7
133
14.2
4
316
27.5
8
3.8
308
32.9
Unknown
331
28.8
8
3.8
323
34.5
Total
1149
100.0
213
100.0
935
100.0
In the period 1994–2002, a total of 1149 patients were diagnosed with esophageal cancer, comprising 796 men and 353 (69.3%) women (30.7%). The median age was 68 years (range, 17–103 years). Patient characteristics are described in Table 1. A large proportion of the patients was diagnosed at an advanced stage of disease; 45.5% were stage III or higher. A further 28.8% were insufficiently staged. Of the 1149 patients, 85 patients (7.4%) were initially diagnosed in the university hospital, 428 patients (37.2%) in teaching nonuniversity hospitals. The remaining 636 patients (55.4%) were diagnosed in the nonteaching hospitals. Patients who were referred from the hospital of initial diagnosis for treatment may have subsequently undergone further diagnostic testing, and previously performed tests determined to be insufficient were repeated. The 5-year relative survival rate for men with esophageal cancer was 12.8% vs. 9.8% for women (P = .496). The 5-year relative survival markedly decreased as the stage advanced. The 5-year relative survival was 71.5% in stage I, 26.5% in stage IIA, 13.3% in stage IIB, 9.2% in stage III, 1.3% in stage IV, and 6.7% for patients with unknown stage (P < .0001).
As Table 1 shows, only 213 patients (18.5%) underwent surgery. Older age (P < .001), advanced or unknown stage (P < .001), and proximal tumor location (P < .001) resulted in a lower probability of tumor resection. Squamous cell carcinoma was also associated with less surgery, but it was highly correlated with the tumor location. In all, 21.4% of the patients diagnosed in nonteaching hospitals underwent surgery, compared with 15.9% and 10.6% for teaching nonuniversity and university hospitals, respectively (P = .011). Adjusted for age, stage, and tumor location, the odds of operation was 1.89 (95% confidence interval [95% CI], 1.26–2.82) for patients diagnosed in a nonteaching hospital compared with patients diagnosed in a teaching nonuniversity hospital.
Of all operated patients 45.1% were referred for surgery after diagnosis in the hospital of presentation (Table 2). The nonteaching hospitals referred 57.4% of patients diagnosed in their hospitals for an operation elsewhere. Of the 14 nonteaching hospitals in which esophageal cancer patients were diagnosed, 12 referred nearly all (75%–100%) patients, and two rarely (.0%–6.9%) referred patients. The teaching nonuniversity hospitals referred 26.5% of the patients diagnosed in their hospitals to a larger institution for therapy, with one hospital referring 63.2% of their patients.
TABLE 2.Referral pattern for esophageal cancer surgery per hospital in the North-Netherlands, 1994–2002HospitalOperated in hospital of diagnosis, n (%)Referred for surgery, n (%)Total (n)High-referral nonteaching hospitals 20 (20.8)76 (79.2)96Hospital A–2 (100.0)2Hospital B–3 (100.0)3Hospital C–3 (100.0)3Hospital D–9 (100.0)9Hospital E2 (11.8)15 (88.2)17Hospital F1 (14.3)6 (85.7)7Hospital G3 (18.8)13 (81.3)16Hospital H3(25.0)9 (75.0)12Hospital I3 (30.0)7 (70.0)10Hospital J1 (33.3)2 (66.7)3Hospital K1 (33.3)2 (66.7)3Hospital L6 (54.5)5 (45.5)11Low-referral nonteaching hospitals 38 (95.0)2 (5.0)40Hospital M27 (93.1)2 (6.9)29Hospital N11 (100.0)–11High-referral teaching, nonuniversity hospitals7 (36.8)12 (63.2)19Hospital O7 (36.8)12 (63.2)19Low-referral teaching, nonuniversity hospitals43 (87.8)6 (12.2)49Hospital P24 (82.8)5 (17.2)29Hospital Q19 (95.0)1 (5.0)20Low-referral university hospital9 (100.0)–9Hospital R9 (100.0)–9117 (54.9)96 (45.1)213
Table 3 compares characteristics of the operated patients in the three hospital types. Of the 213 operated patients, 95 underwent surgery in the university hospital; 86 of these patients were referrals. The three teaching nonuniversity hospitals provided surgery to 60 patients, including 10 referrals: one hospital performed surgery on 7 patients, with the two other hospitals operating on more than 20. The remaining 58 patients underwent surgery at one of the 14 nonteaching hospital after all were initially diagnosed in the same hospital; two low-referral hospitals operated on more than 10 patients, eight hospitals operated on 5 or fewer patients, and the remaining four high-referral hospitals did not perform any esophageal cancer surgery. There were no statistically significant differences in the distribution of age (P = .230), stage (P = .299), or tumor location (P = .130) between the hospital types, again showing little evidence of selective referral. However, of all operated patients per hospital, a slightly larger proportion of stage III/IV tumors were operated on in the nonteaching (46.6%) and university hospitals (41.1%) compared with the teaching nonuniversity hospitals (26.7%), and the portion of stage IIA tumors was somewhat higher in nonteaching (31.0%) and teaching nonuniversity hospitals (38.3%) compared with the university hospital (25.3%).
