Age-period-cohort and bayesian age-period-cohort prediction modeling of hepatitis C incidence and mortality in China

Abstract

Background

Hepatitis C remains a significant public health challenge in China, despite global advancements in treatment and prevention. This study aimed to investigate the age, period, and cohort effects on hepatitis C incidence and mortality trends in China and project future trajectories to inform targeted interventions.

Methods

Data on hepatitis C incidence, mortality, and age-standardized rates (ASIR/ASMR) from 1992 to 2021 were extracted from the Global Burden of Disease (GBD) 2021 database. Joinpoint regression analyzed annual percentage change with 95% confidence intervals (CI). Age-period-cohort (APC) model evaluated relative risks (RR) of age, period, and birth cohort effects using Poisson regression. A Bayesian APC (BAPC) model projected trends from 2022 to 2035.

Results

From 1992 to 2021, hepatitis C incidence cases declined by 31.54% (1,655,914 to 1,133,610 cases), ASIR declined by 39.00% (152.23 to 92.85 per 100,000, estimated annual percentage change [EAPC] = -1.99%, 95% confidence interval [CI]: -2.32%–-1.67%). Mortality counts rose by 28.60% (36,869 to 51,638 deaths), yet ASMR decreased (EAPC=-1.76%, 95%CI: -1.89%–-1.63%). Gender disparities persisted: females had higher ASIR (157.73 vs. males: 147.86 per 100,000 in 1992 and 93.23 vs. males: 93.10 per 100,000 in 2021) but lower ASMR (3.96 vs. males: 4.67 per 100,000 in 1992 and 2.28 vs. males: 2.89 per 100,000 in 2021). The APC model analysis revealed elevated risks for pre-1962 birth cohorts (incidence relative risk [RR] = 1.479, 95%CI: 0.391–5.601; mortality RR = 2.496, 95%CI: 2.094–2.975) and declining period effects post-2004 (incidence RR = 0.757, 95%CI: 0.635–0.902; mortality RR = 0.618, 95%CI: 0.553–0.690). The BAPC model projections indicated continued ASIR declines by 2035, yet female incidence is expected to rise (87.45 to 95.21 per 100,000), contrasting with male declines (83.94 to 74.71 per 100,000). Mortality rate will decrease, but absolute deaths remain substantial.

Conclusions

Declining standardized rates reflect progress in prevention and treatment scale-up, yet rising mortality cases underscore the enduring burden of undiagnosed infections. Targeted interventions for aging cohorts, gender-specific strategies, and equitable access to direct-acting antivirals are critical to achieving hepatitis C elimination. Policymakers must prioritize enhanced screening, public awareness, and resource allocation to mitigate disparities and align with WHO 2030 goals.

Introduction

Hepatitis C is a form of liver inflammation caused by the hepatitis C virus (HCV). HCV is a bloodborne virus, primarily transmitted through blood contact, including unsafe injection practices, inadequate healthcare, transfusion of unscreened blood, injection drug use, and sexual behaviors that can lead to blood exposure [1]. The World Health Organization (WHO) has released the “2024 Global Hepatitis Report,” indicating that approximately 50 million people worldwide were affected by hepatitis C in 2022 [2]. It represents one of the primary causes of liver cirrhosis and hepatocellular carcinoma (HCC) globally. In 2022, approximately 242,000 people died from hepatitis C, with around 1 million new infections reported annually [2, 3]. The number of deaths attributed to HCV is increasing due to aging populations, lack of treatment access, strong concealment, etc [1, 4, 5]. HCV infection poses an international public health threat to nearly all countries, particularly in Africa and Asia [6]. In China, hepatitis C ranks as the fifth largest infectious disease [7]. The incidence of viral hepatitis in China is primarily attributed to hepatitis B virus (HBV) and hepatitis C [7]. While China has successfully reduced hepatitis B incidence through vaccination programs [8], hepatitis C remains a significant concern in some regions of China [4, 9, 10]. Direct antiviral agents (DAAs) can achieve sustained virologic response rates > 95% across genotypes 1–6 [11]; however, accessibility to diagnosis and treatment remains limited [1]. The WHO has launched a global campaign aimed at eliminating the public health threat posed by HCV by 2030 [12]. The campaign aims to reduce the incidence of hepatitis C by 80% and the mortality associated with hepatitis C by 65%, primarily by achieving a diagnosis rate of at least 90% and treatment coverage of at least 80% [12].

