* Department of Zoology, University of Oxford, Oxford, United Kingdom
School of Biological Sciences, University of Auckland, Auckland, New Zealand
Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, Georgia
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Abstract |
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Key Words: hepatitis C Egypt epidemiology Bayesian inference
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Introduction |
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Egypt has possibly the highest HCV prevalence in the world; 10%20% of the general population are infected and HCV is the leading cause of HCC and chronic liver disease in the country (Arthur et al. 1997; Nafeh et al. 2000; El-Zayadi et al. 2001; Habib et al. 2001; Hassan et al. 2001). Approximately 90% of Egyptian HCV isolates belong to a single subtype, 4a, which responds less successfully to interferon therapy than other subtypes (El-Zayadi et al. 1996; Angelico et al. 1997; Ray et al. 2000; Kamal et al. 2000). Furthermore, HCV is less prevalent in countries neighboring Egypt that have similar sociomedical conditions and similar HCV strains (McCarthy et al. 1994; World Health Organization 1997b; Shobokshi et al. 1999).
Why, then, is Egypt so seriously affected? Previous research has suggested that the Egyptian HCV epidemic results from the use of unsterile injection equipment during mass treatment of the general population with parenteral antischistosomal therapy (PAT) (Quinti et al. 1995; Frank et al. 2000). PAT was extensively practiced in Egypt from the 1920s to the 1980s and was gradually replaced by oral treatment from the 1970s onward. The most common PAT drug, tartar emetic (potassium antimony tartarate), was administered over the course of a few weeks as a series of intravenous injections (Maegraith 1964). The potential for transmission of blood-borne pathogens in such circumstances is considerable. Cross-sectional epidemiological analyses have provided evidence for the PAT hypothesis; there is a correlation between the level of exposure to PAT and HCV prevalence among different age groups and geographic regions (Angelico et al. 1997; Frank et al. 2000; Nafeh et al. 2000; Habib et al. 2001).
Although subtype 4a is the dominant Egyptian HCV strain, a survey by Ray et al. (2000) of HCV genetic diversity in the country revealed that other subtypes (provisionally named 4, 4ß, and 1g) are also present at lower prevalences. The most recent common ancestor of each subtype (including 4a) existed approximately 80120 years ago, suggesting the recent and simultaneous appearance of a few pre-existing endemic strains. This is in contrast to the pattern of HCV genetic diversity found in other developing regions, which is more consistent with a long period of endemic infection (Smith et al. 1997; Pybus et al. 2001).
Standard analyses of genetic diversity, however, use only a fraction of the information contained in viral gene sequences. We have previously developed a framework based on coalescent theory that reconstructs the population dynamic history of HCV from sampled sequences, enabling us to date epidemiological events and estimate historical rates of epidemic spread (Pybus et al. 2000, 2001). Here we adapt this framework specifically to investigate the epidemic history of HCV in Egypt. Our results are obtained using a Bayesian inference framework that implements a detailed nucleotide-substitution model and incorporates the uncertainty inherent in phylogenetic reconstruction (Drummond et al. 2002). This method represents a significant improvement on previous analyses that made inferences from a single phylogeny and therefore ignored phylogenetic error (Pybus et al. 2000).
Our results reveal a history of epidemic spread that is unique to Egypt, and they provide strong genetic evidence for the role of PAT in the initiation and propagation of HCV in Egypt. In addition, the quantitative corroboration of our results with a specific epidemiological scenario provides an independent test of inference methods based on the coalescent process.
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Methods |
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In this article we use sample-based Bayesian inference. Drummond et al. (2002) recently described a Metropolis-Hastings Markov chain Monte Carlo (MCMC) method for the joint estimation of genealogy (g), demographic parameters (), and mutational parameters (
). This approach is implemented in the computer program MEPI (Version 1.0) available from http://cebl.auckland.ac.nz/mepi. Given the sampled gene sequences D, parameter estimates are calculated by averaging over many genealogies and weighting the contribution of each genealogy by its likelihood given the data. The MCMC algorithm performs this calculation efficiently by sampling the posterior probability density h
g:
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In the analysis presented below, we are interested in the marginal probability density h(
|D,µ) obtained by sampling from the full posterior probability density h
g. Thus the genealogical information is statistically regarded as "missing data." The following sections provide details of the specific demographic and mutational models used in our analysis of HCV in Egypt.
