Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Health related quality of life among Rheumatic Fever and Rheumatic Heart Disease patients in India

  • Jyoti Dixit,

    Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

  • Gaurav Jyani,

    Roles Validation, Visualization, Writing – review & editing

    Affiliation Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

  • Shankar Prinja ,

    Roles Conceptualization, Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

    Affiliation Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

  • Yashpaul Sharma

    Roles Validation, Visualization, Writing – review & editing

    Affiliation Department of Cardiology, Advanced Cardiac Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Health related quality of life among Rheumatic Fever and Rheumatic Heart Disease patients in India

  • Jyoti Dixit, 
  • Gaurav Jyani, 
  • Shankar Prinja, 
  • Yashpaul Sharma



Measurement of health-related quality of life (HRQOL) of people with chronic illnesses has become extremely important as the mortality rates associated with such illnesses have decreased and survival rates have increased. Thereby, such measurements not only provide insights into physical, mental and social dimensions of patient’s health, but also allow monitoring of the results of interventions, complementing the traditional methods based on morbidity and mortality.


The present study was conducted to describe the HRQOL of patients suffering from Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD), and to identify socio-demographic and clinical factors as predictors of HRQOL.


A cross-sectional study was conducted to assess the HRQOL among 702 RF and RHD patients using EuroQol 5-dimensions 5-levels instrument (EQ-5D-5L), EuroQol Visual Analogue Scale and Time Trade off method. Mean EQ-5D-5L quality of life scores were calculated using EQ5D index value calculator across different stages of RF and RHD. Proportions of patients reporting problems in different attributes of EQ-5D-5L were calculated. The impact of socio-economic determinants on HRQOL was assessed.


The mean EQ-5D-5L utility scores among RF, RHD and RHD with Congestive heart failure patients (CHF) were estimated as 0.952 [95% Confidence Interval (CI): 0.929–0.975], 0.820 [95% CI: 0.799–0.842] and 0.800 [95% CI: 0.772–0.829] respectively. The most frequently reported problem among RF/RHD patients was pain/discomfort (33.8%) followed by difficulty in performing usual activities (23.9%) patients, mobility (22.7%) and anxiety/depression (22%). Patients with an annual income of less than 50,000 Indian National Rupees (INR) reported the highest EQ-5D-5L score of 0.872, followed by those in the income group of more than INR 200,000 (0.835), INR 50,000–100,000 (0.832) and INR 100,000–200,000 (0.828). Better HRQOL was reported by RHD patients (including RHD with CHF) who underwent balloon valvotomy (0.806) as compared to valve replacement surgery (0.645).


RF and RHD significantly impact the HRQOL of patients. Interventions aiming to improve HRQOL of RF/RHD patients should focus upon ameliorating pain and implementation of secondary prevention strategies for reducing the progression from ARF to RHD and prevention of RHD-related complications.


Health related quality of life (HRQOL) encompasses the physical, psychological, and social domains of health, seen as distinct areas that are influenced by a person’s experiences, beliefs, expectations, and perceptions [1]. Over the past few decades, there has been an increasing predominance of chronic disorders resulting from improvement in living conditions and medical technology, proper hygiene, better prevention and management of infectious diseases and overall aging of the population. Currently, there are high numbers of people living with chronic diseases which can adversely affect their quality of life [24]. The majority of chronic diseases hold the potential to worsen the overall health of patients by limiting their capacity to live well, limit productivity as well as HRQOL and thus are major contributors to health care costs [5].

Rheumatic Heart Disease (RHD), a sequela to Rheumatic Fever (RF), is one of the common chronic disorders affecting nearly 20 million people in developing countries [6]. The disease is strongly associated with poverty, poor living conditions and limited access to health care. Global disease estimates in 2005 reported 471,000 RF cases annually across the world, which largely occurred in children aged 5–15 years [6]. India is home to 40% of all people living with RHD. Of the estimated 33 million people with RHD, 13.2 million live in India. Likewise, in 2015, of the 347 000 deaths due to RHD worldwide, over a third are estimated to have occurred in India [7]. The prognosis of patients with RHD is very poor in rural India [8, 9]. The beginning of this chronic state usually results in devastating symptoms and physical presentations, all contributing to poor quality of life in these patients. The literature also suggests that RHD exerts a negative impact on HRQOL [1013].

Despite the increased survival associated with advances in medical technology, problems such as organ dysfunction [14, 15], psychosocial disorders, and effects on neurological development might still occur, limiting patients’ cognitive development and their productivity [10, 16, 17]. The presence of cardiomyopathies and long-term prophylactic treatment with injections of benzathine penicillin often results in psychological disorders and suboptimal treatment adherence, thereby negatively influencing the HRQOL. [18]. Therefore, HRQOL is an important measure to evaluate the impact of a disease and the effects of medical interventions and drug therapies on people with chronic illnesses. In this way, measurement of HRQOL provides an opportunity for health services to be modified to become more patient-centric [19].

