Epidemiology, Risk Factors, Burden and Cost of Acute Rheumatic Fever and Rheumatic Heart Disease


Acute rheumatic fever (ARF) is an immune-mediated, nonsuppurative consequence of group A β-hemolytic streptococcus (GAS) infection. Although episodes of ARF can result in significant disability, the major impact of ARF at the population level is that it causes long-term, irreversible damage to heart valves—termed rheumatic heart disease (RHD)—often as a result of recurrences. For this reason, we discuss the epidemiology of two conditions together emphasizing they continue to be a major public health problem in many parts of the world, resulting in substantial disability, premature mortality, and economic losses.

This chapter summarizes four basic types of information. We first review the primary literature on the epidemiology of ARF and RHD, and then we summarize what is known about major risk factors for the conditions. We go on to discuss “global” disease burden estimates, that is, efforts to synthesize and aggregate the primary descriptive epidemiology literature to estimate levels and trends in fatal and nonfatal RHD. We conclude by highlighting what is known about the economic consequences of RHD.

Epidemiology of Acute Rheumatic Fever and Rheumatic Heart Disease

The epidemiological model of ARF/RHD predominant in the literature suggests a clear stepwise relationship between GAS, ARF, and RHD. Although this relationship remains important, the situation is likely to be more complicated, especially in the settings where RHD is endemic today ( Fig. 1.1 ) For example, in most low- and middle-income countries (LMICs), RHD is reported much more frequently than ARF, with most patients presenting for the first time with complications of RHD in late adolescence or early adulthood.

Fig. 1.1

Epidemiological models describing the relationship between Acute Rheumatic Fever and Rheumatic Heart Disease.

Panel A: classical model. Panel B: contemporary model.

Adapted from Parks T et al.

A variety of approaches (study types) have been used in the literature to measure ARF and RHD incidence, prevalence, and mortality. Each has limitations and advantages, so getting a complete picture of the burden of ARF/RHD in a country will often require triangulating multiple sources of data derived from different types of studies ( Table 1.1 ).

Table 1.1

Epidemiological Methods for Measuring Acute Rheumatic Fever and Rheumatic Heart Disease.

Study Type Limitations Advantages
Clinical registries Heavily dependent on referral pathways: Healthcare infrastructure in the area and willingness of the potential sources of referral
Underreporting: Mild cases, missed diagnosis, marginalized sections of the population may be missed altogether
Overreporting: Sampling from outside the study area due to referral to specialist centers
Denominator: Dependent on census data, errors may result from under or over estimation of the migrant population
Wide coverage
Relatively easy to organize
Community-based surveys Logistics of selecting a representative population of a region can be challenging Regions with low prevalence require very large sample size Better suited for high-prevalence regions
Clearly defined denominator
School surveys Focus entirely on the 5–15-year-old group
Limited value in areas with poor school enrollment rates
Affected children may not attend schools (absenteeism)
School surveys may yield a much lower prevalence in regions where affected RHD patients are older
Clearly defined denominator
Allows systematic and well-organized survey
Better suited for RHD than ARF
Hospital statistics Outpatient clinic records and inpatient admissions: Only those who are relatively sick will be represented
Procedure records: Likely to miss valve lesions that do not require a procedure
Diagnosis is likely to be accurate although usually dependent on clinical coding
ARF/RHD mortality statistics Underreporting in areas with poor health infrastructure
Weak mortality statistics in some regions (e.g., Africa)
Misclassification of underlying cause of deaths (e.g., coded as heart failure or stroke instead of RHD)
Premature mortality is a key measure for estimating disease burden

ARF , acute rheumatic fever; RHD , rheumatic heart disease

Incidence of Acute Rheumatic Fever

Few contemporary studies have been published on the epidemiology of ARF worldwide. The most up-to-date summaries of ARF incidence data come from two systematic reviews and a recent Lancet Seminar. Seckeler and Hoke also review studies conducted before 1970. In addition, some regions of the world—such as Africa—remain underrepresented in the literature. Among the published estimates, rates are reported to vary from 5 to 50 per 100,000 person-years. Rates as high as 194 per 100,000 have been reported among Indigenous populations in the Northern Territory of Australia, but it is not clear whether these figures represent truly “higher” rates than the rest of the world, or whether surveillance is simply better, leading to improved ascertainment. Where it has been measured, the incidence of ARF appears to be declining over time ( Fig. 1.2 ), though again, the regions of the world that are currently very poor and endemic for RHD are underrepresented in this literature.