TABLE 3.Characteristics for operated esophageal cancer patients diagnosed 1994–2002, according to hospital of surgeryCharacteristicTotalTeaching, nonuniversityUniversityNonteachingP valueN%n%n%n%StageStage 13215.51220.01414.7610.3.299aStage 2A6526.81838.32325.32431.0Stage 2B2612.2813.31212.6610.3Stage 3 + 48238.51626.73941.12746.6Unknown87.011.766.311.7Age at diagnosis (y)<502411.3813.31313.735.2.23050–596731.51626.73637.91525.960–698037.62541.73031.62543.170+4219.71118.31616.81525.9HistologySquamous cell carcinoma6229.12135.02728.41424.1.606Adenocarcinoma14065.73558.36366.34272.4Other115.246.755.323.4Tumor locationUpper and middle thoracic3918.31321.71717.9915.5.130Lower thoracic16979.34778.37376.84984.5Not stated52.3––55.3––Total213100.060100.095100.058100.0a Excluding stage unknown.
The cumulative relative survival for university, teaching nonuniversity, and nonteaching hospitals is shown in Fig. 1. Surprisingly, relative survival was markedly better in the university hospital compared with teaching nonuniversity and nonteaching hospitals. The 5-year relative survival was 49.2% for the university hospital versus 32.6% and 27.3% for teaching nonuniversity and nonteaching hospitals, respectively (P = .0039, Table 4). In univariate analysis, the RER for university and teaching nonuniversity was, respectively, .48 (95% CI, .30–.74) and .88 (95% CI, .55–1.42) compared with nonteaching hospitals. The proportion of operated patients who died within 3 months after diagnosis differed between university, teaching nonuniversity and nonteaching hospitals (4.2%, 13.3% and 19.0% respectively, P = .013). Excluding patients who died within the first 3 months, the RER for university and teaching nonuniversity was, respectively, .59 (95% CI, .35–1.00) and .97 (95% CI, .56–1.69) compared with nonteaching hospitals.
FIG. 1.Cumulative relative survival of patients operated for esophageal cancer diagnosed during 1994–2002 according to hospital type.TABLE 4.Overall and relative 5-year survival and estimated excess risk (RER) of death with 95% confidence intervals (95% CI) for operated esophageal cancer patients diagnosed 1994–2002CharacteristicNOS (5 y)RS (5 y)OD (5 y)UnivariateMultivariate95% CIP valueED (5 y)RERa95% CIRERaStage<.0001Stage 1 3287.5%99.1%43.1.06.01–.32.05.01–.22Stage 2A6546.1%52.4%333.9.44.27–.70.39.24–.63Stage 2B2621.8%24.7%191.1.75.43–1.29.72.40–1.27Stage 3+4 (reference)8214.2%14.5%651.61.001.00Unknown816.7%18.0%6.2.99.41–2.351.62.65–4.01Hospital type.0126Nonteaching (reference)5824.9%27.3%402.31.001.00Teaching nonuniversity6029.7%32.6%391.9.89.55–1.421.32.79–2.22University9544.3%49.2%485.6.48.30–.77.57.29–1.12Age (y).0467<50 (reference)2447.6%48.6%11.21.001.0050–596732.1%33.6%421.21.73.87–3.441.51.74–3.0460–698030.9%35.1%523.52.121.07–4.182.361.18–4.7070+4241.9%54.5%224.91.42.64–3.142.05.94–4.46Hospital volume.1125<20 patients operated3819.0%22.3%271.31.001.00≥20 patients operated17537.8%41.7%1008.5.53.33–.83.62.34–1.12Referral rate .8080High (>33.3%)11535.4%39.2%665.21.001.00Low (≤ 33.3%) 9834.1%37.8%614.61.16.79–1.69.94.57–1.54OS, overall survival; RS, relative survival; OD, observed deaths; ED, expected deaths.a Adjusted for time since diagnosis.
