Visual Abstract
Key Words: global health, Mendelian randomization, rheumatic heart disease
Highlights
-
•
From 1990 to 2021, the global age-standardized prevalence and incidence rates of RHD showed a slight increase.
-
•
The RHD burden varied significantly across sexes, age groups, and SDI levels.
-
•
MR analysis identified high systolic blood pressure and high body mass index as causal risk factors for RHD.
-
•
Future projections indicate a continuing upward trend in the age-standardized incidence rate of RHD.
Summary
Rheumatic heart disease (RHD) is a chronic valvular disorder caused by acute rheumatic fever. It mostly affects the mitral valve and leads to thickening. The present study employs the GBD (Global Burden of Diseases, Injuries, and Risk Factors Study 2021) to analyze the global burden of RHD. The study investigates trends in incidence, prevalence, disability-adjusted life-years, and mortality from 1990 to 2021 across regions and 204 countries and territories, exploring difference with sex, age, and sociodemographic index. Two-sample Mendelian randomization identified high systolic blood pressure and high body mass index as causal risk factors for RHD. Although there was a slight increase in age-standardized prevalence and incidence rates, there was a decline in mortality and disability-adjusted life-year rates. Projections indicate a persistent upward trend in the incidence of the condition from 2022 to 2050. The findings emphasize the persistent global burden of RHD and underscore the necessity for targeted interventions.
Rheumatic heart disease (RHD) is a condition that affects the heart valves and is caused by rheumatic fever.1 Acute rheumatic fever begins as an infection with group A streptococcus. If left untreated, acute rheumatic fever can lead to rheumatic myocarditis, resulting in severe valve damage, significant heart disease, and even premature death.2, 3, 4 Although RHD is a preventable disease,5 it remains a major cause of death and disability related to cardiovascular disease. Its high morbidity, disability, and mortality impose a heavy economic burden on society.6,7 Therefore, the study of RHD is crucial for reducing the global and national burden of this disease. However, there is currently limited medical and scientific attention on RHD, with comparatively little research focused on its global burden. Some research has focused on the RHD burden and trends in specific regions, such as Ordunez et al,8 who highlighted the current situation, inequalities, and future trends of RHD in America, and Shi et al,9 who emphasized the burden of RHD in China. Moreover, some studies only analyzed patients with RHD in specific age groups.10 Ruan et al11 and Lv et al12 discussed the overall burden of RHD in 2019 from a global perspective, but did not delve into the detailed risk factors influencing RHD. Therefore, this indicates an urgent need for a comprehensive analysis of the RHD’s burden and its contributing factors, along with an increased understanding of RHD risk factors.
The GBD (Global Burden of Diseases, Injuries, and Risk Factors Study) 2021 serves as a valuable resource for disease burden surveys, helping researchers to better understand the global and regional patterns of RHD. In this study, we grouped the prevalence, incidence rate, disability-adjusted life-years (DALYs), and mortality rate of RHD from GBD 2021 by age, sex, and sociodemographic index (SDI), and performed a detailed comparative analysis.
Methods
Data source
GBD 2021 provided the data for this study. GBD 2021 comprises 371 diseases and injuries from 204 countries and territories, and more than 80 behavioral, environmental, occupational, and metabolic risk factors. In this study, the GBD database was used to extract the estimated incidence rate, prevalence, DALYs, and mortality rate of RHD, and its 95% uncertainty interval (UI).
The GBD study data adhered to the guidelines for Accurate and Transparent Health Estimation Reporting for Population Health Research (GATHER). The checklist is described elsewhere.13
Age-standardized rate
The GBD 2021 was used to obtain the age-standardized rate (ASR) and the corresponding 95% UI for standardized calculations. The ASR is a weighted average of specific age (crude) rates such as prevalence, incidence, DALYs, and mortality, per 100,000 individuals. This can be utilized to quantify the global burden of RHD (Supplemental Appendix). Student’s t-tests were employed to analyze differences in ASRs by sex and across SDI regions.
Average annual percentage change
The Joinpoint Regression Program (software version 5.1.0.0) was utilized to compute the average annual change percentage (AAPC) and the corresponding 95% CI through the joint point regression.14 This analysis evaluated the changes in incidence rate, prevalence, DALYs, and mortality rate of RHD over time (Supplemental Appendix).
