Spatial Epidemiology of Hepatitis C Virus Infection inEgypt: Analyses and Implications

Paper details please read the article carefully which i will provide the link for it to you and summarize it efficiently in 550 words. here is the link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282472/pdf/hep0060-1150.pdf Spatial Epidemiology of Hepatitis C Virus Infection in Egypt: Analyses and Implications Diego F. Cuadros,1,2 Adam J. Branscum,3 F. DeWolfe Miller,4 and Laith J. Abu-Raddad1,2,5 Egypt has the highest hepatitis C virus (HCV) prevalence in the world (14.7%). The drivers of the HCV epidemic in Egypt are not well understood, but the mass parenteral antischistosomal therapy (PAT) campaigns in the second half of the 20th century are believed to be the determinant of the high prevalence. We studied HCV exposure in Egypt at a microscale through spatial mapping and epidemiological description of HCV clustering. The source of data was the 2008 Egypt Demographic and Health Survey. We identified clusters with high and low HCV prevalence and high and low PAT exposure using Kulldorff spatial scan statistics. Correlations across clusters were estimated, and each cluster age-specific HCV prevalence was described. We identified six clusters of high HCV prevalence, three clusters of low HCV prevalence, five clusters of high PAT exposure, and four clusters of low PAT exposure. HCV prevalence and PAT exposure were not significantly associated across clusters (Pearson correlation coefficient [PCC] 5 0.36; 95% confidence interval [CI] 20.12 to 0.71). Meanwhile, there was a strong association between HCV prevalence in individuals older than 30 years of age (who could have been exposed to PAT) and HCV prevalence in individuals 30 years of age or younger (who could not have been exposed to PAT) (PCC 5 0.81; 95% CI 0.55-0.93). Conclusion: The findings illustrate a spatial variation in HCV exposure in Egypt. The observed clustering was suggestive of an array of iatrogenic risk factors, besides past PAT exposure, and ongoing transmission. The role of PAT exposure in the HCV epidemic could have been overstated. Our findings support the rationale for spatially prioritized interventions. (HEPATOLOGY 2014;60:1150-1159) See Editorial on Page 1124 Hepatitis C virus (HCV), first identified in 1989,1 is an RNA virus that is primarily transmitted through direct percutaneous exposure to blood, such as through blood transfusions, sharing of needles, and accidental percutaneous occupational exposures common in healthcare workers and dentists.2 After the discovery of this virus, a flurry of studies around the world was conducted to document its distribution and prevalence in human populations.3 It is now well established that HCV is a global health challenge, with an estimated 130-170 million chronic infections (2-3% of the global population).3,4 An anomaly in the distribution of HCV infection, however, was discovered in Egypt, where the prevalence was 10-fold higher than that in other countries.5,6 The unusual high prevalence in Egypt has stimulated research to identify the factor or factors that contributed to such widespread HCV transmission in this country. Based largely on indirect evidence,7-12 it was believed that an extensive iatrogenic exposure to HCV occurred during the mass parenteral antischistosomal Abbreviations: CAPMAS, Egyptian Central Agency for Public Mobilization and Statistics; CI, confidence interval; CIA, chemiluminescent microplate immunoassay; EDHS, Egypt Demographic and Health Survey; ELISA, enzyme immunoassay; GIS, Geographical information system; GPS, global positioning system; HCV, hepatitis C virus; PAT, parenteral antischistosomal therapy; PCC, Pearson correlation coefficient; RR, relative risk; RT-PCR, real-time reverse-transcription polymerase chain reaction. From the 1 Infectious Disease Epidemiology Group, Weill Cornell Medical College - Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar; 2 Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, NY, USA; 3 College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA; 4 Department of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii, Honolulu, HI, USA; 5 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. Received February 2, 2014; accepted May 29, 2014. This study was made possible by JSREP grant number 3-014-3-007 from the Qatar National Research Fund (a member of Qatar Foundation). Additional support was provided by the Biostatistics, Epidemiology, and Biomathematics Research Core at the Weill Cornell Medical College in Qatar. The statements made herein are solely the responsibility of the authors. 