In a multivariate analysis to adjust for the prognostic effect of patient age, tumor stage, tumor location, hospital volume, frequency of referral, and time since diagnosis, we found that stage, age, hospital type, and time since diagnosis were independently associated with the RER (Table 4). The RER increased with more advanced stage. Patients aged <50 and patients aged ≥70 had a lower RER compared with patients aged 50–69 years. Adjusted for age, stage, and time since diagnosis, the RER for the university hospital was still considerably lower, at .57 (95% CI, .29–1.12), compared with nonteaching hospitals and .43 (95% CI, .24–.76) compared with teaching nonuniversity hospitals (P = .0126). There was some evidence in the data for an independent effect of hospital volume, with a lower RER (.62; 95% CI, .34–1.12) if a hospital operated on ≥20 patients during the study period.
In our study 8.9% of the operated patients received some form of adjuvant therapy (Table 5). Of these patients, 6.1% received preoperative chemotherapy and 2.8% received postoperative radiotherapy. Patients receiving chemotherapy were all treated in the university hospital. Comparing clinical and pathological stage, we found no evidence that chemotherapy led to a marked downstaging.
TABLE 5.Adjuvant therapies for patients operated for esophageal cancer diagnosed 1994–2002, according to hospital of treatmentTreatmentNonteachingTeaching, nonuniversityUniversityTotaln%n%N%N%Surgery5798.35896.77983.219491.1Surgery + radiotherapy11.7––55.362.8Surgery + chemotherapy––23.31111.6136.1Total58100.060100.095100.0213100.0
DISCUSSION
Our study shows that patients who underwent surgery for esophageal cancer in the university hospital had a markedly better relative survival, with a 50% lower risk of death compared with patients treated in nonuniversity hospitals. The risk of death did not differ for patients operated in teaching nonuniversity or nonteaching hospitals. In our study, we found that a higher hospital volume was weakly associated with better survival. Although hospital volume seems to influence better survival, it is unlikely that the difference between the three hospital types can be completely explained by hospital volume; other factors likely play a role. A recent British study found no effect of hospital volume on survival for the operated patients; further, this study did not find teaching hospital status independently associated with survival, but it is unclear whether this reflects the operated patients or the population as a whole.10
Our study is one of the first studies to research the effect of a marker for hospital/surgeon experience on patient survival. Most studies to date have evaluated 30-day postoperative or in-hospital mortality, and several demonstrated an inverse relationship between volume or surgical experience and operative mortality after esophageal resection.8,11,19 A Dutch study found that hospitals performing 1 to 10 operations per year for esophageal cancer and cancer of the gastroesophageal junction had an operative mortality of 12.2%, compared with 4.9% for hospitals performing >50 procedures per year.19 An American study had a similar result, with a mortality of 3.0% among high-volume hospitals and 12.2% among low-volume hospitals for both distal and proximal esophageal cancer,12 but this study used a cutoff point of five procedures per year to differentiate between high- and low-volume hospitals. What threshold distinguishes high-volume from low-volume hospitals remains matter of discussion. The results of these studies do suggest that centralization of esophageal surgery, so that only a few hospitals per region operate on esophageal cancer patients, may improve survival. The results of our study support the recommendation for referral of esophageal cancer patients to a center where there is a specific focus on esophageal cancer treatment. Combined with the fact that the surgical literature is increasingly advocating the need for centralization, we think that further research into the advantages of centralization of esophageal cancer treatment is warranted.
