Determinants and Predictors of Hepatitis C Virus Infection in Gilgit-Baltistan, Pakistan: A Cross-Sectional Study

Determinants and Predictors of Hepatitis C Virus Infection in Gilgit-Baltistan, Pakistan: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants and Predictors of Hepatitis C Virus Infection in Gilgit-Baltistan, Pakistan: A Cross-Sectional Study Nosheen Sadaf, Akbar Khan, Tika Khan, Mehran Kausar, Wajid Ali, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8682253/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hepatitis C virus (HCV) infection continues to be a significant problem to health among the Pakistani population and especially in the underserved and mountainous areas like Gilgit-Baltistan. There is limited comprehensive evidence on the socioeconomic, clinical and behavioral determinants of HCV in this region. Methods A cross-sectional study was done on 238 participants who were recruited into tertiary care hospitals in District Gilgit, Gilgit-Baltistan. A structured questionnaire was used to collect data that included socio-demographic factors, clinical history, risk factors, access to healthcare, and awareness to HCV. Medical records provided clinical parameters, liver functional tests, serological, PCR, and the genotype. The relationship between HCV diagnosis predictors was determined using multivariable logistic regression. The scale reliability was measured on the omega by McDonald and variance inflation factors were used in assessing collinearity diagnostics. Results The sample of the study primarily comprised of adults (young and middle-aged, with a minor majority of males). Many respondents were matric level educated and could not access tertiary healthcare services. Age was found to be a significant predictor of HCV diagnosis and is positive (OR = 1.05, p < 0.001). Matriculated people were more likely to be diagnosed with HCV than illiterate ones (OR = 2.77, p = 0.033). The positive relation with HCV diagnosis was also found in the case of employment in the private sector (OR = 21.24, p = 0.010). Mental health support was also significantly related to increased odds of a diagnosis (OR = 15.47, p = 0.008) and previous HCV cure (OR = 0.04, p < 0.001) and serological testing (OR = 0.03, p = 0.001) were protective. There was no severe multicollinearity of the predictors. Conclusion The paper provides important demographic, socioeconomic and healthcare-based factors that contribute to HCV in Gilgit-Baltistan. The need to reduce HCV in this vulnerable group is to strengthen early screening, access to diagnostic services, and increase the specific awareness campaigns and treatment interventions. Hepatitis C virus Socioeconomic factors Risk behaviors Logistic regression Gilgit-Baltistan Pakistan Figures Figure 1 Figure 2 Figure 3 Introduction Hepatitis C Virus (HCV) is an important and growing worldwide health challenge, as it is considered the primary etiological agent of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC) [ 1 – 4 ]. Since its isolation in 1989 [ 5 ], HCV, an enveloped virus belonging to the Flaviviridae family, has spread widely. Globally, it is estimated that 71 million people have chronic HCV infection [ 6 – 8 ], translating to an approximate prevalence of 3.3% of the world population [ 2 ]. The course of the disease is very alarming; 50% to 80% of the people who are infected with the disease develop chronic hepatitis, which leads to severe outcomes later on by the progressive fibrosis and finally leads to some advanced outcomes like cirrhosis and HCC [ 9 , 10 ]. In addition to hepatic effects, HCV infection has also been learned to be a real metabolic syndrome with extrahepatic conditions such as Type II diabetes, hypertension and cardiovascular disease [ 11 ].. Pakistan is particularly at risk of the epidemic since it is projected to have potentially the second-largest burden of HCV in the world [ 12 , 13 ]. More than 10 million people have HCV with an estimated prevalence rate of 10 percentage of the population [ 14 ]. Critically, roughly 80% of HCV cases in Pakistan proceed to chronic infection [ 2 , 15 ]. Genotyping data confirm that Genotype 3a is the most prevalent, and the predominant type that is 49.05 percent dominant in the national type of 3a [ 2 , 16 ] and reaching as high as 98.1% in some regional studies [ 14 ]. Genotype 3 is significant to be detected because it is commonly linked with steatosis [ 15 , 17 , 18 ]. Genotyping plays a critical role in making clinical decisions since the chances of obtaining a sustained virological response (SVR) of the standard treatment are closely linked to the genotype [ 16 , 19 ]. HCV is acquired in a majorly blood-borne manner. Safe healthcare practices are the biggest determinants of infection in Pakistan with about 70 percent of the cases being contracted in hospitals, most of which are due to syringe reuse and procedure [ 2 , 16 , 20 ]. According to more recent reports, dental procedures have become the main single recognizable risk factor reported (26.2%) [ 14 ]. ). Nonetheless, 20.35% to 35.9% of the infected subjects do not have a definite identifiable risk factor, so such cases can be considered sporadic [ 14 , 21 ]. The fact that the infection has no symptoms implies that most of the infected people are not aware of their condition and therefore they only start treatment when liver damage has progressed making it difficult to curb the spread of the virus [ 19 , 22 , 23 ]. Thus, it is necessary to find the high-risk population groups. Massive population-based studies have shown that the HCV seroprevalence is greatly linked to major socio-demographic drivers such as age, gender, as well as occupation [ 19 , 22 ]. To use an example, it is the highest among the population in older age groups and patients[ 19 ], who have a relatively lower educational background have a high incidence of infection [ 14 ]. Some occupational groups lead to a significantly large number of urine, including farmers (where seroprevalence observed is more than 40 percent) and house residents, laborers, and transporters [ 22 ]. On the other hand, other groups like ones pertaining to academia or businessmen are also showing much less prevalence (less than 3%), implying that awareness of transmission factors has a very important preventative value [ 22 ]. Derives specific intervention solutions, this essential requirement requires a multidimensional analysis with detailed, multidimensional statistical modelling including linear discriminant analysis to determine accurately the pooled predictive impact of socioeconomic and behavioral factors on the Hepatitis C infection status. Methods Study Area The research was done in the District Gilgit, the capital of the Gilgit-Baltistan province of the northern part of Pakistan. This is a part of Gilgit-Baltistan which is made up of twelve administrative districts. The district borders Nagar, Ghizer, Diamer, Skardu and Astore, geographically. The area itself is located at 34.0 N latitude and 71.5 E longitude with the help of rich landscapes consisting of arid plains, riverine belts, semi-mountainous and mountainous landscapes. Study Design and Population The study design that was used in this research is a cross-sectional study design. Its main aim was to determine the prevalence, histopathology, socioeconomic factors, and genomic diversities of Hepatitis C Virus (HCV) in Gilgit-Baltistan in Pakistan. The sample population was composed of patients (hospital visitors) who could be randomly chosen in hospitals which provided tertiary care level in Gilgit to establish the prevalence of HCV. A total of 238 subjects were recruited, and informed consent was taken out on each of the subjects before data and sample collection. Data Collection Personal and Socioeconomic Profiling: The information about the personal and socioeconomic variables was gathered using the structured questionnaire. They were age, gender, level of education, occupation, income, family history of HCV and specific factors like injection drug use, blood transfusion and surgical history. Before using the questionnaire, its validity and reliability were tested. The study was approved by the ethic committee of Karakoram International University, Gilgit. Comprehensive Variables The variables used in the study were very numerous and included demographic (e.g., name, gender, location, household size, income, marital status, employment, education, occupation, house ownership), disease specific (e.g., Hepatitis prevalence and stage, illness duration, co-existing health problems, treatment history, family history, HCV type, treatment side effects, curability, organ effects), risk (e.g., substance use, needle sharing, blood transfusion history, surgery history), and healthcare access and awareness (e.g., social support, healthcare access, mental health support, They also included diagnostic tests, which comprised serological tests, PCR tests, and Liver Function Tests (LFTs) [ 19 ]. Clinical Data Medical records were used to obtain clinical data such as liver function tests (ALT, AST, bilirubin), viral load, and the HCV genotype. Moreover, the participants were also screened against the prevalent comorbidities such as diabetes and hypertension. It is a method to give a systematic examination of the multidimensional risk factors of Hepatitis C diagnosis within the identified area, using both survey- and clinical-based data collection methods. Statistical Analysis A binary logistic regression analysis was done to conduct findings on factors that were related to Hepatitis C virus (HCV) infection. The dependent variable was the status of HCV with 1 representing those with HCV and 0 those without HCV. The age was the primary independent variable, which was a continuous variable and the region, which was a categorical variable, captured the geographical difference in HCV prevalence. The model