Sociodemographic index
The SDI indicates the geometric average of the total fertility rate for those aged <25 years, the average education level of the population aged ≥15 years, and the per capita lagged distribution of income. These values, ranging from 0 to 1, can extensively measure the economic development status of a region (Supplemental Appendix).15
Two-sample Mendelian randomization
Two-sample Mendelian randomization (MR) is a modality to identify causal associations between exposure phenotypes (as risk factors) and outcomes (as diseases). It uses exposed genetic variations as instrumental variables (IVs) based on 3 key assumptions.16, 17, 18 The logistic regression models were used to determine the risk of developing RHD with results presented as the OR with 95% CI. The publicly available IEU OpenGWAS project provided the summary data of the genome-wide association study of risk factors and RHD. The inclusion criteria for IVs were restricted, considering the significance threshold and F-statistic.19 To evaluate heterogeneity and horizontal pleiotropy, Cochrane’s Q-statistic and the MR-Egger regression were used.20,21 The stability of MR results can be determined using the leave-one-out sensitivity test for sensitivity analyses.22 Finally, the classic inverse-variance weighted model was used for primary MR analysis and the adjusted P value after multiple comparisons was 0.0002 (0.05/226). For the selection of IVs and specific analysis of the two-sample MR, details are provided in the attachment (Supplemental Appendix). The R package TwoSampleMR (version 0.6.1) was used for two-sample MR analysis.23
Bayesian age-period-cohort analysis
The age-period-cohort model is commonly used to analyze trends in disease incidence and mortality, taking into account 3 factors: age, period, and cohort.24 The Bayesian age-period-cohort (BAPC) model utilizes a second-order random walk to smooth the prior effects of age, period, and cohort for predicting posterior mortality. To avoid any mixing and convergence challenges because of sampling techniques associated with Markov Chain Monte Carlo, this method combines nested Laplace approximation and demonstrates better coverage and accuracy than other methods, providing stable and reliable results.25 In this study, GBD 2021 data from 1990 to 2021 were used to predict future trends from 2022 to 2050, in RHD incidence, prevalence, DALYs, and mortality for all age groups (with 5-year intervals). The R package BAPC (version 0.0.36) and INLA (version 24.2.9) were used to perform the BAPC analysis.26
Ethical approval
This study is based on publicly available, deidentified summary data from GBD 2021. Because no individual-level data were accessed or analyzed, and the data are fully anonymized, this research did not require review or approval by an Institutional Review Board/ethics committee. Our use of the GBD data complies with the GBD data use agreement.
Results
Global burden overview of RHD
Overall, the global incidence rate and prevalence of RHD increased, while the global rates of DALYs and mortality decreased. Regionally, Central Sub-Saharan Africa had the highest prevalence per 100,000 capita (1,665.95; 95% UI: 1,307.30-2,090.81), whereas the lowest prevalence was found in high-income Asia Pacific (33.31; 95% UI: 28.78-38.14), Australasia (48.10; 95% UI: 40.98-56.21), and Western Europe (43.87; 95% UI: 38.06-51.01). In terms of DALYs, Oceania had the highest rate (525.51; 95% UI: 342.95-854.77), followed by South Asia (453.58; 95% UI: 380.10-580.89). Conversely, lower DALYs were reported in high-income Asia Pacific (13.04; 95% UI: 11.25-14.37), followed by high-income North America (22.72; 95% UI: 19.91-25.14), and Australasia (26.93; 95% UI: 24.50-29.26). Additionally, all regions experienced a downward trend in both DALYs and mortality rates, with the most significant decline observed in Central Europe (AAPC: −5.53; 95% CI: −5.84 to −5.23) (Supplemental Table 1, Supplemental Appendix).
Nationally, Eritrea exhibited a higher age-standardized prevalence rate (ASPR) of 1,865.41 (95% UI: 1,480.34-2,332.94) and age-standardized incidence rate (ASIR) of 130.35 (95% UI: 101.92-163.49). In contrast, Finland had a lower ASPR of 17.44 (95% UI: 14.17-20.98) and an ASIR of 1.43 (95% UI: 1.20-1.66) (Figure 1A, Supplemental Figure 1, Supplemental Tables 2 and 3, Supplemental Appendix). A higher age-standardized disability-adjusted life-years rate (ASDR) of 584.99 (95% UI: 443.30-816.89) was reported by Pakistan with an age-standardized mortality rate (ASMR) of 17.85 (95% UI: 13.13-25.81). In contrast, Colombia exhibited the lowest ASDR of 8.31 (95% UI: 6.65-10.03) and ASMR of 0.20 (95% UI: 0.15-0.25) (Supplemental Figures 2 and 3, Supplemental Tables 4 and 5, Supplemental Appendix). In terms of annual average change trends, prevalence and incidence rates followed similar patterns, and DALYs and deaths also showed similarities. Prevalence increased in Belgium (AAPC: 0.79; 95% CI: 0.17-1.43) and Fiji (AAPC: 0.76; 95% CI: 0.69-0.84), while decreased in Poland (AAPC: −3.31; 95% CI: −3.53 to −3.09) and Singapore (AAPC: −2.62; 95% CI: −2.68 to −2.56) (Figure 1B, Supplemental Appendix). Regarding trends in DALYs and mortality rates, Zimbabwe demonstrated an upward trend (AAPC: 0.78; 95% CI: 0.20-1.36 and AAPC: 0.45; 95% CI: −0.04 to 0.94), while Colombia displayed a downward trend (AAPC: −6.66; 95% CI: −7.76 to −5.54 and AAPC: −7.00; 95% CI: −8.27 to −5.70) (Supplemental Appendix).