Despite global goals and advancements in treatment aimed at eliminating hepatitis C, the task remains arduous due to its strong concealment, low diagnostic rates, and diverse transmission routes [1]. Limited research has explored the specific influence of age, period, and cohort effects on hepatitis C incidence and mortality. Understanding the past and current epidemic patterns in incidence and mortality rates of hepatitis C is crucial for predicting future needs in prevention, treatment, and resource allocation. This study aims to analyze the trends in incidence and mortality of hepatitis C in China from 1992 to 2021 using the Global Burden of Disease (GBD) data from 2021, while exploring the age, period, and cohort effects that influence the risk of hepatitis C and employing a Bayesian age-period-cohort (BAPC) model to predict the trends of the hepatitis C epidemic in China from 2022 to 2035. Notably, this research addresses a significant gap in the existing literature by integrating age, period, and cohort effects to offer a more nuanced understanding of hepatitis C dynamics using the latest released data of GBD in China, which has been underexplored in previous studies. By doing so, we aim to enhance the accuracy of epidemic forecasts and inform targeted public health interventions.

Materials and methods

Data on hepatitis C incidence, mortality counts, age-adjusted standardized incidence rate (ASIR), and age-adjusted mortality rate (ASMR) from 1992 to 2021 were sourced from the Institute for Health Metrics and Evaluation (IHME) GBD 2021 database at the University of Washington (http://www.healthdata.org). The GBD is a collaborative global initiative led by the IHME, aimed at assessing the impact of diseases, disabilities, and deaths on socioeconomic factors and health, including the epidemiology and economic burden of various diseases. The estimated population of China was obtained from the United Nations World Population Prospects 2019 Revision (https://population.un.org/wpp/), while the world standard population data were sourced from the World Health Organization (WHO) standard population for 2000–2025 (https://seer.cancer.gov/stdpopulations/world.who.html) [13].

Joinpoint regression analysis

Joinpoint regression models were employed to analyze the trends in the burden of hepatitis C in China from 1992 to 2021 [14]. We utilized the Joinpoint Regression Program version 4.9.1.0 (Statistical Research and Applications Branch, National Cancer Institute) to calculate the annual percentage change (APC) and average annual percentage change (AAPC), along with their corresponding 95% confidence interval (CI), to assess the trends in hepatitis C burden [14]. We evaluated the significance of these trends by comparing the APC to zero, with a threshold for statistical significance set at P < 0.05. An APC greater than 0 indicates an upward trend in the ASIR or ASMR of hepatitis C in China, while an APC less than 0 signifies a downward trend. An APC equal to 0 suggests that the trends in the ASIR or ASMR of hepatitis C are stable [14].

Age–period–cohort analysis and BAPC models

The age-period-cohort (APC) model was applied to assess the effects of age, period, and birth cohort on hepatitis C incidence and mortality trends. Analyses were conducted using the APC model Online Tool (https://analystools.cancer.gov/apc/). This model estimates time trends in incidence and mortality within each age group, expressed as the annual percentage change [15]. Relative risk (RR) was used to assess age, period, and cohort effects, where an RR greater than 1 indicates a higher relative risk of death or morbidity, while an RR less than 1 indicates a lower relative compared to the reference value. We calculated the net drift (this refers to the linear temporal trend in the incidence or mortality rates of hepatitis C that persists after adjusting for age effects and non-linear period and cohort effects. It represents the underlying direction and magnitude of change over time that cannot be attributed solely to aging populations or specific non-linear historical or generational influences [15]) and the longitudinal age curves of incidence and mortality rates for hepatitis C. The net drift represents the overall APC of ASIR and ASMR over time, reflecting the overall trend of logarithmic incidence rates and mortality, adjusted for period and cohort effects [15]. The significance of the annual percentage change trend was evaluated using the Wald χ² test. The APC model provides insights into the influences of age, period, and cohort effects on hepatitis C incidence and mortality rates over time. For the APC model, data were structured into 18 consecutive 5-year age groups (0–4, 5–9,…, ≥ 85 years) spanning the observation period from 1992 to 2021. This resulted in six 5-year periods (1992–1996, 1997–2001,…, 2017–2021) and 23 five-year birth cohorts (1907–1911, 1912–1916,…, 2017–2021). The APC model is constructed using the Poisson regression model, with its basic form as follows [16, 17]:

