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Abstract

This study aims to assess the status of health-related quality of life (HRQOL) and its associated factors in people with Type 2 diabetes mellitus (T2DM) in Nepal. HRQOL of 481 participants with T2DM in Kavrepalanchok and Nuwakot was measured using European Quality of life Visual Analogue Scale (EQVAS) and European Quality of life 5 dimensions (EQ5D) index from the EQ5D-3L tool. Associated correlates of T2DM were examined using linear regression analyses. The study participants generally reported higher HRQOL. Being 60 years or older was associated with a significantly lower EQVAS whereas, urban residence, higher education, testing glycated haemoglobin regularly were significantly associated with a higher EQVAS. Not having depressive symptoms was significantly associated with higher EQVAS and EQ5D-3L index. Encouraging screening of depressive symptoms, improving awareness on the importance of regular blood glucose monitoring and T2DM self-management education should be incorporated within primary diabetes care to improve HRQOL in Nepal.

Keywords

EuroQOL health-related quality of life Nepal Type 2 diabetes mellitus

Introduction

Type 2 diabetes mellitus (T2DM) continues to be a significant public health burden across the world with consistent increase in its prevalence over the years (International Diabetes Federation, 2021). At present, more than 500 million of the global population live with diabetes, 75% of whom are from low-and-middle-income countries (LMICs); and this number is expected to reach 783 million by 2045 (International Diabetes Federation, 2021). The rising prevalence of T2DM, especially in LMICs, is driven by several factors such as lifestyle changes, rapid urbanization, socioeconomic issues and limited access to and utilization of health services (Dunachie and Chamnan, 2019). The increasing trend of this global pandemic raises concerns not just about its management, but also about its impact on the quality of life of people with T2DM.

Health-related quality of life (HRQOL) is a patient-centred subjective measure used to assess the effect of a disease or its management on the health status of an individual (Oluchi et al., 2021). Over the years, the importance of measuring patient-reported outcomes such as HRQOL in diabetes research has been globally acknowledged as it is known to help health professionals and stakeholders comprehensively understand the physical, psychological and social burden of diabetes and design management strategies tailored to the T2DM population’s specific needs (Cochran and Conn, 2008). Moreover, optimized HRQOL is recognized as a key goal in T2DM management due to direct association of diabetes-related complications, comorbidities and health behaviour choices with HRQOL (Oluchi et al., 2021). HRQOL is therefore regarded as an integral part of T2DM management.

People living with T2DM experience significant impairment in their HRQOL dimensions, comprising of their physical, mental, social, sexual, cognitive and emotional well-being, as compared to those living without the condition (Tusa et al., 2020). The never ending need for daily management of T2DM, strict adherence to prescribed medications and continual health behaviour adjustments explain why T2DM patients experience a deteriorated quality of life (Trikkalinou et al., 2017). In addition, the association between HRQOL and T2DM could be attributed to a range of socio-economic markers such as age, income and behavioural factors such as diet, physical activity, smoking and blood glucose levels (Trikkalinou et al., 2017). Furthermore, HRQOL is significantly compromised among T2DM population with associated comorbidities and complications as opposed to those without any associated comorbidities or complications (Amin et al., 2022).

While a substantial number of studies globally have examined HRQOL in people with T2DM, little is known about HRQOL among people with T2DM in Nepal. Most studies conducted in Nepal to date were hospital-based, with little evidence from larger community-based samples. Further, previous HRQOL-related studies conducted in Nepal had small sample size and data were collected from a single district, administrative division or health centre of the country, which reduced the generalizability of the study findings (Mishra et al., 2015; Shah et al., 2020; Thapa et al., 2019). Therefore, this study aims to bridge these gaps by determining HRQOL status and examining how it is associated with various socio-demographic, behavioural and diabetes-related factors among people with T2DM in Nepal. Findings obtained from this study are expected to inform policies and future strategies for a patient-centred diabetes management model prioritizing people’s HRQOL and well-being.

Methods

Study design and participants

A prospective, community-based randomized controlled trial (RCT) of people with T2DM in Nepal was conducted to assess the effectiveness of a culturally appropriate lifestyle intervention (involving community health workers, peer supporters and regular phone calls) in T2DM management over 6 months after cluster randomization. However, this cross-sectional study only used the baseline data from the overarching RCT, which was collected prior to the intervention. A detailed description of the overarching RCT is provided in a protocol paper published elsewhere (Rawal et al., 2023). Ethics approval for the overarching RCT was obtained from Human Research Ethics Committees (HRECs) of Nepal Health Research Council Nepal (#944/2019P), Kathmandu University Nepal, Tokyo Women’s Medical University Japan and Central Queensland University Australia (#CQU RSH/HE 0000022453). Based on the American Diabetes Association (2014) and WHO criteria for diagnosis and classification of diabetes (World Health Organization, 1999), individuals (30 years and above) with a clinical diagnosis of T2DM that is, with glycated haemoglobin (HbA1c) >6.5% were eligible to take part in the study.

Procedures

Data collection took place from September 2021 to February 2022 from a stratified random sample of 20 healthcare centres of Kavrepalanchowk and 10 healthcare centres of Nuwakot districts. A KoboToolbox/Open Data Kit software was used to gather data digitally so that the potential errors associated with paper-based data collection could be minimized. Health registries of district hospital and health centres were reviewed to identify people with clinically diagnosed T2DM. Then, invitation letters along with detailed study description were sent to the identified individuals. Further, individuals who may have had diagnosed or undiagnosed T2DM or felt they were at risk of developing T2DM were also invited by sending an open invitation to the health centres they visited and their communities. A HbA1c test of people suspected of having T2DM was conducted at the health centre. Then, all those eligible (i.e. those with HbA1c ≥6.5% and that completed the baseline survey, anthropometric and metabolic assessments) were enrolled into the study. Informed written and verbal consent were obtained from all study participants before collecting data.