Figure 1.
The Prevalence of Rheumatic Heart Disease in 204 Countries and Territories in 2021
(A) The age-standardized prevalence rate (ASPR) of rheumatic heart disease in 204 countries and territories in 2021. (B) The average annual change percentage (AAPC) of ASPR of rheumatic heart disease in 204 countries and territories from 1990 to 2021.
The impact of age and sex pattern on the global burden of RHD
The prevalence, incidence rate, DALYs, and mortality rate of RHD varied across all age groups worldwide. The ASPR and ASIR trends were approximately similar, with a gradual rise at a younger age, a peak followed by a decline, and a final rebound in old age (Figures 2A and 2B). This study demonstrated a consistent trend of increase in both ASDR and ASMR with age (Figures 2C and 2D). The peaks in the number of prevalence, incidence, DALYs, and deaths occurred in the age groups of 25-29, 15-19, 55-59, and 65-79 years, respectively, indicating differences in the number of cases in different age groups. The age of onset of DALYs was in adolescents and young adults, with an approximately normal distribution: less in the early and late stages of life, but more in middle age, with most deaths occurring in old age. There were also sex variations in the RHD burden. The incidence, prevalence, and DALYs were higher in women than in men in all the age groups, except in the 5- to 9-year age group, where female patients with RHD had lower DALYs than their male counterparts. In terms of the number of deaths, the number of deaths in women was lower than that in men in the of 5- to 9-year and 15- to 29-year age groups, whereas the number of deaths was higher in women than that in men in other age groups. The Student’s t-tests were conducted on ASIR, ASPR, ASDR, and ASMR for both sexes to determine whether there was a significant statistical difference in RHD burden between sexes (Supplemental Figure 4, Supplemental Appendix), and the results showed that only ASPR exhibited statistical differences between sexes (mean difference = 113.71; 95% CI: 2.30-225.12; P = 0.045).
Figure 2.
The Global Number of Cases and Age-Standardized Rate of Rheumatic Heart Disease by Age and Sex in 2021
(A) Prevalence. (B) Incidence. (C) Disability-adjusted life-years (DALYs). (D) Deaths. The upper and lower dashed lines represent 95% of the upper and lower uncertainty interval, respectively.
The impact of SDI on the burden of RHD
The RHD burden varied across different SDI levels from 1990 to 2021 (Supplemental Figure 5, Supplemental Appendix). The ASPR, ASIR, ASDR, and ASMR in the middle SDI region were similar to their global levels. The ASPR, ASIR, ASDR, and ASMR of RHD of low SDI and low-middle SDI regions were higher than the global level with the middle SDI as the boundary, whereas these were lower than the global level in the high SDI and high-middle SDI regions. Moreover, the observed change trend indicated a decline between 1990 and 2021 in the overall burden of RHD in high, high-middle, and middle SDI regions. In contrast, the prevalence and incidence rate of RHD exhibited an upward trend in the other 2 regions (Figure 3).
Figure 3.
Temporal Trend of ASRs for The Burden of Rheumatic Heart Disease by SD
From 1990 to 2021, the time trend of ASPR, age-standardized incidence rate (ASIR), age-standardized disability-adjusted life-years rate (ASDR), and age-standardized mortality rate (ASMR) of the global burden of rheumatic heart disease was divided by sociodemographic index (SDI). It also showed the AAPC in global and SDI levels from 1990 to 2021. Abbreviations as in Figure 1.
A Pearson’s correlation coefficient of −0.73 and −0.78 for ASPR and ASIR, respectively, was noted, indicating that a negative correlation was observed between the 2 variables. This indicated a decrease in both ASPR and ASIR with the increase in SDI. Moreover, the ASDR and ASMR showed a tendency to increase and subsequently decline in conjunction with the rise in SDI. Among countries with SDI below 60, the relationship between AAPC and SDI indicated a minimal variation in the magnitude of changes in ASPR and ASIR. However, the ASPR and ASIR decreased more in countries with a higher SDI as the SDI increased. Although the ASDR and ASMR of the majority of countries showed an upward trajectory, the improvement in low-SDI countries remained comparatively modest compared with wealthy countries (Figure 4).
