Parental-perceived health-related quality of life of school students with borderline intellectual functioning: A cross-sectional questionnaire-based study in Mumbai, Maharashtra, IndiaS Karande1, NJ Gogtay2, T More1, S Pandit2, Praveenkumar1
1 Department of Pediatrics, Seth G.S. Medical College and K.E.M. Hospital, Mumbai, Maharashtra, India
2 Department of Clinical Pharmacology, Seth G.S. Medical College and K.E.M. Hospital, Mumbai, Maharashtra, India
Correspondence Address: Source of Support: None, Conflict of Interest: None DOI: 10.4103/jpgm.jpgm_310_22
Source of Support: None, Conflict of Interest: None
Keywords: Academic performance, attention-deficit hyperactivity disorder, slow learners, sociodemographic factors, surveys and questionnaires
Children with borderline intellectual functioning (“slow learners”) have an intelligence quotient (IQ) in the range of 71-84, that is, between -2 and -1 standard deviations from the population IQ mean, as per the Diagnostic and Statistical Manual of Mental Disorders-IV-revised and International Classification of Diseases, Tenth Revision, Clinical Modification., Typically, slow learners have difficulties in reading, writing, doing mathematics, and underperforming in all school subjects.,, They are at an increased risk of developing anxiety, depression, becoming school dropouts, experiencing social isolation, and developing lowered self-esteem.,,,
The health-related quality of life (HRQoL) is a multidimensional measure of the overall condition of a human life, namely: (i) psychosocial (mental and social environment), and (ii) physical. We conducted the present study with the primary objective of measuring and analyzing the parent-reported HRQoL of slow learners. The secondary objective was to analyze impact of sociodemographic variables on parent-reported HRQoL of slow learners. Our hypothesis was that parents of slow learners would report their children's HRQoL to be significantly compromised in both psychosocial and physical domains of health.
The present study was approved by the Institutional Ethics Committee [EC/75/2019] and was registered prospectively with the Clinical Trials Registry of India [CTRI/2020/03/023901]. The study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and the 2017 guidelines laid down by the Indian Council of Medical Research. Accordingly, all parents signed an informed consent form before participating in the present study. Additionally, all school students either gave oral assent (≥8 to <12 years) or written assent (≥12 to ≤16 years) prior to parental enrolment. The parents and their children were assured that the answers to the questionnaire would be kept confidential.
Design, setting and sample size calculation
The present cross-sectional single-arm questionnaire-based study was conducted at the Learning Disability (LD) clinic of a public medical college in Mumbai, a megacity in western India, over a period of 21 months, from March 2020 to November 2021. The prevalence of slow learners in India is unknown but has been reported to be up to 7% to 13% among populations in Israel, the United States, and the United Kingdom.,, In the present study, we assumed that 7% of students would be slow learners. With a 95% confidence level and 5% precision, Daniel's formula yielded a sample size of 100.
Inclusion criteria and enrolment process
The study population (recruited by nonprobability sampling) comprised parents of slow learners,, who were ≥8 to ≤16 years of age, and who were able to read and write English. A total of 100 parents were recruited. No exclusion criteria that would preclude participation were used among parents who met the inclusion criteria.
Diagnosis of slow learners
Each student had undergone standard recommended psychological evaluation before the diagnosis of slow learner was confirmed. An otolaryngologist and an ophthalmologist documented that hearing or visual impairment was <40%, if any, respectively. The counsellor ruled out that any environmental deprivation due to poor home or school environment, or any emotional problem was not primarily responsible for a student's poor school performance (PSP). The pediatrician took a detailed clinical history and did a detailed clinical examination. The clinical psychologist used the Wechsler Intelligence Scale for Children-Revised (M.C. Bhatt's Indian adaptation) or Binet-Kamat Test of Intelligence to determine that the student's IQ score was between 71 and 84.
Using information from the student's parents and teachers, a diagnosis of co-occurring attention-deficit/hyperactivity disorder (ADHD), if present, was made by ascertaining that the student's specific behaviors met the required Diagnostic and Statistical Manual of Mental Disorders-5 criteria. Up to 40% of slow learners have associated ADHD, which is characterized by persistent hyperactivity, impulsivity, and inattention, and this comorbidity further impairs their learning.