Instruments

A structured questionnaire was prepared to collect socio-demographic and diabetes-related information from study participants. Health-related quality of life was measured using a Nepali-translated version of EQ5D tool (Balestroni and Bertolotti, 2012), which consists of two parts – the EQ5D-3L descriptive system and the EuroQol Visual Analogue Scale (EQVAS). EQ5D is a valid and reliable tool previously used in similar low-and middle-income settings, with a Cronbach’s alpha of 0.87 and 0.74, and intraclass correlation coefficient (ICC) of 0.76 and 0.64 for EQ5D-3L and EQVAS respectively (Zare et al., 2021). EQ5D tool had a good reliability in this study with a Cronbach’s alpha of 0.75. The descriptive system assesses HRQOL across five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) and each dimension has three response levels (no problems, moderate problems and extreme problems). The participants’ level of reported problems on each dimension were combined to create a 5-digit number for each participant, known as their health state. EQ5D-3L generates 243 (3⁵) various health states, with 11111 being the best health state (full health) and 33333 being the worst health state. If a country has a reference value set, then the health states can be converted to a single utility value (the EQ5D index score). Since there is no EQ5D reference value set for Nepal, the 3L utilities were generated using the reverse crosswalk index value calculator (EuroQol, 2023) with 5L value sets for a neighbouring country, India, for the purpose of this study. Similarly, an EQVAS tool, a 20 cm 100-point scale, was used to allow respondents to rate their health on that day from the worst imaginable health state (0) to the best imaginable health state (100). The EQ5D tool was used in this study due to its universal applicability and ease of use in a resource-constraint setting like Nepal. Further, the five broad domains (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) of the tool align well with the experiences of people with T2DM, including issues with mobility and performing usual tasks due to potential complications, issues with adherence to self-care, mental health issues relating to daily management of the condition, etc.

Physical activity was measured using a 16-question Global Physical Activity Questionnaire (GPAQ), an instrument with demonstrated reliability and validity in South Asian settings (ICC of 0.8 and spearman’s Rho of 0.6; Misra et al., 2014). GPAQ measures physical activity in three domains – physical activity at work, transport-related physical activity and physical activity during leisure-time, by asking intensity, frequency and time for each. For instance, ‘In a typical week, on how many days do you do vigorous-intensity activities as part of your work?’. Those who did moderate to vigorous physical activity for 150 minutes per week were considered physically active in this study (World Health Organization, 2012).

Likewise, diabetes medication adherence was assessed using an eight-item Morisky Medication Adherence Scale (MMAS), which has demonstrated to be a reliable and valid tool in a similar economy, Thailand (Cronbach’s alpha of 0.61 and ICC of 0.83; Sakthong et al., 2009). An example item of MMAS is – ‘Do you sometimes forget to take your diabetes medicine?’. Those scoring <6, 6– <8 and 8 on the scale were considered to have low, medium and high medication adherence respectively (Morisky et al., 2008).

Blood pressure measurement of 130/80 mmHg or higher, taken by a healthcare professional, was established as hypertension (Whitworth, 2003). A sensitive point-of-care testing (POCT) analyzer (SDA1c Care) was used to determine HbA1c by a trained lab assistant. The analyzer has been used previously in South Asian settings and has a sensitivity of 95% and Lin’s concordance correlation coefficient of 0.88 (Khadanga et al., 2021). An HbA1c level of 7% or more was considered uncontrolled HbA1c (American Diabetes Association, 2013). Anthropometric measurements were taken, and body mass index (BMI) was calculated following the World Health Organization (WHO) BMI criteria for Asian classification (World Health Organization, 2000). Those with a BMI of <18.5 kg/m² were classified as ‘underweight’, 18.5–22.9 as ‘normal weight’, 23–24.9 as ‘overweight’ and ≥ 25 as ‘obese’ (World Health Organization, 2000).

Similarly, depressive symptoms were assessed using a nine-item Patient Health Questionnaire (PHQ-9) tool (Kroenke and Spitzer Robert, 2002), which has been used previously in Nepal (Karki et al., 2024; Mishra et al., 2015) and demonstrated high internal consistency (Cronbach’s alpha 0.824) and good validity in a South Asian setting (Rahman et al., 2022). PHQ-9 tool asks respondents how often they have experienced each of the nine symptoms or items in the last 2 weeks on a scale of 0–3, 0 being not at all, 1 being several days, 2 being more than half the days and 3 being nearly every day. For instance, ‘Over the last two weeks, how often have you felt little interest or pleasure in doing things?’. The total score ranges from 0 to 27, with scores of 5, 10, 15 and 20 representing mild, moderate, moderately severe and severe depressive symptoms respectively (Kroenke and Spitzer Robert, 2002). The survey questionnaire was translated into Nepali language to best fit in Nepalese context and pre-tested prior to baseline data collection. The questionnaire was then back translated to English by two independent translators.

Sample size

A sample size calculation was done to estimate the status of HRQOL in target population with good precision. The calculation was based upon a previous study conducted in India using EQ5D (Parik and Patel, 2019). The minimum sample size was determined using the formula, n = z²s²/e² where, z = 1.96 at 95% confidence interval; s, standard deviation (SD) = 15.03 (Parik and Patel, 2019); and e, margin of error = 1.49. Assuming a non-response rate of 10%, an estimated sample size of 434 was calculated. Hence, at least 434 participants were required based on the above calculation and assumptions.