Figure 4.
Correlation Analysis Between ASRs and SDI in Rheumatic Heart Disease
The relationship between SDI and ASPR, ASIR, ASDR, and ASMR of rheumatic heart disease in 2021, as well as corresponding AAPC from 1990 to 2021. The dashed line represented the global age-standardized rate level. Abbreviations as in Figures 1 and 3.
Risk factors associated with RHD
It is possible that clarifying the risk factors for RHD may help to prevent it. However, in GBD 2021, we did not find any risk factors related to RHD. To identify which factors influence the occurrence of RHD, a search was conducted for risk factors in GBD 2019 and OpenGWAS.
In GBD 2019, there were 3 risk factors associated with RHD mortality and DALYs, namely high systolic blood pressure, diet high in sodium, and lead exposure. Among these, high systolic blood pressure was the predominant risk factor, which resulted in a high proportion of RHD in different regions, in addition to diet high in sodium and lead exposure, which were also associated with high risk of pathogenicity (Supplemental Figure 6, Supplemental Appendix).
Among the 226 risk factors in OpenGWAS, it was observed that high systolic blood pressure (P = 2.930 × 10−11) (Figure 5A, Supplemental Figure 7 and Supplemental Table 6, Supplemental Appendix) and high BMI (P = 5.780 × 10−5) (Figure 5B, Supplemental Figure 8 and Supplemental Table 7, Supplemental Appendix) were causally associated with RHD. In particular, for every 1-mm Hg increase in high systolic blood pressure, the risk of developing RHD increased by 58% (OR: 1.58; 95% CI: 1.45-1.72), and for every 1-SD increase in BMI, the risk of developing RHD increased by 19% (OR: 1.19; 95% CI: 1.11-1.27). The identification of these risk factors offered new insights for RHD prevention. Additionally, waist circumference, hip circumference, and glycated hemoglobin (HbA1c) may also be potential risk factors for RHD (all P < 0.05) (Supplemental Figure 9, Supplemental Appendix). The IVs used in the aforementioned 2-sample MR analyses have all passed heterogeneity tests, horizontal pleiotropy tests, and sensitivity analyses. For detailed information, please refer to the Supplemental Appendix, Supplemental Figures 2 and 3, Supplemental Tables 4 and 5).
Figure 5.
Risk Factors That Had Causal Relationships With Rheumatic Heart Disease
(A) The association of systolic blood pressure with risk of rheumatic heart disease. (B) The association of body mass index and the risk of rheumatic heart disease. SNP = single nucleotide polymorphism.
Global burden of RHD forecast by the BAPC model
The ASIR of men and women was indicated to increase continuously from 2022 to 2050 through BAPC analysis (Figure 6A, Supplemental Figure 13, Supplemental Appendix). By 2050, 65 of 100,000 men and 76 of 100,000 women were anticipated to develop the disease. It was determined that in the age range of 60-64 years and younger, the ASIR would demonstrate a relatively flat growth trend after 2021 by forecasting the incidence of different age groups of women from 2022 to 2050 (Figure 6B). In the age range of 65-84 years, the ASIR showed a trend of initial decline and subsequent increase. The ASIR exhibited a consistent decline year on year in the age range of 85-90 years to 95 years and older. In contrast to the incidence rate, the DALYs of men and women reduced year by year and exhibited a downward trend in all age groups (Supplemental Figure 14, Supplemental Appendix). The results for the predictive models for ASPR and ASMR are presented (Supplemental Figures 15 and 16, Supplemental Appendix).
Figure 6.
The Bayesian Age-Period-Cohort Model Predicted the Trend of Age-Standardized Incidence Rate of Women From 2021 to 2050
(A) The overall trend of women’s age-standardized incidence rate from 2021 to 2050. (B) The changing trend of women’s age-standardized incidence rate in different age groups.