Usually, two types of instruments, namely generic and specific, can be used to measure HRQOL [20]. The generic instruments are used to collect information on healthy and ill individuals at the population level and allow for the comparison of HRQOL across different conditions and between healthy and ill individuals [21, 22]. In contrast, disease-specific instruments aim to collect information on health problems that are more specific to a particular disease [21, 22]. The generic instruments used to measure HRQOL among RF and RHD patients include Pediatric Quality of Life Inventory™ (PedsQL™ 3.0) Cardiac Module, Child Health Questionnaire Parent Form—CHQ-PF50 and CHQ-PF28 [23, 24]. The most commonly used generic instruments for measuring the HRQOL are the EuroQol-5 Dimensional 5 Levels instrument (EQ-5D-5L), Short Form-6 Dimension (SF-6D), and Health Utilities Index Mark 2 and Mark 3 (HUI2/3) [2527]. The generic preference-based measures of HRQOL are commonly used in the Health Technology Assessment (HTA) studies, as they provide a multidimensional description of health that is combined with survival to generate quality-adjusted life-years (QALYs). The QALY is an outcome used in the cost utility analysis method of economic evaluation [28, 29].

Against this background, the present study aimed to assess HRQOL of RF and RHD patients who were subjected to a range of treatment approaches, including secondary prophylaxis, conservative management and surgical intervention. We aimed to compare the HRQOL using a range of methods, including the EQ-5D-5L, the EuroQoL Visual Analogue Scale (EQ-VAS) and time trade off (TTO) [30, 31].


A cross-sectional study was conducted to assess the HRQOL of 702 patients suffering from RF and RHD, enrolled in a population-based RF/RHD registry covering two districts of Punjab (Ropar and Mohali) and one union territory, Chandigarh. The patients were interviewed using the EQ-5D-5L, EQ-VAS and TTO tools and followed up for a period of one year. Detailed sample selection is shown in Fig 1.

Description of items in EQ-5D-5L tool, EQ-VAS and TTO for HRQOL assessment

EQ-5D-5L is a generic questionnaire consisting of five attributes: mobility, self-care, usual activity, pain/discomfort and anxiety/depression [32]. Each of these attributes has five levels: no problems, slight problems, moderate problems, severe problems and extreme problem. The EQ-5D health state is converted into a utility score using value set which is a country-specific scoring algorithm. EQ-5D was used to compute a single utility score ranging between <0 and 1 on the basis of individuals’ responses to questions regarding the impact of RF and RHD on their lives. The possible health states that could be defined was 3125 (55), along with ‘unconscious’ and ‘dead’ state making a total of 3127 states [33]. A utility score of ‘1’ implies perfect health and ‘0’ implies death with a range of 1 to -0.549 [34]. The negative value of utility score represents health state worse than death. We used the reference population value set from Thailand to compute the HRQOL index value of individual health states. Given the absence of Indian tariff values, this is in accordance with recommendations made in the draft Indian reference case, developed by ‘Health Technology Assessment in India’ (HTAIn) for conducting HTA in India [3538].

In addition, all the patients were asked to rate their present health state between 0–100 using EQ-VAS [25]. The word “visual” in the term visual analogue scale (VAS) emphasizes the concrete nature of this type of scale (straight line), in contrast to abstract, non-representable evaluation scales (for example-I am unable to walk). A VAS is usually a 100-mm long horizontal line ranging from 0 to 100 at each end to express the extremes of the feeling wherein 0 represents the worst imaginable health state and 100 the best. The patients mark the point on the line that best corresponds to their present health state. They are instructed to put a cross on the straight line at the point that most accurately expresses their degree of agreement.The scores represent the ordinal rankings of the health outcomes, where ‘0’ denotes the worst health state and ‘100’ denotes the best health state from the patients’ perspective [25].

The third method used for HRQOL assessment was TTO wherein the preferences of each respondent for a specific health state are elicited by asking the respondent to choose between two different health states, each assigned a specific number of years followed by death. In the present study, participants were asked to choose between either living a longer life (a remaining life expectancy of 10 years) with same diseased state or living a shorter life (< 10 years) without having the current health condition. There are various methodological differences between these measures as utility scores derived from direct (TTO and VAS) and indirect measures (EQ-5D-5L) represent different respondent perspectives. The direct methods like TTO are used to capture values that patients assign to their own health state while VAS is simply based on rating exercise. However, indirect measures like EQ-5D-5L use published tariffs to assign values of the general population to patients’ description of their health. EQ-VAS and TTO provide information that is complementary to the EQ-5D profile. For instance-patients who report no problems in EQ-5D tool tend to rate their health less than 100 on VAS. Similarly, TTO helps in capturing the patient’s perspective about their own health state. Thus, the present study has used all three measures for HRQOL assessment of RF/RHD patients.

Data collection.