Data analysis

Data analyses were performed using STATA version 18. The general characteristics of the study participants were presented as frequency, percentage, means or standard deviation. Descriptive statistics for each dimension of EQ5D-3L were reported as proportions. Median and interquartile ranges (IQR) were calculated for non-normally distributed variable like EQ5D-3L index. Mean and standard deviation (SD) were calculated for normally distributed variable like EQVAS. 3L utilities were obtained using the ‘reverse crosswalk index value calculator’ (EuroQol, 2023) based on 5L value sets for India. Univariate analysis using simple linear regression was performed to identify the association between independent variables and EQVAS. Independent variables that were statistically significant at p ≤ 0.2 in the univariate analyses (Carter et al., 2022) were included in the multivariate analysis using multiple linear regression. The cutoff of p ≤ 0.2 was used to allow for variables that might not have been strongly associated with the outcome by itself in the univariate analysis to still be included in the multivariate analysis as such variables may become more significant in the presence of other covariates (Hosmer et al., 2013). Since EQ5D index had a heavily left skewed distribution, the assumptions of linear regression were exploited; hence, adjustments to the regression analyses were made by using multiple linear regression with robust standard errors. Pearson’s correlation coefficient was used to determine the correlation between EQ-5D index values and EQVAS scores. Independent variables were tested for multicollinearity using variance inflation factor (VIF). The highest VIF noted was 2.64, hence no multicollinearity was detected. Further, mediation analyses were performed using bootstrapping method with 5000 samples to check whether the associations of variables such as presence of comorbidities and family history of diabetes with outcome variables, EQVAS score and EQ5D-3L index score, were mediated by presence of depressive symptoms or not. Furthermore, we also conducted a mediation analysis to see whether age mediates the association between depressive symptoms and our HRQOL outcomes. Beta-coefficients and 95% CI were computed and an overall p-value of <0.05 was considered statistically significant.

Results

General characteristics of the study participants

The demographic characteristics of the study participants are presented in Table 1. A total of 481 participants, 254 (53%) males and 227 (47%) females, participated in the study. The mean age (±SD) of the participants was 54.44 ± 9.41 years. A large proportion of participants resided in urban area (64%), were married (93%), had gained formal education (59%), relied on agriculture/animal husbandry as their means of livelihood (43%) and had a monthly family income of less than NRs 30,000 (USD 258.6; 60%). Nearly 50% of the participants had reported having at least one of the comorbidities associated with T2DM. The most reported comorbidities were hypertension (38%), hypercholesterolaemia (14%) and heart problems (5%). A large proportion of participants (63%) were living with T2DM for 5 or less years and over 35% had a family history of diabetes. Around 14% of the participants had low T2DM medication adherence. A significant proportion of people reported performing moderate to vigorous physical activity for at least 150 minutes per week (80%). Around one-third (32%) of the participants visited a health facility for HbA1c test in the past 6 months and only 2% visited for their feet examination in the past 12 months. Two-thirds of the participants (67%) had uncontrolled HbA1c (≥7%), 45% had hypertension, 83% were either overweight or obese and nearly 26% experienced depressive symptoms.

Table 1.

General characteristics of the study participants.