Discussion
The ASIR and ASPR of RHD increased between 1990 and 2021 globally; however, the ASDR and ASMR decreased. The burden of RHD significantly varied among countries. For instance, Eritrea showed the highest ASIR and ASPR, while Pakistan exhibited the highest ASDR and ASMR among all countries. In contrast, Finland and Sweden demonstrated the lowest burden of RHD. Eritrea and Pakistan are located in tropical and subtropical regions, with hot and humid climates that are conducive to streptococcal infection, the primary cause of rheumatic fever. Both Eritrea and Pakistan are limited-income countries with lower overall economic levels and relatively scarce medical resources. The majority of these countries’ populations live near the poverty line, making it challenging to diagnose and control rheumatic fever. This increases the risk of developing RHD. The medical and health systems of these countries are relatively underdeveloped, making it challenging to diagnose and treat RHD in a timely manner.27,28 In contrast, as high-income countries, Finland and Sweden have a higher standard of living, robust public health systems, and favorable climate conditions. These contribute to a significantly lower incidence rate of RHD compared with developing nations.29, 30, 31
The study findings based on the GBD 2021 data revealed that RHD burden was inversely proportional to SDI. This suggested a lower prevalence of RHD in countries with higher levels of wealth.32 The socioeconomic and environmental conditions were significant and modifiable factors in RHD prevention.33 Inadequate health infrastructure, subpar medical services and management, overcrowded housing, and low educational attainment significantly hinder efforts to mitigate the burden of RHD in impoverished areas.27 Our research findings support this assertion.
Notably, the age groups with the highest prevalence and incidence rates of RHD are 25 to 29 years and 15 to 19 years. Additionally, the DALYs and deaths among patients with RHD are concentrated in the middle-aged and elderly populations, respectively. Moreover, RHD is more prevalent among women. Previous studies have also shown that there are more female patients with RHD, especially women of childbearing age.4,34,35 These phenomena highlight the requirement for a global realignment of resources, prioritizing impoverished areas and providing increased attention on vulnerable populations, including children, women of childbearing age, and the elderly.36 In addition, it is essential to strengthen education regarding the burden of RHD in society and to raise awareness among children and adolescents. This can contribute to the prevention and early intervention of RHD.37
High systolic blood pressure and high BMI are risk factors for RHD, according to the two-sample MR study. A substantial body of epidemiological evidence has demonstrated that elevated systolic blood pressure represented the most significant risk factor for RHD.38, 39, 40 Systolic pressure and diastolic pressure help to infuse lipid into the intima that forms atherosclerosis, thus increasing the risk of atherosclerosis. On the other hand, the increased level of systolic pressure reflects the rigid and inelastic state of large caliber vessels, ie, these vessels are more vulnerable to mechanical damage caused by lipid deposition and pulse pressure.41 The endothelial damage of this natural valve may increase the susceptibility of the heart to infection.42 A British biological sample bank study also showed that lower blood pressure would lead to lower incidence rate of RHD. In addition, many studies have identified general obesity and abdominal obesity as risk factors for coronary artery disease, including RHD. In a study targeting young women (average age 30 years), obesity (average duration 12 years) was associated with higher left ventricular mass and diastolic and systolic abnormalities, which may be precursors to future cardiovascular disease. Besides high BMI, a potential causal association was also found between waist circumference, hip circumference, and RHD, highlighting the significance of obesity as a risk factor for cardiovascular disease. Additionally, HbA1c has also been identified as a potential risk factor for RHD, providing novel insights into the relationship between diabetes and cardiovascular disease. Previous studies have indicated that reducing HbA1c levels can lead to a decrease in coronary events. The results of this study enhance our understanding of the risk factors associated with RHD in GBD 2021 and provide reliable evidence that contributes to existing research on RHD risk factors. Therefore, it is crucial to acknowledge the importance of this work.
Furthermore, this study used the BAPC method to predict the trend of RHD burden from 2022 to 2050. The prediction results indicate that the incidence rate of RHD is anticipated to increase steadily over the next 30 years. The relatively significant decline in DALYs for RHD suggested that although the global burden of RHD showed a downward trend, it remained an important global public health burden that could not be ignored. Although the overall burden of RHD demonstrated improvement, specific countries such as Eritrea and Pakistan still require special attention.
Conclusions
RHD is a significant global public health burden worldwide, particularly in countries with low SDI, and demonstrates worrying epidemiological trends. ASPR and ASIR are observed to increase with age in children and adolescents. Moreover, the number of female patients with RHD is significantly higher than the number of men. Therefore, these demographic patterns underscore the requirement to pay special attention to high-risk populations affected by this disease. High systolic blood pressure has been the critical risk factor for the burden of RHD in all countries over the past 30 years, with a significant causal relationship between the two. The ASIR of RHD will continue to increase at a stable rate worldwide from 2022 to 2050, making it essential to strengthen early diagnosis and treatment of RHD.
Data availability
GBD study 2021 data sources were available online from Global Health Data exchange tool (https://collab2021.healthdata.org/gbd-results/). The genome-wide association study summary data of risk factors and RHD in this study came from the publicly available IEU OpenGWAS project, at https://gwas.mrcieu.ac.uk/. The code related to the data processing and analysis is freely available from GitHub (https://github.com/onethird-lab/RHD_code).