1150 therapy (PAT) campaigns in Egypt, from as early as 1921, but most intensively during the 1960s and 1970s.11 These campaigns were phased out across Egypt by the late 1970s and early 1980s.11 HCV prevalence levels in Egypt indicate uneven geographic distribution, with higher HCV prevalence found in rural areas compared to urban settings,13-15 and in Lower Egypt compared to the rest of the country.11,15 The factors contributing to the spatial heterogeneity are not well understood, but disparity in the intensity of past PAT campaigns has been proposed as a cause of the geographical variation.10-12,16 A detailed knowledge of the geographical distribution of HCV exposure in Egypt may aid both an elucidation of the drivers of past and present infection transmission and identification of areas with the highest disease burden, where interventions could be prioritized. Aligned with the concept of “Know your epidemic, know your response,” a successful framework and strategy in HIV control,17 we studied HCV prevalence in Egypt at a microscale level through spatial mapping and epidemiological description of the clustering of HCV exposure. With the ultimate aim of developing a more effective strategy to control HCV infection transmission in Egypt, and to clarify the potential drivers of HCV transmission, we implemented a novel approach to analyze the geographical and epidemiological differences in areas where the probability of HCV exposure was higher or lower, and in areas where PAT exposure was higher or lower. Materials and Methods Data Sources. The main source of data in our study was the Egypt Demographic and Health Survey (EDHS) conducted in 2008,6 one of the largest nationally representative studies of HCV infection ever implemented. The survey used a stratified three-stage random cluster sampling to enroll more than 19,500 households. The first stage involved selecting primary sampling units such as towns in urban areas and villages in rural areas. The second stage included mapping and household listing, where the global positioning system (GPS) was used to generate the geographical information system (GIS) dataset that stored the geographical coordinates of each of the EDHS clusters of households. The final stage of the sampling involved the selection of the household sample. All women and men aged 15-59 present in the sampled households were eligible for the survey, and 11,126 (87.1%) of these individuals agreed to and were given an HCV biomarker test. The HCV testing protocol included an initial round of testing to detect the presence of antibodies against the virus. A thirdgeneration enzyme immunoassay (ELISA), Adlatis EIAgen HCV Ab kit, was used to test for antibodies against HCV, and then confirmed by a chemiluminescent microplate immunoassay (CIA) when positive.6 Quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) was also used at the Egyptian Ministry of Health Central Laboratory for the detection of HCV RNA using the RealTime m2000 system (Abbott Laboratories, Abbott Park, IL).6 Further methodological details related to specimen handling and laboratory methods employed for the detection of HCV antibody and HCV RNA can be found in El-Zanaty and Way.6 We used the dichotomous HCV antibody serological status for each individual as the response outcome in our statistical analyses because of our epidemiological interest in investigating exposure to this virus, rather than chronic infection. HCV prevalence in this article refers strictly to the proportion of individuals who are serologically antibody-positive for HCV. We also used the binary answer to the question: “Ever had received an injection to treat for schistosomiasis” as the measure of PAT exposure. Further details related to the EDHS methodology can be found in El-Zanaty and Way.6 Spatial Cluster Analysis. We identified spatial clusters of high and low HCV prevalence, as well as spatial clusters of high and low PAT exposure, using a spatial scan statistical analysis,18 implemented in the SaTScan software.19,20 Scan statistics are one of the most widely used statistical methods for cluster detection in epidemiology.21,22 Briefly, scan statistical analysis uses circular windows of varying radii that span the study region to Address reprint requests to: Diego Cuadros, Ph.D., or Laith Abu-Raddad, Ph.D., Infectious Disease Epidemiology Group, Weill Cornell Medical College, Qatar, Qatar Foundation, Education City, P.O. Box 24144, Doha, Qatar. E-mail: [email protected] or [email protected]; fax: 1(974) 4492-8333. Copyright VC 2014 The Authors. HEPATOLOGY published by Wiley on behalf of the American Association for the Study of Liver Diseases. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. View this article online at wileyonlinelibrary.com. DOI 10.1002/hep.27248 Potential conflict of interest: Nothing to report. HEPATOLOGY, Vol. 60, No. 4, 2014 CUADROS ET AL. 1151 identify areas with exposure clustering. Since we aimed to identify localized clusters, a maximum circular window of 30 Km in radius was used for scanning clusters of HCV exposure and PAT exposure. The circular window was varied continuously in both location and radius size. The radius size was varied from 0 Km up to the fixed maximum radius (30 Km), thus creating and testing a large number of distinct potential clusters of diverse sizes. Each potential cluster was tested using a likelihood ratio test to determine the statistical signifi- cance against the null hypothesis of spatial randomness. Clusters with P < 0.05, calculated through Monte Carlo simulations, were identified as statistically significant, and they were analyzed further for additional epidemiological description. Cluster Characterization and Correlations. After a cluster was identified, the strength of the clustering was estimated using the relative risk (RR) of HCV infection within the cluster versus the area outside the cluster. The fraction of the population living within the cluster and HCV prevalence were also estimated for each cluster. Correlation between HCV prevalence and PAT exposure across the identified clusters was determined using Pearson correlation coefficient (PCC). Agespecific prevalence of HCV exposure for each cluster was described. As mentioned above, PAT campaigns were phased out across Egypt by the late 1970s and early 1980s. Therefore, we assumed that those younger than 30 years of age were virtually unexposed to PAT. This age cutoff was then used to assess the association between HCV prevalence in individuals older than 30 years of age (individuals who could have been exposed to PAT) and HCV prevalence in individuals 30 years of age or younger (individuals who could not have been exposed to PAT). The correlation between HCV prevalence in individuals older than 30 years and HCV prevalence in individuals aged 30 years or younger was calculated across the clusters using PCC. These statistical analyses were conducted using SAS v. 9.3.23 Geographical Distribution of HCV Incidence. We generated a mapping by governorate of the average annual HCV incidence rate experienced by the living Egyptian cohort, using a methodology introduced and developed by Leske et al.,24 among others,25 and applied recently by Miller and Abu-Raddad to study HCV incidence in Egypt at the national level.26 Briefly, the incidence rate was estimated from the age-stratified HCV prevalence per governorate. We assumed that the incidence risk (P) was a cumulative probability, ranging from 0 to 1, of HCV infection over a certain period of time, which we took to be a 5-year age range. It follows that the cumulative probability of incident cases for age interval x is given by Px5 Px112Px Dx ; where Px is the prevalence proportion for age interval x, Px11 is the prevalence proportion for the next older age group (x11), and Dx is the range of ages in interval x. The total population size for each governorate was obtained from the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS), based on 2006 census data.27 To estimate the fraction of the population in each 5-year age group for individuals aged 15- 59 in every governorate, we assumed that these proportions were the same as the proportions estimated for the national sample in the EDHS. GIS analyses, including the production of cartographic displays, were performed with ArcGIS v. 9.2.28 Results Spatial Clustering of HCV Infection. HCV prevalence in responders aged 15-59 years, main outcome in our analysis, was 14.7% (95% confidence interval [CI] 13.9-15.5%). HCV RNA positivity prevalence was 9.8%.6 We identified six clusters of high prevalence of