Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age

Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age | 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 Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age Yutong Kang, Yanying Feng, Qiheng Yuan, Xuanming Xu, Meiqin Zheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5901402/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 Objectives Genital herpes (GH), primarily caused by herpes simplex virus 2, imposes a significant burden on women of childbearing age (WCBA), raising the risk of pregnancy-related complications. Despite the high global burden, comprehensive studies on trends in GH incidence among WCBA are lacking. Methods Using GBD 2021 data, this study analyzed GH incidence in WCBA, explored Socio-demographic Index (SDI) associations and inequality trends, applied decomposition analysis, and predicted future trends with Age-period-cohort (APC) and Bayesian APC (BAPC) models. Results Between 1990 and 2021, global GH cases in WCBA increased from 14.6 million to 22 million, while the age-standardized incidence rate (ASIR) saw a modest annual increase of 0.045%. Regional variations were observed, with low-middle SDI regions showing continued growth in ASIR. Unfavorable period effects were exhibited in low-middle SDI regions. Population growth was identified as the main driver of morbidity trends, with emerging health inequalities over time. By 2030, global GH cases in WCBA are expected to reach 23.6 million, with persistent challenges in low-SDI regions such as Angola, the Democratic Republic of the Congo, Congo, and Equatorial Guinea. Conclusion GH incidence among WCBA is on the rise, underlining the need for focused management strategies, particularly in low-middle SDI regions. Genital herpes Women of childbearing age Age-standardized incidence rate incidence GBD SDI health inequality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Genital herpes (GH) represents a significant public health threat. It is a common sexually transmitted disease (STD) caused primarily by herpes simplex virus (HSV)–2, with HSV-1 playing a lesser role [ 1 – 3 ]. This virus is highly contagious, spreading through sexual contact and vertical transmission from mother to child [ 1 , 3 ]. Women of childbearing age (WCBA) are particularly vulnerable to HSV infection. The World Health Organization (WHO) estimates that around 20 million new HSV-2 infections occur globally each year, and 86 million people are affected by GH, with infection rates in women ranging from 30–80% [ 4 ]. However, there is a lack of comprehensive global analysis on GH incidence specifically in WCBA, with limited attention given to this population. Studies have shown that the global prevalence of GH in WCBA is nearly double that of men, largely due to anatomical differences in the female genital tract that increase susceptibility to infection [ 2 , 5 , 6 ]. GH is strongly associated with WCBA and can elevate the risk of pregnancy complications, leading to considerable maternal morbidity and mortality. Moreover, mother-to-child transmission can result in neonatal herpes, neonatal death, congenital malformations, and other adverse birth outcomes. Neonatal herpes can manifest as localized skin, eye, and mucous membrane infections, encephalitis, or disseminated infections, all of which are associated with high rates of morbidity and mortality [ 1 , 4 , 5 , 7 – 9 ]. As such, GH infection in WCBA requires greater attention and the implementation of targeted interventions. In 2016, epidemiological studies estimated that more than 500 million people worldwide were infected, with the majority of cases occurring in the 16–40 age group, which corresponds with an increase in sexual activity within this age range [ 2 ]. By 2018, it was estimated that 18.6 million individuals in the U.S. were affected by genital HSV-2 infections, with about 65% of these infections occurring in women [ 1 , 3 , 10 ]. The Global Burden of Disease (GBD) study reported a 51.97% increase in global GH cases among individuals aged 15–49 years from 1990 to 2021. Incidence rates also rose in low and middle Socio-demographic Index (SDI) regions, including South Asia, Southern Sub-Saharan Africa, and Central Europe. Among these regions, Southern Sub-Saharan Africa exhibited the highest burden of GH among 15–49-year-olds [ 6 , 11 ]. However, there remains a lack of systematic studies focused specifically on the global, regional, and national disease burden of GH in WCBA, emphasizing the need for a thorough analysis of GH incidence in this group on a global and regional scale. GH episodes in WCBA, particularly during critical stages of reproduction such as pregnancy, can lead to neonatal infections through vertical transmission, which can have long-lasting effects on both the development of the offspring and societal sustainability. Therefore, exploring the epidemiological trends of GH in WCBA is essential to enhance understanding of the disease and support the development of public health strategies. This study utilized a thorough research approach to provide a detailed examination of GH incidence trends among WCBA. Specifically, the number of new cases and age-standardized incidence rate (ASIR) were assessed to identify temporal patterns. The age-period-cohort (APC) model was applied to analyze the temporal dynamics of GH incidence, and the Bayesian age-period-cohort (BAPC) model was integrated to project global epidemiological trends of GH among WCBA until 2030. In addition to these approaches, a health inequality analysis was carried out to explore disparities in GH burden, frontier analysis was used to determine the potential of different countries to improve disease burden and the differences between them, and decomposition analysis was used to determine the key factors driving GH trends. As the first study to quantify the GBD of GH in WCBA and forecast its future trajectory, this research offers important insights that align with the WHO 2030 Sustainable Development Goal to eliminate maternal and child health disparities and prioritize GH as a public health concern. Methods Data sources This study presents a detailed analysis of the epidemiological data on GH in women aged 15 to 49. The foundation of the research is the GBD 2021, a reliable database meticulously curated by the Institute for Health Metrics and Evaluation (IHME) ( https://ghdx.healthdata.org/gbd-2021/sources ). GBD 2021 provides extensive data on disease burden across 204 countries and regions, covering 371 diseases and injuries [ 12 ]. By quantifying health losses across different regions, time periods, and demographics, it offers valuable data that can guide global health system improvements and help reduce health disparities. Within its disease classification, GBD 2021 uses a four-tier system, with genital herpes categorized at the fourth level, highlighting its relevance among infectious diseases. In this study, the Global Health Data Exchange (GHDx) query tool was utilized to extract relevant indicators for GH in women aged 15 to 49 from the GBD 2021 database, including global incidence number and ASIR from 1990 to 2021. The SDI was also incorporated as a composite indicator that reflects economic development, education levels, and fertility rates in a country or region [ 13 ]. Based on SDI values, GBD 2021 classifies countries into five development categories: low, low-middle, middle, high-middle, and high, with higher SDI values typically indicating more advanced development. The aim of this study is to examine the differences in disease burden and prevention strategies for GH in women aged 15 to 49 across countries with varying SDI levels, thereby providing evidence to support the formulation and implementation of global health policies. Study population This study examines GH among WCBA, defined by the WHO as females aged 15 to 49 [ 14 ]. This life stage is marked by reproductive capacity and hormonal fluctuations. Adolescence, spanning ages 10 to 19, is recognized as a transitional phase from childhood to adulthood. Data on GH incidence rates and incidence cases in WCBA from 1990 to 2021 were retrieved from GBD 2021 [ 15 ]. The institutional review board of Beijing Ditan Hospital determined that ethical approval was not necessary, as the study used publicly available data. The research follows the Guidelines for Accurate and Transparent Health Estimates Reporting for cross-sectional studies. Calculation of specific age-standardized incidence of GH in WCBA To calculate the ASIR of GH in WCBA, direct age standardization was applied. This approach assumes that the rates follow a distribution represented by the weighted sum of independent Poisson random variables [ 16 ]. Cross-Country Inequality Analysis The slope index of inequality (SII) and concentration index (CI) are standard metrics used to evaluate absolute and relative gradient inequalities, respectively. These indices provide quantitative assessments of the distributional disparities in the GH burden among WCBA across countries [ 17 ]. The SII is obtained through regression analysis, linking a country’s ASIR to its relative position in the SDI ranking, which is based on the population’s midpoint in the cumulative distribution of SDI. Weighted regression models are used to address heteroscedasticity in the analysis. The CI is determined by integrating the area under the Lorenz curve, which shows the relationship between the cumulative proportion of ASIR and the cumulative distribution of the population ranked by SDI [ 18 ]. Age-period-cohort (APC) model analysis The APC model is a sophisticated analytical approach frequently used in health and socioeconomic research, offering several benefits compared to traditional methods. It allows for the estimation of net drift and local drift, which reflect overall and specific temporal trends, respectively. The model also facilitates the analysis of key temporal factors: age, period, and birth cohort [ 19 – 21 ]. In this study, the APC model was employed to explore the incidence trends of GH among WCBA across various age groups, time periods, and birth cohorts. Specifically, it helps identify changes in disease patterns by isolating the effects of age, period, and cohort, which can be linked to shifts in lifestyle, medical progress, or environmental factors. In this study, the APC model was applied using a method where age intervals were aligned with corresponding time periods, such that each 5-year age group corresponded to a 5-year period. Data on GH incidence from the 2021 GBD database, covering the years 1992–2021, were integrated, with a focus on women of reproductive age and the relevant population data for each region. The target age range of 15 to 49 years was divided into seven age categories for more detailed analysis: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years. The study period (1992–2021) was divided into six 5-year intervals: 1992–1996, 1997–2001, 2002–2006, 2007–2011, 2012–2016, and 2017–2021. This approach covered 12 overlapping 10-year birth cohorts, spanning from 1942–1951 to 1997–2006. The APC model was used to estimate both the overall temporal trend and the trends within individual age groups. The