Quality of life viz-a-viz hypertension, obesity and diabetes: An exploratory study from rural Punjab, India

doi:10.21203/rs.3.rs-4993070/v1

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Quality of life viz-a-viz hypertension, obesity and diabetes: An exploratory study from rural Punjab, India

https://doi.org/10.21203/rs.3.rs-4993070/v1

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Background Adverse health conditions are believed to reduce one’s quality of life (QoL). However, the impact of the most common lifestyle-related adversities like hypertension, obesity, and diabetes on the QoL of rural communities in India is largely unexplored.

Objective The present study aims to understand the QoL with respect to hypertension, diabetes, and obesity in rural areas of Punjab, India.

Methodology The present study was conducted among 931 individuals of both sexes, aged 20-75 years residing in the Mansa district of Punjab, India. Sociodemographic data were collected by using a pre-tested interview schedule. Somatometric measurements and blood pressure (BP) were measured by using standard protocols. QoL was assessed by using the short version of the World Health Organization Quality of Life Instrument (WHOQOL-BREF).

ResultsAlready-diagnosed hypertensives were found at higher risk for poor QoL in the overall analysis as well as in psychological and social-relationship health domains; whereas, underweight individuals were found to be at higher risk for poor QoL in physical health domains. Further, already-diagnosed diabetics were at a higher risk for poor QoL in the physiological health domain. Other health conditions such as high BMI, high WC, and high WHR were not found to be affecting QoL.

Conclusion Findings suggest that proper counselling and awareness regarding hypertension and diabetes should follow the diagnosis to dispel misinformation and minimize the negative consequences of diagnosis labelling.

Diabetes

Hypertension

Punjab

Rural Community

WHOQOL-BREF

The World Health Organization (WHO) identifies obesity, diabetes, and hypertension as the top three major risk factors for mortality worldwide [1]. The increasing prevalence of these conditions in India also contributes to the rising burden of cardiovascular diseases, presenting a significant public health challenge [2–4]. The prevalence of hypertension, diabetes, and obesity in Punjab surpasses the national average [5]. Despite several initiatives to curb the high prevalence of these non-communicable diseases (NCDs) through awareness and treatment programs, the management of hypertension, diabetes, and obesity in the rural areas of Punjab stands out to be a major challenge [5, 6]. Numerous studies reported that individual who suffers from metabolic disorders such as hypertension, diabetes, and obesity experience many negative emotions and impaired physical functioning, which deteriorate their quality of life (QoL) [7]. Therefore, assessing QoL is an important parameter for understanding well-being and treatment outcomes. In recent years, healthcare workers and researchers have paid major attention to understanding QoL with respect to different health conditions through the eyes of those experiencing the health problem. In the given scenario, an improved rate of diagnosis and proper adherence is an essential component of the management of the disease. However, Several studies have reported that patients suffering from acute illnesses adhere to the treatment more seriously than those with asymptomatic health conditions, such as hypertension and diabetes, as they are more likely to be non-compliant [8]. Further, low levels of diagnosis may result in the advent of various complications [3]. To control and manage hypertension prevalence, the government of India (GOI) has launched an Indian hypertension control initiative program (IHCI), that focuses on early and random screening of hypertension cases at the community level so that individuals with previously undiagnosed hypertension can be diagnosed and treated [9].

However early diagnosis can have a downside causing unintended harm and yielding negative labeling consequences [10]. The effect of labeling and its negative consequences were explored in several previous research [11–14]. For instance, Haynes et al.,1978 investigated the impact of labeling among Canadian steelworkers and discovered that informed hypertensive individuals exhibited significantly higher rates of absenteeism in their jobs compared to their uninformed counterparts [11]. Similarly, Mossey et al.,1981 examined the psychosocial ramifications of labeling and found that those recently diagnosed with hypertension experienced deterioration in marital and home life [12]. This unintended harm due to the diagnosis labeling can be prevented by promoting awareness through strategic programs that consider cultural and religious sentiments and perceptions of the community and encourage empathetic communication with the patients [15]. This can potentially eradicate any prevailing misconceptions and stigma associated with the disease.