$$\:\text{ln}\left({Y}_{abc}\right)=\mu\:+{\alpha\:}_{a}+{\beta\:}_{b}+{\gamma\:}_{c}+{\epsilon\:}_{abc}$$

ln(\(\:{Y}_{abc}\)) represents the natural logarithm of the incidence and mortality rates of hepatitis C in China; µ represents the risk level of a certain disease in a specific age group of people; \(\:{\alpha\:}_{a}\)represents the age effect of the a-th age group, where a = 1, 2;\(\:{\beta\:}_{b}\)is the period effect of the b − th period, where b = 1, 2;\(\:\:{\gamma\:}_{c}\) is the cohort effect of the c-th cohort, among them, \(\:{\gamma\:}_{c}\)=b-a+n, where n refers to the number of age groups; \(\:{\epsilon\:}_{abc}\:\)refers to the error term or residual term.

The BAPC model extends the classical APC model framework by incorporating Bayesian inference via integrated nested Laplace approximation (INLA) to enhance predictive accuracy and uncertainty quantification. Unlike traditional APC models, the BAPC approach hierarchically integrates baseline effects (e.g., demographic shifts or healthcare policies) with age, period, and cohort effects, addressing collinearity and overdispersion challenges inherent in longitudinal disease burden analyses [18]. Specifically, the model employs stochastic partial differential equations (SPDEs) to approximate posterior distributions of parameters, enabling robust handling of sparse or missing data while reducing computational complexity [19]. In this study, the BAPC model was applied to project hepatitis C incidence and mortality trends in China (2022–2035). Key steps included [19]: First, prior specification: Weakly informative priors were assigned to age, period, and cohort effects to minimize bias from arbitrary assumptions. Second, posterior estimation: INLA facilitated efficient computation of posterior distributions for incidence/mortality rates, incorporating uncertainty from population dynamics and diagnostic variability. Lastly, validation: Model fit was assessed using deviance information criteria (DIC) and posterior predictive checks to ensure reliability. This approach not only refines trend projections but also quantifies credible intervals for risk assessments, offering policymakers a probabilistic framework to prioritize interventions across heterogeneous age-cohort populations [19].

Statistical analysis

Data were initially collected and organized using Microsoft Excel 2019. Origin software (OriginLab Corporation, Northampton, MA) was then used to visualize the trends in standardized incidence rates and mortality for hepatitis C. To further analyze the trends in APC and AAPC of incidence and mortality rates throughout the study period, joinpoint regression modeling was performed using Joinpoint software [14]. The Monte Carlo permutation test was employed to determine the numbers, positions, and corresponding P-values of connection points; this method is widely recognized for analyzing trends in incidence rates and mortality [14]. To assess age, period, and birth cohort effects on hepatitis C risk, an APC model was implemented using the the APC model Online Tool (https://analystools.cancer.gov/apc/) [15]. A BAPC model was subsequently constructed via the “BAPC” package with INLA algorithms in RStudio (R version 4.2.0; R Foundation for Statistical Computing), enabling projection of incidence and mortality trends from 2022 to 2035. All statistical tests were two-sided, with significance thresholds set at α = 0.05.

Results

Trends in incidence and mortality rates of hepatitis C

From 1992 to 2021, China exhibited distinct trends in hepatitis C incidence and mortality, with standardized rates declining significantly but absolute mortality increasing, alongside persistent gender disparities (Figures S1A-1B, and Table 1). The total number of hepatitis C cases decreased by 31.54%, from 1,655,913.50 (95% uncertainty interval [UI]: 1,414,360.99–1,922,770.10) in 1992 to 1,133,610.21 (95%UI: 956,690.84–1,319,969.28) in 2021, with the ASIR dropping by 39.00% (EAPC=-1.99%, 95%CI: -2.32%– -1.67%). Male cases decreased by 29.33% (833,411.12 [95%UI: 712,728.32–969,262.23] to 588,980.46 [95%UI: 497,075.76–686,416.62]), while female cases declined more sharply by 33.78% (822,502.38 [95%UI: 704,421.73–952,473.71] to 544,629.75 [95%UI: 459,760.92–639,715.71]). Females consistently exhibited higher ASIR than males, declining from 157.73 to 93.23 per 100,000 (EAPC=-2.16%, 95%CI: -2.50%– -1.82%), compared to males’ reduction from 147.86 to 93.10 per 100,000 (EAPC=-1.84%, 95%CI: -2.14%– -1.53%), this gap narrowed but persisted (Figure S1A and Table 1).