Perspectives.
COMPETENCY IN MEDICAL KNOWLEDGE: This study demonstrates that RHD remains a significant public health concern, with women and regions exhibiting low-to-middle SDI levels experiencing the greatest burden. This underscores the pivotal role of both sex and socioeconomic factors in health care access, disease progression, and outcomes. MR provides higher-level evidence that modifiable risk factors, such as high systolic blood pressure and high BMI, are causally related to RHD.
TRANSLATIONAL OUTLOOK: The results of this study emphasize the importance of targeted prevention and screening for RHD. The identified differences require strengthening targeted primary and secondary prevention plans, with special attention to women and populations in low to middle SDI areas. The evidence linking high systolic blood pressure and high BMI to RHD requires incorporating RHD into a broader cardiovascular disease management framework. In the long-term care of RHD patients and high-risk RHD populations, clear emphasis should be placed on blood pressure control and weight management.
Funding Support and Author Disclosures
This work was supported by the National Natural Science Foundation of China (Grant Nos. 31970651, 92046018), Program for Young Talents of Basic Research in Universities of Heilongjiang Province (Grant No.YQJH2023036), Marshal Initiative Funding (Grant No. HMUMIF-22010), and Mathematical Tianyuan Fund of the National Natural Science Foundation of China (Grant No.12026414). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Footnotes
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.
Appendix
For expanded Methods and Results sections as well as supplemental tables and figures, please see the online version of this paper.
Contributor Information
Yongshuai Jiang, Email: jiangyongshuai@hrbmu.edu.cn.
Mingming Zhang, Email: zhangmingming@hrbmu.edu.cn.
Appendix
References
- 1.Marijon E., Mirabel M., Celermajer D.S., Jouven X. Rheumatic heart disease. Lancet (London, England) 2012;379:953–964. doi: 10.1016/S0140-6736(11)61171-9. [DOI] [PubMed] [Google Scholar]
- 2.Essop M.R., Peters F. Contemporary issues in rheumatic fever and chronic rheumatic heart disease. Circulation. 2014;130:2181–2188. doi: 10.1161/CIRCULATIONAHA.114.009857. [DOI] [PubMed] [Google Scholar]
- 3.Carapetis J.R. The stark reality of rheumatic heart disease. Eur Heart J. 2015;36:1070–1073. doi: 10.1093/eurheartj/ehu507. [DOI] [PubMed] [Google Scholar]
- 4.Sharma N., Toor D. Impact of socio-economic factors on increased risk and progression of rheumatic heart disease in developing nations. Curr Infect Dis Rep. 2019;21:21. doi: 10.1007/s11908-019-0677-6. [DOI] [PubMed] [Google Scholar]
- 5.Roberts K., Colquhoun S., Steer A., Reményi B., Carapetis J. Screening for rheumatic heart disease: current approaches and controversies. Nat Rev Cardiol. 2013;10:49–58. doi: 10.1038/nrcardio.2012.157. [DOI] [PubMed] [Google Scholar]
- 6.Arvind B., Ramakrishnan S. Rheumatic fever and rheumatic heart disease in children. Indian J Pediatr. 2020;87:305–311. doi: 10.1007/s12098-019-03128-7. [DOI] [PubMed] [Google Scholar]
- 7.Watkins D.A., Johnson C.O., Colquhoun S.M., et al. Global, regional, and national burden of rheumatic heart disease, 1990-2015. N Engl J Med. 2017;377:713–722. doi: 10.1056/NEJMoa1603693. [DOI] [PubMed] [Google Scholar]
- 8.Ordunez P., Martinez R., Soliz P., Giraldo G., Mujica O.J., Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990-2017: a population-based study. Lancet Glob Health. 2019;7:e1388–e1397. doi: 10.1016/S2214-109X(19)30360-2. [DOI] [PubMed] [Google Scholar]
- 9.Shi L., Bao C., Wen Y., Liu X., You G. Analysis and comparison of the trends in burden of rheumatic heart disease in China and worldwide from 1990 to 2019. BMC Cardiovasc Disord. 2023;23:517. doi: 10.1186/s12872-023-03552-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sun J., Qiao Y., Zhao M., Magnussen C.G., Xi B. Global, regional, and national burden of cardiovascular diseases in youths and young adults aged 15-39 years in 204 countries/territories, 1990-2019: a systematic analysis of global burden of disease study 2019. BMC Med. 2023;21:222. doi: 10.1186/s12916-023-02925-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ruan R., Liu X., Zhang Y., et al. Global, regional, and national advances toward the management of rheumatic heart disease based on the global burden of disease study 2019. J Am Heart Assoc. 2023;12 doi: 10.1161/JAHA.122.028921. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Lv M., Jiang S., Liao D., Lin Z., Chen H., Zhang J. Global burden of rheumatic heart disease and its association with socioeconomic development status, 1990-2019. Eur J Prev Cardiol. 2022;29:1425–1434. doi: 10.1093/eurjpc/zwac044. [DOI] [PubMed] [Google Scholar]