Therefore, assessing the impact of these health conditions on QoL can elucidate such socio-cultural factors crucial in the management and control of diseases which can potentially contribute towards framing efficient community-specific NCD management programs. To the best of our knowledge, no previous community-based study has highlighted the labeling effect of diagnosed health conditions such as hypertension, diabetes, and obesity or explored its impact on the QoL of the general population in India. Considering the gap, the present study aimed to understand the QoL with respect to hypertension, obesity (general and central), and diabetes in rural areas of Punjab, India.

Study area and recruitment of participants

The present cross-sectional study was conducted among a total of 931 adult individuals aged 20 to 75 years of either sex, residing in the Mansa district of Punjab, India. All the participants were recruited through a convenient sampling method. Data were collected using pretested interview schedules. Further, those suffering from a history of chronic diseases like cancers and cardiovascular diseases (CVDs), self-reported mental illness, and pregnant women were excluded from the study.

The present study was approved by the Departmental Ethics Committee (X) (Ref. No./Anth. /2018/2890/ 28-12-18). Written informed consent (in Punjabi) was obtained before data collection from each participant.

Data collection

Data pertaining to sociodemographic variables like age, sex, education, marital status, occupation, and family income were collected by using a pretested and modified interview schedule.

Estimation of hypertension status: Participants were initially interviewed to determine if they had ever undergone blood pressure (BP) measurements. Those who responded affirmatively were subsequently interviewed about their current hypertension status. Based on this collected information, participants were categorized into the following groups: already-diagnosed cases of hypertension, individuals previously diagnosed as normotensive, and those who had never undergone blood pressure (BP) checks. For participants who claimed to be normotensive in prior checkups, as well as those without a documented history of previous checkups, an assessment for hypertension was conducted. BP was measured following standard protocol using a digital BP measuring device (model HEM 7124, Omron Corporation, Tokyo, Japan). Three consecutive readings were recorded at intervals of 5 minutes from each participant, seated upright after resting for at least 10 min. Accordingly, the average of three BP readings was considered final. In the present study, individuals with blood pressure readings < 140 mmHg systolic blood pressure (SBP) and < 90 mmHg diastolic blood pressure (DBP) were considered non-hypertensive, ≥ 140 mmHg SBP and ≥ 90 mmHg DBP were considered hypertensive [16].^` Finally, participants were categorized into three groups: non-hypertensive (those with normal blood pressure readings and no prior hypertension diagnosis), already-diagnosed hypertensives (those with documented hypertension diagnosis prior to the study), and newly diagnosed hypertensives (those newly diagnosed with hypertension during the study, previously unaware).

Estimation of obesity status

Anthropometric variables such as height (in cm), weight (in kg), waist circumferences (WC) (in cm), and hip circumferences (HC) (in cm) were measured using an anthropometric rod, weighing balance, and steel tape, respectively. Three measurements were taken, and the mean of the replicates was used for analysis. Overweight was defined as body mass index (BMI) ≥ 23.0 kg/m² but < 25.0 kg/m^2, while generalized obesity was defined as BMI ≥ 25.0 kg/m² and underweight as BMI < 18.5 kg/m² based on World Health Organization Asia Pacific Guidelines [17]. High WC was defined as WC ≥ 90 cm for men and ≥ 80 cm for women, and high waist-hip ratio as WHR ≥ 0.90 for men and ≥ 0.80 for women and calculated as WC (in cm) divided by HC (in cm) [17].

Estimation of diabetes status: Initially, participants were interviewed to ascertain their history of blood sugar (glucose) measurements. Those confirming previous assessments were further questioned about their current diabetes status. Based on this information, participants were classified into three categories: individuals previously diagnosed with diabetes, Individuals those having no history of diabetes diagnosis but in the present study found high glucose levels from the normal range, and thirdly had no history of diabetes diagnosis and also in the present study found normal glucose levels (normoglycemic). This assessment involved analysing glucose levels using overnight fasting intravenous blood samples collected by a skilled technician. The serum was separated from the blood samples. Serum samples were used for the estimation of fasting blood glucose (FBG) levels. FBG analysis was done using commercial kits (Erba, Germany) through spectrophotometry using a fully automated biochemical analyser (Erba XL-640). In this study, individuals with blood glucose readings < 126 mg/dL were categorized as normoglycemic, while those with readings ≥ 126 mg/dL were considered diabetic [18]. Consequently, participants were grouped into three distinct categories: non-diabetic (normal glucose levels with no prior diabetes diagnosis), already-diagnosed diabetics (documented diabetes diagnosis prior to the study), and newly diagnosed diabetics (diabetes diagnosis made during the study, with no prior awareness).