Fig. 1.2

Estimates of Acute Rheumatic Fever incidence in the published literature.

Data from Tibazarwa KB et al.

A major inconsistency in the literature is a lack of a clear relationship between high incidence of ARF and high prevalence of RHD. For instance, one recent study of ARF epidemiology set in primary care clinics in Fiji (where RHD is known to be highly endemic) found only 25 cases per 100,000 population of definite ARF at ages 4–20 years. However, relative to the number of definite ARF cases, substantially more cases of polyarthralgia and monoarthritis were observed, raising the possibility that ARF is underdiagnosed in these settings. There are several possible explanations for the scarcity of ARF in these settings including (i) a distinct clinical phenotype in which progression to RHD tends to be subclinical, (ii) the relative availability of antiinflammatories and antibiotics that may mask florid ARF, (iii) low rates of presentation to healthcare to receive a diagnosis of ARF, which can reflect both lack of awareness among the general public as well as barriers to accessing primary healthcare services, and (iv) low recognition of ARF among healthcare workers—a problem made more challenging by the 2015 Jones criteria, which require the use of echocardiography to make a diagnosis of ARF in most cases.

One other challenge in interpreting the ARF literature is that many studies fail to differentiate between primary (first) attacks of ARF and recurrences, and some studies have only measured primary ARF. Estimates of the incidence of primary ARF are useful in understanding the proportion of the population affected and in tracking the success of primary prevention programs. Estimates of the incidence of recurrent ARF are useful in tracking the success of secondary prevention programs—that is, high recurrence rates suggest low levels of adherence to antimicrobial prophylaxis. Together, estimates of the incidence of both primary and recurrent ARF paint a picture of the true population exposure to GAS (which is responsible for both types of ARF) and other risk factors.

Prevalence of Rheumatic Heart Disease

A systematic review of RHD prevalence studies conducted in 2016 for the Global Burden of Disease 2015 study found data on RHD prevalence from 59 countries. Most of these datasets were echocardiography-based prevalence studies conducted in LMIC settings; however, this review also included hospital administrative datasets (mostly from high- or upper-middle-income country settings) and auscultation-based prevalence studies. This review complements a 2014 systematic review by Rothenbühler and colleagues that identified 33 datasets from auscultation- or echocardiography-based RHD prevalence studies in LMICs.

School-based echocardiographic surveys of asymptomatic children have become the standard means of assessing the prevalence of RHD. The 2012 World Heart Federation criteria for RHD have helped enforce some standardization in reporting, but questions remain about the epidemiological significance and clinical implications of “latent” RHD (including “borderline” and “subclinical” definite RHD). Short-term follow-up studies suggest that the vast majority of subclinical RHD remains stable or regresses. Subclinical RHD is associated with worse long-term outcomes than similar individuals in the population who do not have subclinical RHD, though the risk of complications is nearly 10 times higher among individuals with clinically diagnosed RHD. Overall, longer-term follow-up studies are needed to understand the link between subclinical RHD in children and RHD-related disability and mortality in later life. Additionally, it remains a possibility that the current definitions of subclinical and borderline RHD are capturing some individuals who do not actually have RHD.

Similar to ARF, RHD is experiencing a decline in many LMICs paralleled by improvements in human development. For example, Negi and colleagues conducted a study using a stratified sample in Northern India using identical survey methods in 1992–93 and 2007–08. They found a fivefold reduction in RHD prevalence in school children between 5 and 15 years together with a sharp decline in recurrent ARF among RHD patients during this period. Sociodevelopmental indices in this region of India improved substantially between 1992 and 2008. Similarly, southern Indian states that have much better sociodevelopmental indices than their northern counterparts have also shown a markedly reduced prevalence of RHD in recent surveys. This was also demonstrated in a large study across 10 districts of India conducted by the Indian Council of Medical Research. The same pattern is also apparent in cross-country comparisons of RHD prevalence.