HCV exposure (Fig. 1A; Table 1): Cluster 1, located at the interface between the governorates of Beni Suef and Minya (HCV prevalence of 33.1%); Cluster 2, Faiyum (23.8%); Cluster 3, Dakahlia (23.4%); Cluster 4, Kafr el-Sheikh (26.5%); Cluster 5, Monufia (22.3%); and Cluster 6, Minya (23.1%). We also identified three clusters of low prevalence of HCV exposure (Fig. 1B): Cluster 7, Alexandria (7.5%); Cluster 8, Cairo (9.4%); and Cluster 9, Luxor (5.8%). Furthermore, 20.8% (95% CI 20.0-21.6%) of the total population of Egypt resided within clusters of high HCV prevalence, whereas 17.6% (95% CI 16.9- 18.4%) of the total population resided within clusters of low HCV prevalence. Spatial Clustering of PAT Exposure. The proportion of the population that was ever exposed to PAT was 9.2% (95% CI 8.6-9.7%). We identified five clusters of high PAT exposure (Fig. 1C): Cluster 10, located at the interface between the governorates of Beni Suef and Minya (PAT exposure of 23.9%); Cluster 11, Sohag (19.4%); Cluster 12, Beni Suef (17.1%); Cluster 13, Kafr el-Sheikh (22.3%); and Cluster 14, Asyut (17.8%). In addition, we identified four clusters of low PAT exposure (Fig. 1D): Cluster 15, Cairo 1152 CUADROS ET AL. HEPATOLOGY, October 2014 (PAT exposure 2.5%); Cluster 16, Alexandria (3.6%); Cluster 17, Monufia (3.3%); and Cluster 18, Suez (0%). Furthermore, 13.1% (95% CI 12.5-13.8%) of the total population was located within clusters of high PAT exposure, whereas 20.8% (95% CI 20.0- 21.6%) of the total population was located within clusters of low PAT exposure. Comparisons Between Clusters of HCV Infection and PAT Exposure and Correlation Between HCV Exposure and PAT Exposure. Cluster 1-Beni Suef/ Mynia, which had the largest relative risk of HCV infection (RR 5 2.4), and implicitly the largest HCV prevalence (33.1%), overlapped with a cluster of high PAT exposure (Cluster 10). In contrast, PAT exposure in the high HCV prevalence clusters of Faiyum, Dakahlia, and Mynia was not statistically significantly different from the national PAT exposure. Moreover, the high PAT exposure clusters of Sohag and Asyut had low HCV prevalence compared to the national level (9.4% and 9.5%, respectively). Figure 2A illustrates a comparison between HCV prevalence and PAT exposure for each cluster identified by scan statistics. Clusters with high or low HCV prevalence, and clusters with high or low PAT exposure, were scattered, with no evident overall pattern of an association between HCV prevalence and PAT exposure. There was a weak association, and with a broad confidence interval consistent with the null hypothesis of no correlation, between HCV prevalence per cluster and the corresponding PAT exposure (PCC 5 0.36; 95% CI 20.12 to 0.71; P 5 0.14). There was a high HCV prevalence cluster of 22.3%, located in the governorate of Monufia (Cluster 5), that had also a low PAT exposure of 4.1% (Fig. 2A). Additionally, a cluster of low PAT exposure in the Suez governorate (Cluster 18), which contained no individuals reporting a PAT exposure, had an HCV prevalence of 12.2%, nearly as high as the national HCV prevalence. Patterns of Age-Specific HCV Prevalence and Correlation Between HCV Exposure Among the Old and Young. The analysis of the age-specific prevalence of HCV exposure reflected regional variations among the clusters identified by scan statistics. The influence of PAT on the age distribution of HCV prevalence was evident in Cluster 1-Beni Suef/Minya, which had the largest HCV prevalence and a high PAT exposure. The epidemiological profile observed in this cluster showed a curve where HCV prevalence was low and constant for young individuals followed by a sharp rise for older individuals, indicative of a cohort effect11 (Fig. 3B). This cohort effect, however, was not observed in other clusters, such as the low PAT exposure clusters (Fig. 3D), where the age-specific HCV prevalence increased steadily with age, indicative of a steady exposure to HCV infection. This pattern of steady increase with age is illustrated in a high HCV prevalence cluster that also had a low PAT exposure (Cluster 5, Monufia; Fig. 3C), and a similar pattern was observed in the other clusters with high HCV prevalence. Table 1. Hepatitis C Virus (HCV) and Parenteral Antischistosomal Therapy (PAT) Clustering Description Cluster Fraction of the Population Within Cluster (%) HCV Prevalence (%) PAT Prevalence (%) Relative*