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For mortality (Figure S1B and Table 1), total deaths increased by 28.6%, from 36,868.51 (95%UI: 31,174.49–43,642.84) in 1992 to 51,637.61 (95%UI: 42,245.65–62,293.11) in 2021; The ASMR declined overall, with an EAPC of -1.76% (95%CI: -1.89%– -1.63%). Males maintained higher ASMR throughout the period: declining from 4.67 to 2.89 per 100,000 (EAPC=-1.62%, 95%CI: -1.72%– -1.52%) versus females’ reduction from 3.96 to 2.28 per 100,000 (EAPC=-1.80%, 95%CI: -1.97%– -1.63%), with male ASMR being 18.0% higher than females in 1992 (4.67 vs. 3.96 per 100,000) and 26.8% higher in 2021 (2.89 vs. 2.28 per 100,000). Despite faster improvements in female ASMR, males retained a larger absolute burden (accounted for 54.90% of deaths in 1992, reducing to 51.9% in 2021), with deaths rising by 24.44% (20,240.33 [95%UI: 16,338.27–25,001.9] to 26,788.09 [95%UI: 20,272.96–35,677.47]), while female deaths surged by 33.08% (16,628.18 [95%UI: 13,791.39–20,176.42] to 24,849.51 [95%UI: 19,275.57–31,222.19]), reflecting slower progress in reducing male mortality.

Joinpoint regression analysis

The joinpoint time trend analysis of the ASIR of hepatitis C in China from 1992 to 2021 demonstrated an overall downward trend (AAPC = -1.68%, 95%CI: -1.84%– -1.62%) (Fig. 1A and Table S1). The overall decline in ASIR for females (AAPC = -1.84%, 95%CI: -1.98%– -1.69%) exceeded that of males (AAPC = -1.66%, 95%CI: -1.83%– -1.49%). The ASIR among females exceeded that among males from 1992 to 2007, while the ASIR among males exceeded that among females from 2007 to 2021. From 1996 to 2000, females experienced a significant decline (APC = -6.26%, 95%CI: -6.68%– -5.83%), while from 2012 to 2021, females exhibited a gradual upward trend with an increase of 0.66% (95%CI: 0.58%– 0.75%). Males experienced a significant decline (APC = -6.32%, 95%CI: -6.97%– -5.66%) from 1996 to 2000, while males exhibited a gradual upward trend from 2010 to 2015, with an increase of 0.90% (95%CI: 0.43%– 1.37%).

The joinpoint time trend of the ASMR of hepatitis C demonstrates that from 1992 to 2021, the ASMR of hepatitis C in China demonstrated an overall downward trend (AAPC = -1.68%, 95%CI: -2.14%– -1.21%) (Fig. 1B and Table S2), with higher mortality rates in males than females. From 2004 to 2007, the ASMR of hepatitis C in China decreased significantly (APC = -3.44%, 95%CI: -7.69%– 1.01%), while the ASMR among females decreased significantly from 2003 to 2007 (APC = -3.69%, 95%CI: -5.87%– -1.47%), and the ASMR among males exhibited a linear downward trend (AAPC = -1.68%, 95%CI: -2.07%– -1.29%).

Age-period-cohort analysis of incidence and mortality rates of hepatitis C

Figures 2 and 3 show the longitudinal age curves for hepatitis C incidence and mortality in China, representing the age-related trends. The net drift of hepatitis C nationwide is -1.88% (95%CI: -2.51%– -1.26%, P < 0.05), indicating that the ASIR is decreasing. In the analysis of the ASMR of hepatitis C, the net drift was − 3.91% (95%CI: -4.57%– -3.23%, P < 0.05), suggesting a decrease in the ASMR associated with hepatitis C.