Demographic Model
Pybus et al. (2001) previously presented a simple demographic model for HCV epidemic behavior. This model assumed a continuous epidemic process in which the viral transmission parameters (such as probability of transmission per contact event) remain constant through time. Such a model is obviously not suitable for HCV in Egypt, which appears to have followed a discontinuous process, putatively as a result of past PAT campaigns. We have therefore developed a new demographic model specifically to investigate the Egyptian HCV epidemic.
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To select an appropriate nucleotide-substitution model for our data, we used an estimated maximum likelihood tree to compare the genealogical likelihood Pr{D|,g} under various substitution models, in a manner similar to that employed by the Modeltest program (Posada and Crandall 1998). Allowing for rate heterogeneity among sites massively improved the log likelihood, and also increased the time to the most recent common ancestor (TMRCA) of the sequences. Thus, in our full Bayesian analysis, we implemented a codon site-specific model of rate heterogeneity. A much smaller improvement in likelihood was obtained when the HKY and GTR substitution models were compared, although this difference was statistically significant according to a likelihood ratio test. As a result, the full MCMC analysis was performed twice, once using HKY and once using GTR. The results were qualitatively identical and quantitatively very similar, so we report here only the results obtained using the HKY model (the GTR results are available on request).
Although the mean mutation rate was fixed, we were able to estimate absolute mutation rates for each codon position (denoted µ1, µ2 and µ3) using MCMC integration. This was achieved using two proposal mechanisms (a random walk and a centered linear transformation) that sampled the mutational parameter space fully and efficiently while maintaining the constraint (µ1 + µ2 + µ3)/3 = µ. We also estimated the HKY and GTR model parameters and the TMRCA of the sequences. MCMC convergence was tested as in Drummond et al. (2002).
Prior Distributions
A uniform prior distribution on r was used, with a lower bound of zero and an upper bound of 0.75. The upper bound represents a maximum plausible growth rate, corresponding to an epidemic doubling time of less than one year. As discussed in Results, this bound was impinged on in the MCMC analysis. However, the posterior probability density of r was focused on substantially lower rates and the lower bound was not reached (fig. 2). All other prior distributions were uniform and no other parameter boundaries were impinged upon.
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The first data set, labeled A, contains 68 sequences 411bp in length (63 type 4 sequences and 5 subtype 1g sequences). This represents all the E1 sequences published by Ray et al. (2000) except for three isolates belonging to subtypes 1a or 1b, which are recent introductions into Egypt rather than endemic strains. Although HCV type 4 is the dominant type in Egypt and appears to have been present in the Middle East for several centuries (Smith et al. 1997), is not clear whether subtype 1g is an endemic or introduced strain. Thus the shared history of type 4 and subtype 1g could possibly represent a non-Egyptian ancestral population. To exclude any potential estimation bias arising from this, we created a second data set, labeled B, which contains the 63 type 4 sequences only.
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Results |
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The mean posterior estimate of the HCV growth rate in Egypt is r = 0.25 year-1. This point estimate is not particularly informative because the posterior distribution of r is right skewed (see fig. 2). Because the posterior density is focused in the range 0.10.3, it would be more appropriate use this range as an estimate for r. Thus the spread of HCV in Egypt was rapid, with an estimated epidemic doubling time of 2 to 7 years. Even the lower confidence limit of the Egyptian growth rate (r 0.07) is greater than the rate estimated for type 4 infections outside Egypt (r
0.04) (Pybus et al. 2001). In fact, the growth rate in Egypt is as fast, or faster, than that estimated for HCV subtypes 1a and 1b (r
0.09), which have spread primarily through highly efficient parenteral transmission networks, namely injecting drug use and infected blood products (Pybus et al. 2001). Thus our growth rate result indicates that an equally efficient route must have been at work during the twentieth century in Egypt. Parenteral transmission via PAT treatment is the only viable candidate route.
As shown in figure 2, the upper bound on the prior distribution for r was impinged upon in the MCMC analysis, and the posterior distribution of r is right skewed, indicating that the data do not reject exceptionally high growth rates. However, the lower bound of zero was not reached, and we can further conclude that r is at least 0.07. Thus the data do strongly support a switch from low to high numbers of infections sometime between 1924 and 1966, but do not contain much information about the upper limit of the epidemic growth rate.