At the same time, “hot spots” of RHD have been documented in middle-income countries, such as Brazil, that have high rates of economic and health inequality across subnational units. These subnational hot spots could be masked by country-level estimates, which reflect the average of low- and high-risk populations. Within relatively small regions, there are consistently demonstrable gradients in RHD prevalence that point toward the influence of poverty and access to healthcare on the disease. Rural populations have consistently higher prevalence in comparison to their urban counterparts. This has been demonstrated in both clinical and echocardiographic surveys. Some of the highest RHD prevalence estimates ever reported have come from studies involving the poorest sections of the society. These studies also report aggressive disease patterns such as early onset mitral stenosis that have almost disappeared from low-prevalence regions.

At an ecological level, a strong correlation was found between increasing ARF/RHD rates and decline in access to primary care in central Asia after the breakup of the Soviet Union. Conversely, some LMICs with excellent primary care such as Cuba, Thailand, and Sri Lanka have levels of ARF and RHD that are comparable to high-income countries.

Heart Failure due to Rheumatic Heart Disease

The Global Burden of Disease study also estimated the worldwide prevalence of heart failure because of RHD over 1990–2015. For 1990, the estimated prevalence of mild, moderate, and severe heart failure was 160,000, 130,000, and 350,000 cases, respectively. The corresponding prevalence for 2015 was 300,000, 240,000, and 660,000 cases. The increase in the number of prevalent cases of RHD-associated heart failure has primarily been due to population growth and aging, a trend that will probably continue over the coming decades. Heart failure carries a significant risk for death and other adverse health outcomes (discussed in detail in Chapter 16 ).

Excess Mortality from Rheumatic Heart Disease

Relatively little is known about mortality from RHD in some world regions, particularly in African countries without vital registration systems (i.e., standardized approaches to recording and certifying deaths, including their cause). Even in countries with complete vital registration, accurate measurement of RHD-related mortality can be challenging for a number of reasons. Perhaps the most significant of these is that the WHO International Form of Medical Certificate of Cause of Death specifies that there can only be one “underlying” cause of death. Among individuals with RHD, it is likely that many deaths are being coded to sequelae such as atrial fibrillation, endocarditis, and stroke, or to nonspecific causes of death such as heart failure, rather than RHD. In the settings where RHD is endemic, clinical differentiation between the causes of heart failure or the underlying etiology of stroke is typically challenging, particularly if availability of echocardiography is limited. Consequently, if an individual with RHD dies after an ischemic stroke, there is no clear consensus on whether such a death should be assigned to RHD itself or to stroke (or to atrial fibrillation, for that matter), and it is likely that coding practices vary greatly by region. As the global population with RHD ages and becomes increasingly exposed to risk factors for atherosclerotic coronary and cerebrovascular disease, these sorts of confusions will probably become amplified and will require better coding guidelines to track RHD mortality accurately.

Despite the challenges in assigning deaths to RHD, there is a strong consensus that in endemic settings RHD is a significant contributor to risk of mortality. For example, in one study, at least 10% of patients with RHD who were admitted to the hospital (for any reason) die from their disease. However, few studies have examined rates of RHD-attributable mortality in the general population, not least because of the difficulties assigning the underlying cause of death outlined earlier. One alternative approach to this problem is to estimate how often patients with RHD die compared to individuals in the general population who have similar characteristics. (This approach is widely used in cancer epidemiology because, similar to RHD, patients with cancer often die of complications rather than cancer itself.) In Fiji, for example, excess mortality due to RHD was examined by combining multiple sources of routine data using probabilistic record linkage. The authors were able to provide the first population-based estimate of RHD-related mortality in an LMIC setting, 9.9 deaths per 100,000 population. In this study, almost half of RHD-attributable deaths occurred before age 40, and there were 1.6-fold more deaths than reported through the death certification process and twofold higher rates in some age groups than estimated in the Global Burden of Disease 2013 study. Although the generalizability of this study beyond the Pacific region is limited, it is clear that RHD can be a significant contributor to mortality in endemic countries beyond what is reflected in vital registration statistics.