Measuring the HRQoL
HRQoL was assessed using the English DISABKIDS chronic generic module parent (proxy) long-version, namely the DCGM-37-P., The parents (either the father or mother, if both were present) were asked to complete the questionnaire during their visit to the clinic. Completion of the questionnaires lasted 15 to 25 minutes and took place in a quiet room without the presence of their child.
The DCGM-37-P is an instrument which has been developed cross-nationally across 7 European Union (EU) countries, namely Austria, France, Greece, Germany, the Netherlands, Sweden, and the United Kingdom, to measure the HRQoL of children and adolescents with any chronic medical condition or disability., One aim of the developers of the DCGM-37-P was to enable cross-cultural pediatric HRQoL research internationally, namely, in different national and cultural contexts., The English version of the DCGM-37-P can be used in any country for parents who can read and understand English., The DCGM-37-P offers the following six facets or sub-scales [Table 1]: “independence” (autonomy and living without impairments), “emotion” (emotional worries and concerns), “social inclusion” (acceptance of others, positive relationships), “social exclusion” (stigma, feeling left out), “limitation” (functional limitations, perceived health), and “treatment” (impact of receiving treatment). These six facets are associated with three HRQoL domains, namely: mental, social, and physical [Table 1]. All 37 items have a 5-point Likert-type response scale with options ranging from 1 (never), to 5 (always). The parent used the recommended 4-week recall period for scoring all 37 items. Each item question is simple and easy to understand. Following the recommendations of the developers of the DISABKIDS, we offered an assistance, if necessary, to read out the question and explain before a parent marked his/her response.,
For data analysis, item raw scores of each facet of the DCGM-37-P were to be summed and transformed into a score from 0 (worst possible HRQoL) to 100 (best possible HRQoL)., Additionally, a “total score” was to be calculated by combining the scores of the six facets representing a “total score” for HRQoL., Satisfactory internal consistency has been reported for all the six facets with Cronbach's alpha coefficient ranging from 0.77 to 0.90 while the total score has displayed a consistency coefficient of α = 0.95., Pilot testing has also revealed satisfactory results concerning the validity of the DCGM-37-P instrument.,
Data related to 19 sociodemographic variables (“potential confounders”), namely 13 student variables and 6 parent variables were documented using a supplementary questionnaire. The 13 student variables included: (i) age; (ii) gender; (iii) birth order; (iv) being an only child; (v) presence or absence of co-occurring ADHD; (vi) presence or absence of co-occurring chronic medical illness; (vii) duration of PSP; (viii) IQ; (ix) school class standard; (x) medium of instruction in school; (xi) school ownership; (xii) type of school attended, viz., “co-educational” or “single-gender education”; and, (xiii) type of school board curriculum. The six parental variables included: (i) age; (ii) gender; (iii) education status; (iv) work status; (v) socioeconomic status; and, (vi) type of family. Socioeconomic strata was determined by Kuppuswamy's socioeconomic scale.
Analysis was done using the Statistical Package for Social Sciences, version 25.0 for Windows (Chicago, Illinois, USA). Demographic data were expressed using descriptive statistics. First, the mean proxy facet scores and total HRQoL scores of the slow learners were computed using SPSS files provided by the developers of the DCGM-37-P., These scores were tested for normality using the Shapiro–Wilk test, that indicated non-normal distributions. Since population proxy norms for Indian children and adolescents with chronic medical conditions or disabilities are not available, mean scores of the study children were compared with the published EU proxy norms, as recommended., These EU norms have been computed from the results of the DISABKIDS field study, wherein the proxy HRQoL of 1,152 of children and adolescents with seven different chronic conditions or disabilities (namely, asthma, juvenile arthritis, atopic dermatitis, diabetes, cerebral palsy, cystic fibrosis, and epilepsy) were measured., To investigate the clinical importance of potential differences between the study sample of the present study and the EU norm data, we used effect sizes, which were calculated by dividing their mean difference in scores by the EU children's SD., Cohen's guidelines were used for interpretation of effect size (-0.2 to < -0.5 = small, -0.5 to < -0.8 = medium, and ≥ -0.8 = large). Second, to investigate the reliability of the DCGM-37-P in the present study, internal consistencies (Cronbach's alpha) were calculated for each facet score and for the total score. Third, in order to measure the degree of association between the facet scores and total scores of slow learners, their correlation coefficients (as measured by Spearman's rho) were computed. Fourth, univariate regression analysis was carried out to evaluate the unadjusted impact of each of the 19 sociodemographic variables (“independent variables”) on the facet and total facet scores (both “dependent variables”). Subsequently, using the binary logistic regression analysis, a multivariate analysis was performed to determine the “independent” impact that the sociodemographic (“categorical”) variables had on a poor DCGM-37-P facet score “outcome” and on a poor total score “outcome.” Accordingly, each DCGM-37-P facet score and the total score were dichotomized into a “poor” score (score of ≤ mean -1 SD) or “good” score (score of > mean -1 SD) and used as “dependent variables” in the models. Wherever appropriate, the odds ratio (OR) was calculated, and 95% confidence intervals (CI) were estimated around the OR. A two-tailed P value of < 0.05 was considered significant.