used indicator (dummy) variables to enter regions with one category being used as the reference group. Data was first reviewed as complete and the descriptive statistics produced on all variables before model estimation. The hypothesis of linearity among continuous predictors and the logit of the outcome was tested. Multicollinearity was tested using variance inflation factors (VIFs). The logistic regression model was specified as follows: $$\:\text{l}\text{o}\text{g}\left(\frac{p}{1-p}\right)={\beta\:}_{0}+{\beta\:}_{1}\left(\text{Age}\right)+\sum\:_{i=1}^{k}{\beta\:}_{2i}\left({\text{Region}}_{i}\right)$$ where \(\:p\) represents the probability of HCV infection, \(\:{\beta\:}_{0}\) is the interception, \(\:{\beta\:}_{1}\) is the regression coefficient for age, and \(\:{\beta\:}_{2i}\) denotes the coefficients for each regional category relative to the reference region. The corresponding probability form of the model is given by: $$\:p=\frac{\text{exp}\left({\beta\:}_{0}+{\beta\:}_{1}\left(\text{Age}\right)+\sum\:_{i=1}^{k}{\beta\:}_{2i}\left({\text{Region}}_{i}\right)\right)}{1+\text{e}\text{x}\text{p}\left({\beta\:}_{0}+{\beta\:}_{1}\left(\text{Age}\right)+\sum\:_{i=1}^{k}{\beta\:}_{2i}\left({\text{Region}}_{i}\right)\right)}$$ The maximum likelihood estimation was used to estimate model parameters. The findings are described in odds ratios (ORs) and the associated 95% confidence interval (CI). The Hosmer-emeshow goodness-of-fit test was used to determine model fitness and two-sided tests with a p-value of less than 0.05 were used to determine statistical significance. Standard statistical analysis was used in all statistical analysis [ 20 ]. Results Socio-Demographic Characteristics of the Study Population Figure 1 (a -i) provides the socio-demographic attributes of the respondents. The age structure (Fig. 1 a) shows that the age population of the study sample consisted mainly of young and middle-aged adults. The gender structure (Fig. 1 b) represented a little bit more among the males than the females. The respondents (Fig. 1 c) educational status showed that most of the respondents completed matric and graduates showed the next rank and those with no formal education were a lesser percentage. The distribution of the respondents in terms of the districts (Fig. 1 d) revealed that a greater number of the respondents were obtained in the Gilgit district and a relatively smaller number of the respondents in the other districts. Marital status (Fig. 1 e) indicated that most of the respondents were either married or not, with widowed people and children having a small percentage. The employment status (Fig. 1 f) showed that a high percentage of the respondents were either employed or unemployed with a smaller percentage of the respondents being in business or private-sector employment. The housing status (Fig. 1 g) implied that most respondents owned houses. Occupation (Fig. 1 h) indicated that students were the best group with government employees, the unemployed, business workers, and farmers following them. Distribution of household size (Fig. 1 i) revealed that most of the household sizes included five to six individuals. Figure 1 presents a multi-panel summary of the demographic and socioeconomic characteristics of the surveyed population, and panel (a) to panel (i) indicates the frequency distribution of each variable. Factors Associated with Hepatitis C The clinical features, social background, and access to healthcare services among respondents are described in Fig. 2 (a–l). Most of the participants were diagnosed with the disease at the early stages (Fig. 2 a), and most of them had a history of the disease that did not exceed three years (Fig. 2 b). The respondents had low treatment history and adherence in general (Fig. 2 c). The main source of social support was the family members, and community or peer networks were not involved (Fig. 2 d). The situation was predominantly that primary and secondary healthcare services were accessible, whereas tertiary healthcare facilities were not as prevalent (Fig. 2 e). Most of the respondents have stated that they had access to mental health support services (Fig. 2 f); yet large percentages of them have reported stigma and social isolation (Fig. 2 g). The use of substances was common among the subjects (Fig. 2 h), but the risky behavior of sharing needles was rarely reported (Fig. 2 i). Treatment cost was viewed as a significant obstacle and a significant proportion of respondents (Fig. 2 j) indicated unresolved and unaffordable costs. The level of awareness about hepatitis C was low and most of the participants exhibited limited to moderate levels of awareness with only a tiny fraction portraying adequate awareness (Fig. 2 k). The practices of diagnostic were not the most optimal because most of the respondents had not yet been subjected to serological antibody testing (Fig. 2 l), confirmatory PCR testing (Fig. 2 n), or genotyping (Fig. 2 o). The prevalence of co-existing health conditions among the study population was also commonly presented (Fig. 2 m). Knowledge, Medical History, and Perceptions Related to Hepatitis C Figure 3 (a–j) provides the medical history of the respondents and their perceptions to hepatitis C. The majority of those interviewed denounced a family history of hepatitis C (Fig. 3 a), a history of blood transfusion (Fig. 3 b), and a history of surgery (Fig. 3 c). In the same way, most of them did not have a history of liver function testing before (Fig. 3 d). The treatment methods were reported differently, including dietary measures, most frequently, and medications and vaccination among the most frequently observed with fewer participants adopting combined or alternative measures (Fig. 3 e). Concerning perceived methods of enhancing the outcomes of hepatitis C, awareness and hygiene practices appeared the most common ones, and a smaller number of respondents mentioned infection control measures or formal medical interventions (Fig. 3 f). Most subjects indicated the lack of side effects of treatment (Fig. 3 g). There was limited knowledge about the curability of hepatitis C with more respondents giving uncertain or unawareness statements (Fig. 3 h). Most of the respondents were not diagnosed with a particular form of hepatitis, and a small percentage of respondents were diagnosed with hepatitis C (Fig. 3 i). Also, most of the respondents were not aware of the fact that hepatitis C can attack other body organs other than the liver (Fig. 3 j). Figure 3 depicts the knowledge, medical history and perceptions of respondents with reference to hepatitis C. Scale Reliability and Model Fit McDonald omega (ω) was used to determine the scale reliability where omega showed that the overall scale had acceptable internal consistency (0.733). Some of them, house situation, hepatitis stage, social support network, healthcare access, treatment control history, and policies exhibited negative correlations with the total scale, which implies that these items must be reverse coded before further analysis (Table 1 ). Table 1 Scale reliability (McDonald’s ω) with items requiring reverse coding McDonald's ω Scale 0.733 Note. items 'House situation, 'Hepatitis stages', 'Social support network, 'Health care access', 'Treatment control history., and 'Policies' correlate negatively with the total scale and probably should be reversed The model fit statistics showed that there was good performance in the model with the value of deviance of 217 and the Akaike Information Criterion (AIC) of 235. The R 2 of 0.130 of McFadden indicates that the model had anticipated the outcome variable variation to be approximately 13 percent. A sample size of 238 was used in estimating all the models (Table 2 ). Table 2 Model fit statistics for the estimated regression model (N = 238) Model Deviance AIC R² McF 1 217 235 0.130 Note. Models estimated using sample size of N = 238 Predictors of Hepatitis C Diagnosis Table 3 shows the results of logistic regression analysis that investigated the existence of socio-demographic predictors of hepatitis C diagnosis. The age was a strong positive predictor of hepatitis C diagnosis (= 0.051, p < 0.001), where one or more years of age was likely to increase the odds of diagnosis by around 5.3% points. The status of diagnosis did not have significant correlation with income level (p = 0.225). Table 3 Logistic regression coefficients for predictors of Hepatitis C diagnosis (N = 238). Predictor Estimate SE Z p Odds ratio Intercept -2.8614 0.7354 -3.891 < .001 0.0572 Age 0.0515 0.0127 4.051 < .001 1.0528 Income Level -8.72e − 6 7.19e-6 -1.212 0.225 1.0000 Education Matric – Illiterate 1.0180 0.4784 2.128 0.033 2.7676 Graduate – Illiterate 0.4148 0.5070 0.818 0.413 1.5140 Gender: Male – Female -0.5815 0.3790 -1.534 0.125 0.5591 Employment. Status: Unemployed – Employed 0.4203 0.3846 1.093 0.274 1.5225 Business – Employed -0.4499 0.5383 -0.836 0.403 0.6377 Private – Employed 3.0557 1.1890 2.570 0.010 21.2354 Note. Estimates represent the log odds of "Diagnosed = Hep C" vs. "Diagnosed = None" The level of education had a considerable impact, as those with matric-level education had more chances to be diagnosed with hepatitis C than illiterate participants (OR = 2.77, p = 0.033), the difference between graduates and illiterate participants was not statistically significant. Gender and general employment status were found to be not significantly related to diagnosis; the participants in the private sector were equally found to be much more likely to be diagnosed with hepatitis C than the employees (OR = 21.24, p = 0.010). Clinical and Healthcare-Related Predictors Table 4 shows overall model fit statistics of the extended logistic regression model. The model fitted well and its deviance was 95.9 and the AIC was 122. The R 2 of McFadden was 0.616, which implied that the model accounted for a significant