The EQ-5D-5L tool was administered to all 702 RF and RHD patients by trained field investigators. The present study has elicited the HRQOL directly from the patients and no proxies were used. House-to-house visits were made in order to conduct face-to-face interviews, which lasted for average 20–25 minutes. The information related to socio-demographic characteristics, disease condition whether RF/RHD/RHD with CHF, type of intervention such as secondary prophylaxis/conservative management/both conservative management and secondary prophylaxis/surgical intervention (balloon valvotomy or valve replacement surgery) were collected, allowing categorisation into 9 disease and intervention groups.

Disease and intervention categories

  1. Rheumatic Fever Remission on ‘No intervention’(N = 80): This refers to patients diagnosed with rheumatic fever as per modified Jones criteria [39], among whom treatment (secondary prophylaxis) was stopped by the doctor due to cessation of RF symptoms and completion of secondary prophylaxis regime according to guidelines. These patients were referred as rheumatic fever remission cases as RF symptoms were subsided among them.
  2. Rheumatic Fever on ‘Secondary Prophylaxis (N = 19): This refers to patients suffering from rheumatic fever diagnosed on the basis of modified Jones criteria [39] and who were active treatment i.e. on secondary prevention i.e. 4-weekly Inj. Benzathine penicillin.
  3. Rheumatic Heart Disease on ‘Secondary Prophylaxis’ (N = 45): This refers to the patients who had rheumatic carditis and were on secondary prophylaxis i.e. 4-weekly Inj. Benzathine penicillin.
  4. Rheumatic Heart Disease on ‘Conservative treatment’ (N = 153): This refers to the patients suffering from RHD diagnosed using echocardiography and on medical management.
  5. Rheumatic Heart Disease on ‘Secondary prophylaxis and Conservative management (N = 22): This refers to the patients suffering from RHD diagnosed using echocardiography and who were on two interventions namely secondary prophylaxis and medical management.
  6. Severe Rheumatic Heart Disease (N = 22): This refers to the patients suffering from RHD having valvular lesions who were being advised surgery by the doctor but they did not avail it and were on medical management.
  7. Rheumatic heart disease with Congestive heart failure on ‘Conservative treatment’ (N = 148): This refers to the patients suffering from RHD along with congestive heart failure as a complication and who were on medical management.
  8. Rheumatic heart disease on ‘valve replacement surgery’ (N = 32): This refers to the RHD patients who had valvular lesions, were advised surgery and underwent valve replacement surgery.
  9. Rheumatic heart disease on ‘balloon valvotomy’ (N = 68): This refers to the RHD patients who had valvular lesions, were advised and underwent balloon valvotomy.

Data analysis.

The EQ-5D-5L, EQ-VAS and TTO data were analysed using Microsoft Excel and Statistical Package for Social Sciences (SPSS) for windows version 21.0 Chicago, SPSS Inc. Percentages of patients reporting problems in each attribute of EQ-5D-5L were calculated. Mean stage-specific utility scores for patients falling into different categories namely RF, RHD, RHD with congestive heart failure (CHF) were calculated. In order to assess the impact of intervention, intervention-specific utility scores were also computed for each disease stage using EQ-5D-5L, EQ-VAS and Time Trade Off (TTO) methods. The description of the nine disease and intervention categories is given below:

Difference of HRQOL among patients of different socio-demographic characteristics

The difference in the mean HRQOL scores among the patients of different socio- demographic characteristics was assessed using the statistical tools ANOVA, independent samples t- test and multiple linear regression. ANOVA was used to assess the difference of mean HRQOL scores among patients of different age, religion, residence, education, annual household income and clinical severity, whereas independent samples t-test was used to investigate the difference across gender. Multiple linear regression was used to assess the determinants of HRQOL. The regression equation so formed can be written as:

Where α is constant, α1–α9 denote gender, religion, annual household income, clinical severity, residence, education, occupation, marital status and age (independent variables), β1–β6 are regression coefficients for all the independent variables, and y is mean EQ-5D-5L score (independent variable).

Ethical considerations.

The study complies with the Declaration of Helsinki and an ethical approval to undertake the study was obtained from Institute Ethics Committee of the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh. Informed written consent was obtained from all the study participants. All participants above the age of 18 years gave the consent for themselves, while parental or guardian consent was sought for the participants below the 18 years of age.


The HRQOL of 702 patients was assessed using EQ-5D-5L, EQ-VAS and TTO methods. The majority of patients were in the 15–30 years age group (41%) followed by 30–45 years (31.6%) and 45–60 years (20.8%). The disease was found to be more prevalent among females (58.1%). More than half of the participants were residing in rural areas (51%) followed by urban areas (47.7%) with only 1.3% in slums. Just over two-thirds were married and just under one-third were unmarried, with 2% being widowed/separated from their spouses. Almost t\wo thirds (64%). The 36% unemployed people included regular salaried/wage employees (18%), wage labourers (7%), own account workers (6%) and cultivators (3%). The majority of the patients were found to be suffering from RHD (54.3%), followed by RHD with CHF (31.6%) and RF (14%). Detailed sample characteristics are summarised in Table 1.

Table 1. EQ-5D-5L scores among different socio-demographic groups of Rheumatic fever and Rheumatic heart disease patients.