Characteristics of the students
The median age of students was 14.7 years (IQR 14.0 – 15.2). The boys: girls ratio was 1.9:1. Only two students had a co-occurring chronic medical illness (epilepsy and acute lymphocytic leukemia, respectively). The median duration of PSP in students was 4 years (IQR 2.0 – 5.5). Their median IQ score was 77 (IQR 74 – 80). Other sociodemographic characteristics of students are shown in [Table 2]a.
Their median age was 45.5 years (IQR 41.00 – 50.00). The fathers: mothers ratio was 1.17:1. Other sociodemographic characteristics of parents are shown in [Table 2]b. The time taken by the parents to fill out the DCGM-37-P questionnaire ranged from 20 to 30 min. No parent or student declined consent/assent for participation in the study.
Comparison of proxy HRQoL of slow learners with EU children proxy norms
Since all parents answered that their child was not taking any medicine for his/her condition, treatment facet scores could not be calculated in the present study. There were no missing data for all the other DCGM-37-P items. Four of the five (all except for the “limitation” facet) DCGM-37-P mean facet scores and the total score were lower in the study subjects compared to the EU children proxy norms [Table 3]. Calculated effect sizes indicated clinically relevant deficits of slow learners in four HRQoL facet scores as well as in the total score. [Table 3].
Reliability of DCGM-37-P scores
Testing for reliability (“internal consistency”) involves estimating how consistently individuals respond to the items within a scale., Internal consistencies of HRQoL facets due to the DCGM-37-P were ranging between “independence,” (alpha = 0.82); “emotion,” (alpha = 0.79); “social exclusion,” (alpha = 0.78); “social inclusion,” (alpha = 0.77); and “limitation,” (alpha = 0.68); while the “total score” displayed a consistency coefficient of alpha = 0.78. Where items within a facet measure different elements of an individual's experience (as in the multi-dimensional DCGM-37-P instrument), a moderate Cronbach alpha (i.e., approximately 0.5), rather than a high alpha (i.e., ≥0.7), can be considered satisfactory.
Correlations between DCGM-37-P facet scores and total score
[Table 4] shows the correlations between the five DCGM-37-P facets and total scores for the whole sample. These can be used as another test of the convergent and divergent validity of the constructs. There was a “moderately strong” relationship between: (i) the “emotion” facet scores and the “social exclusion” and “limitation” facets' scores; and (ii) the “social exclusion” and “limitation” facets' scores. There was a “highly strong” relationship between: (i) the “independence” and “social inclusion” facets' scores; and (ii) the “independence”, “emotion”, “social inclusion”, “social exclusion”, and “limitation” facets scores and the “total” scores, indicating a good convergent validity for these constructs.