overall temporal trend, known as net drift, was expressed as the annual percentage change in incidence, reflecting the impact of calendar time and continuous birth cohorts. The trend within each age group, referred to as local drift, was expressed as the annual percentage change in age-specific incidence. The significance of these annual percentage change trends was assessed using the Wald χ 2 test. Decomposition Analysis A decomposition analysis was conducted to identify the key factors driving changes in the burden of GH among WCBA from 1990 to 2021. The purpose of this analysis was to quantify the individual contributions of population growth, aging, and epidemiological transitions. Each factor’s effect was isolated by holding the other two variables constant during the assessment [ 22 ]. Frontier analysis Frontier analysis was used to explore the relationship between the burden of GH among WCBA and sociodemographic development. The frontier was defined as a nonlinear boundary that represents the minimum achievable burden, dependent on the current development status of a country or region. Non-parametric data envelope analysis was applied to construct this frontier, as described in earlier studies [ 23 ]. The gap between the observed ASIR in a given country and its corresponding frontier, referred to as the “effective difference,” indicates the potential health improvements that have yet to be achieved, given the region’s current development level. Prediction The future incidence and number of GH cases among WCBA from 2022 to 2030 were projected using the BAPC model. To enhance prediction accuracy, the Integrated Nested Laplace Approximation (INLA) framework was incorporated with the BAPC model. This approach addresses challenges related to mixing and convergence commonly encountered in traditional Bayesian methods that rely on Markov Chain Monte Carlo (MCMC) sampling. The analysis was conducted using the R packages “BAPC” (version 0.0.36) and “INLA” (version 24.02.09), and all statistical analyses and data visualizations were performed using Stata 16.0 ( https://www.stata.com ) and R 4.4.2. Statistical analysis All metrics (counts, rates) were reported with 95% confidence intervals (95%CI) or 95% uncertainty intervals (UI), derived from the 25th and 975th percentiles of 1,000 posterior draws. Rates were presented per 100,000 population. Statistical analyses were carried out using R (version 4.4.2, R Core Team). Results Trends in genital herpes incidence globally and across SDI regions The number of GH cases increased in the low SDI, low-middle SDI, middle SDI, and middle-high SDI regions by 2021, with only high SDI regions showing a decline. Over the past three decades, the global burden of GH has risen, with varied patterns across SDI regions. The greatest burden was observed in middle SDI regions, while the largest growth occurred in low-middle SDI areas (Table 1 and Fig. 1 A). Notably, the ASIR in low SDI and middle SDI regions remained above the global average, while the high-middle SDI, low-middle SDI, and high SDI regions stayed below it. These regional differences in GH incidence and ASIR trends highlight the importance of further analysis (Fig. 2 B). The APC model was used to estimate the net drift of incidence, shown in Table 1 . Globally, GH incidence among WCBA followed an upward trend, with an annual net drift of 0.05% (95% CI: − 0.01 to 0.10). Interestingly, the low-middle SDI region continued this trend, showing an annual increase with a positive net drift of 0.11% (95% CI: 0.04 to 0.18). Table 1 Global and SDI trends of acute genital herpes incidence in WCBA from 1992 to 2021. Characteristics 1990 2021 1990–2021 Incident cases No. (95% UI) ASIR per 100000 No. (95% UI) Incident cases No. (95% UI) ASIR per 100000 No. (95% UI) Net Drift (%/year) Global 14631443.7 (10559685.65 ~ 19532383.01) 1061.7 (761.49 ~ 1424.52) 22010115.9 (15441987.71 ~ 29880978.73) 1140.4 (800.89 ~ 1546.63) 0.05 (-0.01 to 0.10) Low SDI 1899707.4 (1412099.57 ~ 2468988.77) 1554.4 (1144.48 ~ 2036.46) 4670980.7 (3372038.14 ~ 6187170.62) 1574.7 (1129.38 ~ 2098.91) -0.10 (-0.17 to -0.02) Low-middle SDI 2747080.6 (1962753.77 ~ 3697347.65) 971.5 (689.28 ~ 1315.63) 5373664.2 (3792640.24 ~ 7266861.6) 1042.7 (733.55 ~ 1413.61) 0.11 (0.04 to 0.18) Middle SDI 5132730.7 (3715023.55 ~ 6834371.98) 1111.6 (797.46 ~ 1490.31) 6976352.5 (4860056.48 ~ 9512758.21) 1142.9 (797.76 ~ 1555.69) -0.14 (-0.20 to -0.08) High-middle SDI 2556759.7 (1809780.11 ~ 3478625.72) 901.7 (635.3 ~ 1230.7) 2779906.3 (1896660.8 ~ 3844744.9) 934.2 (641.9 ~ 1283.66) -0.10 (-0.16 to -0.05) High SDI 2281198 (1643690.84 ~ 3059166.01) 1013.2 (733.27 ~ 1354.56) 2190951.3 (1484391.84 ~ 3064663.95) 930.3 (635.37 ~ 1291.88) -0.08 (-0.14 to -0.02) 95% UI = 95% uncertainty intervals; 95% CI = 95% Conffdence Interval; APC = age period cohort; SDI = sociodemographic index; WCBA = women of childbearing age. Relationship between ASIR of GH and SDI Across Regions Figure 2 A presents the changes in ASIR across regions from 1990 to 2021 in relation to the increase in SDI. In eight regions with low and high SDI, ASIR for GH in WCBA decreased in five regions, except for Central Sub-Saharan Africa, South Asia, and Western Europe, where ASIR remained relatively constant from 1990 to 2021. In regions with middle-high SDI and in about half of the middle SDI regions, ASIR remained largely stable over the same period. Interestingly, in certain middle SDI regions, ASIR first increased and then decreased as SDI rose, with the most noticeable change occurring in Southern Sub-Saharan Africa, where ASIR peaked at an SDI around 0.6 (Fig. 2 A). Figure 2 B illustrates the relationship between ASIR and SDI in various countries in 2021. In these countries, ASIR decreased with an increase in SDI. When SDI reached about 0.5625, ASIR began to rise, peaking at an SDI of 0.625, before decreasing again as SDI continued to increase. Moreover, based on SDI alone, the ASIR in the Central African Republic, Angola, Gabon, and Equatorial Guinea far exceeds the global mean ASIR for GH in WCBA (Fig. 2 B). Health inequalities analysis The findings of the study, as shown in Fig. 3 A, reveal that the blue and red solid lines representing the global incidence of GH in 1990 and 2021, respectively, follow a pattern from the upper left to the lower right as SDI increases, indicating a higher disease burden in areas with lower SDI. Specifically, the SII was − 1175 in 1990 and − 1216 in 2021. The absolute value of the SII in 2021 exceeds that of 1990 and displays a steeper slope, clearly indicating a rise in absolute disparities between income groups in 2021 compared to 1990 (Fig. 3 A). Furthermore, as shown in Fig. 3 B, the CI for 1990 and 2021 were − 0.01 and − 0.11, respectively, both negative, suggesting that the global burden of GH disease in WCBA is more concentrated in lower-income groups. An increase in health inequality was observed in 2021 (Fig. 3 B). Frontier analysis based on agestandardized incidence rates Using data from 1990 to 2021, this study analyzed the association between the GH burden in WCBA and national development levels through frontier analysis, incorporating ASIR and SDI. The frontier line represents the theoretically achievable ASIR based on the SDI. The effective difference, or the distance from the frontier, reflects the gap between the observed and achievable ASIR for each country or region, considering its SDI. In general, countries with higher SDI exhibited smaller effective differences, while the largest differences were observed in countries with intermediate SDI values (Fig. 4 A). The analysis identified 15 countries with the highest potential for improvement, including Malawi, Zimbabwe, Botswana, South Africa, Mozambique, Uganda, Namibia, Eswatini, Lesotho, Congo, the Central African Republic, Angola, Gabon, and Equatorial Guinea. Countries with low SDI, including Somalia, Yemen, Nepal, Bangladesh, and Bhutan, were also identified as frontier nations. Moreover, high-SDI countries showing substantial room for improvement relative to their development stage included Singapore, Sweden, Taiwan (Province of China), Lithuania, and the United States (Fig. 4 B). Decomposition analysis To assess the primary factors driving changes in the GH disease burden among WCBA globally from 1990 to 2021, decomposition analysis was applied to quantify and evaluate the relative contributions of aging, population growth, and epidemiological changes. Figure 5 shows the results of the decomposition analysis, illustrating how aging, population growth, and epidemiological changes affected variations in ASIR across the five SDI regions globally. The findings indicate that, globally, population growth is the primary factor influencing changes in ASIR for GH among WCBA, followed by epidemiological changes, while aging has a negative effect on ASIR changes. However, the contributions of these factors to ASIR changes vary significantly across the five SDI regions. In low SDI and low-middle SDI regions, the effects of aging, population growth, and epidemiological changes on ASIR changes are consistent with the global trends. In middle SDI regions, population growth is the most important driver of ASIR changes, with the contribution of aging to ASIR changes slightly surpassing that of epidemiological changes, contrary to the global pattern. In middle-high SDI and high SDI regions, the contributions of aging, population growth, and epidemiological changes to ASIR changes are about equal. Notably, in high SDI regions, both aging and epidemiological changes have a significant effect on ASIR changes among WCBA (Fig. 5 ). Temporal Trends in GH Incidence Among WCBA Across Different Age Groups Figure 6 A illustrates the variation in GH incidence number across different age groups within the WCBA population globally, as well as by SDI levels. Figure 6 B depicts the annual percentage change in incidence rate for each age group, derived from the local drift calculated using the APC model. A positive annual percentage change indicates an increase in incidence, while a negative value signifies a decrease. Temporal trends in GH incidence among WCBA showed notable differences across SDI regions and age groups. Globally, the highest incidence occurred in the 20–29 age group over the past decade, with the average annual percent changes (AAPC) peaking in the 25–29 age group (local drift coefficient: 0.04), indicating increasing rates in the 20–34 age range. In high SDI regions, incidence rose for individuals aged 25–49, while it declined for those under 25. In contrast, middle and middle-high SDI regions experienced a general decline in incidence across all age groups, with most AAPC values below 0. Similarly, in low SDI regions, incidence declined in individuals under 29 but increased in those aged 29 and older. A notable decrease in ASR among females aged 15–19 was observed globally and across all five SDI regions. Effect of age, period, and birth cohort on the Incidence of GH disease in WCBA The effects of age, period, and birth cohort on GH incidence, derived from the APC model, are shown in Fig. 7 . Across SDI regions, age effects displayed a consistent pattern, with the highest risk seen in women aged 25–29 