Socioeconomic variations of the EQ-5D-5L index among RF/RHD patients

The mean EQ-5D-5L indices by socioeconomic groups of RF and RHD patients are presented in Table 1. Males were found to have a higher HRQOL (EQ-5D-5L) index (0.854 [95% CI: 0.831–0.878]) compared to females (0.817 [95% CI: 0.796–0.837]). The highest mean EQ-5D-5L score among patients aged 15 years old or less was 0.920 [95%CI: 0.840–0.999]. All scores were lower in the older age groups, including 0.856 [95%CI:0.833–0.879], 0.814 [95%CI: 0.787–0.840], 0.806 [95%CI: 0.764–0.847] and 0.834 [95% CI: 0.768–0.900] among patients aged 15–30 years, 30–45 years, 45–60 years and 60 years and above respectively.

Higher HRQOL was observed among RF/RHD patients of urban and rural areas (0.833 and 0.833 respectively) as compared to those of slum area (0.782). Furthermore, HRQOL of RF and RHD patients were found to be highest (0.872) among lowest income group having an annual household income of less than INR 50,000 Indian National Rupees (USD 67 or less). This is followed by those in the income group of more than INR 200,000 (USD 2683 and above), INR 50,000–100,000 (USD 67 to 1341) and INR 100,000–200,000 (USD 1341 to 2683) reporting EQ-5D-5L scores of 0.835, 0.832 and 0.828 respectively. (Table 1)

The mean EQ-5D-5L utility scores among RF, RHD and RHD with CHF patients were estimated as 0.952 [95% CI: 0.929–0.975], 0.820 [95% CI: 0.799–0.842] and 0.800 [95% CI: 0.772–0.829] respectively. The HRQOL decreased with the increase in clinical severity (p value < 0.001). However, there were no differences observed among the mean EQ-5D-5L scores within these groups (age, gender, marital status, religion, residence, education, occupation and annual household income). (Table 1)

Results of multiple linear regression implied that even after controlling the socio-demographic variables, HRQOL of patients decreased with the increase in the clinical severity of the disease. However, the other independent variables considered in the regression model were not found to be the significant predictors for HRQOL. (Table 2)

Table 2. Determinants of health related quality of life (EQ-5D-5L score) among RF/RHD patients.

Quality of life assessment using EQ-VAS and TTO

Mean EQ-VAS utility scores for RF, RHD and RHD with CHF were estimated as 0.940 [95% CI: 0.927–0.953], 0.840 [95% CI: 0.828–0.853] and 0.798 [95% CI: 0.785–0.811] respectively. Mean TTO utility score for RF, RHD and RHD with CHF were estimated as 0.897 [95% CI: 0.883–0.911], 0.837 [95% CI: 0.829–0.843] and 0.823 [95% CI: 0.815–0.830] respectively.

Distribution of RF and RHD patients by EQ-5D-5L

Table 3 describes the distribution of RF and RHD patients according to EQ-5D-5L dimensions and levels. Across the five dimensions, pain/discomfort is the most reported problem and was 1.5 to 3 times more frequently reported (33.8%) than the other dimensions, followed by difficulty in performing usual activities reported by 23.9% patients. While 22.7% RF/RHD patients reported to have problems in walking about, 22% patients had anxiety/depression. One in ten (10.1%) patients reported to have difficulties in activities pertaining to self-care.

Table 3. Self-reported problems by patients suffering from Rheumatic Fever and Rheumatic Heart Disease in five domains of EQ-5D-5L.

Stage specific mean EQ-5D-5L, EQ-VAS and TTO scores based on various interventions along with confidence intervals are presented in Table 4. Better HRQOL was reported by RF patients on ‘No intervention’ (referred as RF remission) using EQ-5D-5L dimensions as compared to RF patients on secondary prophylaxis (0.955 versus 0.937). The HRQOL scores of RHD patients on secondary prophylaxis, conservative treatment and both secondary prophylaxis and conservative treatment using EQ-5D-5L dimensions were estimated as 0.888, 0.795 and 0.742 respectively. The HRQOL among RHD patients was found to be decreasing with increase in intensity of the intervention as per clinical severity of the disease. Better HRQOL was reported by RHD patients (including RHD with CHF) who underwent balloon valvotomy (0.806) as compared to valve replacement surgery (0.645).

Table 4. Stage-wise quality of life scores of patients suffering from Rheumatic Fever and Rheumatic Heart Disease on the basis of different interventions.