Impact of sociodemographic variables on HRQOL
At the univariate level: (i) a longer duration of PSP and higher parental age were significantly associated with a lower “independence” facet score (F = 4.532, df = 1, 98, B = -0.173, 95% CI: -0.334 to -0.012, P = 0.036; and F = 4.057, df = 1, 98, B = -0.759, 95% CI: -1.508 to -0.011, P = 0.047, respectively; (ii) lower birth order, a longer duration of PSP and a higher class standard were associated with lower social inclusion facet score (F = 9.109, df = 1, 98, B = 7.867, 95% CI: 2.694 to 13.040, P = 0.003; F = 4.004, df = 1, 98, B = -0.172, 95% CI: -0.343 to -0.001, P = 0.048; F = 5.818, df = 1, 98, B = -3.246, 95% CI: -5.917 to -0.575, P = 0.018, respectively); (iii) higher IQ and higher socioeconomic status were associated with lower social exclusion facet score (F = 5.215, df = 1, 98, B = -1.462, 95% CI: -2.732 to -0.192, P = 0.025 and F = 4.294, df = 1, 98, B = -5.951, 95% CI: -11.651 to -0.252, P = 0.041, respectively). No sociodemographic variable was significantly associated with the emotion and limitation facet and the total scores.
Multivariate analysis [Table 5]a,[Table 5]b,[Table 5]c,[Table 5]d,[Table 5]e,[Table 5]f revealed that no variable predicted a poor “independence” facet score outcome or a poor “total” score outcome [Table 5]a and [Table 5]f. Only three variables predicted a poor facet score outcome: (i) being an only child predicted poor “emotion” and “social exclusion” facet score outcomes (P = 0.039, OR = 0.26, 95% CI: 0.07 – 0.94; and, P = 0.024, OR = 0.21, 95% CI: 0.06 – 0.82, respectively) [Table 5]b and [Table 5]d, (ii) being a female predicted a poor “social inclusion” facet score outcome (P = 0.022, OR = 4.24, 95% CI: 1.24 – 14.56) [Table 5]c. and, (iii) studying in a single-gender school predicted a poor “limitation” facet score outcome (P = 0.020, OR = 6.93, 95% CI: 1.36 – 35.17) [Table 5]e.
In partial support of our hypothesis, the present study confirms that the parental-reported HRQoL of slow learners was significantly compromised only in the psychosocial facets of health (independence > social exclusion > emotion > social inclusion). Our study documents that parents perceive that their children: (i) lack “independence”, viz., they are insecure about their future and unable to live an autonomous life; (ii) are “socially excluded,” viz., they feel different from their peers, are lonely, perceive being stigmatized by their teachers/peers, have problems concentrating at school and feel left out; (iii) have developed “emotional reactions,” viz., worries, concerns, anger, and problems; and, (iv) lack in qualities for “social inclusiveness,” viz., they feel that their peers and friends do not enjoy their company, do not understand their problems, do not care about their condition, and find it difficult to develop social relationships. However, parents perceived that their children had no limitations in performing physical activities, had a good health status, and had no difficulties with sleeping. The present study also documents that parents perceive that the 'total' HRQoL of slow learners is significantly compromised. Also, in the present study, multivariate analysis revealed that: (i) being an only child predicted a poor “emotion” and “social exclusion” facet score outcomes; (ii) being a female predicted a poor “social inclusion” facet score outcome; and, (iii) studying in a single-gender school predicted a poor “limitation” facet score outcome.
A detailed Medline search using the keywords “health-related quality of life” and “borderline intellectual functioning” or “slow learners” did not find any study which had documented and analyzed the HRQoL of slow learners. Hence, we cannot compare the present study with previous work because there is not any.