percentage of the variance in the diagnosis of hepatitis C. The general model test was found to be statistically significant (2 = 154, 12, p = 0.001). Table 4 Logistic regression model fit statistics for Hepatitis C diagnosis (N = 238) Overall Model Test Model Deviance AIC R² McF χ² df P 1 95.9 122 0.616 154 12 < .001 Note. Models estimated using sample size of N = 238 Table 5 Logistic regression coefficients predicting Hepatitis C diagnosis (log odds and odds ratios, N = 238) Predictor Estimate SE Z p Odds ratio Intercept 3.4526 2.490 1.3866 0.166 31.5812 Health are access Primary – None -1.0828 0.943 -1.1487 0.251 0.3386 secondary – None -0.5538 0.955 -0.5799 0.562 0.5748 Tertiary – None -2.3214 1.803 -1.2873 0.198 0.0981 LFT. History No – Yes 2.0455 1.633 1.2529 0.210 7.7332 Mental health Support: Yes – No 2.7391 1.028 2.6640 0.008 15.4729 Risk Behavior No – yes 0.3256 1.964 0.1658 0.868 1.3849 Genotology No – yes -0.3968 1.010 -0.3928 0.694 0.6725 Blood. Transfusion No – Yes -2.0213 1.173 -1.7237 0.085 0.1325 Family History No – Yes -1.8064 1.312 -1.3772 0.168 0.1643 Hepatitis Cure No – Yes -3.3118 0.712 -4.6519 <.001 0.0365 Serological Test No – Yes -3.4336 1.060 -3.2395 0.001 0.0323 Treatment Cost 1 Lak – Not fixed 0.0141 0.594 0.0238 0.981 1.0142 Note. Estimates represent the log odds of "Diagnosed = Hep C" vs. "Diagnosed = None" Table 6 Collinearity diagnostics (VIF and tolerance) for predictors in the logistic regression model (N = 238) Variables VIF Tolerance Health care access 1.14 0.876 LFT test history 1.57 0.638 Mental health support 1.29 0.772 Risk behavior 1.24 0.806 Gerontology 1.53 0.654 Blood transfusion 1.07 0.935 Family history 1.31 0.761 Hepatitis care 1.28 0.784 Serological test 1.42 0.704 Treatment cost 1.05 0.952 The support related to mental health was strongly linked with the increased odds of hepatitis C diagnosis (OR = 15.47, p = 0.008), as demonstrated in Table 5. Conversely, the cure for hepatitis C in the past (OR = 0.037, p = 0.001) and serological testing (OR = 0.032, p = 0.001) had a significant negative correlation with hepatitis C diagnosis today. Other predictors such as healthcare access, liver function test history, risk behaviors, gerontology, blood transfusion history, family history and treatment cost were found not to be statistically significant Collinearity Diagnostics Variance Inflation Factor (VIF) and tolerance were used to measure collinearity among the predictors (Table 7). Tolerance values were over 0.6 and all the VIF values were under 2.0, which shows that there is no significant multicollinearity between independent variables. These findings affirm the fact that the attributes that have been incorporated in the logistic regression model are interpretable with high degrees of reliability and that they are not all about inflated standard errors or redundancy. Discussion The present study presents subnational epidemiological data on the Hepatitis C virus (HCV) infection in Gilgit-Baltistan which is both geographically remote and underrepresented in history in Pakistan. The results are generally in line with national data with its narrowing down of the existing knowledge of demographic, socioeconomic, and healthcare-related predictors of HCV diagnosis. HCV continues to be a serious public health issue in the world and Pakistan. Since its identification in 1989 (Choo et al., 1989), approximately 71 million individuals have been chronically infected worldwide (WHO, 2018; Mohd Hanafiah et al., 2013). Pakistan is one of the nations with one of the largest national burdens with an estimated prevalence rate of almost 10 which translates to over 10 million people infected [ 2 , 13 , 14 ]. In this regard, regional studies like the current one are necessary in determining local pertinent determinants of disease detection and control. Older age was also found to be an independent predictor of HCV diagnosis with odds increasing 5.3 percent each year. This observation is congruent with national epidemiological surveys with higher prevalence in older age groups, which could be due to cumulative lifetime exposure to unsafe medical and community-based practices [ 14 , 24 ]. Recent data, however, showing increased seroprevalence in younger adults (1835 years) are indicative of changing patterns of transmission, or simply greater uptake of screening by younger adults [ 4 , 12 ], indicating the possibility of regional heterogeneity. Education showed no-linear correlation with HCV diagnosis. The odds of diagnosis were greatly higher among participants who were receiving matric-level education than those who were illiterate. Though the educational level tends to be lower in such cases, exposure risk, and thus this result are presumably due to the variation in healthcare access, health literacy, and uptake of diagnoses as opposed to a higher infection risk [ 23 , 25 ]. This difference implies the significance of defining education as a determinant of detection instead of transmission only. The employment in the private sector was closely related to the diagnosis of HCV. This is unlike in previous research where agricultural workers, labourers, and transport workers were considered to be high-risk occupational groups whilst professional groups had traditionally lower prevalence rates [ 22 , 26 ]. The observed correlation in this study is probably due to more contact with formal healthcare systems and screening with the help of employers, which also highlights that the occupational status might be more likely to contribute to diagnosis than the exposure risk. Engagement in healthcare proved to be a significant diagnosis determinant. Availability of mental health care was significantly linked with a significant increase in the likelihood of diagnosis of HCV, which implies that people who receive structured healthcare or psychosocial services are more likely to receive a screening. Since chronic HCV infection is asymptomatic, these healthcare contact points are critical in identification of cases [ 23 , 27 ]. Conversely, history of HCV treatment and history of serological tests in the past was very strong protection, which means that the probability of current infection was considerably lower. These results support the efficacy of early diagnosis and antiviral therapy in the process of decreasing the burden of the disease and avoiding advanced liver disease [ 22 , 28 ]. They also promote the growth of routine and repeat screening plans especially when the resources are limited. Although the transmission via blood transfusion is a recognized route of transmission in this country, it was not statistically significant in this study. This can be an indication of better blood safety precautions or low statistical power. Nationally, unsafe medical procedures such as the reuse of needles, unsafe dental treatment and barber exposures continue to be the most common causes of the spread of HCV [ 2 , 14 , 16 , 21 , 29 ]. A significant percentage of infections remains unidentified about any prior exposure history, and these include the cases of sporadic cases that were earlier reported. The history of genotyping did not play a major role in the diagnosis in this analysis. However, Genotype 3a is still the most common circulating strain in Pakistan and has significant clinical interests related to the fact that this virus is associated with hepatic steatosis, and varies in terms of response to treatment [ 2 , 14 , 30 ]. Conclusion Taking together, these data points to the focal role of healthcare access and involvement in HCV cases detection in Gilgit-Baltistan. Enhancement of screening during the early years as well as the incorporation of HCV into routine and mental health delivery and the issue of infection during healthcare are significant in decreasing disease burden. Since the prevalence of Genotype 3a still exists, the introduction of genotyping into routine treatment regimens will be necessary to maximize treatment outcomes and to secure long-term virological control. Declarations Competing Interest The authors of this article have no competing interest. Author Contribution Nosheen Sadaf was a PhD student, and she was involved in all the study from conceptualization to the development of the manuscriptAkbar Khan is involved in conceptualization, data arrangement and involved in writing of the manuscript. Tika Khan involved in writing the manuscript Mehran Kausar is involved in study design and field work Wajid Al is involved in the analysis of the dataSaad Abdullah is involved in analysis and data arrangement Imran Hassan is involved in lab related maters AKNOWLEDGEMETS We acknowledge gracious support from Karakoram International University and the staff of PHQ Hospital Gilgit. References Suhail M, et al. Role of hepatitis c virus in hepatocellular carcinoma and neurological disorders: an overview. Front Oncol. 2022;12:913231. Akbar HO et al. Hepatitis C virus infection: A review of the current and future aspects and concerns in Pakistan. 2009. Shepard CW, Finelli L, Alter MJ. Global epidemiology of hepatitis C virus infection. Lancet Infect Dis. 2005;5(9):558–67. Alamneh TS, et al. Hepatitis C virus cascade of care among adults in Sindh province, Pakistan: Findings from 2019–2020 household sero-survey. PLOS Global Public Health. 2025;5(7):e0004706. Choo Q-L, et al. Isolation of a cDNA clone derived from a blood-borne non-A, non-B viral hepatitis genome. Science. 1989;244(4902):359–62. WHO. Hepatitis C, Key Facts report, 18 July, 2018. Retrieved from Discussion references 2018. Mohd Hanafiah K, et al. Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence. Hepatology. 2013;57(4):1333–42. Khedhiri M et al. Overview of the epidemic history of Hepatitis C uncommon subtypes 2i and 4d in Tunisia and in the world. Infection, Genetics and Evolution, 2022. 105: p. 105375. Muzaffar F, Hussain I, Haroon TS. Hepatitis C: the dermatologic profile. J Pakistan Association Dermatologists. 