The present study is the first in India to measure HRQOL of RF and RHD patients using EQ-5D-5L, VAS and TTO methods. The majority of participants were in the 15–30 years age group (41%) followed by 30–45 years (31.6%) and 45–60 years (20.8%). Thus, it clearly states that RF and RHD continues to affect patients throughout the life and affects most productive years of life. The mean EQ-5D utility score among RF, RHD and RHD with CHF patients was estimated as 0.952 [95% CI: 0.929–0.975], 0.820 [95% CI: 0.799–0.842] and 0.800 [95% CI: 0.772–0.829] respectively. Mean EQ-VAS utility scores for RF, RHD and RHD with CHF were estimated as 0.940 [95% CI: 0.927–0.953], 0.840 [95% CI: 0.828–0.853] and 0.798 [95% CI: 0.785–0.811] respectively. Mean TTO utility score for RF, RHD and RHD with CHF were estimated as 0.897 [95% CI: 0.883–0.911], 0.837 [95% CI: 0.829–0.843] and 0.823 [95% CI: 0.815–0.830] respectively. We also found a declining trend in EQ-5D-5L, EQ-VAS and TTO utility scores across RF, RHD and RHD with CHF disease states. These findings are in line with other studies, as well as biological understanding of the disease, that HRQOL of patients declines as the disease progresses [40, 41]. The study findings also revealed that utility scores using TTO are lower as compared to VAS in less severe stages (RF, RF remission and RHD on secondary prophylaxis). However, as the clinical severity increases, the opposite trend is found, resulting in higher TTO utility scores as compared to VAS. This implies that patients are willing to trade off fewer years in severe stages although report lower quality of life on VAS [4244]. In the context of surgical intervention, better HRQOL has been reported among patients who underwent balloon valvotomy (0.806) as compared to valve replacement surgery (0.645) as balloon valvotomy is advised in less severe stages of RHD contrary to valve replacement wherein condition is more critical.

Measurement of utility scores for RF and RHD has been attempted in various countries among children aged 5–18 years using other HRQOL assessment tools such as SF-36, CHQ, PedsQL™ 3.0 Cardiac Module [45, 46]. However, most studies used parents as proxy for valuation of quality of life among children, despite parents of children with heart diseases known to report significantly poorer HRQOL than the children themselves [47]. None of these have assessed the HRQOL of RF, RHD and RHD associated with CHF using EQ-5D-5L tool and thus cannot be compared with our findings. The most commonly reported problem by RF and RHD patients in India was found to be pain/ discomfort which is also consistent with findings of other studies [40, 41]. This implies that interventions aiming to improve HRQOL of RF and RHD patients should be more patient-focussed and should give more attention to this aspect in order to achieve better patient outcomes. Further, the present study found that the HRQOL of RF/RHD patients is primarily determined by the clinical severity as study findings showed the declining trend in HRQOL with increase in severity of the disease. Thus, the interventions focussing on reducing the progression of RF to RHD and the prevention of RHD-related complications would have a significant impact on quality of life. In order to achieve this, early diagnosis and secondary prophylaxis is the key intervention for treatment of RF patients as these reduce the progression of ARF to RHD [48].

In this regard, various initiatives had been taken by the Government of India, such as launch of Rashtriya Bal Swasthaya Karyakram (RBSK) in 2013 [49]. Screening and early intervention services under RBSK scheme aim to identify 30 health conditions, including RHD, to provide free treatment for children from birth to 18 years of age. Another such initiative is the establishment of RF/RHD registries at primary and secondary level of health-care facilities [50]. These registries aim to register RF/RHD patients to provide them with early diagnosis and treatment (4-weekly Benzathine penicillin). The multidisciplinary health-care workers, including medical officers, pharmacists, laboratory technicians, health supervisors and multipurpose health workers, are oriented to RF/RHD surveillance and case registration. Patients reporting to either a general outpatient department or referred by health workers or schoolteachers are examined by doctors who follow standard diagnostic procedures in accordance with the Revised Jones criteria 2015 for confirmation of diagnosis and registration of the case [39]. Clinical history and examination by doctors are used to diagnose cases with RHD. Suspected cases are confirmed by the cardiologist in a tertiary care hospital using echocardiography. The confirmed cases are administered benzathine pencillin in the registry setting and are followed up every four weeks.

However, such registries are currently not functional across many regions of the country. A major barrier to RF/RHD registry-based programs in India remains the lack of a continuous supply of benzathine penicillin. Therefore, adequate supply of Benzathine penicillin is urgently needed as an area of public health system strengthening. A recent meta-analysis by Abrams et al 2020 also showed that secondary prevention led to decrease in incidence of RF recurrence (6.4 to 0.4 per 1000), hospitalization rate (41.1% to 8.3%) and prevalence of RF/RHD (8.0 to 2.0 cases per 1000) along with 86.1% decline in cost of managing the disease [51]. Hence, secondary prophylaxis should be promoted as a means of achieving better quality of life among RF/RHD patients and reducing the burden of this preventable cardiac disease.

Additionally, the Central government has introduced Ayushman Bharat Pradhan Mantri -Jan Arogya Yojana (ABPM-JAY) which is a large tax-funded national health insurance scheme for the provision of secondary and tertiary care services in public and private hospitals [52]. The scheme covers the component of tertiary prevention of RF/RHD by providing reimbursement to RHD patients undergoing surgical procedures like valve replacement surgery and balloon valvotomy.