Assessment of Quality of Life: The WHQOL-BREF tool, Hindi version, was used for assessing participants. It consists of 26 items, and these items are categorized into four health domains, namely: physical domain (7 items), psychological (6 items), social relationship (3 items), environment (8 items), two general questions. Two general questions were analyzed separately without transforming the scores. These questions addressed the overall assessment of individuals’ quality of life and health. The first question asked was, “How would you rate your quality of life?” and the second was, “How satisfied are you with your health?” The WHOQOL-BREF scores can be calculated in three ways: raw scores, scores ranging from 4 to 20, and scores ranging from 0 to 100, with higher scores indicating better quality of life (QoL). For this study, the raw scores for each domain were scaled to a range of 0 to 100. The average scores of all four domains were then used to estimate overall well-being. Medians of the overall and domain-specific scores were used as cut-off points to define good and poor QoL [19].

Statistical analysis

Data analysis for this study was performed using SPSS version 22. The Kolmogorov-Smirnov test was used to determine whether the data was distributed normally or not. The WHOQOL-BREF instrument's reliability was assessed using Cronbach’s alpha, which was found to be 0.947. Continuous variables are expressed as mean value and standard deviation (SD). Categorical variables are described as frequencies and percentages. Differences in means of two and more than two groups were established using t-test and ANOVA, respectively. Pearson correlation was used to determine the level of agreement between health conditions and WHOQOL-BREF domains. The odds ratio was calculated after adjusting age, and sex. A value of p < 0.05 was considered the level of significance for all the statistical tests used in the present study.

General characteristics of the studied population

The present study was conducted on 931 individuals (59.6% females) aged 20–75 years, with 96.9% being Sikh, 84.7% married, over half having fewer than five family members, 59.2% literate, 56.4% unemployed, and 56% having an annual family income of less than 2 lakh INR.

In addition, more than half of the studied population was found to be hypertensive, including both already diagnosed (27.8%) and newly diagnosed (26.5%), compared to the non-hypertensive group (45.6%) respectively. In terms of BMI, 55.5% of individuals were found to have general obesity. While a greater proportion of individuals were found to have high WC (76.5%) and WHR (92.6%). Moreover, the majority (84.1%) of individuals were found non-diabetic (Table 1).

Table 1

General characteristics of the studied population.
Sociodemographic variable	Status	Frequency (%)
Age group (in years)	20–29	28 (3.0)
	30–39	225 (24.16)
	40–49	199(21.37)
	50–59	201(21.5)
	60–69	200 (21.48)
	70 and above	78 (8.3)
Sex	Male	382 (41.03)
Sex	Female	549(58.96)
Blood pressure	Non- hypertensive	425 (45.64)
	Newly-diagnosed hypertensive	247 (26.53)
	Already-diagnosed hypertensive	259 (27.81)
BMI	Underweight	49(5.26)
	Normal	223(23.95)
	Overweight	142 (15.25)
	Obese	517 (55.53)
Waist circumferences	Normal	218(23.41)
Waist circumferences	High	713(76.58)
Waist hip circumferences	Normal	68(7.30)
Waist hip circumferences	High	863 (92.69)
Blood glucose	Non-diabetic	635(84.1)
	Newly-diagnosed diabetic	45 (5.96)
	Already-diagnosed diabetic	75 (9.93)

Variations in mean QoL scores with respect to selected health condition

The mean scores for Q1 (overall QoL rating) and Q2 (satisfaction with health) were found to be significantly lower among already diagnosed hypertensives, diabetics, and also in individuals with high WC, and high WHR compared to their healthy counterparts and newly diagnosed (diagnosed during the study and unaware of their condition) cases. Further, in the overall QoL (average of all domains), significantly lower scores for QoL were observed among individuals who were already diagnosed with hypertension and diabetes in comparison to the other two categories (newly diagnosed and healthy controls).