2008;18(3):171–81. Fasano M, et al. Acute hepatitis C: current status and future perspectives. Viruses. 2024;16(11):1739. Sheikh MY, et al. Hepatitis C virus infection: molecular pathways to metabolic syndrome. Hepatology. 2008;47(6):2127–33. Tirmizi R, Munir R, Zaidi N. Trends in hepatitis C virus seroprevalence and associated risk factors among msm in Pakistan: insights from a community-based study. Sci Rep. 2024;14(1):16551. Umer M, Iqbal M. Hepatitis C virus prevalence and genotype distribution in Pakistan: Comprehensive review of recent data. World J Gastroenterol. 2016;22(4):1684. Ali A, Abbasi AS, Shaikh JI. DEMOGRAPHIC FEATURES OF HEPATITIS C IN PAKISTANI POPULATION. J Med Sci. 2017;25(2):252–6. Zarębska-Michaluk D. Genotype 3-hepatitis C virus’ last line of defense. World J Gastroenterol. 2021;27(11):1006. Idrees M. Development of an improved genotyping assay for the detection of hepatitis C virus genotypes and subtypes in Pakistan. J Virol Methods. 2008;150(1–2):50–6. Roingeard P, Hourioux C. Hepatitis C virus core protein, lipid droplets and steatosis. J Viral Hepatitis. 2008;15(3):157–64. Rubbia-Brandt L, et al. Hepatocyte steatosis is a cytopathic effect of hepatitis C virus genotype 3. J Hepatol. 2000;33(1):106–15. Mansoor M, et al. Prevalence and risk factors for hepatitis C virus infection in an informal settlement in Karachi, Pakistan. PLOS Global Public Health. 2023;3(9):e0002076. Ullah S, et al. Illness perception about hepatitis C virus infection: a cross-sectional study from Khyber Pakhtunkhwa Pakistan. BMC Infect Dis. 2022;22(1):74. Idrees M, Riazuddin S. Frequency distribution of hepatitis C virus genotypes in different geographical regions of Pakistan and their possible routes of transmission. BMC Infect Dis. 2008;8(1):69. Ahsan A, et al. Estimation of hepatitis C prevalence in the Punjab province of Pakistan: A retrospective study on general population. PLoS ONE. 2019;14(4):e0214435. Hoofnagle JH. Course and outcome of hepatitis C. Hepatology. 2002;36:S21–9. Akhtar N, et al. Prevalence of Hepatitis C virus infections among the general population of Buner, Khyber Pakhtunkhwa, Pakistan. Biomedical Res Therapy. 2016;3(12):1003–17. Alzahrani MS et al. Knowledge of and testing rate for hepatitis C infection among the general public of Saudi Arabia: a cross-sectional study. International Journal of Environmental Research and Public Health, 2023. 20(3): p. 2080. Westermann C, et al. The prevalence of hepatitis C among healthcare workers: a systematic review and meta-analysis. Occup Environ Med. 2015;72(12):880–8. Assoumou SA, et al. Relationship between hepatitis C clinical testing site and linkage to care. Open forum infectious diseases. Oxford University Press; 2014. Smith-Palmer J, Cerri K, Valentine W. Achieving sustained virologic response in hepatitis C: a systematic review of the clinical, economic and quality of life benefits. BMC Infect Dis. 2015;15(1):19. Saleem U, et al. Hepatitis C virus: Its prevalence, risk factors and genotype distribution in Pakistan. Eur J Inflamm. 2022;20:1721727X221144391. Sherwani RAK et al. Prevalence of Genotype 3a in Different Regions of Pakistan: A systematic Review and Meta-Analysis. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8682253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580510021,"identity":"5f9b88d6-3e43-4ff5-9084-b0d584faa132","order_by":0,"name":"Nosheen Sadaf","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Nosheen","middleName":"","lastName":"Sadaf","suffix":""},{"id":580510023,"identity":"f772a9df-5d2a-4307-b4f3-08b8f7ea00bb","order_by":1,"name":"Akbar Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACHh4gUZEAIhkkgNiASC1nSNbC2JbAQLwW/p6zBz9XzkuTMTjAfPA2D8MdY4JaJM72JUue3ZbDY3CALdmah+GZGWGHnecxkGzcVgHUwmMmzcNw2IagDvnzPMY/G+eAtPB/I06LwdkeM8nGBpDDeNhAWgg7zPDMuTTLhmNpPJKH2Ywt5xg8I+x9uTO5h2821CTb8x1vfnjjTcUdwwaCeuCAGezOA8RrgAEytIyCUTAKRsGwBwCSbzhQlHNGpgAAAABJRU5ErkJggg==","orcid":"","institution":"Karakoram International University","correspondingAuthor":true,"prefix":"","firstName":"Akbar","middleName":"","lastName":"Khan","suffix":""},{"id":580510027,"identity":"aa606188-c35c-4205-93d7-5880ebef0fde","order_by":2,"name":"Tika Khan","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Tika","middleName":"","lastName":"Khan","suffix":""},{"id":580510033,"identity":"a37763df-c996-4e6f-9aa9-e51bd45fb079","order_by":3,"name":"Mehran Kausar","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Mehran","middleName":"","lastName":"Kausar","suffix":""},{"id":580510034,"identity":"4e04cead-7d34-416e-b1cd-b8f376faac45","order_by":4,"name":"Wajid Ali","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Wajid","middleName":"","lastName":"Ali","suffix":""},{"id":580510036,"identity":"ffcfaf68-d07d-44f6-9f8e-5bd3a28fb957","order_by":5,"name":"Saad Abdullah","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Saad","middleName":"","lastName":"Abdullah","suffix":""},{"id":580510038,"identity":"ffd46105-ce35-41a2-b5be-b96a50bb7c5e","order_by":6,"name":"Imran Hassan","email":"","orcid":"","institution":"Karakoram International University","correspondingAuthor":false,"prefix":"","firstName":"Imran","middleName":"","lastName":"Hassan","suffix":""}],"badges":[],"createdAt":"2026-01-23 20:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8682253/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8682253/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101214434,"identity":"1e62297e-d690-4c69-b321-428ada77d1ae","added_by":"auto","created_at":"2026-01-27 10:35:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":525003,"visible":true,"origin":"","legend":"\u003cp\u003eFig.1 is a multi-panel summary of demographic and socioeconomic characteristics of a surveyed population, labeled (a) through (i). Each panel shows the frequency distribution of a different variable.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8682253/v1/bf7c4102058d4df36a5770d0.png"},{"id":101214433,"identity":"bcdf0ac9-9a58-4db5-97ca-58878131b74e","added_by":"auto","created_at":"2026-01-27 10:35:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":694305,"visible":true,"origin":"","legend":"\u003cp\u003esummarizes the clinical profile, social context, and healthcare access of individuals affected by hepatitis\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8682253/v1/56e07d0a46885779620a1995.png"},{"id":101214436,"identity":"13f1062e-6d32-4c58-bc4b-3a96dc07dd9f","added_by":"auto","created_at":"2026-01-27 10:35:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444686,"visible":true,"origin":"","legend":"\u003cp\u003epresents respondents’ knowledge, medical history, and perceptions related to hepatitis C\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8682253/v1/4c3f370a7c04f303d06dd9e1.png"},{"id":101880602,"identity":"800dc630-07d7-4f8d-a303-26ce1dda4f18","added_by":"auto","created_at":"2026-02-04 15:04:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2653130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8682253/v1/f25af986-be38-49cb-975b-563a6e13af22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants and Predictors of Hepatitis C Virus Infection in Gilgit-Baltistan, Pakistan: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis C Virus (HCV) is an important and growing worldwide health challenge, as it is considered the primary etiological agent of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since its isolation in 1989 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], HCV, an enveloped virus belonging to the \u003cem\u003eFlaviviridae\u003c/em\u003e family, has spread widely. Globally, it is estimated that 71\u0026nbsp;million people have chronic HCV infection [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], translating to an approximate prevalence of 3.3% of the world population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The course of the disease is very alarming; 50% to 80% of the people who are infected with the disease develop chronic hepatitis, which leads to severe outcomes later on by the progressive fibrosis and finally leads to some advanced outcomes like cirrhosis and HCC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition to hepatic effects, HCV infection has also been learned to be a real metabolic syndrome with extrahepatic conditions such as Type II diabetes, hypertension and cardiovascular disease [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]..