years (20–24 years in low SDI regions), followed by a decrease in risk with age. High SDI and high-middle SDI regions consistently exhibited lower prevalence across all age groups, with minor variation between groups (Fig. 7 A). The period effect revealed a global upward trend in hazard ratios after 2012–2016, evident across all five SDI regions. Throughout the study period, hazard ratios were highest in low-middle SDI regions, surpassing the 1992–1996 baseline. The relative risks for 2012–2016 and 2017–2021 were 1.01 (95% CI: 0.999–1.036) and 1.03 (95% CI: 1.017–1.056), respectively. Globally, the relative risk for 2017–2021 was 1.022 (95% CI: 1.006–1.037) (Fig. 7 B). Concerning birth cohort effects, the global cohort exhibited incidence risk in the period after 1972–1981, with incidence exceeding the baseline set in that cohort. Notably, among cohorts born before 1982–1991, risk trends varied across SDI regions. After this cohort, however, risk consistently declined across all regions. The cohorts with the highest risk included the global cohort from 1987 to 1996, the low-middle SDI and high SDI cohorts from 1982–1991, the 1947–1956 cohort in the middle SDI region, and the 1957–1966 cohort in the high-middle SDI region. Projected Global Burden among WCAB of Genital Herpes by 2030 The BAPC model projects global mortality and population size from 2022 to 2030. The global total of GH cases is anticipated to steadily increase, reaching an estimated 23.57 million (95% UI: 19.85–27.30 million) by 2030. At the same time, ASIR is expected to rise moderately, reaching a peak of 1163.35 (95% UI: 991.46–1335.23), marking a historical high (Fig. 8 A). Projections for China and India, the two most populous countries, indicate a decline in ASIR for both nations, with China experiencing a slightly higher rate compared to India (Fig. 8 B, Figure S1). The study also predicts that the incidence of GH in the 15 countries furthest from the frontier fit line identified in the frontier analysis, including Angola, the Democratic Republic of the Congo, Congo, and Equatorial Guinea, will continue to increase both in terms of total cases and ASIR (Fig. 8 C, 8 D, 8 E, 8 F). These results suggest that, without substantial interventions, the impact of GH in these regions is unlikely to decrease in the near future. Additionally, the disparity between disease burden and development levels may grow. For the remaining 11 countries that were also far from the frontier fit line, projections indicate continued increases in GH cases in Botswana, the Central African Republic, Namibia, and Zimbabwe, despite a predicted decrease in ASIR. Similarly, GH cases in Ecuador, Eswatini, Gabon, and Lesotho are expected to rise, while ASIR remains stable. In contrast, both case numbers and ASIR are expected to decline in Peru and South Africa, while Jamaica is projected to experience a decrease in cases, with ASIR remaining unchanged (Figure S1). Discussion GH presents a significant global public health threat, especially to high-risk populations, with women of reproductive age being a major affected group [ 6 , 24 ]. GH infection in WCBA before or during pregnancy often leads to severe health consequences. Initially, infection can worsen GH symptoms in WCBA, affecting physical function. Furthermore, this infection can cause adverse pregnancy outcomes, including premature birth, abortion, and congenital malformations. More critically, the vertical transmission of the virus from mother to child can result in neonatal disease, with neonatal herpes being the most severe direct consequence, leading to high morbidity and mortality [ 25 – 29 ]. Given these serious risks, the incidence of GH in WCBA must be addressed. This study sheds light on the global incidence and regional disparities of GH among WCBA through indicators such as case numbers and age-standardized incidence rates, and applies the APC and BAPC models to further investigate the temporal dynamics and SDI-related changes of the disease. It also provides a well-founded projection of its future development. These findings aim to offer solid data support and a reliable basis for effectively addressing this disease. To the best of our knowledge, this is the first study to examine the incidence and trends of GH among WCBA aged 15–49 years from 1990 to 2021 across 204 countries at global, regional, and national levels, as well as to forecast future trends. The relationship between GH incidence and SDI is particularly noteworthy. The significant differences in the incidence and ASIR of GH across different regions provide valuable information to help us understand the epidemiological trends of this sexually transmitted infection and its influencing factors in various socio-economic contexts. While high-SDI regions have seen a stabilization or decline in GH rates, low-middle and low-SDI regions continue to experience increased burden. These findings echo earlier studies [ 8 , 30 ] that suggest socioeconomic development and healthcare infrastructure significantly impact the disease burden. For instance, the significant increase in GH cases in Southern Sub-Saharan Africa [ 31 ] aligns with studies showing that regions with poorer healthcare access have higher infection rates [ 32 ]. Similarly, our analysis of the SII and CI revealed a growing disparity in the burden of GH, with the disease being more concentrated in lower-income groups, a trend that has worsened over the past three decades. This observation aligns with findings from Stebbins et al. (2019), who demonstrated that socioeconomic and racial disparities contribute to higher pathogen burdens, including HSV infections [ 33 ]. One of the key findings of this study is the significant health inequality present in the global burden of GH. The frontier analysis demonstrated that many countries, especially those in sub-Saharan Africa, could achieve substantial improvements in GH control, given their current SDI. This provides a clear roadmap for health policy makers to focus on these regions, investing in both preventive measures such as education and vaccination, and in strengthening healthcare systems to reduce the disease burden. Cao et al. (2024) emphasized the need for increased vaccination efforts in low-middle SDI regions to combat the rising incidence of genital herpes [ 31 ].The increasing inequalities in health outcomes, as shown by the CI and SII indices, suggest that without focused interventions, the global disparities in GH burden may widen, exacerbating existing health inequities. This aligns with findings from Mayaud and Mabey (2004), who highlighted the urgent need for enhanced health infrastructure and comprehensive preventive strategies—such as vaginal microbicides, vaccines, and behavior change interventions—in regions with high disease burdens, including sub-Saharan Africa [ 34 ]. The APC analysis show that younger women, especially those in the 20–29 age group, have the highest incidence rates of GH globally. This aligns with other studies' findings. For instance, Purva et al. reported that GH was most prevalent in women aged 25–29 years [ 35 ], and Spicknall et al. found the incidence was highest in women aged 18–24 years, with over 80% of cases in women aged 18–29 years [ 10 ]. The likely reason is the peak in sexual activity and risk behavior in the late teens and twenties [ 30 , 36 ]. And a consistent pattern was observed across regions with different SDI levels, where the incidence of GH decreases with age [ 37 ]. In low - SDI regions, there's an increasing incidence of GH in women aged 30 and above, possibly due to limited access to prevention and treatment. In contrast, in high - SDI regions, the decline in GH incidence among women under 25 may be attributed to better awareness, prevention programs, and healthcare access. Our study on WCBA also indicates that the risk of developing GH decreases with age. Younger WCBA, being most active in reproductive and sexual activities, face higher risks related to pregnancy - related conditions and HSV infection [ 38 ]. Given these findings, targeted prevention and intervention efforts should prioritize women in the 18–29 age group, particularly those aged 20–29. This study provides a comprehensive global analysis of GH incidence trends among WCBA over the past three decades, with projections up to 2030. By 2030, the global incidence is projected to reach 23.6 million, with continued difficulty in controlling the disease in low-SDI regions such as Angola, Equatorial Guinea, Congo, and Democratic Republic of the Congo. The ASIR is expected to reach 1,163.35per 100,000 population during the same period. These results suggest gradual success in the global GH control efforts. The rising number of cases is primarily attributed to demographic changes, while the relatively stable incidence highlights the preliminary success of implemented control measures [ 39 , 40 ]. In terms of policy implications, these results underscore the urgent need for region-specific strategies. High-SDI regions, where the burden is relatively stable or declining, may benefit from targeted interventions focusing on reducing vertical transmission, improving management of neonatal herpes, and maintaining effective prevention programs. In contrast, low-middle SDI regions should prioritize expanding access to sexual health education, improving the availability of antiviral treatments, and enhancing healthcare infrastructure to reduce the incidence of GH [ 30 ]. Additionally, increased funding and international support are critical to reduce the disease burden in these regions. Conclusion In conclusion, genital herpes remains a major public health issue among women of childbearing age, with rising incidence rates globally, particularly in low-middle SDI regions. The study's findings emphasize the need for targeted interventions that address the specific needs of different regions, especially those with low SDI. As the global burden continues to grow, it is crucial to implement strategies that reduce health inequalities and promote equitable access to prevention and treatment. Further research and stronger health systems are key to mitigating the impact of GH on women’s health and maternal and child outcomes worldwide. Declarations Declarations of interest: none Author Contribution Y. K., Y. F., and Q. Y.: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation. X. X.: Methodology, Software, Data curation. Y. W. and M. Z.: Reviewing and Editing. Z. L.: Investigation, Reviewing and Editing. Funding Declaration This study was supported by a grant from Research on the Technical Specifications and Standard System for Biological Safety Sample Banks (Project number: 2019YFC1200700) References Johnston C, Wald A, Genital Herpes. JAMA. 2024;332:835–6. Jaishankar D, Shukla D. Genital herpes: insights into sexually transmitted infectious disease. Microb Cell. 2016;3:438. Voelker R. What Is Genital Herpes? JAMA. 2024. https://doi.org/10.1001/jama.2024.21537 Plagens-Rotman K, Przybylska R, Gerke K, Adamski Z, Czarnecka-Operacz M. Genital herpes as still significant dermatological, gynaecological and venereological problem. Adv Dermatology Allergol Dermatologii i Alergol. 2021;38:210–3. Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW, García FAR, et al. Serologic screening for genital herpes infection: US Preventive Services Task Force recommendation statement. JAMA. 2016;316:2525–30. Cao G, Liu J, Liu M, Liang W. 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Estimates of the prevalence and incidence of genital herpes, United States, 2018. Sex Transm Dis. 2021;48:260–5. Van Wagoner N, Qushair F, Johnston C. Genital herpes infection: progress and problems. Infect Dis Clin. 2023;37:351–67. Ferrari AJ, Santomauro DF, Aali A, Abate YH, Abbafati C, Abbastabar H, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systema. Lancet. 2024;403:2133–61. YU C, BAI J. The concept of Socio-Demographic Index (SDI) and its application. J Public Heal Prev Med. 2020;:5–10. Women of reproductive. age (15–49 years) population (thousands). https://www.who.int/data/gho/indicator-metadata-registry/imr-details/women-of-reproductive-age-(15-49-years)-population-(thousands ). Accessed 29 Dec 2024. 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Revisiting Das Gupta: Refinement and extension of standardization and decomposition. Demography. 2009;46:429–49. Barber RM, Fullman N, Sorensen RJD, Bollyky T, McKee M, Nolte E, et al. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet. 2017;390:231–66. Roe VA. Living with genital herpes: how effective is antiviral therapy? J Perinat Neonatal Nurs. 2004;18:206–15. Corey L, Handsfield HH. Genital herpes and public health: addressing a global problem. JAMA. 2000;283:791–4. Gnann JW Jr, Whitley RJ. Genital herpes. N Engl J Med. 2016;375:666–74. Rathore S, Jamwal A, Gupta V. Herpes simplex virus type 2: Seroprevalence in antenatal women. Indian J Sex Transm Dis AIDS. 2010;31:11–5. Guerra B, Puccetti C, Cervi F. The genital herpes problem in pregnancy. G Ital di Dermatologia e Venereol. 2012;147:455. Bhatta AK, Keyal U, Liu Y, Gellen E. Vertical transmission of herpes simplex virus: an update. JDDG J der Dtsch Dermatologischen Gesellschaft. 2018;16:685–92. Zheng Y, Yu Q, Lin Y, Zhou Y, Lan L, Yang S, et al. Global burden and trends of sexually transmitted infections from 1990 to 2019: an observational trend study. Lancet Infect Dis. 2022;22:541–51. Guiying C, Liu J, Liu M, Liang W. Global, Regional, and National Trends Analysis in Incidence of Genital Herpes Among the Population Aged 15–49 Years — Worldwide, 1990–2021. China CDC Wkly. 2024;6:1033–7. Huda MN, Ahmed MU, Uddin MB, Hasan MK, Uddin J, Dune TM. Prevalence and demographic, socioeconomic, and behavioral risk factors of self-reported symptoms of sexually transmitted infections (STIs) among ever-married women: Evidence from Nationally representative surveys in Bangladesh. Int J Environ Res Public Health. 2022;19:1906. Stebbins RC, Noppert GA, Aiello AE, Cordoba E, Ward JB, Feinstein L. Persistent socioeconomic and racial and ethnic disparities in pathogen burden in the United States, 1999–2014. Epidemiol Infect. 2019;147:e301. Mayaud P, Mabey D. Approaches to the control of sexually transmitted infections in developing countries: old problems and modern challenges. Sex Transm Infect. 2004;80:174–82. Jain P, Embry A, Arakaki B, Estevez I, Marcum ZA, Viscidi E. Prevalence of Genital Herpes and Antiviral Treatment. Sex Transm Dis. 2024;51:686–93. Shannon CL, Klausner JD. The growing epidemic of sexually transmitted infections in adolescents: a neglected population. Curr Opin Pediatr. 2018;30:137–43. James C, Harfouche M, Welton NJ, Turner KME, Abu-Raddad LJ, Gottlieb SL, et al. Herpes simplex virus: global infection prevalence and incidence estimates, 2016. Bull World Health Organ. 2020;98:315. Zhang J, Ma B, Han X, Ding S, Li Y. Global, regional, and national burdens of HIV and other sexually transmitted infections in adolescents and young adults aged 10–24 years from 1990 to 2019: a trend analysis based on the Global Burden of Disease Study 2019. Lancet Child Adolesc Heal. 2022;6:763–76. Herpes simplex virus. https://www.who.int/news-room/fact-sheets/detail/herpes-simplex-virus . Accessed 29 Dec 2024. Bai L, Xu J, Zeng L, Zhang L, Zhou F. A review of HSV pathogenesis, vaccine development, and advanced applications. Mol Biomed. 2024;5:35. Additional Declarations No competing interests reported. Supplementary Files floatimage9.png Figure S1. The number of GH cases in WCBA and ASIR (with the right vertical axis as the unit) from 1990 to 2030 in different countries. Countries included are Botswana, the Central African Republic, Ecuador, Eswatini, Gabon, India, Jamaica, Lesotho, Namibia, Peru, South Africa, and Zimbabwe. The title of each subplot indicates the country’s name. The blue bar charts show the annual number of GH cases, while the red lines show the ASIR trend per 100,000 people. The black vertical lines represent error bars, indicating the uncertainty range in the number of cases for each year. Abbreviations: GH = Genital herpes; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5901402","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411887526,"identity":"f21b2a4d-3640-4791-b16d-c5f6f8b6f479","order_by":0,"name":"Yutong Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3RMWrDMBSAYRuBp2dEN6suPsMDgU1IaK/iEvBkSqfOKgH3CirkCBkyZZbx2sZroEshYzzIW4cMlQNppygZC9UPQkLoGyR5nsv1B4t+l/7zZ44ToFRcRNAM0qB+LG6YVBeToLiSupmgyO2Evbw3u7DaJxldpASwBfSUr/vyNInhoRiHFfKR7NIt4AdkRBD2ujpNEq9MuSH3y81bxgcyEiogoY3Q7oekMeAaUOV2Ekcl3x5IWxVMojpPmLmCP19zjpvhkXEKTNYz612ituS6e0oSbGvzlfvbO0pnte4txBRcwwHnxw1fWM+bSP81TFSdO+hyuVz/tW9U81Os/nXl0QAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Kang","suffix":""},{"id":411887529,"identity":"21324634-8e30-4601-8625-29718f67831b","order_by":1,"name":"Yanying Feng","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanying","middleName":"","lastName":"Feng","suffix":""},{"id":411887531,"identity":"46fa6f3f-fbb4-415e-a2cd-f8a33b7a1802","order_by":2,"name":"Qiheng Yuan","email":"","orcid":"","institution":"Ministry of Education, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiheng","middleName":"","lastName":"Yuan","suffix":""},{"id":411887533,"identity":"63dbf03b-1b23-4c7d-be15-ad723d9935c5","order_by":3,"name":"Xuanming Xu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuanming","middleName":"","lastName":"Xu","suffix":""},{"id":411887537,"identity":"63161180-760d-4795-aea3-7a53a2bda3bf","order_by":4,"name":"Meiqin Zheng","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiqin","middleName":"","lastName":"Zheng","suffix":""},{"id":411887538,"identity":"2af6cfff-77b4-4114-9865-9aff7bc4065e","order_by":5,"name":"Yajie Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yajie","middleName":"","lastName":"Wang","suffix":""},{"id":411887539,"identity":"d71303a8-5c6c-4bc3-ae0b-ce93d7955853","order_by":6,"name":"Zhenjun Li","email":"","orcid":"","institution":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zhenjun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-01-25 11:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5901402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5901402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76088395,"identity":"29e14d4a-ab84-4d1a-8358-4de8d2f4b0ce","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":359102,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal incidence of GH in women of childbearing age (WCBA) across five SDI regions from 1990 to 2021 (A), and the corresponding age-standardized incidence rate (ASIR) (B). Abbreviations: GH = Genital herpes; SDI = Socio-demographic Index; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/26435701a4d98da150a7bca3.png"},{"id":76088425,"identity":"79e5f06a-4eca-4171-b790-300dafe8f3e3","added_by":"auto","created_at":"2025-02-12 08:07:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":423844,"visible":true,"origin":"","legend":"\u003cp\u003eASIR for GH across 21 regions and 204 countries and territories by SDI. (A) ASIRs for GH in 21 regions from 1990–2021 according to SDI. (B) ASIRs for GH in 204 countries and territories in 2021, categorized by SDI.Abbreviations: GH = Genital herpes; SDI = Socio-demographic Index; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/eb0e36e9cfbf508fbd8bf917.png"},{"id":76088400,"identity":"57cf0c6e-3cdc-4fc0-b3ce-ffc85b01bacd","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284164,"visible":true,"origin":"","legend":"\u003cp\u003eSlope Index of Inequality (SII) and Concentration Index (CI) for ASIR of GH among WCBA globally in 1990 and 2021. Panel A shows the SII, illustrating the relationship between SDI and ASIR for each country, with data points sized according to population. Panel B presents the CI, quantifying relative health disparities by measuring the area under the Lorenz curve, aligning ASIR distribution with population distribution based on SDI. Data from 1990 are depicted in blue, and data from 2021 are presented in red. Abbreviations:SII =Slope Index of Inequality; CI= Concentration Index; GH = Genital herpes; SDI = Socio-demographic Index; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/dde5df0248826682fb4d2d5c.png"},{"id":76088422,"identity":"e1807597-5448-4c6b-9b25-16285cda3111","added_by":"auto","created_at":"2025-02-12 08:07:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355149,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The frontier line (black) represents the theoretical achievable age-standardized genital herpes incidence rate (ASIR) based on SDI. Each dot reflects the actual ASIR per 100,000 women in a specific country or territory, with the color gradient indicating the year of data collection, from dark blue (1990) to light blue (2019). (b) Frontier analysis of ASIR per 100,000 women in 2019, with increases in incidence from 1990 to 2019 indicated by red dots, and decreases shown by green dots. The black line represents the frontier of achievable ASIR according to SDI. The 15 countries with the greatest effective difference—the largest gap between observed and achievable incidence rates—are labeled in black. The top five countries with the smallest effective difference in low SDI (\u0026lt;0.50) are labeled in blue, while those in high SDI regions (\u0026gt;0.85) with the greatest effective difference are labeled in red. Abbreviations: ASIR = Age-Standardized Incidence Rate; SDI = Socio-demographic Index.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/4f9f349a8069c8bc3e8fc8ee.png"},{"id":76088396,"identity":"08a9b48c-2fa6-4dbe-ab9b-35fdbdde212a","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116348,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative contributions of aging, population dynamics, and epidemiological changes to the variation in ASIR from 1990 to 2021, compared across global and five SDI regions. The black dot represents the overall change in ASIR from 1990 to 2021. Abbreviations: ASIR = Age-Standardized Incidence Rate; SDI = Socio-demographic Index.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/fecfbee2ed1b8a613b135922.png"},{"id":76088411,"identity":"947a9d46-e0a7-4db3-9af5-5295b56674d7","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":351452,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in local drift and age-specific incidence distribution of genital herpes in WCBA across SDI quintiles from 1990 to 2021. (A) Shows the temporal shifts in age-specific incidence among WCBA during this period. (B) Illustrates the local drift of incidence rates, represented by the annual percentage change (APC) for seven age groups, with dots showing APC values and shaded areas indicating their 95% confidence intervals (CI). Abbreviations: SDI = Socio-demographic Index; WCBA = Women of Childbearing Age.