One of the possible pitfalls in our study remains selective referral. We showed that the likelihood of being operated on was 47% higher for patients diagnosed in a nonteaching hospital than for those diagnosed in teaching nonuniversity hospitals. Adjusted for age, stage, and tumor location, this was even higher, 73%, indicating that nonteaching hospitals considered patients with a worse prognosis possible candidates for surgery, or at least for referral to evaluate resectability. However, adding hospital referral frequency to our multivariate analyses showed no influence of referral on overall and relative survival or RER. The referral pattern showed little evidence for selective referral. Those nonteaching hospitals that referred patients referred almost all, and the teaching nonuniversity hospitals referred only few patients for surgery (Table 2). The referral pattern of the nonteaching hospitals implies that the university hospital operated on a priori prognostically worse patients. This is a likely explanation for the higher number of stage III patients in the university hospital.
To minimize the eventual effect of any residual selection referral, the relative survival rate was adjusted for case mix, despite there being no statistically significant differences in the distribution of age, stage, and sex between the different types of hospital.
One may suggest that patients who underwent esophagectomy in the university hospital were mostly referred for treatment, thus adding a delay before surgery. A consequence could be that these patients had thus a slightly longer preoperative survival time, estimated to be between 2 and 4 weeks in our study. This short delay could mean that prognostically worse patients scheduled for surgery eventually fall out of the surgery category through disease progression during the delay period. However, little is known in the literature about the effect of longer preoperative delays on surgical outcome or eligibility in esophageal cancer. Although patients with advanced disease may miss surgery through stage progression, patients who do end up having surgery also progress, meaning the university hospital operates on patients with more advanced disease. This should negatively influence the survival outcome and would not explain the better performance by the university hospital. Furthermore, even if we were very conservative and excluded all patients who died in the first 3 months of our study, the university hospital still performs far better than teaching nonuniversity and nonteaching hospitals. So although we cannot discount early mortality as a factor in survival, we think that it is unlikely that the difference in performance can be fully explained by this.
We had no information about the operative procedure that had been performed. Treatment guidelines indicated a curative surgical approach for tumors encompassing ≤5 cm of the length of the esophagus, as based on ultrasonographic or radiological examination. Surgical resection could be attempted for tumors 5–8 cm in length. For this last group, neoadjuvant chemotherapy, which was provided in the university hospital after proof of locally advanced disease, could be attempted to improve resectability. For adenocarcinomas, a transthoracoabdominal approach with two-field lymphadenectomy was advised, combining a midline laparotomy and a right-sided thoracotomy. Alternatively a transhiatal blind esophagectomy could be performed with a cervical esophagogastrostomy. For distal adenocarcinomas without Barrett dysplasia, a left-sided thoracotomy with intrathoracic anastomosis was an alternative approach.
In general, patients in our population who were operated on in the university hospital underwent a transthoracic esophagectomy, with the exception of superficial T1 tumors, whereas patients treated in regional hospitals frequently underwent surgery with a transhiatal approach. Therefore, the two main operative strategies we encountered were transhiatal resection and transthoracoabdominal resection with a two-field lymph node dissection. There is no evidence in literature that the outcome differs for these two procedures,14,20 except for a tendency toward an improved long-term survival in the extended transthoracic group in the study of Hulscher et al.21 So the clinical outcome in our population is likely uninfluenced by differences in surgical procedure. However, there is possibly a stage migration effect between the more thorough pathological staging in operations with lymphadenectomy, as in the university hospital, and understaging in patients undergoing a transhiatal esophageal resection.