\u003c/p\u003e \u003cp\u003ePakistan is particularly at risk of the epidemic since it is projected to have potentially the second-largest burden of HCV in the world [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. More than 10\u0026nbsp;million people have HCV with an estimated prevalence rate of 10 percentage of the population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Critically, roughly \u003cb\u003e80% of HCV cases\u003c/b\u003e in Pakistan proceed to chronic infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Genotyping data confirm that Genotype 3a is the most prevalent, and the predominant type that is 49.05 percent dominant in the national type of 3a [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and reaching as high as 98.1% in some regional studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Genotype 3 is significant to be detected because it is commonly linked with steatosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Genotyping plays a critical role in making clinical decisions since the chances of obtaining a sustained virological response (SVR) of the standard treatment are closely linked to the genotype [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHCV is acquired in a majorly blood-borne manner. Safe healthcare practices are the biggest determinants of infection in Pakistan with about 70 percent of the cases being contracted in hospitals, most of which are due to syringe reuse and procedure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to more recent reports, dental procedures have become the main single recognizable risk factor reported (26.2%) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. ). Nonetheless, 20.35% to 35.9% of the infected subjects do not have a definite identifiable risk factor, so such cases can be considered sporadic [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe fact that the infection has no symptoms implies that most of the infected people are not aware of their condition and therefore they only start treatment when liver damage has progressed making it difficult to curb the spread of the virus [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, it is necessary to find the high-risk population groups. Massive population-based studies have shown that the HCV seroprevalence is greatly linked to major socio-demographic drivers such as age, gender, as well as occupation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To use an example, it is the highest among the population in older age groups and patients[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who have a relatively lower educational background have a high incidence of infection [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some occupational groups lead to a significantly large number of urine, including farmers (where seroprevalence observed is more than 40 percent) and house residents, laborers, and transporters [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On the other hand, other groups like ones pertaining to academia or businessmen are also showing much less prevalence (less than 3%), implying that awareness of transmission factors has a very important preventative value [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Derives specific intervention solutions, this essential requirement requires a multidimensional analysis with detailed, multidimensional statistical modelling including linear discriminant analysis to determine accurately the pooled predictive impact of socioeconomic and behavioral factors on the Hepatitis C infection status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe research was done in the District Gilgit, the capital of the Gilgit-Baltistan province of the northern part of Pakistan. This is a part of Gilgit-Baltistan which is made up of twelve administrative districts. The district borders Nagar, Ghizer, Diamer, Skardu and Astore, geographically. The area itself is located at 34.0 N latitude and 71.5 E longitude with the help of rich landscapes consisting of arid plains, riverine belts, semi-mountainous and mountainous landscapes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Population\u003c/h3\u003e\n\u003cp\u003eThe study design that was used in this research is a cross-sectional study design. Its main aim was to determine the prevalence, histopathology, socioeconomic factors, and genomic diversities of Hepatitis C Virus (HCV) in Gilgit-Baltistan in Pakistan.\u003c/p\u003e \u003cp\u003eThe sample population was composed of patients (hospital visitors) who could be randomly chosen in hospitals which provided tertiary care level in Gilgit to establish the prevalence of HCV. A total of 238 subjects were recruited, and informed consent was taken out on each of the subjects before data and sample collection.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003ePersonal and Socioeconomic Profiling: The information about the personal and socioeconomic variables was gathered using the structured questionnaire. They were age, gender, level of education, occupation, income, family history of HCV and specific factors like injection drug use, blood transfusion and surgical history. Before using the questionnaire, its validity and reliability were tested. The study was approved by the ethic committee of Karakoram International University, Gilgit.\u003c/p\u003e\n\u003ch3\u003eComprehensive Variables\u003c/h3\u003e\n\u003cp\u003eThe variables used in the study were very numerous and included demographic (e.g., name, gender, location, household size, income, marital status, employment, education, occupation, house ownership), disease specific (e.g., Hepatitis prevalence and stage, illness duration, co-existing health problems, treatment history, family history, HCV type, treatment side effects, curability, organ effects), risk (e.g., substance use, needle sharing, blood transfusion history, surgery history), and healthcare access and awareness (e.g., social support, healthcare access, mental health support, They also included diagnostic tests, which comprised serological tests, PCR tests, and Liver Function Tests (LFTs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eClinical Data\u003c/h3\u003e\n\u003cp\u003eMedical records were used to obtain clinical data such as liver function tests (ALT, AST, bilirubin), viral load, and the HCV genotype. Moreover, the participants were also screened against the prevalent comorbidities such as diabetes and hypertension. It is a method to give a systematic examination of the multidimensional risk factors of Hepatitis C diagnosis within the identified area, using both survey- and clinical-based data collection methods.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eA binary logistic regression analysis was done to conduct findings on factors that were related to Hepatitis C virus (HCV) infection. The dependent variable was the status of HCV with 1 representing those with HCV and 0 those without HCV.\u003c/p\u003e \u003cp\u003eThe age was the primary independent variable, which was a continuous variable and the region, which was a categorical variable, captured the geographical difference in HCV prevalence. The model used indicator (dummy) variables to enter regions with one category being used as the reference group. Data was first reviewed as complete and the descriptive statistics produced on all variables before model estimation. The hypothesis of linearity among continuous predictors and the logit of the outcome was tested. Multicollinearity was tested using variance inflation factors (VIFs).\u003c/p\u003e \u003cp\u003eThe logistic regression model was specified as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{o}\\text{g}\\left(\\frac{p}{1-p}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}\\left(\\text{Age}\\right)+\\sum\\:_{i=1}^{k}{\\beta\\:}_{2i}\\left({\\text{Region}}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003erepresents the probability of HCV infection, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003eis the interception, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003eis the regression coefficient for age, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2i}\\)\u003c/span\u003e\u003c/span\u003edenotes the coefficients for each regional category relative to the reference region.\u003c/p\u003e \u003cp\u003eThe corresponding probability form of the model is given by:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:p=\\frac{\\text{exp}\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}\\left(\\text{Age}\\right)+\\sum\\:_{i=1}^{k}{\\beta\\:}_{2i}\\left({\\text{Region}}_{i}\\right)\\right)}{1+\\text{e}\\text{x}\\text{p}\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}\\left(\\text{Age}\\right)+\\sum\\:_{i=1}^{k}{\\beta\\:}_{2i}\\left({\\text{Region}}_{i}\\right)\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe maximum likelihood estimation was used to estimate model parameters. The findings are described in odds ratios (ORs) and the associated 95% confidence interval (CI). The Hosmer-emeshow goodness-of-fit test was used to determine model fitness and two-sided tests with a p-value of less than 0.05 were used to determine statistical significance. Standard statistical analysis was used in all statistical analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSocio-Demographic Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a -i) provides the socio-demographic attributes of the respondents. The age structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) shows that the age population of the study sample consisted mainly of young and middle-aged adults. The gender structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) represented a little bit more among the males than the females. The respondents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) educational status showed that most of the respondents completed matric and graduates showed the next rank and those with no formal education were a lesser percentage. The distribution of the respondents in terms of the districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) revealed that a greater number of the respondents were obtained in the Gilgit district and a relatively smaller number of the respondents in the other districts. Marital status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee) indicated that most of the respondents were either married or not, with widowed people and children having a small percentage. The employment status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef) showed that a high percentage of the respondents were either employed or unemployed with a smaller percentage of the respondents being in business or private-sector employment. The housing status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg) implied that most respondents owned houses. Occupation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh) indicated that students were the best group with government employees, the unemployed, business workers, and farmers following them. Distribution of household size (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei) revealed that most of the household sizes included five to six individuals. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a multi-panel summary of the demographic and socioeconomic characteristics of the surveyed population, and panel (a) to panel (i) indicates the frequency distribution of each variable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Hepatitis C\u003c/h2\u003e \u003cp\u003eThe clinical features, social background, and access to healthcare services among respondents are described in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a\u0026ndash;l). Most of the participants were diagnosed with the disease at the early stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), and most of them had a history of the disease that did not exceed three years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The respondents had low treatment history and adherence in general (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The main source of social support was the family members, and community or peer networks were not involved (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe situation was predominantly that primary and secondary healthcare services were accessible, whereas tertiary healthcare facilities were not as prevalent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Most of the respondents have stated that they had access to mental health support services (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef); yet large percentages of them have reported stigma and social isolation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). The use of substances was common among the subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh), but the risky behavior of sharing needles was rarely reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eTreatment cost was viewed as a significant obstacle and a significant proportion of respondents (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej) indicated unresolved and unaffordable costs. The level of awareness about hepatitis C was low and most of the participants exhibited limited to moderate levels of awareness with only a tiny fraction portraying adequate awareness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek). The practices of diagnostic were not the most optimal because most of the respondents had not yet been subjected to serological antibody testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el), confirmatory PCR testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003en), or genotyping (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eo). The prevalence of co-existing health conditions among the study population was also commonly presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKnowledge, Medical History, and Perceptions Related to Hepatitis C\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (a\u0026ndash;j) provides the medical history of the respondents and their perceptions to hepatitis C. The majority of those interviewed denounced a family history of hepatitis C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), a history of blood transfusion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), and a history of surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In the same way, most of them did not have a history of liver function testing before (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe treatment methods were reported differently, including dietary measures, most frequently, and medications and vaccination among the most frequently observed with fewer participants adopting combined or alternative measures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Concerning perceived methods of enhancing the outcomes of hepatitis C, awareness and hygiene practices appeared the most common ones, and a smaller number of respondents mentioned infection control measures or formal medical interventions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Most subjects indicated the lack of side effects of treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eThere was limited knowledge about the curability of hepatitis C with more respondents giving uncertain or unawareness statements (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). Most of the respondents were not diagnosed with a particular form of hepatitis, and a small percentage of respondents were diagnosed with hepatitis C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). Also, most of the respondents were not aware of the fact that hepatitis C can attack other body organs other than the liver (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the knowledge, medical history and perceptions of respondents with reference to hepatitis C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScale Reliability and Model Fit\u003c/h2\u003e \u003cp\u003eMcDonald omega (ω) was used to determine the scale reliability where omega showed that the overall scale had acceptable internal consistency (0.733). Some of them, house situation, hepatitis stage, social support network, healthcare access, treatment control history, and policies exhibited negative correlations with the total scale, which implies that these items must be reverse coded before further analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable 1 Scale reliability (McDonald\u0026rsquo;s \u0026omega;) with items requiring reverse coding\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMcDonald\u0026apos;s \u0026omega;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eNote. items \u0026apos;House situation, \u0026apos;Hepatitis stages\u0026apos;, \u0026apos;Social support network, \u0026apos;Health care access\u0026apos;, \u0026apos;Treatment control history., and \u0026apos;Policies\u0026apos; correlate negatively with the total scale and probably should be reversed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003eThe model fit statistics showed that there was good performance in the model with the value of deviance of 217 and the Akaike Information Criterion (AIC) of 235. The R 2 of 0.130 of McFadden indicates that the model had anticipated the outcome variable variation to be approximately 13 percent. A sample size of 238 was used in estimating all the models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit statistics for the estimated regression model (N\u0026thinsp;=\u0026thinsp;238)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeviance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eMcF\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. Models estimated using sample size of N\u0026thinsp;=\u0026thinsp;238\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of Hepatitis C Diagnosis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of logistic regression analysis that investigated the existence of socio-demographic predictors of hepatitis C diagnosis. The age was a strong positive predictor of hepatitis C diagnosis (=\u0026thinsp;0.051, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), where one or more years of age was likely to increase the odds of diagnosis by around 5.3% points. The status of diagnosis did not have significant correlation with income level (p\u0026thinsp;=\u0026thinsp;0.225).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression coefficients for predictors of Hepatitis C diagnosis (N\u0026thinsp;=\u0026thinsp;238).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.8614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.72e\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.19e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatric \u0026ndash; Illiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate \u0026ndash; Illiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale \u0026ndash; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment. Status:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed \u0026ndash; Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness \u0026ndash; Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate \u0026ndash; Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.2354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. Estimates represent the log odds of \"Diagnosed\u0026thinsp;=\u0026thinsp;Hep C\" vs. \"Diagnosed\u0026thinsp;=\u0026thinsp;None\"\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe level of education had a considerable impact, as those with matric-level education had more chances to be diagnosed with hepatitis C than illiterate participants (OR\u0026thinsp;=\u0026thinsp;2.77, p\u0026thinsp;=\u0026thinsp;0.033), the difference between graduates and illiterate participants was not statistically significant. Gender and general employment status were found to be not significantly related to diagnosis; the participants in the private sector were equally found to be much more likely to be diagnosed with hepatitis C than the employees (OR\u0026thinsp;=\u0026thinsp;21.24, p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Healthcare-Related Predictors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows overall model fit statistics of the extended logistic regression model. The model fitted well and its deviance was 95.9 and the AIC was 122. The R 2 of McFadden was 0.616, which implied that the model accounted for a significant percentage of the variance in the diagnosis of hepatitis C. The general model test was found to be statistically significant (2\u0026thinsp;=\u0026thinsp;154, 12, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression model fit statistics for Hepatitis C diagnosis (N\u0026thinsp;=\u0026thinsp;238)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eOverall Model Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDeviance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003csub\u003e\u003cb\u003eMcF\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eχ\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. Models estimated using sample size of N\u0026thinsp;=\u0026thinsp;238\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 5 \u003c/strong\u003eLogistic regression coefficients predicting Hepatitis C diagnosis (log odds and odds ratios, N = 238)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"646\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.5812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth are access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary \u0026ndash; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.1487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esecondary \u0026ndash; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTertiary \u0026ndash; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.3214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.2873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLFT. History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.7332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental health Support:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes \u0026ndash; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.4729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenotology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlood. Transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.0213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.7237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.8064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.3772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHepatitis Cure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.3118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.6519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSerological Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.4336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.2395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment Cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 Lak \u0026ndash; Not fixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eNote. Estimates represent the log odds of \u0026quot;Diagnosed = Hep C\u0026quot; vs. \u0026quot;Diagnosed = None\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u003c/strong\u003eCollinearity diagnostics (VIF and tolerance) for predictors in the logistic regression model (N = 238)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"657\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth care access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLFT test history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental health support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGerontology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlood transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHepatitis care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSerological test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe support related to mental health was strongly linked with the increased odds of hepatitis C diagnosis (OR\u0026thinsp;=\u0026thinsp;15.47, p\u0026thinsp;=\u0026thinsp;0.008), as demonstrated in Table\u0026nbsp;5. Conversely, the cure for hepatitis C in the past (OR\u0026thinsp;=\u0026thinsp;0.037, p\u0026thinsp;=\u0026thinsp;0.001) and serological testing (OR\u0026thinsp;=\u0026thinsp;0.032, p\u0026thinsp;=\u0026thinsp;0.001) had a significant negative correlation with hepatitis C diagnosis today. Other predictors such as healthcare access, liver function test history, risk behaviors, gerontology, blood transfusion history, family history and treatment cost were found not to be statistically significant\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCollinearity Diagnostics\u003c/h2\u003e \u003cp\u003eVariance Inflation Factor (VIF) and tolerance were used to measure collinearity among the predictors (Table\u0026nbsp;7). Tolerance values were over 0.6 and all the VIF values were under 2.0, which shows that there is no significant multicollinearity between independent variables. These findings affirm the fact that the attributes that have been incorporated in the logistic regression model are interpretable with high degrees of reliability and that they are not all about inflated standard errors or redundancy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study presents subnational epidemiological data on the Hepatitis C virus (HCV) infection in Gilgit-Baltistan which is both geographically remote and underrepresented in history in Pakistan. The results are generally in line with national data with its narrowing down of the existing knowledge of demographic, socioeconomic, and healthcare-related predictors of HCV diagnosis. HCV continues to be a serious public health issue in the world and Pakistan. Since its identification in 1989 (Choo et al., 1989), approximately 71\u0026nbsp;million individuals have been chronically infected worldwide (WHO, 2018; Mohd Hanafiah et al., 2013). Pakistan is one of the nations with one of the largest national burdens with an estimated prevalence rate of almost 10 which translates to over 10\u0026nbsp;million people infected [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this regard, regional studies like the current one are necessary in determining local pertinent determinants of disease detection and control. Older age was also found to be an independent predictor of HCV diagnosis with odds increasing 5.3 percent each year. This observation is congruent with national epidemiological surveys with higher prevalence in older age groups, which could be due to cumulative lifetime exposure to unsafe medical and community-based practices [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recent data, however, showing increased seroprevalence in younger adults (1835 years) are indicative of changing patterns of transmission, or simply greater uptake of screening by younger adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], indicating the possibility of regional heterogeneity. Education showed no-linear correlation with HCV diagnosis. The odds of diagnosis were greatly higher among participants who were receiving matric-level education than those who were illiterate. Though the educational level tends to be lower in such cases, exposure risk, and thus this result are presumably due to the variation in healthcare access, health literacy, and uptake of diagnoses as opposed to a higher infection risk [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This difference implies the significance of defining education as a determinant of detection instead of transmission only. The employment in the private sector was closely related to the diagnosis of HCV. This is unlike in previous research where agricultural workers, labourers, and transport workers were considered to be high-risk occupational groups whilst professional groups had traditionally lower prevalence rates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The observed correlation in this study is probably due to more contact with formal healthcare systems and screening with the help of employers, which also highlights that the occupational status might be more likely to contribute to diagnosis than the exposure risk. Engagement in healthcare proved to be a significant diagnosis determinant. Availability of mental health care was significantly linked with a significant increase in the likelihood of diagnosis of HCV, which implies that people who receive structured healthcare or psychosocial services are more likely to receive a screening. Since chronic HCV infection is asymptomatic, these healthcare contact points are critical in identification of cases [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, history of HCV treatment and history of serological tests in the past was very strong protection, which means that the probability of current infection was considerably lower. These results support the efficacy of early diagnosis and antiviral therapy in the process of decreasing the burden of the disease and avoiding advanced liver disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. They also promote the growth of routine and repeat screening plans especially when the resources are limited. Although the transmission via blood transfusion is a recognized route of transmission in this country, it was not statistically significant in this study. This can be an indication of better blood safety precautions or low statistical power. Nationally, unsafe medical procedures such as the reuse of needles, unsafe dental treatment and barber exposures continue to be the most common causes of the spread of HCV [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A significant percentage of infections remains unidentified about any prior exposure history, and these include the cases of sporadic cases that were earlier reported. The history of genotyping did not play a major role in the diagnosis in this analysis. However, Genotype 3a is still the most common circulating strain in Pakistan and has significant clinical interests related to the fact that this virus is associated with hepatic steatosis, and varies in terms of response to treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaking together, these data points to the focal role of healthcare access and involvement in HCV cases detection in Gilgit-Baltistan. Enhancement of screening during the early years as well as the incorporation of HCV into routine and mental health delivery and the issue of infection during healthcare are significant in decreasing disease burden. Since the prevalence of Genotype 3a still exists, the introduction of genotyping into routine treatment regimens will be necessary to maximize treatment outcomes and to secure long-term virological control.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors of this article have no competing interest.