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Figure 2 and Table 2 reveal distinct age-related patterns in hepatitis C incidence and mortality in China, with notable gender disparities in risk trends and threshold ages. For incidence, the RR declines steadily with age, but females exhibit persistently higher risks than males across all age groups. For example, in the 0–4 years age group, female incidence RR is 2.435 (95% CI: 1.711–3.465) compared to males’ 1.962 (95% CI: 1.566–2.458), and this disparity persists until older ages. Females’ incidence risk remains elevated until 65–69 years (RR = 1.341, 1.014–1.774), whereas males’ risk drops below 1 earlier at 45–49 years (RR = 0.997; 0.851–1.168) (Fig. 2A, D and G, and Table 2). For mortality, risks peak in early childhood (e.g., 0–4 years: females: RR = 9.826 [95%CI: 6.105–15.816]; males: RR = 6.604 [95%CI: 5.144–8.478]) but decline with age, though absolute mortality increases due to aging populations. Both sexes reach a mortality risk threshold (RR < 1) around 50–54 years, but females experience steeper declines post-threshold (e.g., 85–89 years: female RR = 0.207 [95%CI: 0.162–0.263] vs. male RR = 0.262 [95%CI: 0.230–0.298]) (Fig. 3A, D and G, and Table 2). Gender differences are pronounced: females face higher incidence and mortality risks in early life, while males retain slower risk reductions in older ages.

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As shown in Figs. 2 and 3 and Table 3, in the period RR results of this study, after controlling for age and birth cohort effects, there were distinct temporal trends in hepatitis C burden. For incidence (Fig. 2B, E and H, and Table 3), the period effect declined significantly over time, with RR decreasing from 1.259 (95% CI: 1.065–1.488) in 1992–1996 to 0.757 (95%CI: 0.635–0.902) in 2017–2021. Males consistently exhibited higher incidence risks than females, particularly in early periods (e.g., 1992–1996: male RR = 1.319 [95%CI: 1.190–1.462] vs. female RR = 1.151 [95%CI: 0.983–1.349]). By 2017–2021, male risks remained elevated (RR = 0.904 [95%CI: 0.812–1.006]) compared to females (RR = 0.875 [95%CI: 0.743–1.032]). For mortality (Fig. 3B, E and H, and Table 3), period effect also declined, with RR dropping from 1.681 (95%CI: 1.550–1.824) in 1992–1996 to 0.618 (95%CI: 0.553–0.690) in 2017–2021. Females faced higher mortality risks in early periods (e.g., 1992–1996: RR = 1.843 [95%CI: 1.681–2.021] vs. males: 1.628 [95%CI: 1.544–1.716]), but both sexes showed accelerated declines post-2002, aligning by 2017–2021 (male RR = 0.634 [95%CI: 0.592–0.680]; female RR = 0.596 [95%CI: 0.525–0.678]).

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Birth cohort trends highlight generational shifts in hepatitis C burden. For incidence (Fig. 2C, F and I, and Table 4), earlier cohorts (e.g., 1907: RR = 1.479 [95%CI: 0.391–5.601]) showed elevated but unstable risks, while later cohorts (e.g., 2017: RR = 0.380 [95%CI: 0.240–0.601]) experienced steep declines. Females born after 1982 saw sharper reductions (e.g., 1992 cohort: RR = 0.354 [95%CI: 0.241–0.520]) compared to males (1992 cohort: RR = 0.640 [95%CI: 0.502–0.817]). For mortality (Fig. 3C, F and I, and Table 4), early cohorts (e.g., 1907: RR = 2.496 [95%CI: 2.094–2.975]) faced significantly higher risks, with females consistently exceeding males (e.g., 1907 cohort: female RR = 2.808 [95%CI: 2.424–3.253] vs. male RR = 2.427 [95%CI: 2.075–2.839]). However, post-1962 cohorts demonstrated dramatic declines, particularly for females (2017 cohort: RR = 0.004 [95%CI: 0.000–0.627]) versus males (RR = 0.008 [95%CI: 0.001–0.099]). Gender disparities narrowed in younger cohorts, but males retained higher residual risks in later periods. These findings underscore the interplay of generational exposures and sex-specific vulnerabilities in HCV burden.