The HRQOL of RF and RHD patients was found to be less in households with higher annual household income. This could be because people with higher incomes tend to have better access to health care, higher utilisation of health services and lower thresholds of discomfort than those with lower incomes and who live in poverty. As HRQOL is a subjective health outcome, people with different education levels can perceive its dimensions differently, particularly with regard to psychological functions. As with other studies, the present study found better HRQOL among patients who have attained some education as compared to those with lower literacy [53, 54].

A pertinent strength of the present analysis is that all participants were RF and RHD patients themselves, in contrast to some earlier studies where parents were asked to report health states of behalf of their children [42, 43]. As a result, the utility scores obtained via interviewing parents of affected children might not depict the true picture. The present study depicts comparatively more accurate representation of HRQOL of RF and RHD patients owing to the large sample size of the study covering a range of disease severity and treatment groups.

We would like to acknowledge that there are certain study limitations. Firstly, the study findings might not be generalizable to communities beyond these regions as the valuation of HRQOL is strongly influences by culture, ethnicity, age and region. However, our study sample was drawn from a northern population based RF-RHD registry and the population characteristics are similar to the overall characteristics of RF/RHD patients residing in other parts of the nation. Secondly, utility scores were calculated using value set from another country which might not represent actual perception of Indian population. However, it is worthwhile to mention that value set for Indian population has not been prepared so far, necessitating the use of value set from another country [3537]. As per standard recommendations in selecting other country value set to be used for converting local health states to utility scores, Thailand appears to be the most appropriate among the countries with a value set [55, 56]. Moreover, the draft Indian reference case for undertaking HTA in India recommends using the Thailand value-set to calculate quality of life index scores [38]. Another limitation is the fact that the analysis by severity-treatment category involved nine strata, such that the precision within small groups was not optimal and apparent differences did not always reach statistical significance.


It is worthwhile to mention that in contemporary clinical practise, which is predominantly value-based, the true measures of quality of care are the outcomes that matter to patients. When these outcomes are measured and reported, it fosters improvement and adoption of best practices, thus upstreaming the standards of clinical care. Efforts to improve HRQOL of RF and RHD patients should focus primarily upon ameliorating pain and implementation of secondary prevention strategies for reducing the progression from ARF to RHD and prevention of RHD-related complications. As the present study generated utility scores for RF and RHD patients in the local population, its results may be used for conducting cost-effectiveness analysis of the interventions for the prevention and control of RF and RHD in India. However, further studies are needed to develop a local EQ-5D tariff value-set in order to facilitate evidence-informed decision making in India.