Moreover, in domain-wise analysis, it was observed that individuals already diagnosed with hypertension and diabetes exhibited significantly lower QoL scores in the physical health and psychological health domains. Additionally, the mean QoL score in the social relationship domain was observed to be significantly lower among already-diagnosed hypertensives than the other two groups. Further, in the environmental health domain, individuals who were already diagnosed with hypertension showed significantly lower QoL scores, whereas obese individuals exhibited significantly higher scores for quality of life in this domain than their respective counterparts. Overall, poor QoL was observed across all QoL domains among the already-diagnosed hypertensive group than newly diagnosed and normotensives (Table 2).

Table 2

Variations in mean quality of life (QoL) score with respect to selected health conditions
Variables	Status	Q1	Q2	Physical health	Psychological health	Social-relationship	Environmental Health	Overall QoL
Variables	Status	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Blood pressure status	Non-hypertensive	3.98 ± 1.08	2.98 ± 1.08	61.90 ± 12.31	68.08 ± 17.24	71.49 ± 27.24	76.25 ± 19.59	69.43 ± 13.65
	Newly diagnosed hypertensive	3.99 ± 1.10	2.99 ± 1.10	60.32 ± 12.94	67.22 ± 18.09	72.89 ± 25.71	75.88 ± 19.36	69.08 ± 13.91
	Already diagnosed hypertensive	3.30 ± 1.18	2.30 ± 1.18	57.28 ± 15.28	60.22 ± 19.24	64.35 ± 24.97	68.54 ± 22.31	62.60 ± 16.24
	p-value	< 0.001*	< 0.001*	< 0.001*	< 0.001*	< 0.001*	<0.001*	< 0.001*
BMI	Normal	3.91 ± 1.15	2.91 ± 1.15	59.76 ± 12.45	66.58 ± 18.17	72.00 ± 27.01	73.10 ± 20.67	67.86 ± 14.61
	Underweight	3.79 ± 1.06	2.79 ± 1.06	56.69 ± 13.61	64.16 ± 15.87	69.10 ± 25.35	70.06 ± 19.37	65.00 ± 13.50
	Overweight	3.80 ± 1.21	2.80 ± 1.21	59.02 ± 15.21	63.54 ± 18.78	72.54 ± 24.57	71.31 ± 21.85	66.60 ± 15.50
	Obese	3.74 ± 1.15	2.74 ± 1.15	61.05 ± 13.35	66.00 ± 18.50	68.30 ± 26.71	75.52 ± 20.21	67.72 ± 14.77
	p-value	0.374	0.374	0.083	0.400	0.191	0.058*	0.543
Waist circumferences	Normal	3.99 ± 1.09	2.99 ± 1.09	59.78 ± 12.29	66.09 ± 18.48	70.95 ± 26.47	72.95 ± 20.17	67.44 ± 14.65
	High	3.73 ± 1.16	2.73 ± 1.16	60.33 ± 13.84	65.54 ± 18.31	69.55 ± 26.42	74.34 ± 20.71	67.44 ± 14.83
	p-value	0.005*	0.005*	0.597	0.699	0.495	0.383	1.00
Waist hip ratio	Normal	4.17 ± 1.00	3.17 ± 1.00	60.08 ± 11.95	66.47 ± 17.85	72.60 ± 27.03	71.83 ± 17.89	67.75 ± 12.80
	High	3.76 ± 1.16	2.76 ± 1.16	60.21 ± 13.61	65.60 ± 18.39	69.66 ± 26.38	74.18 ± 20.78	67.41 ± 14.93
	p-value	0.005*	0.005*	0.942	0.709	0.378	0.365	0.859
Blood glucose status	Non-diabetic	3.85 ± 1.13	2.85 ± 1.13	60.72 ± 13.30	66.59 ± 17.54	69.52 ± 26.15	74.50 ± 19.91	67.83 ± 14.24
	Newly-diagnosed diabetic	3.77 ± 1.06	2.77 ± 1.06	61.71 ± 12.56	68.26 ± 17.86	68.48 ± 27.72	75.37 ± 15.70	68.46 ± 12.47
	Already-diagnosed diabetic	3.20 ± 1.23	2.20 ± 1.23	56.85 ± 15.46	58.32 ± 22.04	68.33 ± 25.27	70.10 ± 23.35	63.40 ± 17.29
	p-value	0.001*	0.001*	0.020*	0.001*	0.909	0.183	0.039*
*Significant at p-value < 0.05; SD = Standard Deviation, QoL = Quality of life; Q1 = question1; Q2 = question 2; HTN = hypertension