One important subpopulation at excess risk of mortality from RHD are pregnant females. Hemodynamic changes during pregnancy are poorly tolerated in the presence of some lesions such as severe mitral and aortic stenosis and among individuals with pulmonary hypertension (see Chapter 9 ). In countries where there is low capacity to diagnose RHD during pregnancy and refer for specialist antenatal care and delivery, a significant proportion of pregnant women with severe RHD experience fetal loss, critical illness, or even death. The most striking example is a report from Senegal that estimated a case fatality ratio of 34% for pregnant patients with structural heart disease.

Risk Factors for Acute Rheumatic Fever and Rheumatic Heart Disease

There are considerable gaps in knowledge about the etiology, pathogenesis, and risk factors for ARF/RHD that currently limit our ability to develop and implement effective interventions for this disease. Drawing on studies identified in prior literature reviews, we discuss the factors that have been associated with an increased or decreased risk of ARF and RHD. A variety of study designs have been used to address this issue, including case-control studies, cross-sectional studies, and cohort studies. Interpretation and generalization of study findings is difficult, because the majority of such studies have been small and frequently of poor quality. Still, broad conclusions are possible, and the wide range of potential risk factors can be organized according to a conceptual model ( Fig. 1.3 ). These factors are discussed in the following sections.

Fig. 1.3

Major hypothesized risk and protective factors along the causal pathway from group A streptococcal exposure to Acute Rheumatic Fever and Rheumatic Heart Disease.

Information synthesized from the studies reviewed in this chapter. ETS , environmental tobacco smoke, GAS , group A streptococcus.

Adapted from Baker M et al.

Pathogen and Host Factors that Lead to Acute Rheumatic Fever and Rheumatic Heart Disease

Factors specific to the streptococcal organism

Exposure to GAS is necessary for the development of ARF, and evidence of this exposure is a prerequisite for ARF diagnosis. Unfortunately, only one-third to two-thirds of ARF cases report a sore throat (presumed to be caused by GAS pharyngitis) in the preceding weeks. Contemporary studies in settings with high endemic ARF suggest a diverse array of organism-specific genetic factors are likely to play a role in the epidemiology of ARF. Other infectious agents could potentially act as cofactors to influence the risk of either GAS infection or ARF.

It is possible that GAS skin infections (impetigo) can also initiate the autoimmune processes leading to ARF either directly or via pharyngitis. In Australian Aboriginal populations, streptococcal skin infections are far more commonly associated with ARF than streptococcal pharyngitis. In New Zealand, genetic typing of GAS strains obtained from ARF cases showed a strong association with strains usually identified in pyoderma cases. There is also evidence that scabies skin infections may be important as a site of GAS coinfection, especially in Aboriginal and Pacific Island populations that experience high rates of ARF. Molecular mechanisms that may allow scabies infestations to facilitate GAS infection of skin lesions have been identified.

Host demographics

Host factors include those that are largely fixed (such as demographics, ancestry, and genetics) and those that are influenced by early life exposure to other risk factors. The risk of ARF is strongly influenced by specific demographic factors, particularly age and ethnicity (discussed further later). ARF is rare in children under 4 years old; incidence rises to a peak at around 9–12 years then declines in those over 20 years old. This very specific age group vulnerability to ARF suggests a strong contribution from maturation processes in the immune system. Some studies report a higher risk for females, particularly for RHD in LMICs, which may be at least partially associated with healthcare seeking behaviors resulting from pregnancy.