  1. 1. Testa MA, Simonson DC. Assessment of quality-of life outcomes. N Engl J Med. 1996;334:835–40. pmid:8596551
  2. 2. Last JM, Spasoff RA, Harris SS, Thuriaux MC, Anderson JB. A dictionary of Epidemiology. 4. New York: Oxford University Press; 2001. p. 148.
  3. 3. Mathle WT, Spray TL, Wernovsky G, Gaynor JW, Clark BJ 3rd. Survival after reconstructive surgery for hypoplastic left syndrome: a 15-year experience from a single institution. Circulation. 2000; 102:136–141.
  4. 4. Allen SW, Gauvreau K, Bloom BT, Jenkins KJ. Evidence-base referral results in significantly reduced mortality after congenital heart surgery.
  5. 5. Devins GM, Blinik YM, Hutchinson TA, et al. The emotional impact of end-stage renal disease: Importance of patients’ perceptions of intrusiveness and control. Int J Psychiatry Med 1983;13:327–43. pmid:6671863
  6. 6. Carapetis J. The current evidence for the burden of group A streptococcal diseases. WHO/FCH/CAH/05.07. Geneva: World Health Organization. Available from:
  7. 7. Watkins DA, Johnson CO, Colquhoun SM, Karthikeyan G, Beaton A, Bukhman G, et al. Global, regional, and national burden of rheumatic heart disease, 1990–2015. N Engl J Med 2017;377:713–22. pmid:28834488
  8. 8. Sriharibabu M., et al. (2013). "Rheumatic heart disease in rural south India: A clinico-observational study." Journal of Cardiovascular Disease Research 4(1): 25–29 pmid:24023467
  9. 9. Riaz A, Hanif MI, Khan IH, Hanif A, Mughal S, Anwer A. Quality of life in patients with rheumatic heart disease. J Pak Med Assoc. 2018 Mar;68(3):370–375. pmid:29540870.
  10. 10. Brosig CL, Mussatto KA, Kuhn EM, Tweddell JS. Psychosocial outcomes for preschool children and families after surgery congenital heart disease. Pediatr Cardiol. 2007;28:255–262. pmid:17486393
  11. 11. Uzark K, Jones K, Slusher J, Limberts CA, Burwinkle TM. Quality of life in children with heart disease as perceived by children and parents. Pediatrics. 2008;121:e1060–e1067. pmid:18450848
  12. 12. Berkes A, Varni JW, Pataki I, Kardos Kemeny C, Mogyorosy G. Measuring health-related quality of life in Hungarian children attending a cardiology clinic with the pediatric quality of life inventory. Eur J Pediatr. 2010;169:337–347. pmid:19768468
  13. 13. Moons P, Van Deyk K, De Geest S, Gewillig M, Budts W. Is the severity of congenital heart disease associated with the quality of life and perceived health of adult patients. Heart. 2005;91:1193–1198. pmid:16103557
  14. 14. Schultz AH, Wernovsky G. Late outcomes in patients with surgically treated congenital heart disease. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu. 2005;1:145–156. pmid:15818371
  15. 15. Driscoll DJ, Durongpisitkul K. Exercise testing after the Fontan operation. Pediatr Cardiol. 1999;20:57–59. pmid:9861079
  16. 16. Mahle WT, Clancy RR, Moss E, Gerds M, Jobes DR, Wernovsky G. Neurodevelopmental outcomes and lifestyle assessment in school-age and adolescents children with hypoplastic left heart syndrome. Pediatrics. 2000;105:1082–1089. pmid:10790466
  17. 17. Kamphuis M, Ottenkam J, Vliegen HW, Vogels T, Zwinderman KH, Kamphuis RP, et al. Health related quality of life and health status in adult survivors with previously operated complex congenital heart disease. Heart. 2002;87:356–362. pmid:11907011
  18. 18. Lima AB, Barros M, Santos M. Description of social indicators in adolescents with rheumatic fever. Magazine of SOCERJ. 2005; 18: 30–5.
  19. 19. Staquet MJ, Hays RD, Fayers PM. Quality of life assessment in clinical trials. New York: Oxford University Press; 1998.
  20. 20. Teckle P, Peacock S, McTaggart-Cowan H, van der Hoek K, Chia S, Melosky B, et al. The ability of cancer-specific and generic preference-based instruments to discriminate across clinical and self-reported measures of cancer severities. Health Qual Life Outcomes. 2011 Nov 28;9:106. pmid:22123196
  21. 21. Fayers PM, Machin D. Quality of Life. Assessment, Analysis and Interpretation. West Sussex, UK: John Wiley & Sons Ltd, 2000.
  22. 22. Solans M, Pane S, Estrada MD, Serra-Sutton V, Berra S, Herdman M, et al. Health-related quality of life measurement in children and adolescents: a systematic review of generic and disease-specific instruments. Value Health. 2008 Jul-Aug;11(4):742–64.
  23. 23. Uzark K, Jones K, Burwinkle TM, Varni JW. The pediatric quality of life inventory in children with heart disease. Prog Pediatr Cardiol. 2003;18:141–148.
  24. 24. Raat H, Bonsel GJ, Essink-Bot ML, Landgraf JM, Gemke RJ. Reliability and validity of comprehensive health status measures in children: The Child Health Questionnaire in relation to the Health Utilities Index. J Clin Epidemiol. 2002;55:67–76. pmid:11781124
  25. 25. Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001 Jul;33(5):337–43. pmid:11491192
  26. 26. Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002 Mar;21(2):271–92. pmid:11939242
  27. 27. Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003 Oct 16;1:54. pmid:14613568
  28. 28. Whitehead SJ, Ali S. Health outcomes in economic evaluation: the QALY and utilities. Br Med Bull 2010;96:5–21. pmid:21037243
  29. 29. Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford, UK: Oxford University Press, 2015.
  30. 30. Longworth L, Yang Y, Young T, Mulhern B, Alava MH, Mukuria C, et al. Use of generic and condition specific measures of health related quality of life in NICE decision making: a systematic review, statistical modelling and survey. Health Technol Assess 2014;18:1–224. pmid:24524660