Correlation analysis

A significant inverse correlation was found between SBP and QoL scores in overall QoL rating (Q1), satisfaction with health conditions (Q2), and in all health subdomains. However, no significant correlation was observed between DBP and QoL scores except for a negative correlation with the social-relationship subdomain. Further, WC, WHR, and diabetes status were found inversely correlated with overall QoL rating (Q1) and satisfaction with health conditions (Q2). Conversely, BMI was observed to be positively correlated with overall QoL rating (Q1), physical health, and environmental health domains, while WC was found positively correlated with scores environmental health subdomain (Table 3).

Table 3

Correlation of two overall questions and four domains of WHOQOL-BREFF with SBP, DBP, BMI, WC, WHR and diabetes status
QoL Subdomains		Mean SBP	Mean DBP	BMI	WC	WHR	Diabetes Status
Q1. How would you rate your quality of life	Pearson Correlation	− .100^**	− .044	− .032	− .068^*	− .088^**	− .107^**
Q1. How would you rate your quality of life	p-value	.002*	.180	.326	.039*	.007	.003*
Q2. How satisfied are you with your health	Pearson Correlation	− .100^**	− .044	− .032	− .068^*	− .088^**	− .107^**
Q2. How satisfied are you with your health	p-value	.002*	.180	.326	.039	.007	.003*
Physical Health	Pearson Correlation	− .106^**	− .037	.090^**	.052	− .010	− .005
Physical Health	p-value	.001*	.264	.006	.116	.760	.886
Psychological Health	Pearson Correlation	− .092^**	− .032	.043	.006	− .030	− .056
Psychological Health	p-value	.005*	.326	.186	.861	.366	.125
Social-relationship	Pearson Correlation	− .085^**	− .069^*	− .034	.003	.014	.000
Social-relationship	p-value	.010*	.036*	.295	.929	.674	.996
Environmental	Pearson Correlation	− .099^**	− .042	.089^**	.085^**	.046	− .019
Environmental	p-value	.002*	.196	.006	.009	.160	.601
Overall	Pearson Correlation	− .125^**	− .064	.050	.045	.011	− .026
Overall	p-value	.000*	.052*	.130	.174	.744	.486
*Significant at p-value < 0.05; SBP = systolic blood pressure; DBP = Diastolic blood pressure; BMI = Body mass index; WC = waist circumferences; WHR = waist hip ratio

Odds ratio Analysis

Adjusted logistic regression analysis was performed to understand the association of poor QoL in various subdomains with hypertension, diabetes, obesity, abnormal WC, and abnormal WHR. As compared to non-hypertensive, already-diagnosed hypertensive individuals were found to have a significantly increasing risk for poor QOL in the overall assessment (OR = 1.560, p = 0.010), as well as in psychological (OR = 1.593, p = 0.007), and social relationships (OR = 1.520, p = 0.015) subdomains. Whereas, compared to normal BMI, underweight individuals were found to have a significantly higher risk for poor QoL in the physical health domain (OR = 2.159, p = 0.023). Abnormal WHR and WC were not found to be associated with QoL subdomains (Table 4). Further, newly diagnosed diabetic individuals were found to significantly increase the risk for poor QoL in the psychological health domain (OR = 1.816, p = 0.021) (Table 4).