- 13.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 systematic analysis for the global burden of disease study 2021. Lancet. 2024;403:2133–2161. doi: 10.1016/S0140-6736(24)00757-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim H.J., Fay M.P., Feuer E.J., Midthune D.N. Permutation tests for Joinpoint regression with applications to cancer rates. Stat Med. 2000;19:335–351. doi: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z. [DOI] [PubMed] [Google Scholar]
- 15.GBD 2016 Mortality Collaborators Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390:1084–1150. doi: 10.1016/S0140-6736(17)31833-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Burgess S., Davey Smith G., Davies N.M., et al. Guidelines for performing mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. doi: 10.12688/wellcomeopenres.15555.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Skrivankova V.W., Richmond R.C., Woolf B.A.R., et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: The STROBE-MR statement. JAMA. 2021;326:1614–1621. doi: 10.1001/jama.2021.18236. [DOI] [PubMed] [Google Scholar]
- 18.Cao Z., Wu Y., Li Q., Li Y., Wu J. A causal relationship between childhood obesity and risk of osteoarthritis: results from a two-sample Mendelian randomization analysis. Ann Med. 2022;54:1636–1645. doi: 10.1080/07853890.2022.2085883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Pierce B.L., Ahsan H., Vanderweele T.J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–752. doi: 10.1093/ije/dyq151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Bowden J., Hemani G., Davey Smith G. Invited commentary: detecting individual and global horizontal pleiotropy in Mendelian randomization-a job for the humble heterogeneity statistic? Am J Epidemiol. 2018;187:2681–2685. doi: 10.1093/aje/kwy185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bowden J., Davey Smith G., Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression. Int J Epidemiol. 2015;44:512–525. doi: 10.1093/ije/dyv080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zheng J., Baird D., Borges M.C., et al. Recent developments in Mendelian randomization studies. Curr Epidemiol Rep. 2017;4:330–345. doi: 10.1007/s40471-017-0128-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hemani G., Zheng J., Elsworth B., et al. The MR-BASE platform supports systematic causal inference across the human phenome. eLife. 2018;7 doi: 10.7554/eLife.34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.He Q., Mok T.N., Sin T.H., et al. Global, regional, and national prevalence of gout from 1990 to 2019: age-period-cohort analysis with future burden prediction. JMIR Public Health Surveill. 2023;9 doi: 10.2196/45943. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yan M., Wu B. Hepatitis b and c mortality from 1990 to 2019 in China: A Bayesian age-period-cohort analysis. Ann Transl Med. 2022;10:1384. doi: 10.21037/atm-22-5676. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Baghfalaki T., Ganjali M. Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data. Stat Methods Med Res. 2021;30:1484–1501. doi: 10.1177/09622802211002868. [DOI] [PubMed] [Google Scholar]
- 27.Laudari S., Subramanyam G. A study of spectrum of rheumatic heart disease in a tertiary care hospital in central Nepal. Int J Cardiol Heart Vasc. 2017;15:26–30. doi: 10.1016/j.ijcha.2017.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Shehu M.N., Adamu U.G., Ojji D.B., Ogah O.S., Sani M.U. The pandemic of coronary artery disease in the Sub-Saharan Africa: what clinicians need to know. Curr Atheroscler Rep. 2023;25:571–578. doi: 10.1007/s11883-023-01136-9. [DOI] [PubMed] [Google Scholar]
- 29.Mensah G.A., Roth G.A., Sampson U.K., et al. Mortality from cardiovascular diseases in Sub-Saharan Africa, 1990-2013: a systematic analysis of data from the global burden of disease study 2013. Cardiovasc J Africa. 2015;26:S6–S10. doi: 10.5830/CVJA-2015-036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Atun R., de Andrade L.O., Almeida G., et al. Health-system reform and universal health coverage in latin america. Lancet. 2015;385:1230–1247. doi: 10.1016/S0140-6736(14)61646-9. [DOI] [PubMed] [Google Scholar]