Characteristics of the participants	Frequency (%)	EQVAS	EQ5D
Characteristics of the participants	Frequency (%)	Mean ± SD	Median (IQR)
Total population (N)	481 (100)	71.67 ± 15.74	0.97 (0.12)
Socio-demographic factors
Gender
Male	254 (52.8)	73.48 ± 15.00	0.97 (0.12)
Female	227 (47.2)	69.65 ± 16.32	0.97 (0.12)
Age (years) (mean ± SD: 54.44 ± 9.41)
<45	71 (14.8)	76.95 ± 14.70	0.97 (0.00)
45–59	248 (51.5)	72.99 ± 15.80	0.97 (0.12)
≥60	162 (33.7)	67.34 ± 15.08	0.97 (0.12)
Ethnicity
Janajati	208 (43.2)	72.66 ± 15.97	0.97 (0.12)
Brahmin/Chhetri	240 (49.9)	71.17 ± 15.34	0.97 (0.12)
Others (Madhesi/Dalit)	33 (6.9)	69.15 ± 17.10	0.97 (0.10)
Religion
Hindu	429 (89.2)	71.97 ± 15.51	0.97 (0.12)
Others (Buddhist/Christian)	52 (10.8)	69.23 ± 17.44	0.97 (0.12)
Marital status
Currently married	447 (92.9)	71.72 ± 15.70	0.97 (0.12)
Never married/divorced/separated	34 (7.1)	71.03 ± 16.46	0.97 (0.12)
Area of residence
Urban	309 (64.2)	73.42 ± 15.57	0.97 (0.12)
Rural	172 (35.8)	68.54 ± 15.58	0.97 (0.12)
Education
No formal education	199 (41.4)	67.15 ± 16.25	0.97 (0.12)
Below secondary education	138 (28.7)	75.45 ± 14.26	0.97 (0.12)
Secondary and higher education	144 (29.9)	74.32 ± 14.88	0.97 (0.12)
Occupation
Housewife	106 (22.0)	67.21 ± 16.86	0.97 (0.12)
Agriculture/animal husbandry	206 (42.8)	71.37 ± 15.09	0.97 (0.12)
Business/service	112 (23.3)	76.61 ± 13.76	0.97 (0.12)
Others (student/retired/wage/labour)	57 (11.9)	71.35 ± 17.13	0.97 (0.00)
Monthly family income (NRs) (1 USD = 116 NRs)
≥30,000 (>USD 258.62)	192 (39.9)	74.88 ± 15.01	0.97 (0.12)
20,000–29,999 (USD 172.41–258.61)	91 (18.9)	70.71 ± 14.94	0.97 (0.12)
10,001–19,999 (USD 86.20–172.40)	73 (15.2)	69.65 ± 15.37	0.97 (0.12)
≤10,000 (USD <86.20)	125 (26.0)	68.63 ± 16.86	0.97 (0.12)
Diabetes-related factors
Presence of comorbidity
No	243 (50.5)	73.11 ± 16.08	0.97 (0.12)
Yes	238 (49.5)	70.21 ± 15.27	0.97 (0.12)
Duration of diabetes (years)
≤5	302 (62.8)	71.31 ± 16.01	0.97 (0.12)
>5	179 (37.2)	72.28 ± 15.16	0.97 (0.12)
Family history of diabetes
Yes	169 (35.1)	73.39 ± 14.75	0.97 (0.12)
No	312 (64.9)	70.74 ± 16.19	0.97 (0.12)
Diabetes medication adherence
Low	63 (14.1)	71.11 ± 16.85	0.97 (0.12)
Moderate	169 (37.7)	72.98 ± 16.79	0.97 (0.12)
High	216 (48.2)	72.08 ± 14.47	0.97 (0.12)
Use of tobacco products
Yes	134 (27.9)	70.09 ± 15.62	0.97 (0.12)
No	347 (72.1)	72.28 ± 15.76	0.97 (0.12)
Current alcohol consumption
Yes	109 (22.7)	72.61 ± 15.92	0.97 (0.12)
No	372 (77.3)	71.40 ± 15.69	0.97 (0.12)
Moderate to vigorous physical activity for at least 150 minutes per week
Yes	385 (80.0)	71.87 ± 15.85	0.97 (0.12)
No	96 (20.0)	70.91 ± 15.31	0.97 (0.12)
HbA1c test in the past 6 months
Yes	155 (32.2)	76.70 ± 14.21	0.97 (0.12)
No	326 (67.8)	69.28 ± 15.88	0.97 (0.12)
Feet examination in the past 12 months
Yes	8 (1.7)	68.13 ± 16.89	0.79 (0.37)
No	473 (98.3)	71.74 ± 15.73	0.97 (0.12)
Hypertension
Yes	216 (44.9)	72.19 ± 16.12	0.97 (0.12)
No	265 (55.1)	71.25 ± 15.43	0.97 (0.12)
HbA1c
Controlled <7%	160 (33.3)	72.96 ± 15.98	0.97 (0.12)
Uncontrolled ≥7%	321 (66.7)	71.03 ± 15.60	0.97 (0.12)
Depressive symptoms
Yes	123 (25.6)	60.32 ± 11.81	0.86 (0.26)
No	358 (74.4)	75.57 ± 15.02	0.97 (0.05)
Body mass index (BMI)
Underweight/normal weight	80 (16.63)	69.93 ± 16.19	0.97 (0.12)
Overweight	98 (20.37)	71.97 ± 14.91	0.97 (0.12)
Obese	303 (62.99)	72.03 ± 15.89	0.97 (0.12)

EQVAS: European quality of life visual analytical scale; EQ5D: European quality of life 5D; IQR: inter-quartile range.

EQ5D-3L and EQVAS scores

The quality of life profile of the participants based on their responses to the five dimensions of EQ-5D is presented in Table 2. Many participants reported having moderate to extreme problems in the domains of pain/discomfort (26%), anxiety/depression (15%), mobility (13%), usual activities (8%) and self-care (5%). A high EQ5D index score was reported by the study participants with a median (IQR) of 0.97 (0.12) and a mean (SD) of 0.90 (0.13). The participants reported a total of 26 different health states. The top 10 health states which constituted 95% of the responses from participants are presented in the Supplemental File 1. Around 64% (n = 310) of the participants reported having a full health state (11111) while none of the participants reported having the worst health state (33333). The mean ± SD EQVAS score was 71.67 ± 15.74. A statistically significant positive correlation was found between EQ5D index values and EQVAS score (Pearson correlation coefficient of 0.36; p < 0.001).

Table 2.

EQ5D-3L domains by gender.

EQ5D-3L domains	Male (n = 254) n (%^a)	Female (n = 227) n (%^a)	Total (n = 481) n (%)
Mobility
I have no problems in walking about	220 (86.6)	192 (84.6)	412 (85.7)
I have some problems in walking about	34 (13.4)	35 (15.4)	69 (14.3)
I am confined to bed	0	0	0
Self-care
I have no problems with self-care	241 (94.9)	211 (93.0)	452 (94.0)
I have some problems washing or dressing myself	13 (5.1)	16 (7.0)	29 (6.0)
I am unable to wash or dress myself	0	0	0
Usual activities
I have no problems with performing my usual activities	233 (91.7)	199 (87.7)	432 (89.8)
I have some problems with performing my usual activities	21 (8.3)	28 (12.3)	49 (10.2)
I am unable to perform my usual activities	0	0	0
Pain/discomfort
I have no pain or discomfort	203 (79.9)	155 (68.3)	358 (74.4)
I have moderate pain or discomfort	50 (19.7)	70 (30.8)	120 (24.9)
I have extreme pain or discomfort	1 (0.4)	2 (0.9)	3 (0.6)
Anxiety/depression
I am not anxious or depressed	221 (87.0)	190 (83.7)	411 (85.4)
I am moderately anxious or depressed	32 (12.6)	35 (15.4)	67 (13.9)
I am extremely anxious or depressed	1 (0.4)	2 (0.9)	3 (0.6)

%^a: percentage within gender.