Host genetic factors

Inherited genetic variants are likely to be important in ARF susceptibility but are poorly understood. Familial ARF has been described for more than a century. Further evidence for a genetic component comes from the finding that the pooled proband-wise concordance risk for ARF is 44% in monozygotic twins and 12% in dizygotic twins, with an estimated heritability of 60%. The differences in incidence in relation to ethnicity have often been taken as evidence that host genetic factors influence susceptibility. In New Zealand, for example, the elevated risk for Māori and Pacific children is marked, even after stratifying for deprivation. A Hawaiian study found that risk of ARF was significantly higher for people of Pacific ethnicity compared with other ethnicities, despite similar living conditions. Nonetheless, it is clear high rates of ARF were observed internationally in all ethnicities earlier in the 20th century and before. Thus, while host genetic factors may play a role in determining susceptibility among individuals within a community (typically of the same ethnic group) with the same nongenetic risk factors, they are likely to contribute very little to the differences in incidence in relation to ethnicity.

Several small candidate gene studies have been published, but their results are mostly inconsistent and have been difficult to interpret. A number of genetic polymorphisms have been significantly associated with ARF and RHD overlapping genes including IFN-γ, ACE, FCN, FcgammaRIIA, TLR-2, and HLA. Different HLA class II antigen associations with ARF have been observed in several populations, which are perhaps unsurprising given the HLA class II region is strongly associated with a wide spectrum of autoimmune disorders.

To overcome the problems of small datasets, international multicountry transethnic genome-wide association study meta-analyses are now underway to identify genetic determinants of RHD susceptibility. The first such study was based on 2852 individuals recruited in eight Oceanian countries. It identified a novel susceptibility signal in the immunoglobulin heavy chain locus. A more recent study in the Australian Aboriginal population identified the HLA-DQ locus as being the strongest genetic marker associated with RHD, with the data supporting a role for cross-reactivity with GAS epitopes in etiology. These findings were significant in that they have provided additional insight into how the disease develops, with potential implications for vaccine development. Nevertheless, genetic susceptibility probably remains a minor determinant of the epidemiology of RHD relative to factors that influence the acquisition of streptococcal disease in the population.

Host oral health status

Some observational studies have found an association between dental caries and ARF. Both RHD and poor oral health are more prevalent in deprived populations, potentially due to common bacterial causes. GAS can live in dental plaque, and dental microbiota have been linked to endocarditis and a multitude of systemic diseases. It has also been suggested that certain oral bacteria produce an enzyme that can weaken tissue resistance to bacterial penetration, which could influence the risk of ARF by creating conditions that enhance bacterial spread.

One compelling hypothesis is that exposure to sugar drives the association between poor oral health and ARF. A cohort study of 20,333 children in Auckland found that those with five or more primary teeth affected by caries were 57% more likely to develop ARF or RHD compared with those who were caries-free. There is biological plausibility for high sugar intake being a risk factor for ARF: GAS can ferment sucrose (table sugar) and fructose (which along with glucose forms the disaccharide sucrose). High sucrose intake may well enhance conditions that promote the growth of GAS in the oral cavity, increasing the likelihood of developing GAS pharyngitis and thus ARF. A study in Bangladesh found that not brushing teeth after a meal increased the chances of developing ARF by 2.5-fold. Of course, dental caries is a multifactorial condition that does not correlate perfectly with sugar intake; in many cases other socioeconomic factors may influence caries development and contribute to ARF risk through unrelated mechanisms.

Host nutritional status

Macronutrient intake and body-mass index have been proposed as factors that influence development of ARF. Low body-mass index and low birth weight were found to be significant risk factors for RHD in Congo, and low body-mass index was associated with RHD in India. In Bangladesh, low consumption of certain foodstuffs (eggs, milk, chicken, pulses, fruits, and bread) was associated with ARF risk, and increased consumption of soybean oil appeared to be protective. This study also documented an increased risk of ARF in children with an upper arm circumference that was <80% of normal for their age, that is, children at past or present risk of protein-energy malnutrition. The authors postulated that malnutrition could inhibit the immune response to GAS and predisposing ARF. These findings are not universal; however, a study in Fiji did not identify low weight, height, or body-mass index for age as significant risk factors for RHD.