The mutational parameter estimates show a strong bias toward transitions over transversions in the E1 gene region, as previously reported (Salemi and Vandamme 2002). Our estimates of codon-positionspecific mutation rates indicate a faster rate at third codon positions, which is commonly observed and is most likely due to the increased opportunity for unconstrained "silent" (synonymous) nucleotide substitution at third positions.
By sampling parameter values from the marginal probability density h(
|D,µ) we were able to reconstruct an estimated growth curve for the Egyptian HCV epidemic (fig. 3). The reconstructed epidemic histories are similar for both data sets and show a rapid increase from 1930 to 1960 in the effective number of infections. This growth period lies entirely within the period of widespread PAT treatment in Egypt. In the context of cross-sectional epidemiological evidence (Frank et al. 2000), this temporal correlation constitutes independent genetic evidence for the involvement of PAT in the initiation and generation of the Egyptian HCV epidemic.
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Conclusion |
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From an epidemiological perspective, the course of the Egyptian HCV epidemic resulted from a temporary increase in R0, the basic reproductive number of the virus, during the PAT treatment period. R0 represents the average number of secondary infections created by one primary infection in a susceptible population. An increase in R0 can result from three changes: increased probability of transmission per contact event, more frequent exposure to infection, and a longer period of infectiousness. In the absence of adequate needle hygiene, it is clear that the injectable nature of PAT could produce the first change, while the widespread administration of PAT could produce the second. Prior to PAT, HCV infection in Egypt was probably at endemic equilibrium with an R0 close to 1.7, which is the value estimated for current HCV type 4 infections outside Egypt (Pybus et al. 2001). If the general relationship R0 = rD + 1 holds true for the exponential phase of the Egyptian epidemic, then R0 was raised to approximately 37 during the period of PAT treatment (D is the mean duration of infectiousness, and following Pybus et al. (2001) it is averaged across the range 10 to 30 years).
Hepatitis C virus infection in Egypt increases with age (e.g., Frank et al. 2000), suggesting that transmission has considerably decreased since the end of PAT treatment, such that the R0 of HCV in Egypt today has probably declined to its pre-PAT level. This temporal change in R0 provides further evidence that the epidemic behavior of HCV is primarily determined by the transmission routes and networks that individual HCV strains find themselves in. However, even if contemporary levels of transmissibility and exposure to infection are small, the high HCV prevalence in Egypt means that the absolute number of new infections per year may still be significant. The public health situation is compounded by the long duration of chronic HCV infection and frequent co-infection with schistosomes (Kamal et al. 2000; Hassan et al. 2001; Gad et al. 2001). Although our results show that infection via PAT occurred several decades ago, the slow development of HCV-related liver cirrhosis and cancer (Centers for Disease Control and Prevention 1998) means that the incidence of these diseases in Egypt may not yet have peaked. Even under the most optimistic scenario of zero contemporary transmission, a standard dynamical model of prevalence decay (Anderson and May 1991) indicates that HCV infection in Egypt will remain above 5% for at least the next 50 years. Transmission prevention strategies, vaccine development, and cheaper antiviral drugs are all required to reduce the current and future HCV disease burden in Egypt. Anti-viral combination drug therapy (interferon -2b or
-2a plus ribavirin) is effective against HCV type 4 infections in 40%60% of cases (El-Zayadi et al. 1999; Shobokshi et al. 2002), but the high cost of this treatment may affect the extent of its use in Egypt.
On a more theoretical note, the PAT hypothesis provides a quantitative a priori scenario and can therefore be used to judge the accuracy of parameter estimates obtained from sequence data using coalescent theory. Such test cases are relatively rare and enable us to objectively evaluate the performance of analytical methods. Coalescent theory has been similarly investigated in the past by Rodrigo et al. (1999), who used a coalescent model to estimate the generation time of HIV-1 (human immunodeficiency virus type 1) from viral gene sequence data. Their estimated generation time was close to that obtained using alternative nongenetic mathematical models. The correspondence of our estimated dates and growth rates with independently obtained epidemiological results also suggests that the coalescent framework is reliable in this case. Thus it appears that possible confounding factors (substitution rate variation among lineages, or epidemic differences among regions in Egypt) have not significantly biased our estimates. Viral recombination, which could potentially strongly affect coalescent analyses (Schierup and Hein 2000; Worobey 2001), is very rare in HCV, having only been detected once (Kalinina et al. 2002).
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Acknowledgements |
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Footnotes |
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Keith Crandall, Associate Editor
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