Factors associated with HRQOL

As presented in Table 3, several socio-demographic, behavioural and diabetes-related factors were significantly associated with EQVAS in the multiple linear regression after adjusting for gender, age, area of residence, education, occupation, monthly family income, presence of T2DM comorbidities, family history of diabetes, use of tobacco products, depressive symptoms, HbA1c test done in the past 6 months and HbA1c level. Being 60 years or older was associated with a significantly lower EQVAS (β = −5.16; 95% CI: −9.13, −1.02) as compared to 45 years or less of age. People with T2DM residing in urban areas had a mean EQVAS score of nearly three points higher (β = 2.76; 95% CI: 0.12, 5.41) than those residing in rural areas. Having up to secondary level of education was significantly associated with nearly four-points higher mean EQVAS score (β = 3.73; 95% CI: 0.14, 7.32) than having no formal education. Getting a HbA1c test done in the past 6 months elevated the EQVAS score by nearly five units (β = 4.51; 95% CI: 1.81, 7.21) compared to not getting the test done. Not having depressive symptoms resulted in the EQVAS score being significantly higher by 13 points (β = 13.35; 95% CI: 10.49, 16.19) compared to living with the symptoms.

Table 3.

Factors associated with EQVAS assessed by using multiple linear regression.

Characteristics of the participants	B-coefficients from univariate analysis [95% CI]^a	p-Value	B-coefficients from multivariate analysis [95% CI]^b	p-Value
Gender		0.01
Male (ref)
Female	−3.83 [−6.64,−1.03]		−1.20 [−4.95, 2.54]	0.53
Age		<0.001
≤45 (ref)
45–59	−3.96 [−8.03, 0.11]		−1.81 [−5.53, 1.92]	0.34
≥60	−9.62 [−13.92, −5.13]		−5.16 [−9.31,−1.02]	0.01
Ethnicity		0.38
Others (Madhesi/Dalit) (ref)
Janajati	3.51 [−2.28, 9.30]
Brahmin/Chhetri	2.02 [−3.72, 7.76]
Religion		0.23
Hindu (ref)
Others (Buddhist/Christian)	−2.74 [−7.28, 1.79]		-	-
Marital status		0.80
Never married/widowed/separated (ref)			-	-
Currently married	0.69 [−4.81, 6.20]
Area of residence		0.01
Rural (ref)
Urban	4.88 [1.96, 7.79]		2.76 [0.12, 5.41]	0.04
Education		<0.001
No formal education (ref)
Below secondary	8.30 [4.97, 11.63]		3.73 [0.14, 7.32]	0.04
Secondary and higher	7.17 [3.88, 10.46]		−0.02 [−4.08, 4.04]	0.99
Occupation		<0.001
Housewife (ref)
Agriculture/animal husbandry	4.16 [0.53, 7.79]		2.09 [−1.62, 5.81]	0.27
Business/service	9.49 [5.37, 13.60]		3.83 [−0.43, 8.08]	0.08
Others (wage/labour/student/retired)	3.95 [−1.03, 8.95]		0.83 [−4.45, 6.12]	0.76
Monthly family income (NRs) (1 USD = 116 NRs)		0.01
≤10,000 (USD <86.20) (ref)
10,001–19,999 (USD 86.20–172.40)	1.02 [−3.47, 5.53]		−0.31 [−4.28, 3.65]	0.87
20,000–29,999 (USD 172.41–258.61)	2.08 [−2.13, 6.29]		0.51 [−3.24, 4.26]	0.79
≥30,000 (>USD 258.62)	6.25 [2.74, 9.76]		2.25 [−1.04, 5.54]	0.18
Presence of comorbidities		0.04
Yes (ref)
No	2.91 [0.09, 5.72]		2.29 [−0.22, 4.81]	0.07
Duration of diabetes		0.52
≤5 years (ref)
>5 years	9.61 [−1.96, 3.88]
Family history of diabetes		0.07
Yes (ref)
No	−2.65 [−5.59, 2.94]		−2.35 [−5.02, 0.31]	0.08
Diabetes medication adherence		0.69
Low (ref)
Moderate	1.87 [−2.68, 6.44]
High	0.97 [−3.45, 5.40]
Use of tobacco products		0.17
Yes (ref)
No	2.19 [−0.95, 5.33]		2.39 [−0.43, 5.23]	0.09
Current alcohol consumption		0.48
Yes (ref)
No	−1.20 [−4.57, 2.17]
Moderate to vigorous PA for at least 150 minutes per week		0.59
No (ref)
Yes	0.96 [−2.57, 4.49]
HbA1c test in the past 6 months		<0.01
No (ref)
Yes	7.42 [4.47, 10.36]		4.51 [1.81, 7.21]	0.01
Feet examination in the past 12 months		0.52
No (ref)
Yes	−3.61 [−14.64, 7.42]
Hypertension		0.51
Yes (ref)
No	−0.94 [−3.78, 1.89]
Depressive symptoms		<0.01
Yes (ref)
No	15.25 [12.32, 18.18]		13.35 [10.49, 16.19]	<0.01
HbA1c		0.20
Uncontrolled ≥7 (ref)
Controlled <7	1.92 [−1.06, 4.91]		2.44 [−0.20, 5.07]	0.07
Body mass index		0.56
Normal/underweight (ref)
Overweight	2.04 [−2.62, 6.70]
Obese	2.09 [−1.79, 5.98]

Adjusted R ² of multiple linear regression model = 0.267; highest variance inflation factor (VIF) noted was 2.64, hence no multicollinearity was detected; bold figures indicate statistical significance at p < 0.05.