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/4c612dbb87d5157150495302.png"},{"id":76088399,"identity":"6fec704d-1a82-4629-a0c0-50ccbeb3c8b3","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":481064,"visible":true,"origin":"","legend":"\u003cp\u003eAge, period, and birth cohort effects on GH incidence in WCBA based on APC models. (A) The age effect is illustrated through age-specific longitudinal rates, adjusted for differences across birth cohorts, while accounting for deviations in different periods. (B) The period effect is represented by the relative risk of GH incidence across various time periods, with the ratio of age-specific rates from 1992–1996 to 2017–2021, using 1992–1996 as the baseline period. (C) The birth cohort effect is shown by the cohort-specific relative risk of incidence, calculated as the ratio of age-specific rates between the 1942–1951 cohort and the 1997–2006 cohort, with the 1972–1981 cohort as the reference. The points and shaded regions represent the incidence rates or rate ratios, with their respective 95% confidence intervals (CI). Abbreviations: GH = Genital herpes; SDI = Socio-demographic Index; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate; APC = annual percentage change.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/feb53e1c10f7d8cc51a56057.png"},{"id":76088418,"identity":"150e0873-177a-4483-8b6a-57d176b91363","added_by":"auto","created_at":"2025-02-12 08:07:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":441651,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of ASIR and numbers of GH in WCBA for (A) global, (B) China, (C) Angola, (D) Congo, (E) the Democratic Republic of the Congo, and (F) Equatorial Guinea from 2022 to 2030. Abbreviations: GH = Genital herpes; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/ca84532b4cba90da93fe0a0c.png"},{"id":104426219,"identity":"44347c66-d7ce-4f15-b043-2e60a0191d59","added_by":"auto","created_at":"2026-03-11 14:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3690834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/1b93c8a7-5542-46a5-9752-96a80f4f384c.pdf"},{"id":76088410,"identity":"0394443d-b35d-4bb9-9d95-25d44d41c0d9","added_by":"auto","created_at":"2025-02-12 08:07:07","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":539754,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. The number of GH cases in WCBA and ASIR (with the right vertical axis as the unit) from 1990 to 2030 in different countries. Countries included are Botswana, the Central African Republic, Ecuador, Eswatini, Gabon, India, Jamaica, Lesotho, Namibia, Peru, South Africa, and Zimbabwe. The title of each subplot indicates the country’s name. The blue bar charts show the annual number of GH cases, while the red lines show the ASIR trend per 100,000 people. The black vertical lines represent error bars, indicating the uncertainty range in the number of cases for each year. Abbreviations: GH = Genital herpes; WCBA = Women of Childbearing Age; ASIR = Age-Standardized Incidence Rate.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5901402/v1/f82fb66b1f3b4b9864af9c33.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenital herpes (GH) represents a significant public health threat. It is a common sexually transmitted disease (STD) caused primarily by herpes simplex virus (HSV)\u0026ndash;2, with HSV-1 playing a lesser role [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This virus is highly contagious, spreading through sexual contact and vertical transmission from mother to child [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Women of childbearing age (WCBA) are particularly vulnerable to HSV infection. The World Health Organization (WHO) estimates that around 20\u0026nbsp;million new HSV-2 infections occur globally each year, and 86\u0026nbsp;million people are affected by GH, with infection rates in women ranging from 30\u0026ndash;80% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there is a lack of comprehensive global analysis on GH incidence specifically in WCBA, with limited attention given to this population.\u003c/p\u003e \u003cp\u003eStudies have shown that the global prevalence of GH in WCBA is nearly double that of men, largely due to anatomical differences in the female genital tract that increase susceptibility to infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. GH is strongly associated with WCBA and can elevate the risk of pregnancy complications, leading to considerable maternal morbidity and mortality. Moreover, mother-to-child transmission can result in neonatal herpes, neonatal death, congenital malformations, and other adverse birth outcomes. Neonatal herpes can manifest as localized skin, eye, and mucous membrane infections, encephalitis, or disseminated infections, all of which are associated with high rates of morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As such, GH infection in WCBA requires greater attention and the implementation of targeted interventions.\u003c/p\u003e \u003cp\u003eIn 2016, epidemiological studies estimated that more than 500\u0026nbsp;million people worldwide were infected, with the majority of cases occurring in the 16\u0026ndash;40 age group, which corresponds with an increase in sexual activity within this age range [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2018, it was estimated that 18.6\u0026nbsp;million individuals in the U.S. were affected by genital HSV-2 infections, with about 65% of these infections occurring in women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Global Burden of Disease (GBD) study reported a 51.97% increase in global GH cases among individuals aged 15\u0026ndash;49 years from 1990 to 2021. Incidence rates also rose in low and middle Socio-demographic Index (SDI) regions, including South Asia, Southern Sub-Saharan Africa, and Central Europe. Among these regions, Southern Sub-Saharan Africa exhibited the highest burden of GH among 15\u0026ndash;49-year-olds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, there remains a lack of systematic studies focused specifically on the global, regional, and national disease burden of GH in WCBA, emphasizing the need for a thorough analysis of GH incidence in this group on a global and regional scale.\u003c/p\u003e \u003cp\u003eGH episodes in WCBA, particularly during critical stages of reproduction such as pregnancy, can lead to neonatal infections through vertical transmission, which can have long-lasting effects on both the development of the offspring and societal sustainability. Therefore, exploring the epidemiological trends of GH in WCBA is essential to enhance understanding of the disease and support the development of public health strategies.\u003c/p\u003e \u003cp\u003eThis study utilized a thorough research approach to provide a detailed examination of GH incidence trends among WCBA. Specifically, the number of new cases and age-standardized incidence rate (ASIR) were assessed to identify temporal patterns. The age-period-cohort (APC) model was applied to analyze the temporal dynamics of GH incidence, and the Bayesian age-period-cohort (BAPC) model was integrated to project global epidemiological trends of GH among WCBA until 2030. In addition to these approaches, a health inequality analysis was carried out to explore disparities in GH burden, frontier analysis was used to determine the potential of different countries to improve disease burden and the differences between them, and decomposition analysis was used to determine the key factors driving GH trends. As the first study to quantify the GBD of GH in WCBA and forecast its future trajectory, this research offers important insights that align with the WHO 2030 Sustainable Development Goal to eliminate maternal and child health disparities and prioritize GH as a public health concern.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThis study presents a detailed analysis of the epidemiological data on GH in women aged 15 to 49. The foundation of the research is the GBD 2021, a reliable database meticulously curated by the Institute for Health Metrics and Evaluation (IHME) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/gbd-2021/sources\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/gbd-2021/sources\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GBD 2021 provides extensive data on disease burden across 204 countries and regions, covering 371 diseases and injuries [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By quantifying health losses across different regions, time periods, and demographics, it offers valuable data that can guide global health system improvements and help reduce health disparities. Within its disease classification, GBD 2021 uses a four-tier system, with genital herpes categorized at the fourth level, highlighting its relevance among infectious diseases.\u003c/p\u003e \u003cp\u003eIn this study, the Global Health Data Exchange (GHDx) query tool was utilized to extract relevant indicators for GH in women aged 15 to 49 from the GBD 2021 database, including global incidence number and ASIR from 1990 to 2021. The SDI was also incorporated as a composite indicator that reflects economic development, education levels, and fertility rates in a country or region [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on SDI values, GBD 2021 classifies countries into five development categories: low, low-middle, middle, high-middle, and high, with higher SDI values typically indicating more advanced development. The aim of this study is to examine the differences in disease burden and prevention strategies for GH in women aged 15 to 49 across countries with varying SDI levels, thereby providing evidence to support the formulation and implementation of global health policies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThis study examines GH among WCBA, defined by the WHO as females aged 15 to 49 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This life stage is marked by reproductive capacity and hormonal fluctuations. Adolescence, spanning ages 10 to 19, is recognized as a transitional phase from childhood to adulthood. Data on GH incidence rates and incidence cases in WCBA from 1990 to 2021 were retrieved from GBD 2021 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The institutional review board of Beijing Ditan Hospital determined that ethical approval was not necessary, as the study used publicly available data. The research follows the Guidelines for Accurate and Transparent Health Estimates Reporting for cross-sectional studies.