A few of the operated patients received neoadjuvant therapy in our study, mostly in the university setting, which might account for a small part of the better survival in the university hospital. Separate analysis, however, showed that patients in our study, who received neoadjuvant therapy with surgical removal of the tumor, did not perform better than patients who underwent surgical removal alone in the university hospital. In other studies, the preoperative effect of cisplatin-based chemotherapy on both adenocarcinoma and squamous cell carcinoma showed no increase in overall survival.22–25 Preoperative chemotherapy or radiotherapy can result in downstaging and thus lead to a better resectability, but no clear downstaging was seen in our study when comparing pre- and postoperative clinical and pathological stage. In several randomized, controlled studies, postoperative radiotherapy demonstrated either no increase26,27 or a decrease28 in survival compared with resection alone. Postoperative chemotherapy has also been compared with surgical management alone in several randomized controlled trials, without demonstrating an improvement in survival.29 According to these results, the effect of perioperative treatment is not likely to influence our data.
We found a tentative relationship between higher volume and a better relative survival. However, this issue still is a debatable problem in determining treatment guidelines. Therefore, we suggest that guidelines concerning specific referral of esophageal cancer patients should be based on hospital outcomes, preferably in experienced centers, rather than on annual numbers of procedures as long as the factor that is determining patient survival is still unknown.
The individual surgeon could be an important parameter in determining the hospital outcome. Although the implications of the assertion that some surgeons have better outcomes than others make clinicians uncomfortable, there should be little doubt that it is true. Variation in performance has been shown to be related to surgeon characteristics, including surgical volume, subspecialization, and the hospital setting in which they operate.7,30 Individual surgical experience has been associated with the postoperative mortality of esophageal cancer. Sutton et al.9 showed a reduction in mortality from 6% to 3% after 150 procedures. Miller et al.14 published results in one center demonstrating an operative mortality of 22% among esophageal resections performed by a surgeon who performed fewer than six procedures a year. These results are widely quoted in the surgical literature as proving that surgeons without the necessary expertise should not perform esophageal resections.
There is some evidence that subspecialization improves outcomes. Herr et al.31 found that patients who underwent radical cystectomy by urology oncologists had substantially lower rates of local tumor recurrence than those who were operated on by general urologists (6% vs. 23% (P = .006). Dueck et al.32 reported that patients who underwent surgery for a ruptured abdominal aortic aneurysm had markedly better outcomes when the surgery was performed by a vascular surgeon rather than a general surgeon. The effect of subspecialization of the surgeon on the outcome of esophageal cancer has not yet been studied, but it may be a promising factor for decisions with regard to centralization.
It has been suggested in previous reports that the skill of the anesthesia and nursing staff affects morbidity and relative survival of esophagectomy patients and that it confounds the surgeon’s personal outcome.7,9 Better critical-care experience of the support staff may explain a higher relative survival in university hospitals; staff may be more adept at caring for esophagectomy patients. Some authors have suggested that the expertise of the anesthesia and nurses in a hospital is directly correlated to the hospital and surgical load in that hospital.9 However, expertise can be acquired elsewhere, and expertise only develops through effective feedback, not only by number of patients.
Although referral to dedicated centers possibly results in improved relative survival, the focus entails some disadvantages, which should be considered. Referral to centers means that many patients have to travel to distant sites, which can create hardship for the patient and his or her family. In-hospital family support and postoperative follow-up are more difficult when the hospital is farther away. Finlayson et al.33 demonstrated that 45% of the patients prefer to stay in their local area even if the projected operative mortality is doubled. However, that study represents the American situation, and it is questionable whether distance is perceived to be a problem in the Dutch situation. In our region, which has a relatively high density of hospitals, a recent patient survey showed that traveling distance was not considered a critical issue.
In conclusion, we demonstrated that in our region, the relative survival for patients operated on for esophageal cancer is better in the university hospital compared with teaching nonuniversity and nonteaching hospitals, emphasizing the need for referral to centers focused on the treatment of esophageal cancer. The underlying parameter for the observed difference remains unclear. We suggest that centers at least periodically review the morbidity and mortality rates of esophageal resections to assess their outcome and the possibility of referral. Eligibility for centers focused on esophageal cancer treatment should therefore be based on patient outcomes rather than on patient numbers.

expertise
referral
survival benefit
esophageal carcinoma