B: unstandardized regression coefficient; CI: confidence interval; HbA1c: glycated haemoglobin.

Simple linear regression; ^bMultiple linear regression; Adjusted for gender, age, area of residence, education, occupation, monthly family income, presence of comorbidities, family history of diabetes, use of tobacco products, HbA1c test done in the past 6 months, presence of depressive symptoms and HbA1c level. Other variables were excluded from the adjusted model as those variables were not statistically associated with EQVAS at p ≤ 0.2 in the simple linear regression model.

There was no evidence for a mediating role of depressive symptoms in the association between comorbidity and EQVAS indicated by a natural indirect effect (nie; β = −0.64; 95% CI: −1.92, 0.64) or between family history of T2DM and EQVAS (β = −0.95; 95% CI: −2.18, 0.27) after adjusting for covariates (Supplemental Figures 1a and 2a). Further, we found no mediating effects of age between depressive symptoms and EQVAS (β = −0.05, 95% CI: −0.29, 0.18; Supplemental Figure 3).

In a multivariable analysis of EQ5D-3L index as presented in Table 4, not having depressive symptoms was significantly associated with a significantly higher EQ5D-3L index score of 0.11 (95% CI: 0.07, 0.14) compared to those experiencing depressive symptoms. The results were adjusted for gender, age, area of residence, education, occupation, monthly family income, presence of T2DM comorbidities, family history of diabetes, physical activity, health facility visit for feet examination in the past 12 months, presence of depressive symptoms and HbA1c level.

Table 4.

Factors associated with EQ5D-3L index assessed by using multiple linear regression with robust standard errors.

Characteristics of the participants	B-coefficients from univariate analysis [95% CI]^a	p-Value	B-coefficients from multivariate analysis [95% CI]^b	p-Value
Gender		0.03
Male (ref)
Female	−0.02 [−0.04, −0.002]		0.01 [−0.03, 0.03]	0.98
Age		0.01
≤45 (ref)
45–59	−0.01 [−0.04, 0.02]		−0.01 [−0.03, 0.03]	0.98
≥60	−0.02 [−0.08, −0.02]		−0.02 [−0.06, 0.01]	0.18
Ethnicity		0.36
Others (Madhesi/Dalit) (ref)
Janajati	0.03 [−0.03, 0.10]
Brahmin/Chhetri	0.02 [−0.04, 0.09]
Religion		0.38
Hindu (ref)
Others (Buddhist/Christian)	−0.02 [−0.06, 0.02]
Marital status		0.62
Never married/widowed/separated (ref)
Currently married	0.01 [−0.03, 0.06]
Area of residence		0.18
Rural (ref)
Urban	−0.02 [−0.04, 0.01]		−0.02 [−0.04, 0.01]	0.06
Education		0.01
No formal education (ref)
Below secondary	0.04 [0.01, 0.07]		0.02 [−0.01, 0.05]	0.22
Secondary and higher	0.05 [0.02, 0.08]		0.02 [−0.01, 0.05]	0.19
Occupation		0.05
Housewife (ref)
Agriculture/animal husbandry	0.02 [−0.01, 0.06]		0.01 [−0.02, 0.04]	0.59
Business/service	0.04 [0.01, 0.07]		0.02 [−0.02, 0.05]	0.41
Others (wage/labour/student/retired)	0.05 [0.01, 0.09]		0.04 [−0.01, 0.08]	0.09
Monthly family income (NRs) (1 USD = 116 NRs)		0.14
≤10,000 (USD <86.20) (ref)
10,001–19,999 (USD 86.20–172.40)	0.02 [−0.01, 0.05]		0.01 [−0.02, 0.04]	0.51
20,000–29,999 (USD 172.41–258.61)	−0.02 [−0.05, 0.02]		−0.02 [−0.05, 0.01]	0.24
≥30,000 (>USD 258.62)	0.02 [−0.01, 0.05]		−0.01 [−0.03, 0.03]	0.88
Presence of comorbidities		0.03
Yes (ref)
No	0.03 [0.01, 0.05]		0.01 [−0.01, 0.03]	0.21
Duration of diabetes		0.46
≤5 years (ref)
>5 years	0.01 [−0.01, 0.03]
Family history of diabetes		0.01
Yes (ref)
No	−0.02 [−0.05, −0.01]		−0.02 [−0.04, 0.01]	0.13
Diabetes medication adherence		0.98
Low (ref)
Moderate	0.01 [−0.03, 0.04]
High	0.01 [−0.03, 0.04]
Use of tobacco products		0.89
Yes (ref)
No	−0.01 [−0.03, 0.02]
Current alcohol consumption		0.30
Yes (ref)
No	−0.01 [−0.04, 0.01]
Moderate to vigorous PA for at least 150 minutes per week		0.02
No (ref)
Yes	0.04 [0.01, 0.07]		0.03 [−0.01, 0.06]	0.09
HbA1c test in the past 6 months		0.27
No (ref)
Yes	0.01 [−0.01, 0.04]
Feet examination in the past 12 months		0.06
No (ref)
Yes	−0.11 [−0.23, 0.01]		−0.11 [−0.21, 0.01]	0.15
Hypertension		0.50
Yes (ref)
No	−0.01 [−0.03, 0.02]
HbA1c		0.06
Uncontrolled ≥7 (ref)
Controlled <7	−0.03 [−0.05, 0.01]		−0.01 [−0.03, 0.01]	0.20
Depressive symptoms		<0.01
Yes (ref)
No	0.11 [0.08, 0.14]		0.11 [0.07, 0.14]	<0.01
Body mass index		0.23
Normal/underweight (ref)
Overweight	0.03 [−0.01, 0.07]
Obese	0.02 [−0.01, 0.06]

Adjusted R ² of the multivariate GLM model = 0.224; highest variance inflation factor (VIF) noted was 2.64, hence no multicollinearity was detected; bold figures indicate statistical significance at p < 0.05.