What is the utility of the present study? First, our study has revealed that parents perceive that the total HRQoL of slow learners is significantly compromised. It is well known that class standards in our city have, on an average 45 to 60 students and the classroom teacher is often unable to pay individual attention to the students. In such a crowded classroom, it is common for a slow learner to lag behind, as he/she is unable to cope with the speed of teaching, resulting not only in chronic PSP but also in a significantly lower total HRQoL. Hence, all primary school students with PSP should undergo IQ testing to aid its early diagnosis and academic rehabilitation. Second, our study has revealed that parents of slow learners perceive that their children have clinically significant deficits in their independence, social exclusion, social inclusion, emotion and social inclusion facets of health. School managements should become proactive in setting up resource rooms wherein special educators/remedial teachers impart regular and affordable remedial education to reduce their academic difficulties in reading, writing, and doing mathematics., For each slow learner, the special educator should formulate an individualized education program (IEP), which is integrated with the normal school curriculum.,, Indian studies have shown that IEP is effective in improving the academic functioning and self-esteem of slow learners., School counselors, educators, and mental health professionals should plan targeted support interventions for these students to equip them with skills to cope with their emotional experiences and deal with their negative feelings and stressful events in their daily functioning. These interventions should also aim to improve the knowledge of classroom teachers, classmates, and family members about slow learners to help reduce the unpleasant experiences these students undergo, both at school and at home. Third, our study has identified the significant sociodemographic variables, which impact the HRQoL of these students, and which need to be addressed by counselors right at the time their disability is diagnosed. In our society, it is well known that parents often have high expectations from their only child and consider achieving good school grades as the single most important factor that would help their child have a bright future and climb up socially. An only child who is a slow learner may therefore be experiencing a lot more stress at home due to parental expectations for good school grades. Such parents would be unable to cope-up with their child's academic underperformance. They would react by making their children study more hours and by scolding them repeatedly for their poor school performance. This may be an explanation for “being an only child” being an independent predictor of a poor “emotion” facet outcome, viz. for these children to have significantly more emotional worries, concerns, anger, and problems because of their condition. In general, girls in our society are more sensitive and reticent than boys. This would explain why “female gender” was an independent predictor of a poor “social inclusion” facet score outcome, viz. for girls perceiving that other people (peers, friends) do not understand their condition and finding it difficult to develop social relationships.
We have no proper explanation for (i) “why being an only child” was an independent predictor of a poor “social exclusion” facet outcome; and (i) “why studying in a single-gender school” was an independent predictor of a poor limitation” facet score outcome. These aspects are beyond the scope of the present study. Future studies are required to evaluate the role of these sociodemographic factors in influencing the HRQoL of slow learners.
The strengths of the present study include adequate sampling size, high participation and response rates, and the use of a validated instrument. The reliability for the total DCGM-37-P score and three facet scores was good, and for one facet score (“limitation”) was acceptable. The convergent validity for all the constructs of the DCGM-37-P questionnaire was good.
The present study has its limitations. First, our study relied on information obtained from parents rather than from the students themselves since most slow learners may have limited reading ability to complete an HRQoL questionnaire properly. It is possible that the health perceptions of these students might have differed from those of their parents. However, for the pediatric population who are unable to meet the cognitive and communication demands involved, parent-proxy tools are generally accepted as being reliable measures of child health status. Second, the gender ratio between the study and norms group was not matched. It is well-known that in our society, more boys are referred for assessment of academic problems as parents generally have higher expectations from their sons. This could have led to an ascertainment bias in the present study. Second, since non-English reading and writing parents were not included in the present study there may be a potential language bias in our findings. Fourth, slow learners from the lower socioeconomic strata of society were not represented in our study population. Probably their parents were not motivated enough to bring their children to our clinic for assessment. Fifth, the non-probability sampling of the present study, may have led to a recruitment bias in our findings. However, we do not believe that these limitations unfavorably affect the utility of our results. Both due to the limitations outlined above and the general paucity of data on HRQoL of slow learners, future researchers should investigate whether the present study's results can be generalized to the population level.
There is an urgent need to start assessing the HRQoL of slow learners, and the DCGM-37-P questionnaire can help in this process. Early diagnosis of deficits in HRQoL would help to optimize the management of these students and may lead to favorable long-term academic and social outcomes.
We thank all the parents and students who participated in the present study. We also thank Professor Dr. Monika Bullinger, DISABKIDS Project Coordinator, Department of Medical Psychology, University Hospital of Hamburg-Eppendorf, Hamburg, Germany for providing us with the DISABKIDS manual free of cost and granting us permission to use the DCGM-37-Proxy instrument. The material in this publication is the result of the use of the DCGM-37-Proxy instrument, and the assistance of the EUROPEAN DISABKIDS GROUP is gratefully acknowledged.
Financial support and sponsorship
The Learning Disability Clinic at our institute is partially funded by a research grant from MPS Interactive Systems, Mumbai, Maharashtra, India.
Conflict of interest
Dr. Sunil Karande is the Editor of the Journal of Postgraduate Medicine.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]