Low intake of certain micronutrients may also play a role in development of ARF. The importance of vitamin D to human immune system function is increasingly being recognized. Although there have been no reported associations of low vitamin D intake with ARF, one study noted an association between low serum vitamin D levels and recurrent GAS tonsillopharyngitis. Similarly, iron deficiency may predispose to repeated GAS infections. A protective association between increased serum iron and ARF was documented in a case-control study in Bangladesh.

Social and Environmental Conditions

Socioeconomic status is a key determinant that influences multiple potential risk factors for ARF and RHD. ARF has been associated with low maternal education in Yugoslavia and with low income in Bangladesh and Australia. RHD has been associated with low household income in Yemen and Uganda. On an ecological level, ARF is clearly associated with socioeconomic deprivation across Africa, the Americas, Asia, Europe, and the Pacific. However, socioeconomic status in itself is a “distal” risk factor that influences a variety of “proximal” risk behaviors and patterns of environmental exposure ( Fig. 1.3 ). This section reviews the specific proximal risk factors that have been implicated in the acquisition of GAS infection and in the development of ARF and RHD.

GAS is highly infectious and spread via salivary and nasal droplet transmission ; almost half of the siblings of cases with GAS pharyngitis become infected. Hence, environmental factors that influence survival and transmission of GAS organisms are important, as are health-seeking behaviors that serve to interrupt transmission.

Household crowding, including bed sharing

Humans are the established reservoir for GAS. Being near others is a known risk factor for transmission, with outbreaks well documented in schools, daycare centers, military barracks, and crowded homes. Transmission occurs rapidly in cramped living conditions. This situation has been implicated as a key factor mediating ARF outbreaks in US military camps.

Household overcrowding is a highly plausible risk factor for ARF, as it increases the effective reproduction number for GAS infections in the home. Household crowding can manifest in a range of ways, including high household occupancy, bedroom deficits, and bed sharing. One study found a significant positive association between crowding (children per bedroom) and GAS carriage. Among remote Australian communities at risk of ARF, one cohort study found a correlation between the number of cases of pyoderma per household and number of people per bedroom. Another found a correlation between acquisition of pathogenic GAS strains and household size.

Historically, there is evidence from the United States and New Zealand that ARF is associated with poor housing and crowding at a neighborhood level. Among a substantial literature of small studies, four higher-quality studies in LMICs have reported associations between RHD and measures of household crowding. A cross-sectional study in Congo and a case-control study in Uganda identified significant associations between larger household size and RHD. Similarly, a cross-sectional study in South Africa identified having more than three siblings as a risk factor for RHD. Conversely, a cohort study in New Caledonia did not identify number of siblings as a risk factor for RHD, but did identify an association with more than two people sharing a bedroom. Similarly, a Yugoslavian case-control study found an association with bed sharing (≥2 people per bed). Two cohort studies have reported on these associations in high-income countries: a UK study found no significant association between measured household crowding as a child and death from RHD in later life, while a Finnish study found that growing up in large households was associated with an increased risk of occurrence and death from RHD. It should be noted that many of these studies found significant associations only on univariate analyses, suggesting that housing conditions are linked with other, more proximal, exposures and behaviors.

Household resources, including washing and laundry

GAS has been reported to survive on inanimate objects for more than 6 months. Handwashing may be protective, as is regular bathing, such as swimming in chlorinated pools. Removing dust, handwashing, and disinfecting surfaces are used as control measures in hospitals affected by GAS outbreaks. Lack of washing facilities and resources may contribute to an increase in bacterial load on the skin of household members or on inanimate objects, resulting in increased transmission of bacteria and associated skin and pharyngeal infections. However, it has not been definitively proven that lack of washing is a significant independent risk factor for ARF.

Housing conditions, including tenure, damp, and cold

Many housing factors, including crowding, household facilities, and the indoor air environment are influenced by housing tenure. Poor housing conditions (e.g., cold, damp, mold) could potentially contribute to an indoor environment that increases the risk of GAS transmission. GAS incidence was significantly higher in social housing compared with private housing in Singapore and in households that lacked a kitchen in India. In an outbreak in a UK boarding school, the attack rate was significantly higher in poorly ventilated dormitories compared with those that were well ventilated. In Yugoslavia, home dampness and a change in place of residence in the last 5 years were significantly associated with ARF. Substandard housing was associated with ARF in Bangladesh.