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BAPC model predicts incidence and mortality rates of hepatitis C

As shown in Fig. 4 and Tables S3-S4, the BAPC model was employed to predict the trend in the incidence and mortality rates of hepatitis C from 2022 to 2035. It can be seen from Fig. 4A and D and Tables S3-S4, the BAPC model predicts a declining trend in ASIR of hepatitis C among males in China from 83.94 (2022) to 74.71 (2035) per 100,000, while females show a gradual increase from 87.45 to 95.21 per 100,000 during the same period. ASMR decline for both genders, with males decreasing from 2.64 to 2.61 per 100,000 and females from 2.10 to 1.76 per 100,000. In absolute terms, male incidence cases drop from 608,940 (2022) to 519,971 (2035), whereas female cases rise from 607,970 to 653,241. Deaths decline significantly for both genders: male deaths fall from 19,179 to 18,169, and female deaths plummet from 14,623 to 12,123. Notably, while female ASIR increases, the ASMR improves more sharply compared to males, highlighting gender-specific disparities in disease progression and healthcare outcomes.

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Disscussion

This study identified significant declines in ASIR, ASMR, and incidence counts of hepatitis C alongside rising mortality counts in China from 1992 to 2021, driven by distinct age, period, and cohort effects. Key findings include: (1) elevated risks for individuals born before 1962 (RR > 1) and reduced post-2004 period effects (RR < 1), (2) persistent gender disparities, with males exhibiting slower mortality declines (-1.62% vs. -1.80% in females), and (3) a predicted decline in male ASIR contrasting with rising female ASIR, alongside decreasing ASMR for both genders. These results underscore the need for targeted interventions addressing aging cohorts, sex-specific disparities, and equitable access to therapies to mitigate the enduring burden of undiagnosed infections and achieve hepatitis C elimination goals [2, 20].

The observed decline in ASIR and ASMR aligns with global trends [21, 22], this can be attributable to improved prevention strategies, safer blood transfusion practices, and expanded access to DAAs since 2014 [11, 23, 24]. However, the 28.6% increase in absolute mortality counts contrasts sharply with global trends [2, 21, 22], which achieved a 16% reduction in HCV-related deaths between 2019 and 2022 through mass screening and free DAAs programs [2]. This disparity underscores the lingering burden of undiagnosed and untreated chronic infections, particularly among aging cohort. This paradox—declining rates but rising deaths—mirrors patterns also seen in countries with aging populations [25, 26], where decades-long HCV progression to cirrhosis and HCC manifests as delayed mortality [26]. For instance, Japan observed similar mortality trends despite universal DAAs coverage [27], driven by late-stage diagnoses in older adults. China’s success in reducing HBV incidence through vaccination contrasts with its slower progress against HCV, likely due to the absence of an HCV vaccine and historically fragmented screening programs—particularly in rural areas [10, 28, 29]. The stagnation in female mortality rate projected by the BAPC model further emphasizes systemic gaps in reaching high-risk populations, such as people who inject drugs (PWID) and rural communities with limited healthcare access [10, 11, 23, 29].

Persistent gender disparities in hepatitis C burden warrant closer scrutiny. While females exhibited higher ASIR but steeper declines, males faced slower mortality improvements and higher absolute deaths. This aligns with studies showing that women are more likely to engage with prenatal and routine healthcare [30], leading to earlier diagnosis and treatment initiation. Conversely, males in China are disproportionately exposed to occupational hazards (e.g., unregulated medical procedures, tattooing, mining, construction) and high-risk behaviors (e.g., injection drug use), which contribute to delayed presentations and advanced liver disease [31]. Biological factors may also play a role. Estrogen’s hepatoprotective effects in premenopausal women could mitigate fibrosis progression, whereas testosterone in males may accelerate liver damage [31, 32]. However, the narrowing gender gap in younger cohorts (post-2004) suggests that sociocultural shifts, such as increased health literacy and gender-neutral harm reduction programs [11], are moderating traditional risk disparities. For example, a recent study indicated that integration of HCV screening and treatment into harm reduction programs (HRPs) for PWID will show the most significant decrease in HCV burden in China, projecting a 91.94% reduction in HCV-related deaths by 2030 [23].