B: unstandardized regression coefficient; CI: confidence interval; HbA1c: glycated haemoglobin.

Simple linear regression using robust standard error; ^bMultiple linear regression using robust standard error; Adjusted for gender, age, area of residence, education, occupation, income, presence of comorbidities, family history of diabetes, physical activity, HbA1c test done in the past 6 months, feet examined in the past 12 months, presence of depressive symptoms and HbA1c level. Other variables were excluded from the adjusted model as those variables were not statistically associated with EQ5D-3L index at p ≤ 0.2 in the simple linear regression.

There was no evidence for a mediating role of depressive symptoms in the association between comorbidity and EQ5D-3L (β = −0.004; 95% CI: −0.01, 0.004) or between family history of T2DM and EQ5D-3L (β = −0.007; 95% CI: −0.01, 0.002) after adjusting for covariates (Supplemental Figures 1b and 2b).

Discussion

This is the first study to examine the HRQOL among people with T2DM in Nepal using the EQ5D-3L and EQVAS instrument. Study participants reported a very high EQ5D-3L index scores and high, though relatively lower, EQVAS scores. The EQ5D index is commonly seen to be largely negatively skewed, as a large proportion of people tend to report not having any problems in the EQ5D domains (Carter et al., 2022). As such, there is often a discrepancy between EQ5D index scores and EQVAS scores, because of varying nature of the questions and calculations (Jo et al., 2020). Furthermore, the utility estimates are calculated using a country-specific population weights based on time trade-off values (Oppe et al., 2016), whereas EQVAS scores represents an overall estimate of one’s self-reported health on a scale. As such, EQ5D index score represents the preference of the general population for a given health state, whereas EQVAS reflects an individual’s own valuation of their health. This further exacerbates the differences seen between both scores. EQ5D scores reported in studies conducted in other LMICs like Iran (Abedini et al., 2020) and Malaysia (Butt et al., 2018) are very similar to the score reported in this study. Similarly, EQVAS scores reported in studies conducted in LMICs including India (Parik and Patel, 2019), Bangladesh (Safita et al., 2016) and Iran (Zare et al., 2020), are also very similar to the scores reported in our study.

Over a quarter participants in this study were affected in the pain/discomfort domain, followed by anxiety/depression and mobility. The study by Abedini et al. (2020) has also stated that T2DM patients mostly faced challenges in the pain (45%), depression (43%) and mobility (34%) domains. T2DM patients experiencing acute or chronic pain could be attributed to diabetes-associated complications such as neuropathy, which is commonly accompanied by pain and also significantly associated with depressive symptoms and reduced quality of life (Alghafri et al., 2020). Our finding corroborates previous studies from similar South Asian settings (Parik and Patel, 2019; Safita et al., 2016).

Women with T2DM were found to report more problems in the domains than their male counterparts, aligning with findings from other T2DM studies (Alsuwayt et al., 2021). Women are more often the caregiver in the family and in most cases, have to look after meals and daily routines of their family members. In doing so, they might experience challenges in meeting their own dietary needs, staying mentally and physically active, or adhering to their medication regimen (Misra and Lager, 2009). On the other hand, the conventional concept of ‘masculinity’ and societal expectation of males to be physically and psychologically stronger than females, especially in Nepalese societies, could restrict them from openly expressing and admitting the impact of the health problems that they might have (Siddiqui et al., 2013). Hence, gender stereotypes could have come into play for participants when responding to the domains. However, whether the influence of this factor is greater than the actual difference in health problems or not requires further exploration.

In the present study, T2DM residents of urban areas had a significantly higher HRQOL compared to rural residents. Better means of living, relatively improved socio-economic status of people and more accessible healthcare facilities in urban areas could attribute to higher HRQOL in urban residents than rural residents (Abedini et al., 2020). Similarly, people who had secondary level of education reported significantly better HRQOL estimates as compared to those that had no formal education. These results are in line with findings reported in other developing nations such as Iran (Abedini et al., 2020; Zare et al., 2020) and Bangladesh (Safita et al., 2016). People with better education might have higher levels of knowledge of T2DM, its associated complications and self-management practices compared to people with a lower education, which ultimately aids in enhancing their quality of life (Zare et al., 2020). Further, better education attracts better occupation and affordability to healthcare services for T2DM management, ultimately enhancing the quality of life of an individual (Zare et al., 2020).

Age was found to have a negative association with HRQOL in this study. People with T2DM over 60 years of age had a significantly lower HRQOL compared to people 45 years old or younger, which is consistent with previous studies conducted in Iran (Zare et al., 2020) and Bangladesh (Alsuwayt et al., 2021). Older people with T2DM have higher chances of developing comorbidities and diabetes-related complications, as a result, their quality of life is compromised (Alsuwayt et al., 2021). On the contrary, studies by Carter et al. (2022) and O’Reilly et al. (2011) reported that increased age was associated with better HRQOL in people with T2DM in China and Canada respectively. This contrast could be explained by the differences in socio-economic situation, accessibility of health services and the governmental or systemic factors between the countries (Al Hayek et al., 2014).