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNosheen Sadaf was a PhD student, and she was involved in all the study from conceptualization to the development of the manuscriptAkbar Khan is involved in conceptualization, data arrangement and involved in writing of the manuscript. Tika Khan involved in writing the manuscript Mehran Kausar is involved in study design and field work Wajid Al is involved in the analysis of the dataSaad Abdullah is involved in analysis and data arrangement Imran Hassan is involved in lab related maters\u003c/p\u003e\n\u003ch2\u003eAKNOWLEDGEMETS \u003c/h2\u003e\n\u003cp\u003eWe acknowledge gracious support from Karakoram International University and the staff of PHQ Hospital Gilgit.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSuhail M, et al. Role of hepatitis c virus in hepatocellular carcinoma and neurological disorders: an overview. Front Oncol. 2022;12:913231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkbar HO et al. \u003cem\u003eHepatitis C virus infection: A review of the current and future aspects and concerns in Pakistan.\u003c/em\u003e 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShepard CW, Finelli L, Alter MJ. Global epidemiology of hepatitis C virus infection. Lancet Infect Dis. 2005;5(9):558\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamneh TS, et al. Hepatitis C virus cascade of care among adults in Sindh province, Pakistan: Findings from 2019\u0026ndash;2020 household sero-survey. PLOS Global Public Health. 2025;5(7):e0004706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoo Q-L, et al. Isolation of a cDNA clone derived from a blood-borne non-A, non-B viral hepatitis genome. Science. 1989;244(4902):359\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Hepatitis C, \u003cem\u003eKey Facts report, 18 July, 2018. Retrieved from Discussion references\u003c/em\u003e 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd Hanafiah K, et al. Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence. Hepatology. 2013;57(4):1333\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhedhiri M et al. \u003cem\u003eOverview of the epidemic history of Hepatitis C uncommon subtypes 2i and 4d in Tunisia and in the world.\u003c/em\u003e Infection, Genetics and Evolution, 2022. 105: p. 105375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuzaffar F, Hussain I, Haroon TS. Hepatitis C: the dermatologic profile. J Pakistan Association Dermatologists. 2008;18(3):171\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFasano M, et al. Acute hepatitis C: current status and future perspectives. Viruses. 2024;16(11):1739.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikh MY, et al. Hepatitis C virus infection: molecular pathways to metabolic syndrome. Hepatology. 2008;47(6):2127\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTirmizi R, Munir R, Zaidi N. Trends in hepatitis C virus seroprevalence and associated risk factors among msm in Pakistan: insights from a community-based study. Sci Rep. 2024;14(1):16551.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmer M, Iqbal M. Hepatitis C virus prevalence and genotype distribution in Pakistan: Comprehensive review of recent data. World J Gastroenterol. 2016;22(4):1684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli A, Abbasi AS, Shaikh JI. DEMOGRAPHIC FEATURES OF HEPATITIS C IN PAKISTANI POPULATION. J Med Sci. 2017;25(2):252\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarębska-Michaluk D. Genotype 3-hepatitis C virus\u0026rsquo; last line of defense. World J Gastroenterol. 2021;27(11):1006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdrees M. Development of an improved genotyping assay for the detection of hepatitis C virus genotypes and subtypes in Pakistan. J Virol Methods. 2008;150(1\u0026ndash;2):50\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoingeard P, Hourioux C. Hepatitis C virus core protein, lipid droplets and steatosis. J Viral Hepatitis. 2008;15(3):157\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubbia-Brandt L, et al. Hepatocyte steatosis is a cytopathic effect of hepatitis C virus genotype 3. J Hepatol. 2000;33(1):106\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansoor M, et al. Prevalence and risk factors for hepatitis C virus infection in an informal settlement in Karachi, Pakistan. PLOS Global Public Health. 2023;3(9):e0002076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUllah S, et al. Illness perception about hepatitis C virus infection: a cross-sectional study from Khyber Pakhtunkhwa Pakistan. BMC Infect Dis. 2022;22(1):74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdrees M, Riazuddin S. Frequency distribution of hepatitis C virus genotypes in different geographical regions of Pakistan and their possible routes of transmission. BMC Infect Dis. 2008;8(1):69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhsan A, et al. Estimation of hepatitis C prevalence in the Punjab province of Pakistan: A retrospective study on general population. PLoS ONE. 2019;14(4):e0214435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoofnagle JH. Course and outcome of hepatitis C. Hepatology. 2002;36:S21\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar N, et al. Prevalence of Hepatitis C virus infections among the general population of Buner, Khyber Pakhtunkhwa, Pakistan. Biomedical Res Therapy. 2016;3(12):1003\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlzahrani MS et al. \u003cem\u003eKnowledge of and testing rate for hepatitis C infection among the general public of Saudi Arabia: a cross-sectional study.\u003c/em\u003e International Journal of Environmental Research and Public Health, 2023. 20(3): p. 2080.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestermann C, et al. The prevalence of hepatitis C among healthcare workers: a systematic review and meta-analysis. Occup Environ Med. 2015;72(12):880\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssoumou SA, et al. Relationship between hepatitis C clinical testing site and linkage to care. Open forum infectious diseases. Oxford University Press; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith-Palmer J, Cerri K, Valentine W. Achieving sustained virologic response in hepatitis C: a systematic review of the clinical, economic and quality of life benefits. BMC Infect Dis. 2015;15(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleem U, et al. Hepatitis C virus: Its prevalence, risk factors and genotype distribution in Pakistan. Eur J Inflamm. 2022;20:1721727X221144391.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherwani RAK et al. \u003cem\u003ePrevalence of Genotype 3a in Different Regions of Pakistan: A systematic Review and Meta-Analysis.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatitis C virus, Socioeconomic factors, Risk behaviors, Logistic regression, Gilgit-Baltistan, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-8682253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8682253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatitis C virus (HCV) infection continues to be a significant problem to health among the Pakistani population and especially in the underserved and mountainous areas like Gilgit-Baltistan. There is limited comprehensive evidence on the socioeconomic, clinical and behavioral determinants of HCV in this region.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was done on 238 participants who were recruited into tertiary care hospitals in District Gilgit, Gilgit-Baltistan. A structured questionnaire was used to collect data that included socio-demographic factors, clinical history, risk factors, access to healthcare, and awareness to HCV. Medical records provided clinical parameters, liver functional tests, serological, PCR, and the genotype. The relationship between HCV diagnosis predictors was determined using multivariable logistic regression. The scale reliability was measured on the omega by McDonald and variance inflation factors were used in assessing collinearity diagnostics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe sample of the study primarily comprised of adults (young and middle-aged, with a minor majority of males). Many respondents were matric level educated and could not access tertiary healthcare services. Age was found to be a significant predictor of HCV diagnosis and is positive (OR\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Matriculated people were more likely to be diagnosed with HCV than illiterate ones (OR\u0026thinsp;=\u0026thinsp;2.77, p\u0026thinsp;=\u0026thinsp;0.033). The positive relation with HCV diagnosis was also found in the case of employment in the private sector (OR\u0026thinsp;=\u0026thinsp;21.24, p\u0026thinsp;=\u0026thinsp;0.010). Mental health support was also significantly related to increased odds of a diagnosis (OR\u0026thinsp;=\u0026thinsp;15.47, p\u0026thinsp;=\u0026thinsp;0.008) and previous HCV cure (OR\u0026thinsp;=\u0026thinsp;0.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and serological testing (OR\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.001) were protective. There was no severe multicollinearity of the predictors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe paper provides important demographic, socioeconomic and healthcare-based factors that contribute to HCV in Gilgit-Baltistan. The need to reduce HCV in this vulnerable group is to strengthen early screening, access to diagnostic services, and increase the specific awareness campaigns and treatment interventions.\u003c/p\u003e","manuscriptTitle":"Determinants and Predictors of Hepatitis C Virus Infection in Gilgit-Baltistan, Pakistan: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 10:34:57","doi":"10.21203/rs.3.rs-8682253/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"225ac4af-6cfe-4cf5-bb8f-b283c9715645","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T03:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 10:34:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8682253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8682253","identity":"rs-8682253","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}