The APC analysis revealed elevated risks for pre-1962 birth cohorts, likely reflecting historical exposures to unsafe medical practices, including widespread use of unscreened blood products and non-sterile injections during the 1970–1990 s. These cohorts now face advanced liver disease, driving the rise in absolute mortality despite declining ASMR. Similar generational patterns have been documented in the U.S., where baby boomers (born 1945–1965) account for most of HCV-related deaths [33]. The post-2004 decline in period effects (RR < 1) reflects China’s healthcare reforms, including the China’s 1993 mandate for universal blood screening and the 1998 ban on paid plasma donation reduced transmission risks, the 2003 introduction of free antiretroviral therapy and the 2019 inclusion of DAAs in national insurance [34]. However, regional disparities persist: only the minority of rural HCV patients receive DAAs due to cost barriers and stigma, compared to the most in urban areas [35]. Besides, residual risks in older cohorts highlight the long-term consequences of past exposures, necessitating targeted screening for individuals born before 1980. The cohort effect’s diminishing influence in younger generations aligns with global trends where DAAs availability and preventive education have curtailed new infections [2]. Yet, the BAPC model’s prediction of increased female mortality post-2030 signals unmet needs among aging female with advanced liver disease, who may require palliative care and HCC surveillance rather than curative antiviral therapy [36].

China’s progress in reducing ASIR and ASMR aligns with the WHO’s elimination framework but falls short of the required acceleration to meet the 80% incidence and 65% mortality reduction targets [2, 37]. The increase of female mortality post-2030 signals a “legacy cohort” effect, where aging individuals with untreated cirrhosis or HCC offset treatment gains. This trend was also observed in high-income countries, where late-stage HCV complications now dominate liver-related mortality [22]. To accelerate progress, China must address three systemic gaps: First, diagnostic infrastructure: less than 20% of HCV cases are diagnosed nationally [38], far below the WHO’s 90% target [2]. Decentralized rapid testing and telemedicine platforms could bridge this gap, as demonstrated in pilot programs in some regions, where community-based screening significantly increased diagnosis rates [39, 40]. Second, treatment equity: Despite DAAs’ inclusion in insurance, out-of-pocket costs remain prohibitive for low-income populations [35]. In China, most of rural patients delay treatment due to financial constraints [2]. Subsidies for vulnerable groups and price negotiations with pharmaceutical companies are urgently needed [35]. Lastly, high-risk populations: PWID account for 40% of new HCV infections, yet harm reduction programs remain limited to prefectures [1, 2, 5]. Scaling up needle-exchange programs and integrating HCV care into opioid agonist therapy could disrupt transmission [2, 5].

While this study advances understanding of HCV dynamics, several limitations warrant attention. First, the GBD data are based on estimates and models, which may be subject to uncertainty and bias, particularly in under-resourced provinces [35]. Second, the APC model’s collinearity [41], though mitigated by Bayesian methods, complicates causal inference. Triangulating results with qualitative studies on behavioral risk factors could enhance robustness. Third, the analysis did not account for genotype-specific variations in treatment response, which could influence mortality projections. Lastly, due to the lack of provincial data in the GBD database, the study is unable to delineate the spatial distribution of hepatitis C across China [24]. The size and distribution of China’s population introduce uncertainties into the forecast results.

Conclusion

While the decline in the ASIR and ASMR of hepatitis C in China is a positive development, the rising mortality counts highlight the ongoing challenges in managing this public health issue. The stagnation in female mortality rate projected by the BAPC model further emphasizes the need for gender-specific strengthening measures. Understanding the age, period, and cohort effects provides valuable insights into the dynamics of hepatitis C and underscores the need for targeted interventions. As China strives to meet the WHO’s elimination goals by 2030, a multifaceted approach that prioritizes screening, equitable access to treatment, and tailored interventions for vulnerable populations will be essential to mitigate the burden of hepatitis C and ultimately achieve elimination.

Data availability

The datasets analysed during the current study are available at (http://www.healthdata.org/). The estimated population of China was retrieved from the United Nations World Population Prospects 2019 Revision (https://population.un.org/wpp/). The world standard population data were obtained from the World Standard (WHO 2000-2025) (https://seer.cancer.gov/stdpopulations/world.who.html).

Abbreviations

ASIR :

Age-standardized incidence rate

ASMR:

Age-standardized mortality rate

APC:

Age-period-cohort

BAPC:

Bayesian age-period-cohort

HCV:

Hepatitis C virus

GBD:

Global Burden of Disease

DAAs:

Direct antiviral agents

AAPC:

Average annual percentage change

CI:

Confidence interval

UI:

Uncertainty interval

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