Regular HbA1c testing was found to be significantly associated with HRQOL in this study. Although a direct relation between regular HbA1c measurement and HRQOL is not evident, there is a potential link of regular monitoring of HbA1c with better glycaemic outcomes and subsequent reduced risk of complications, both of which are established predictors of improved HRQOL in T2DM population (Mohan et al., 2018). Moreover, HbA1c measurements are routinely performed because of patient awareness and motivation to manage their condition. As such, the quality of life of T2DM patients is likely to improve when they are self-aware and motivated to adhere to diabetes management measures (Sebire et al., 2018). HbA1c level, however, was not significantly associated with HRQOL in this study, although previous studies have indicated that better glucose level is associated with lowered risk of complication, leading to enhanced HRQOL (Gebremedhin et al., 2019; Mohan et al., 2018). Nepalese cultural context, varying perception regarding quality of life and family support in managing T2DM might have influenced this non-association (Adhikari and Mishra, 2019). Further research is required to explore the association between HbA1c levels and HRQOL in Nepali people with T2DM.

Depressive symptoms were found to be strongly associated with HRQOL. A previous study from Nepal has demonstrated that depressive symptoms reduced the HRQOL of people with T2DM in the social relation domain (Mishra et al., 2015). Another study from Turkey showed that people with T2DM and depression had a significantly lower HRQOL compared to non-depressed group (Eren et al., 2008). Depressive symptoms may impose functional restrictions and threaten one’s ability to self-manage their T2DM condition (Schram et al., 2009). Further, low medication adherence, and increased health care costs associated with the diabetes-depression comorbidity and exacerbates the negative association between depressive symptoms and HRQOL (Tusa et al., 2020). Hence, regular screening and timely treatment of mental health problems should be encouraged in routine primary care of T2DM patients.

This study has some important strengths and limitations to note. To the best of our knowledge, this is the first study to assess HRQOL in people with T2DM in Nepal using a validated and reliable tool – EQ5D and EQVAS. The tool’s universal applicability, ease of use and utility in economic analysis make it preferable over other HRQOL measurement tools, especially in a resource-constraint setting like Nepal. A relatively larger sample size (n = 481) as compared to other QOL studies conducted previously among people with T2DM in Nepal, for example, by Mishra et al. (2015; n = 167), Thapa et al. (2019; n = 102) and Shah et al. (2020; n = 270), is another strength of this study. There are also limitations to this study. Firstly, generalizability of the findings of this study warrants caution as they might not be representative of the entire Nepali population with T2DM. Secondly, due to cross-sectional nature of the study, causal inferences could not be drawn. Thirdly, due to not having an EQ5D reference value set for Nepal, the 3L utilities were generated corresponding to the 5L value sets of India, which may or may not have provided an accurate assessment of HRQOL in terms of EQ5D-3L among people with T2DM in this study.

Conclusions

This study identified several socio-demographic, diabetes-related and behavioural factors affecting the HRQOL of people with T2DM in Nepal. Urban residence, higher level of education, regularly taking HbA1c tests and not having depressive symptoms were significantly associated with better HRQOL whereas, age was found to be a negative predictor of HRQOL. Screening of depressive symptoms should be encouraged in the routine primary care of people with T2DM. Further, people with T2DM should be educated, both at health facilities as well as at community level, about the importance of regular blood glucose monitoring and practising self-management behaviours to maintain glycaemic control. A qualitative study further exploring the HRQOL aspects of people with T2DM in Nepal could provide a deeper understanding of their experiences. Longitudinal intervention studies are recommended to examine if improvements in QOL outcome can be generated. Large population surveys across the country are recommended to generate a representative HRQOL scoring algorithm for the EQ5D with a country-specific value set for Nepal. Lastly, economic evaluations are recommended to identify evidence-based and cost-effective strategies for T2DM management in a resource-constraint setting such as Nepal. Overall, the findings obtained from this study have not only identified the existing gaps in HRQOL research in Nepal but is also expected to inform policy reform for culturally tailored and patient-centred holistic care model for people with T2DM.

Supplemental Material

sj-docx-1-hpq-10.1177_13591053241302877 – Supplemental material for Health-related quality of life and associated factors in people with Type 2 diabetes mellitus in Nepal: Baseline findings from a cluster-randomized controlled trial

Supplemental material, sj-docx-1-hpq-10.1177_13591053241302877 for Health-related quality of life and associated factors in people with Type 2 diabetes mellitus in Nepal: Baseline findings from a cluster-randomized controlled trial by Ashmita Karki, Corneel Vandelanotte, Stephanie Alley and Lal B Rawal in Journal of Health Psychology

Footnotes

Author contributions

AK conceptualized the study under the supervision of LR and CV. The project research assistants collected the data. AK conducted data cleaning and analysis. SA provided guidance on statistical analysis. AK prepared a draft of the manuscript. LR, CV and SA critically reviewed the manuscript and AK prepared the final manuscript. AK, LR, CV and SA agreed to the final version of the manuscript.

Data sharing statement

The data that support the findings of this study are available upon reasonable request.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author was supported by the Australian Government Research Training Program Scholarship.

Ethics approval

Ethical clearance of the overarching project was obtained from Human Research Ethics Committees (HRECs) of Nepal Health Research Council Nepal (#944/2019P), Kathmandu University Nepal, Tokyo Women’s Medical University Japan and Central Queensland University Australia (#CQU RSH/HE 0000022453).

Informed consent

Informed written and verbal consent were obtained from all study participants before collecting data.

ORCID iDs

Ashmita Karki

Corneel Vandelanotte

Stephanie Alley

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