  31. 31. Devlin NJ, Krabbe PF. The development of new research methods for the valuation of EQ-5D-5L. Eur J Health Econ 2013;14(Suppl. 1):S1–3. pmid:23900659
  32. 32. Kind P. The EuroQol instrument: an index of health-related quality of life. In Quality of life and pharmacoeconomics in clinical trials. Edited by: Spiker B. Philadelphia: Lippincott-Raven Publishers; 1996:191–201.
  33. 33. Brooks R. EuroQol: the current state of play. Health Policy. 1996 Jul;37(1):53–72. pmid:10158943
  34. 34. Prieto L, Sacristán JA. What is the value of social values? The uselessness of assessing health-related quality of life through preference measures. BMC Med Res Methodol. 2004 Apr 29;4:10. pmid:15117417
  35. 35. The EuroQol Group. Valuation–EQ-5D [Internet]. 2018 [cited 2 April 2019]. Available from:
  36. 36. Prinja S, Downey LE, Gauba VK, Swaminathan S. Health Technology Assessment for Policy Making in India: Current Scenario and Way Forward. PharmacoEconomics—Open. 2018;2(1):1–3. pmid:29464668
  37. 37. Downey L, Rao N, Guinness L, Asaria M, Prinja S, Sinha A, et al. Identification of publicly available data sources to inform the conduct of Health Technology Assessment in India [version 1; referees: 1 approved, 1 approved with reservations]. 1000Research 2018, 7:245 pmid:29770210
  38. 38. Rajsekar K. [Personal Communication]. Indian reference case for undertaking economic evaluation for Health Technology Assessment in India. New Delhi: Department of Health Research, Ministry of Health and Family Welfare, Government of India; 2018.
  39. 39. Gewitz MH, Baltimore RS, Tani LY, et al. Revision of the Jones Criteria for the diagnosis of acute rheumatic fever in the era of Doppler echocardiography: a scientific statement from the American Heart Association. Circulation 2015; 131: 1806−18. pmid:25908771
  40. 40. Goldie S J, Kohli M, Grima D, Weinstein M C, Wright T C, Bosch F X, et al. Projected clinical benefits and cost-effectiveness of a human papillomavirus 16/18 vaccine. J Natl Cancer Inst. 2004; 96(8): 604–15. pmid:15100338
  41. 41. Zhao ZM, Pan XF, Lv SH, Xie Y, Zhang SK, Qiao YL, et al. Quality of life in women with cervical precursor lesions and cancer: a prospective, 6-month, hospital-based study in China. Chin J Cancer. 2014 Jul;33(7):339–45. pmid:24698497
  42. 42. do Nascimento Moraes A., Ramos Ascensão Terreri M.T., Esteves Hilário M.O. et al. Health related quality of life of children with rheumatic heart diseases: reliability of the Brazilian version of the pediatric quality of life inventory™ cardiac module scale. Health Qual Life Outcomes 11, 198 (2013). pmid:24284003
  43. 43. Torrance GW. Utility approach to measuring health-related quality of life. Journal of Chronic Disease 1987;40:593–603. pmid:3298297
  44. 44. Read JL, Quinn RJ, Berwick DM, Fineberg HV, Weinstein MC. Preferences for health outcomes. Comparison of assessment methods. Med Decis Making 1984;4:315–29. pmid:6335216
  45. 45. Tijhuis GJ, Jansen SJ, Stiggelbout AM, Zwinderman AH, Hazes JM, Vliet Vlieland TP. Value of the time trade off method for measuring utilities in patients with rheumatoid arthritis. Ann Rheum Dis. 2000;59 (11):892–7. pmid:11053068
  46. 46. Zhang Y, Ou F, Gao S, Gao Q, Hu L, Liu Y. Effect of low income on health-related quality of life: a cross-sectional study in northeast China. Asia Pac J Public Health. 2015 Mar;27(2):NP1013–25. pmid:24097919
  47. 47. Arafa M.A., Zaher S.R., El-Dowaty A.A. et al. Quality of life among parents of children with heart disease. Health Qual Life Outcomes 6, 91 (2008). pmid:18980676
  48. 48. WHO Expert Consultation on Rheumatic Fever and Rheumatic Heart Disease. Rheumatic fever and rheumatic heart disease: report of a WHO Expert Consultation, 29 October—1 November 2001.[Internet] Geneva; 2004. [updated31.01.2021 cited 31.01.2021] Available from:
  49. 49. Rashtriya Bal Swasthya Karyakram (RBSK)—Vikaspedia [Internet]. 2019 [cited 14 May 2019]. Available from:
  50. 50. Kumar R, Sharma M. COMMUNITY CONTROL OF RHEUMATIC FEVER/ RHEUMATIC HEART DISEASE IN INDIA [Internet]. 2010 [cited 1 January 2021]. Available from:
  51. 51. Abrams J, Watkins DA, Abdullahi LH, Zühlke LJ, Engel ME. Integrating the Prevention and Control of Rheumatic Heart Disease into Country Health Systems: A Systematic Review and Meta-Analysis. Glob Heart. 2020 Sep 14;15(1):62. pmid:33150127; PMCID: PMC7500229.
  52. 52. National Health Authority. Ayushman Bharat–Pradhan Mantri Jan Aarogya Yojana (AB—PMJAY), 2019. Available: [Accessed 24 Aug 2019].
  53. 53. Häggström MK, Jonsson B, Isacson D, Bingefors K: Using EQ-5D to derive general population-based utilities for the quality of life assessment of growth hormone deficiency in adults (QoL-AGHDA). Value in Health. 2007, 10: 73–81. pmid:17261118
  54. 54. Essawy MA, Bahgat ZS, Kassem HA. Health-related quality of life of school-age children with rheumatic fever.J Egypt Public Health Assoc. 2010; 85: 205–22. pmid:21244818
  55. 55. Norman R, Cronin P, Viney R, King M, Street D, Ratcliffe J. International comparisons in valuing EQ-5D health states: a review and analysis. Value Health. 2009 Nov-Dec;12(8):1194–200. pmid:19695009
  56. 56. Bailey H, Kind P. Preliminary findings of an investigation into the relationship between national culture and EQ-5D value sets. Qual Life Res. 2010 Oct; 19(8):1145–54. pmid:20496167