\u003c/p\u003e\n\u003ch3\u003eCalculation of specific age-standardized incidence of GH in WCBA\u003c/h3\u003e\n\u003cp\u003eTo calculate the ASIR of GH in WCBA, direct age standardization was applied. This approach assumes that the rates follow a distribution represented by the weighted sum of independent Poisson random variables [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCross-Country Inequality Analysis\u003c/h3\u003e\n\u003cp\u003eThe slope index of inequality (SII) and concentration index (CI) are standard metrics used to evaluate absolute and relative gradient inequalities, respectively. These indices provide quantitative assessments of the distributional disparities in the GH burden among WCBA across countries [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The SII is obtained through regression analysis, linking a country\u0026rsquo;s ASIR to its relative position in the SDI ranking, which is based on the population\u0026rsquo;s midpoint in the cumulative distribution of SDI. Weighted regression models are used to address heteroscedasticity in the analysis. The CI is determined by integrating the area under the Lorenz curve, which shows the relationship between the cumulative proportion of ASIR and the cumulative distribution of the population ranked by SDI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAge-period-cohort (APC) model analysis\u003c/h3\u003e\n\u003cp\u003eThe APC model is a sophisticated analytical approach frequently used in health and socioeconomic research, offering several benefits compared to traditional methods. It allows for the estimation of net drift and local drift, which reflect overall and specific temporal trends, respectively. The model also facilitates the analysis of key temporal factors: age, period, and birth cohort [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, the APC model was employed to explore the incidence trends of GH among WCBA across various age groups, time periods, and birth cohorts. Specifically, it helps identify changes in disease patterns by isolating the effects of age, period, and cohort, which can be linked to shifts in lifestyle, medical progress, or environmental factors.\u003c/p\u003e \u003cp\u003eIn this study, the APC model was applied using a method where age intervals were aligned with corresponding time periods, such that each 5-year age group corresponded to a 5-year period. Data on GH incidence from the 2021 GBD database, covering the years 1992\u0026ndash;2021, were integrated, with a focus on women of reproductive age and the relevant population data for each region. The target age range of 15 to 49 years was divided into seven age categories for more detailed analysis: 15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, and 45\u0026ndash;49 years. The study period (1992\u0026ndash;2021) was divided into six 5-year intervals: 1992\u0026ndash;1996, 1997\u0026ndash;2001, 2002\u0026ndash;2006, 2007\u0026ndash;2011, 2012\u0026ndash;2016, and 2017\u0026ndash;2021. This approach covered 12 overlapping 10-year birth cohorts, spanning from 1942\u0026ndash;1951 to 1997\u0026ndash;2006.\u003c/p\u003e \u003cp\u003eThe APC model was used to estimate both the overall temporal trend and the trends within individual age groups. The overall temporal trend, known as net drift, was expressed as the annual percentage change in incidence, reflecting the impact of calendar time and continuous birth cohorts. The trend within each age group, referred to as local drift, was expressed as the annual percentage change in age-specific incidence. The significance of these annual percentage change trends was assessed using the Wald χ\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition Analysis\u003c/h2\u003e \u003cp\u003eA decomposition analysis was conducted to identify the key factors driving changes in the burden of GH among WCBA from 1990 to 2021. The purpose of this analysis was to quantify the individual contributions of population growth, aging, and epidemiological transitions. Each factor\u0026rsquo;s effect was isolated by holding the other two variables constant during the assessment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFrontier analysis\u003c/h3\u003e\n\u003cp\u003eFrontier analysis was used to explore the relationship between the burden of GH among WCBA and sociodemographic development. The frontier was defined as a nonlinear boundary that represents the minimum achievable burden, dependent on the current development status of a country or region. Non-parametric data envelope analysis was applied to construct this frontier, as described in earlier studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The gap between the observed ASIR in a given country and its corresponding frontier, referred to as the \u0026ldquo;effective difference,\u0026rdquo; indicates the potential health improvements that have yet to be achieved, given the region\u0026rsquo;s current development level.\u003c/p\u003e\n\u003ch3\u003ePrediction\u003c/h3\u003e\n\u003cp\u003eThe future incidence and number of GH cases among WCBA from 2022 to 2030 were projected using the BAPC model. To enhance prediction accuracy, the Integrated Nested Laplace Approximation (INLA) framework was incorporated with the BAPC model. This approach addresses challenges related to mixing and convergence commonly encountered in traditional Bayesian methods that rely on Markov Chain Monte Carlo (MCMC) sampling. The analysis was conducted using the R packages \u0026ldquo;BAPC\u0026rdquo; (version 0.0.36) and \u0026ldquo;INLA\u0026rdquo; (version 24.02.09), and all statistical analyses and data visualizations were performed using Stata 16.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stata.com\u003c/span\u003e\u003cspan address=\"https://www.stata.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R 4.4.2.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll metrics (counts, rates) were reported with 95% confidence intervals (95%CI) or 95% uncertainty intervals (UI), derived from the 25th and 975th percentiles of 1,000 posterior draws. Rates were presented per 100,000 population. Statistical analyses were carried out using R (version 4.4.2, R Core Team).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTrends in genital herpes incidence globally and across SDI regions\u003c/h2\u003e \u003cp\u003eThe number of GH cases increased in the low SDI, low-middle SDI, middle SDI, and middle-high SDI regions by 2021, with only high SDI regions showing a decline. Over the past three decades, the global burden of GH has risen, with varied patterns across SDI regions. The greatest burden was observed in middle SDI regions, while the largest growth occurred in low-middle SDI areas (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Notably, the ASIR in low SDI and middle SDI regions remained above the global average, while the high-middle SDI, low-middle SDI, and high SDI regions stayed below it. These regional differences in GH incidence and ASIR trends highlight the importance of further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The APC model was used to estimate the net drift of incidence, shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Globally, GH incidence among WCBA followed an upward trend, with an annual net drift of 0.05% (95% CI: \u0026minus;\u0026thinsp;0.01 to 0.10). Interestingly, the low-middle SDI region continued this trend, showing an annual increase with a positive net drift of 0.11% (95% CI: 0.04 to 0.18).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlobal and SDI trends of acute genital herpes incidence in WCBA from 1992 to 2021.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncident cases\u003c/p\u003e \u003cp\u003eNo. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASIR per 100000\u003c/p\u003e \u003cp\u003eNo. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncident cases\u003c/p\u003e \u003cp\u003eNo. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASIR per 100000\u003c/p\u003e \u003cp\u003eNo. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNet Drift\u003c/p\u003e \u003cp\u003e(%/year)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14631443.7\u003c/p\u003e \u003cp\u003e(10559685.65\u0026thinsp;~\u0026thinsp;19532383.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1061.7\u003c/p\u003e \u003cp\u003e(761.49\u0026thinsp;~\u0026thinsp;1424.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22010115.9\u003c/p\u003e \u003cp\u003e(15441987.71\u0026thinsp;~\u0026thinsp;29880978.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1140.4 (800.89\u0026thinsp;~\u0026thinsp;1546.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(-0.01 to 0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1899707.4\u003c/p\u003e \u003cp\u003e(1412099.57\u0026thinsp;~\u0026thinsp;2468988.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1554.4\u003c/p\u003e \u003cp\u003e(1144.48\u0026thinsp;~\u0026thinsp;2036.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4670980.7\u003c/p\u003e \u003cp\u003e(3372038.14\u0026thinsp;~\u0026thinsp;6187170.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1574.7\u003c/p\u003e \u003cp\u003e(1129.38\u0026thinsp;~\u0026thinsp;2098.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.17 to -0.02)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2747080.6\u003c/p\u003e \u003cp\u003e(1962753.77\u0026thinsp;~\u0026thinsp;3697347.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e971.5\u003c/p\u003e \u003cp\u003e(689.28\u0026thinsp;~\u0026thinsp;1315.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5373664.2\u003c/p\u003e \u003cp\u003e(3792640.24\u0026thinsp;~\u0026thinsp;7266861.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1042.7\u003c/p\u003e \u003cp\u003e(733.55\u0026thinsp;~\u0026thinsp;1413.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e(0.04 to 0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5132730.7 (3715023.55\u0026thinsp;~\u0026thinsp;6834371.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1111.6\u003c/p\u003e \u003cp\u003e(797.46\u0026thinsp;~\u0026thinsp;1490.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6976352.5\u003c/p\u003e \u003cp\u003e(4860056.48\u0026thinsp;~\u0026thinsp;9512758.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1142.9\u003c/p\u003e \u003cp\u003e(797.76\u0026thinsp;~\u0026thinsp;1555.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e(-0.20 to -0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2556759.7 (1809780.11\u0026thinsp;~\u0026thinsp;3478625.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e901.7\u003c/p\u003e \u003cp\u003e(635.3\u0026thinsp;~\u0026thinsp;1230.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2779906.3\u003c/p\u003e \u003cp\u003e(1896660.8\u0026thinsp;~\u0026thinsp;3844744.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e934.2\u003c/p\u003e \u003cp\u003e(641.9\u0026thinsp;~\u0026thinsp;1283.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.16 to -0.05)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2281198\u003c/p\u003e \u003cp\u003e(1643690.84\u0026thinsp;~\u0026thinsp;3059166.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1013.2\u003c/p\u003e \u003cp\u003e(733.27\u0026thinsp;~\u0026thinsp;1354.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2190951.3\u003c/p\u003e \u003cp\u003e(1484391.84\u0026thinsp;~\u0026thinsp;3064663.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e930.3\u003c/p\u003e \u003cp\u003e(635.37\u0026thinsp;~\u0026thinsp;1291.