Table 4

Adjusted odds ratio analysis for poor QoL
		Poor physical health		Poor psychological health		Poor social-relationship		Poor environmental health		Overall poor QoL
		OR^# (95% CI)	p-value	OR^# (95% CI)	p-value	OR^# (95% CI)	p-value	OR^# (95% CI)	p-value	OR^# (95% CI)	p-value
Blood pressure status	Non-hypertensive	Ref		Ref		Ref		Ref		Ref
	Newly-diagnosed hypertensive	1.193(0.848–1.677)	0.312	0.906 (0.640–1.282)	0.837	0.960(0.683–1.350)	0.856	0.975 (0.693–1.371)	0.882	0.913(0.652–1.279	0.994
	Already-diagnosed hypertensive	1.156(0.824–1.623)	0.402	1.593 (1.137–2.33)	0.007*	1.520 (1.086–2.126)	0.015*	1.185 (0.847–1.659)	0.322	1.560(1.112–2.18)	0.010*
BMI	Normal	Ref
	Underweight	2.159(1.110–4.20)	0.023*	1.314(0.691–2.498)	0.405	1.191(0.629–2.256)	0.591	1.066(0.565–2.013)	0.844	1.613(0.846–3.076)	0.146
	Overweight	1.075(0.691–1.672)	0.750	1.236(0.796–1.919)	0.346	1.114(0.718–1.728)	0.630	1.201(0.779–1.853)	0.407	1.185(0.767–1.833)	0.444
	Obese	0.848(0.591–1.148)	0.253	0.982(0.705–1.369)	0.916	1.320(0.950–1.834)	0.097	0.779(0.561–1.080)	0.134	1.129(0.815–1.563)	0.465
WC	Normal	Ref
WC	High	0.731(0.524–1.019)	0.065	0.754(0.541–1.051)	0.096	0.976(0.705–1.353)	0.886	0.696(0.501–0.967)	0.031*	0.794 (0.573-1.100)	0.166
WHR	Normal	Ref
WHR	High	0.783 (0.461–1.331)	0.366	0.648(0.384–1.096)	0.106	1.138 (0.670–1.934)	0.631	0.650(0.387–1.093)	0.104	0.756 (0.450–1.270)	0.290
Blood glucose status	Non-diabetic	Ref
	Newly-diagnosed diabetic	1.278(0.680–2.403)	0.446	0.701 (0.359–1.371)	0.300	0.823(0.431–1.572)	0.556	0.808(0.419–1.558)	0.524	0.973(0.515–1.837)	0.932
	Already-diagnosed diabetic	1.441(0.866–2.398)	0.160	1.816(1.092–3.018)	0.021*	0.903 (0.5411.509)	0.698	1.178(0.708–1.958)	0.528	1.506(0.898–2.524)	0.120
*Significant at p-value < 0.05; OR = odds ratio; CI = confidence interval; QoL = Quality of life; HTN = hypertension; BMI = Body mass index; WC = Waist circumference, WHR = Waist hip ratio; Ref = reference; ^#OR adjusted for age and sex

The present study was conducted to assess the subjective aspect of QoL with respect to selected commonly prevalent asymptomatic health conditions, namely hypertension, diabetes, and obesity in rural Punjab, India. Findings revealed that already-diagnosed individuals with hypertension exhibited lower satisfaction with their health and significantly lower mean QoL scores across all health subdomains compared to newly diagnosed hypertensive and non-hypertensive individuals. Align with present findings a population-based study by Martin et al., (2003) reported that awareness of hypertension status may contribute to poor perceived health and QoL [20]. Similarly, Rostrup and his colleagues conducted a study where participants with similar BP readings (156/96 and 159/95) were classified into two groups: Informed about blood pressure status and uninformed. In follow-up, the uninformed group showed a greater decrease in BP reading than the informed group [21]. A study conducted by Bloom and Monterosso also revealed similar findings that those who were earlier mislabelled as hypertensive but later found normotensive exhibited more depressive symptoms and, bad present health and worsening of health over time [22]. Worsing QoL due to diagnosis indicates that the act of labeling someone as hypertensive may cause a subsequent increase in BP and that is most probably mediated by increased sympathetic activity [21]. That, in turn, can significantly impact an individual's quality of life (QoL). Consequently, it is crucial to emphasize the importance of assessing QoL when evaluating the outcomes of hypertensive treatment.