- 31.Frenk J., Gómez-Dantés O., Knaul F.M. The democratization of health in Mexico: financial innovations for universal coverage. Bull World Health Organ. 2009;87:542–548. doi: 10.2471/BLT.08.053199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kumar R.K., Antunes M.J., Beaton A., et al. Contemporary diagnosis and management of rheumatic heart disease: implications for closing the gap: a scientific statement from the American Heart Association. Circulation. 2020;142:e337–e357. doi: 10.1161/CIR.0000000000000921. [DOI] [PubMed] [Google Scholar]
- 33.Fossépré M., Trévisan M.E., Cyriaque V., et al. Detection of the enzymatic cleavage of DNA through supramolecular chiral induction to a cationic polythiophene. ACS Appl Bio Mater. 2019;2:2125–2136. doi: 10.1021/acsabm.9b00123. [DOI] [PubMed] [Google Scholar]
- 34.Sriha Belguith A., Koubaa Abdelkafi A., El Mhamdi S., et al. Rheumatic heart disease in a developing country: incidence and trend (Monastir; Tunisia: 2000-2013) Int J Cardiol. 2017;228:628–632. doi: 10.1016/j.ijcard.2016.11.249. [DOI] [PubMed] [Google Scholar]
- 35.Negi P.C., Kandoria A., Asotra S., et al. Gender differences in the epidemiology of rheumatic fever/rheumatic heart disease (RF/RHD) patient population of hill state of northern India; 9 years prospective hospital based, HP-RHD registry. Indian Heart J. 2020;72:552–556. doi: 10.1016/j.ihj.2020.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Passos L.S.A., Nizet V., Levine R.A., Aikawa E. Can we diagnose acute rheumatic fever early to maximize the success of secondary prophylaxis in rheumatic heart valve disease? Cardiovasc Res. 2022;118:e62–e65. doi: 10.1093/cvr/cvac071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Marijon E., Mocumbi A., Narayanan K., Jouven X., Celermajer D.S. Persisting burden and challenges of rheumatic heart disease. Eur Heart J. 2021;42:3338–3348. doi: 10.1093/eurheartj/ehab407. [DOI] [PubMed] [Google Scholar]
- 38.Gölbaşý Z., Uçar O., Yüksel A.G., Gülel O., Aydoğdu S., Ulusoy V. Plasma brain natriuretic peptide levels in patients with rheumatic heart disease. Eur J Heart Fail. 2004;6:757–760. doi: 10.1016/j.ejheart.2004.04.018. [DOI] [PubMed] [Google Scholar]
- 39.Xu Z., Pan J., Chen T., et al. A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery. Interact Cardiovasc Thorac Surg. 2018;26:623–630. doi: 10.1093/icvts/ivx408. [DOI] [PubMed] [Google Scholar]
- 40.Shi A., Tao Z., Wei P., Zhao J. Epidemiological aspects of heart diseases. Exp Ther Med. 2016;12:1645–1650. doi: 10.3892/etm.2016.3541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kannel W.B., Gordon T., Schwartz M.J. Systolic versus diastolic blood pressure and risk of coronary heart disease. The Framingham Study. Am J Cardiol. 1971;27:335–346. doi: 10.1016/0002-9149(71)90428-0. [DOI] [PubMed] [Google Scholar]
- 42.Lee G.B., Shin K.E., Han K., et al. Association between hypertension and incident infective endocarditis. Hypertension. 2022;79:1466–1474. doi: 10.1161/HYPERTENSIONAHA.122.19185. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
GBD study 2021 data sources were available online from Global Health Data exchange tool (https://collab2021.healthdata.org/gbd-results/). The genome-wide association study summary data of risk factors and RHD in this study came from the publicly available IEU OpenGWAS project, at https://gwas.mrcieu.ac.uk/. The code related to the data processing and analysis is freely available from GitHub (https://github.com/onethird-lab/RHD_code).
Perspectives.
COMPETENCY IN MEDICAL KNOWLEDGE: This study demonstrates that RHD remains a significant public health concern, with women and regions exhibiting low-to-middle SDI levels experiencing the greatest burden. This underscores the pivotal role of both sex and socioeconomic factors in health care access, disease progression, and outcomes. MR provides higher-level evidence that modifiable risk factors, such as high systolic blood pressure and high BMI, are causally related to RHD.
TRANSLATIONAL OUTLOOK: The results of this study emphasize the importance of targeted prevention and screening for RHD. The identified differences require strengthening targeted primary and secondary prevention plans, with special attention to women and populations in low to middle SDI areas. The evidence linking high systolic blood pressure and high BMI to RHD requires incorporating RHD into a broader cardiovascular disease management framework. In the long-term care of RHD patients and high-risk RHD populations, clear emphasis should be placed on blood pressure control and weight management.