Healthcare utilization and health literacy

Effective antibiotic treatment of GAS infections interrupts the development of ARF and RHD. (The epidemiological evidence for the effectiveness of primary and secondary prevention strategies is reviewed elsewhere. ) Widespread availability of comprehensive primary and secondary prevention measures in Baltimore, Cuba, and Costa Rica coincided with significant reductions in ARF incidence rates documented in ecological evaluations. Other studies were successful in reducing ARF in part because they expanded access to secondary prevention measures using an active (community-based) case-finding approach. Conversely, ARF remains relatively common in many populations where access to healthcare is a known public health problem.

A key component of the success of large-scale ARF/RHD programs has been the education of the public and healthcare providers, resulting in increased use of evidence-based primary and secondary prevention measures. Low levels of maternal education were a significant risk factor for ARF in Yugoslavia, and ARF and RHD were associated with maternal illiteracy in Bangladesh. Enhancing awareness of ARF and its prevention was a major aspect of the Cuban intervention program that occurred from 1986 to 96—during which period the ARF incidence declined 7.4-fold. Martinique and Guadeloupe also received a 10-year ARF control and prevention intervention that included educating healthcare professionals on ARF, and emphasizing to the public the importance of primary prevention among schoolchildren. Increasing awareness of the need for primary prevention in children with symptoms of pharyngitis is a major focus of the New Zealand Rheumatic Fever Prevention Programme, though the effectiveness of this program remains uncertain.

Summary of Risk and Protective Factors for Acute Rheumatic Fever and Rheumatic Heart Disease

The literature review earlier illustrates the breadth, complexity, and gaps in current knowledge of risk factors for ARF and RHD. We can broadly conclude, however, that the major risk factors influencing ARF and RHD are well established: children have a period of vulnerability to ARF starting from about age four until about age 20. If they are exposed to GAS infection, in the throat and probably the skin also, then they have a small risk of developing the autoimmune disease ARF about 3 weeks later. Individual risk is partly influenced by genetic factors, but the most consistent distal risk factor is poverty and social deprivation, which creates the conditions for exposure to more proximal risk factors including (i) poor-quality housing and household crowding; (ii) poor nutritional status, and (iii) low utilization of and access to healthcare. The published evidence to date is based on relatively poor-quality studies, however, and it has been difficult to identify the most important mediators of ARF/RHD risk that might provide effective points of intervention.

Measures of global disease burden

Two groups routinely release estimates of the burden of RHD: the Institute for Health Metrics and Evaluation (IHME), which publishes the Global Burden of Disease studies, and the Metrics, Measurement, & Evaluation department at the World Health Organization (WHO), which publishes the Global Health Estimates reports. RHD is included among the causes of death and disability that are routinely tracked by IHME and WHO in their health estimates. Although ARF is not included in these health estimates, its contributions to health loss globally are largely reflected in the estimates of RHD mortality, as nearly all individuals who die of ARF die from rheumatic carditis. The data inputs and methods used by IHME and WHO are somewhat different, but they lead to broadly similar conclusions about the relative magnitude of the burden of RHD globally.

In 2000, there were an estimated 320,000–380,000 deaths and 12–13 million disability-adjusted life-years (DALYs) from RHD ( Table 1.2 ). (DALYs are a summary measure of health that incorporates premature mortality and disability into a single number that can be compared across diseases and age groups. Over 80% of DALYs from RHD are due to premature mortality, with the remainder being due to nonfatal disease that causes disability.) By 2016, the number of deaths had declined to 290,000–310,000 and the number of DALYs to 9.8–10 million, indicating progress on reducing RHD at a global level.

Feb 2, 2021 | Posted by in RHEUMATOLOGY | Comments Off on Epidemiology, Risk Factors, Burden and Cost of Acute Rheumatic Fever and Rheumatic Heart Disease
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