A decline in QoL among already diagnosed hypertensive individuals may be attributed to three key mechanisms. Firstly, the direct effect of Blood pressure itself. Secondly, the side effects associated with antihypertensive treatments can further contribute to an impaired QoL. Thirdly, the consequences of labeling (the awareness of having hypertension can lead to excessive worry and apprehension [10].

Walker et al. (2017), through the lens of "Narrative Identity Theory," explained how receiving a diagnosis for an asymptomatic disease can trigger often overlooked forms of harm. While diagnosing asymptomatic diseases can bring medical and social benefits, it is essential to recognize the potential negative effects. Beyond the practical and psychological consequences, such a diagnosis can significantly impact a person’s identity and self-perception. These identity-related harms may lead to ongoing anxiety, alter beliefs about one's health, and foster a false sense of control over well-being. This highlights the need for healthcare providers to approach the diagnosis of asymptomatic conditions with caution, carefully weighing the potential effects on an individual’s identity and quality of life [23].

In the present study, informal interactions with participants revealed a notable psychological barrier to seeking medical evaluations, specifically blood pressure checks. Many individuals who perceive themselves as healthy tend to avoid these checks out of fear that a diagnosis of hypertension would disrupt their self-image of being disease-free. This fear stems from a deeper concern that acknowledging a health condition might alter their identity from being well to being sick, which can be a significant psychological burden. Moreover, the anticipation that awareness of hypertension could lead to the onset of symptoms contributes to this avoidance. Individuals often worry that learning about their condition might induce anxiety about managing it, including adhering to medication and making lifestyle changes. This anxiety over potential health changes further deters them from seeking a diagnosis, as they prefer to remain unaware rather than face the stress associated with a new health regimen.

This behavior reflects the concepts in Talcott Parsons’ sick role theory, which posits that a diagnosed individual is exempt from regular social responsibilities, such as work, and is expected to focus on recovery. However, for those with socio-economic constraints, adopting the “sick role” can be impractical. The prospect of losing income or facing other economic hardships can make the sick role more of a burden than a relief, leading individuals to avoid seeking a diagnosis to maintain their socio-economic stability. Consequently, the reluctance to undergo health checks is not merely about avoiding medical labels but also about managing socio-economic responsibilities. This avoidance strategy, driven by both psychological fears and practical constraints, highlights a significant challenge in health promotion. Addressing these barriers requires an understanding of how identity, anxiety, and economic factors interplay in shaping health behaviors. Recognizing and addressing these factors can lead to more effective strategies for encouraging proactive health management while considering individuals' socio-economic realities [23,24].

Coming to other health conditions, centrally obese individuals were found to have better QoL in environmental health domains. Further, underweight individuals were at risk for having poor QoL in the physical health domain. This observation aligns with previous studies from China and northern England, where individuals with higher BMI have been reported to perceive their health and social functioning more positively [23,25].

Additionally, studies among Asian populations suggest that overweight and obese individuals often report better physical and mental well-being compared to underweight individuals [26]. In many cultures, including some regions of Asia, larger body sizes are traditionally associated with wealth and prosperity, which could contribute to these perceptions of enhanced well-being.

In contrast, research among the Brazilian population indicates that individuals with higher BMI often report lower QoL scores. Historically, Brazilian culture favored curvier body types, but this preference has shifted towards thinner physiques. This cultural transition might explain the lower QoL scores among those with higher BMI, as societal norms now favor leaner body types [27]. Furthermore, in rural farming communities, higher body weight may be associated with better physical strength and an increased ability to handle demanding work. This perception could lead to overweight and obese individuals feeling more capable and confident in managing their workloads, thus contributing to their positive self-assessment of physical and mental well-being.