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003cp\u003e(-0.14 to -0.02)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e95% UI\u0026thinsp;=\u0026thinsp;95% uncertainty intervals; 95% CI\u0026thinsp;=\u0026thinsp;95% Conffdence Interval; APC\u0026thinsp;=\u0026thinsp;age period cohort; SDI\u0026thinsp;=\u0026thinsp;sociodemographic index; WCBA\u0026thinsp;=\u0026thinsp;women of childbearing age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between ASIR of GH and SDI Across Regions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA presents the changes in ASIR across regions from 1990 to 2021 in relation to the increase in SDI. In eight regions with low and high SDI, ASIR for GH in WCBA decreased in five regions, except for Central Sub-Saharan Africa, South Asia, and Western Europe, where ASIR remained relatively constant from 1990 to 2021. In regions with middle-high SDI and in about half of the middle SDI regions, ASIR remained largely stable over the same period. Interestingly, in certain middle SDI regions, ASIR first increased and then decreased as SDI rose, with the most noticeable change occurring in Southern Sub-Saharan Africa, where ASIR peaked at an SDI around 0.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB illustrates the relationship between ASIR and SDI in various countries in 2021. In these countries, ASIR decreased with an increase in SDI. When SDI reached about 0.5625, ASIR began to rise, peaking at an SDI of 0.625, before decreasing again as SDI continued to increase. Moreover, based on SDI alone, the ASIR in the Central African Republic, Angola, Gabon, and Equatorial Guinea far exceeds the global mean ASIR for GH in WCBA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHealth inequalities analysis\u003c/h2\u003e \u003cp\u003eThe findings of the study, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, reveal that the blue and red solid lines representing the global incidence of GH in 1990 and 2021, respectively, follow a pattern from the upper left to the lower right as SDI increases, indicating a higher disease burden in areas with lower SDI. Specifically, the SII was \u0026minus;\u0026thinsp;1175 in 1990 and \u0026minus;\u0026thinsp;1216 in 2021. The absolute value of the SII in 2021 exceeds that of 1990 and displays a steeper slope, clearly indicating a rise in absolute disparities between income groups in 2021 compared to 1990 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, the CI for 1990 and 2021 were \u0026minus;\u0026thinsp;0.01 and \u0026minus;\u0026thinsp;0.11, respectively, both negative, suggesting that the global burden of GH disease in WCBA is more concentrated in lower-income groups. An increase in health inequality was observed in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFrontier analysis based on agestandardized incidence rates\u003c/h2\u003e \u003cp\u003eUsing data from 1990 to 2021, this study analyzed the association between the GH burden in WCBA and national development levels through frontier analysis, incorporating ASIR and SDI. The frontier line represents the theoretically achievable ASIR based on the SDI. The effective difference, or the distance from the frontier, reflects the gap between the observed and achievable ASIR for each country or region, considering its SDI. In general, countries with higher SDI exhibited smaller effective differences, while the largest differences were observed in countries with intermediate SDI values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The analysis identified 15 countries with the highest potential for improvement, including Malawi, Zimbabwe, Botswana, South Africa, Mozambique, Uganda, Namibia, Eswatini, Lesotho, Congo, the Central African Republic, Angola, Gabon, and Equatorial Guinea. Countries with low SDI, including Somalia, Yemen, Nepal, Bangladesh, and Bhutan, were also identified as frontier nations. Moreover, high-SDI countries showing substantial room for improvement relative to their development stage included Singapore, Sweden, Taiwan (Province of China), Lithuania, and the United States (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition analysis\u003c/h2\u003e \u003cp\u003eTo assess the primary factors driving changes in the GH disease burden among WCBA globally from 1990 to 2021, decomposition analysis was applied to quantify and evaluate the relative contributions of aging, population growth, and epidemiological changes. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of the decomposition analysis, illustrating how aging, population growth, and epidemiological changes affected variations in ASIR across the five SDI regions globally. The findings indicate that, globally, population growth is the primary factor influencing changes in ASIR for GH among WCBA, followed by epidemiological changes, while aging has a negative effect on ASIR changes. However, the contributions of these factors to ASIR changes vary significantly across the five SDI regions. In low SDI and low-middle SDI regions, the effects of aging, population growth, and epidemiological changes on ASIR changes are consistent with the global trends. In middle SDI regions, population growth is the most important driver of ASIR changes, with the contribution of aging to ASIR changes slightly surpassing that of epidemiological changes, contrary to the global pattern. In middle-high SDI and high SDI regions, the contributions of aging, population growth, and epidemiological changes to ASIR changes are about equal. Notably, in high SDI regions, both aging and epidemiological changes have a significant effect on ASIR changes among WCBA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTemporal Trends in GH Incidence Among WCBA Across Different Age Groups\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA illustrates the variation in GH incidence number across different age groups within the WCBA population globally, as well as by SDI levels. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB depicts the annual percentage change in incidence rate for each age group, derived from the local drift calculated using the APC model. A positive annual percentage change indicates an increase in incidence, while a negative value signifies a decrease. Temporal trends in GH incidence among WCBA showed notable differences across SDI regions and age groups. Globally, the highest incidence occurred in the 20\u0026ndash;29 age group over the past decade, with the average annual percent changes (AAPC) peaking in the 25\u0026ndash;29 age group (local drift coefficient: 0.04), indicating increasing rates in the 20\u0026ndash;34 age range. In high SDI regions, incidence rose for individuals aged 25\u0026ndash;49, while it declined for those under 25. In contrast, middle and middle-high SDI regions experienced a general decline in incidence across all age groups, with most AAPC values below 0. Similarly, in low SDI regions, incidence declined in individuals under 29 but increased in those aged 29 and older. A notable decrease in ASR among females aged 15\u0026ndash;19 was observed globally and across all five SDI regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of age, period, and birth cohort on the Incidence of GH disease in WCBA\u003c/h2\u003e \u003cp\u003eThe effects of age, period, and birth cohort on GH incidence, derived from the APC model, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Across SDI regions, age effects displayed a consistent pattern, with the highest risk seen in women aged 25\u0026ndash;29 years (20\u0026ndash;24 years in low SDI regions), followed by a decrease in risk with age. High SDI and high-middle SDI regions consistently exhibited lower prevalence across all age groups, with minor variation between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The period effect revealed a global upward trend in hazard ratios after 2012\u0026ndash;2016, evident across all five SDI regions. Throughout the study period, hazard ratios were highest in low-middle SDI regions, surpassing the 1992\u0026ndash;1996 baseline. The relative risks for 2012\u0026ndash;2016 and 2017\u0026ndash;2021 were 1.01 (95% CI: 0.999\u0026ndash;1.036) and 1.03 (95% CI: 1.017\u0026ndash;1.056), respectively. Globally, the relative risk for 2017\u0026ndash;2021 was 1.022 (95% CI: 1.006\u0026ndash;1.037) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eConcerning birth cohort effects, the global cohort exhibited incidence risk in the period after 1972\u0026ndash;1981, with incidence exceeding the baseline set in that cohort. Notably, among cohorts born before 1982\u0026ndash;1991, risk trends varied across SDI regions. After this cohort, however, risk consistently declined across all regions. The cohorts with the highest risk included the global cohort from 1987 to 1996, the low-middle SDI and high SDI cohorts from 1982\u0026ndash;1991, the 1947\u0026ndash;1956 cohort in the middle SDI region, and the 1957\u0026ndash;1966 cohort in the high-middle SDI region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProjected Global Burden among WCAB of Genital Herpes by 2030\u003c/h2\u003e \u003cp\u003eThe BAPC model projects global mortality and population size from 2022 to 2030. The global total of GH cases is anticipated to steadily increase, reaching an estimated 23.57\u0026nbsp;million (95% UI: 19.85\u0026ndash;27.30\u0026nbsp;million) by 2030. At the same time, ASIR is expected to rise moderately, reaching a peak of 1163.35 (95% UI: 991.46\u0026ndash;1335.23), marking a historical high (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Projections for China and India, the two most populous countries, indicate a decline in ASIR for both nations, with China experiencing a slightly higher rate compared to India (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, Figure S1). The study also predicts that the incidence of GH in the 15 countries furthest from the frontier fit line identified in the frontier analysis, including Angola, the Democratic Republic of the Congo, Congo, and Equatorial Guinea, will continue to increase both in terms of total cases and ASIR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). These results suggest that, without substantial interventions, the impact of GH in these regions is unlikely to decrease in the near future. Additionally, the disparity between disease burden and development levels may grow. For the remaining 11 countries that were also far from the frontier fit line, projections indicate continued increases in GH cases in Botswana, the Central African Republic, Namibia, and Zimbabwe, despite a predicted decrease in ASIR. Similarly, GH cases in Ecuador, Eswatini, Gabon, and Lesotho are expected to rise, while ASIR remains stable. In contrast, both case numbers and ASIR are expected to decline in Peru and South Africa, while Jamaica is projected to experience a decrease in cases, with ASIR remaining unchanged (Figure S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGH presents a significant global public health threat, especially to high-risk populations, with women of reproductive age being a major affected group [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. GH infection in WCBA before or during pregnancy often leads to severe health consequences. Initially, infection can worsen GH symptoms in WCBA, affecting physical function. Furthermore, this infection can cause adverse pregnancy outcomes, including premature birth, abortion, and congenital malformations. More critically, the vertical transmission of the virus from mother to child can result in neonatal disease, with neonatal herpes being the most severe direct consequence, leading to high morbidity and mortality [\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Given these serious risks, the incidence of GH in WCBA must be addressed. This study sheds light on the global incidence and regional disparities of GH among WCBA through indicators such as case numbers and age-standardized incidence rates, and applies the APC and BAPC models to further investigate the temporal dynamics and SDI-related changes of the disease. It also provides a well-founded projection of its future development. These findings aim to offer solid data support and a reliable basis for effectively addressing this disease. To the best of our knowledge, this is the first study to examine the incidence and trends of GH among WCBA aged 15\u0026ndash;49 years from 1990 to 2021 across 204 countries at global, regional, and national levels, as well as to forecast future trends.\u003c/p\u003e \u003cp\u003eThe relationship between GH incidence and SDI is particularly noteworthy. The significant differences in the incidence and ASIR of GH across different regions provide valuable information to help us understand the epidemiological trends of this sexually transmitted infection and its influencing factors in various socio-economic contexts. While high-SDI regions have seen a stabilization or decline in GH rates, low-middle and low-SDI regions continue to experience increased burden. These findings echo earlier studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] that suggest socioeconomic development and healthcare infrastructure significantly impact the disease burden. For instance, the significant increase in GH cases in Southern Sub-Saharan Africa [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] aligns with studies showing that regions with poorer healthcare access have higher infection rates [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, our analysis of the SII and CI revealed a growing disparity in the burden of GH, with the disease being more concentrated in lower-income groups, a trend that has worsened over the past three decades. This observation aligns with findings from Stebbins et al. (2019), who demonstrated that socioeconomic and racial disparities contribute to higher pathogen burdens, including HSV infections [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the key findings of this study is the significant health inequality present in the global burden of GH. The frontier analysis demonstrated that many countries, especially those in sub-Saharan Africa, could achieve substantial improvements in GH control, given their current SDI. This provides a clear roadmap for health policy makers to focus on these regions, investing in both preventive measures such as education and vaccination, and in strengthening healthcare systems to reduce the disease burden. Cao et al. (2024) emphasized the need for increased vaccination efforts in low-middle SDI regions to combat the rising incidence of genital herpes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].The increasing inequalities in health outcomes, as shown by the CI and SII indices, suggest that without focused interventions, the global disparities in GH burden may widen, exacerbating existing health inequities. This aligns with findings from Mayaud and Mabey (2004), who highlighted the urgent need for enhanced health infrastructure and comprehensive preventive strategies\u0026mdash;such as vaginal microbicides, vaccines, and behavior change interventions\u0026mdash;in regions with high disease burdens, including sub-Saharan Africa [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe APC analysis show that younger women, especially those in the 20\u0026ndash;29 age group, have the highest incidence rates of GH globally. This aligns with other studies' findings. For instance, Purva et al. reported that GH was most prevalent in women aged 25\u0026ndash;29 years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and Spicknall et al. found the incidence was highest in women aged 18\u0026ndash;24 years, with over 80% of cases in women aged 18\u0026ndash;29 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The likely reason is the peak in sexual activity and risk behavior in the late teens and twenties [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. And a consistent pattern was observed across regions with different SDI levels, where the incidence of GH decreases with age [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In low - SDI regions, there's an increasing incidence of GH in women aged 30 and above, possibly due to limited access to prevention and treatment. In contrast, in high - SDI regions, the decline in GH incidence among women under 25 may be attributed to better awareness, prevention programs, and healthcare access. Our study on WCBA also indicates that the risk of developing GH decreases with age. Younger WCBA, being most active in reproductive and sexual activities, face higher risks related to pregnancy - related conditions and HSV infection [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given these findings, targeted prevention and intervention efforts should prioritize women in the 18\u0026ndash;29 age group, particularly those aged 20\u0026ndash;29.\u003c/p\u003e \u003cp\u003eThis study provides a comprehensive global analysis of GH incidence trends among WCBA over the past three decades, with projections up to 2030. By 2030, the global incidence is projected to reach 23.6\u0026nbsp;million, with continued difficulty in controlling the disease in low-SDI regions such as Angola, Equatorial Guinea, Congo, and Democratic Republic of the Congo. The ASIR is expected to reach 1,163.35per 100,000 population during the same period. These results suggest gradual success in the global GH control efforts. The rising number of cases is primarily attributed to demographic changes, while the relatively stable incidence highlights the preliminary success of implemented control measures [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of policy implications, these results underscore the urgent need for region-specific strategies. High-SDI regions, where the burden is relatively stable or declining, may benefit from targeted interventions focusing on reducing vertical transmission, improving management of neonatal herpes, and maintaining effective prevention programs. In contrast, low-middle SDI regions should prioritize expanding access to sexual health education, improving the availability of antiviral treatments, and enhancing healthcare infrastructure to reduce the incidence of GH [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, increased funding and international support are critical to reduce the disease burden in these regions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, genital herpes remains a major public health issue among women of childbearing age, with rising incidence rates globally, particularly in low-middle SDI regions. The study's findings emphasize the need for targeted interventions that address the specific needs of different regions, especially those with low SDI. As the global burden continues to grow, it is crucial to implement strategies that reduce health inequalities and promote equitable access to prevention and treatment. Further research and stronger health systems are key to mitigating the impact of GH on women\u0026rsquo;s health and maternal and child outcomes worldwide.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eDeclarations of interest: none\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. K., Y. F., and Q. Y.: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation. X. X.: Methodology, Software, Data curation. Y. W. and M. Z.: Reviewing and Editing. Z. L.: Investigation, Reviewing and Editing.\u003c/p\u003e\u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study was supported by a grant from Research on the Technical Specifications and Standard System for Biological Safety Sample Banks (Project number: 2019YFC1200700)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnston C, Wald A, Genital Herpes. JAMA. 2024;332:835\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaishankar D, Shukla D. Genital herpes: insights into sexually transmitted infectious disease. Microb Cell. 2016;3:438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoelker R. What Is Genital Herpes? 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Mol Biomed. 2024;5:35.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Genital herpes, Women of childbearing age, Age-standardized incidence rate, incidence, GBD, SDI, health inequality","lastPublishedDoi":"10.21203/rs.3.rs-5901402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5901402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eGenital herpes (GH), primarily caused by herpes simplex virus 2, imposes a significant burden on women of childbearing age (WCBA), raising the risk of pregnancy-related complications. Despite the high global burden, comprehensive studies on trends in GH incidence among WCBA are lacking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing GBD 2021 data, this study analyzed GH incidence in WCBA, explored Socio-demographic Index (SDI) associations and inequality trends, applied decomposition analysis, and predicted future trends with Age-period-cohort (APC) and Bayesian APC (BAPC) models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween 1990 and 2021, global GH cases in WCBA increased from 14.6\u0026nbsp;million to 22\u0026nbsp;million, while the age-standardized incidence rate (ASIR) saw a modest annual increase of 0.045%. Regional variations were observed, with low-middle SDI regions showing continued growth in ASIR. Unfavorable period effects were exhibited in low-middle SDI regions. Population growth was identified as the main driver of morbidity trends, with emerging health inequalities over time. By 2030, global GH cases in WCBA are expected to reach 23.6\u0026nbsp;million, with persistent challenges in low-SDI regions such as Angola, the Democratic Republic of the Congo, Congo, and Equatorial Guinea.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGH incidence among WCBA is on the rise, underlining the need for focused management strategies, particularly in low-middle SDI regions.\u003c/p\u003e","manuscriptTitle":"Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-12 08:07:02","doi":"10.21203/rs.3.rs-5901402/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":"e305a687-de2f-4bcb-858d-6363db040406","owner":[],"postedDate":"February 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T14:41:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-12 08:07:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5901402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5901402","identity":"rs-5901402","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}