These contrasting findings underscore the necessity of understanding cultural contexts when evaluating the impact of body weight on QoL. Cultural perceptions significantly influence how body weight is associated with social and economic status, and thus impact overall well-being. Effective weight management and health promotion strategies should be tailored to reflect these cultural differences to improve QoL across diverse populations. By incorporating cultural perspectives into health interventions, we can better address the needs and improve the well-being of individuals in varying socio-cultural contexts.

Regarding diabetes, already-diagnosed diabetic individuals were found to have lower QoL mean scores in physical, psychological, and overall health domains than non-diabetics. However, odds ratio analysis revealed that already diagnosed diabetic individuals were at a risk for poor QoL in the psychological health domain. These findings suggest that diabetes may have a significant impact on various aspects of individual well-being, affecting both their physical and mental health. Similar findings were reported among the Iranian population; diabetic individuals have poor QoL in the psychological domain [28]. Conversely among the Bangladeshi population, Diabetic individuals were reported to have impaired physical health and better psychological health conditions [29]. Some of the previous studies have demonstrated that poor psychological health in patients with diabetes may be associated with poor glycemic control [30]. Therefore, addressing psychological well-being appears to be crucial for optimizing diabetes care and improving overall health outcomes.

Study Limitations

The present study has some limitations firstly hypertension diabetes and obesity tend to have some other co-morbidities, data on the presence of other co-morbidities was not collected in this study, so it is difficult to find out how the presence of other co-morbidities is responsible for reducing the QoL. Secondly, Data regarding treatment adherence and the number of drugs per day taken by diagnosed individuals was not collected, lack of this data it is difficult to find out how the number of drugs affects individual well-being and deteriorates QoL. Thirdly this was a cross-sectional study; it had some limitations because outcome and predictor variables were measured at the same time, and it is relatively difficult to establish a causal relationship from a cross-sectional study.

The present study revealed that individuals who were already diagnosed with hypertension, diabetes, and being underweight had poor QoL in rural communities of Punjab. These findings indicate that only clinical interventions for highly prevalent conditions like hypertension and diabetes may not be sufficient for improving QoL. Along with clinical intervention, it is important to provide proper knowledge, awareness, and management regarding hypertension, diabetes, and other non-communicable disease. Moreover, meaningful patient-doctor interactions should be prioritized for proper counselling and lifestyle modification regarding health conditions. These interactions may help to mitigate the potential negative consequences of labelling and improve the well-being of the diagnosed individuals. Additionally, this study also underscores the importance of understanding how communities perceive various health conditions, as these perceptions can significantly shape healthcare and disease management in communities.

Acknowledgments

The author would like to acknowledge the grant received from the Indian Councill of Medical Research (ICMR), the University of Delhi of India, and the participants of Mansa district, Punjab India for their participation in the present study.

Author’s contributions

SK, VC, KNS, RKD, KSSM, and NKD) the conception and design of the study, acquisition of data, or analysis and interpretation of data; NKD, VC and SK drafting the article or revising it critically for important intellectual content, RKD and PCJ has final approval of the version to be submitted.

Funding This work was supported by the Indian Council of Medical Research (ICMR), under grant number 5/4/1-33 2020-NCD-I, and the University of Delhi.

Data Availability Statement Data supporting the study’s findings are available from the corresponding author (NKD) upon reasonable request.

Declarations

Ethical approval and consent to participate All procedures involving human participants were done in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki. The study was approved by the Departmental Ethics Committee, Department of Anthropology, University of Delhi, Delhi-110007, India (Ref No. Anth/2010/455/1).

Conflicts of interests The authors have no relevant financial or non-financial interests to disclose.

Informed Consent Informed written consent, typed in the local language, was obtained from each participant prior to their recruitment.

Consent to Publish Not applicable.

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No competing interests reported.

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Quality of life viz-a-viz hypertension, obesity and diabetes: An exploratory study from rural Punjab, India

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Abstract

Introduction

Materials and Methods

Study area and recruitment of participants

Data collection

Statistical analysis

Results

General characteristics of the studied population

Variations in mean QoL scores with respect to selected health condition

Correlation analysis

Odds ratio Analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

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