1 Introduction

Sociocultural determinants of health are non-medical elements, sociopolitical, economic forces, and demographic and systemic factors, and living conditions which affect health and social outcomes in individuals [1]. Research demonstrated the negative impact of sociocultural determinants on the general population’s physical, social, and mental well-being [2, 3], and coping abilities of people with chronic illnesses [4, 5]. Psychological distress is described as non-specific symptoms of anxiety and depression [6] and emotional suffering due to demands and inability to cope with everyday stressors [7]. People with chronic illness are prone to experience distress because disease chronicity results in psychological burden, complicated treatment and care, and impacts self-care management at home and community [8,9,10].

Previous research demonstrated the negative impact of sociocultural determinants on the mental well-being and distress of people with chronic illnesses in North American, Australian, and Asian context [4, 5, 11, 12]. Studies have also determined psychological distress among people with chronic kidney disease [13], cancer [14], Chronic Obstructive Pulmonary Disease (COPD) [15], psoriasis [16], diabetes [17], endometritis [18], arthritis [19], and fibromyalgia [20] among individuals from South East Asia, Europe, and North America. These studies reported more disease-specific determinants such as packed smoking years with COPD [15], HbA1c levels influencing perceived diabetes severity and self-efficacy [17], stable relationship, shorter time for diagnosis, and pelvic pain with distress in endometriosis [18]. However, very limited information was provided about general determinants of psychological distress. These studies reported that commonly reported determinants of psychological distress were age, gender, marital status, education, income, ethnicity, precarious living conditions, access to health care services and caregivers, and type of health service use [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. While these studies offer valuable disease-specific and some sociocultural determinants of psychological distress, there is an apparent gap in the literature concerning a more detailed examination of determinants that may be more prevalent in low and middle-income countries and in South Asian context (e.g., Pakistan, Afghanistan, Bangladesh, India, and Sri Lanka). Additionally, no studies were located to have focused on examining the relationship of sociocultural determinants with psychological distress and coping strategies in chronic illness. Studies and systematic reviews noted an increasing prevalence of chronic diseases among South Asian individuals [21, 22] with higher global prevalence of diabetes, Chronic Kidney Disease (CKD), and cardiovascular disorders [22, 23]. For example, Shrestha et al. [22] based on a systematic review and meta-analysis 24 studies reported a pooled prevalence of 14% for CKD, and prevalence of 27% for CKD with hypertension, and prevalence of 31% with CKD and diabetes among South Asian individuals. South Asian individuals also have increasing prevalence of multiple chronic conditions [21, 24]. For example, Eto et al. [24] based on data of 837,869 individuals reported that the prevalence of early onset multimorbidity among South Asian individuals was 59%. Pati et al. [21] based on a systematic review of 13 studies reported the multimorbidity among South Asian individuals ranged from 4.5 to 83%.

While the evidence indicates increased burden of chronic disease among South Asian individuals, there has been limited research on examining sociocultural determinants of distress, coping, and mental well-being of those who live with chronic diseases. Additionally, most of the research in South Asian context focused on population in India with limited studies from other South Asian countries [25]. A qualitative systematic review of 21 studies reported that South Asian individuals with chronic conditions experience depression, anxiety, and maladaptive coping [26]. Psychological distress and maladaptive coping can have detrimental effects on personal, family, and social lives of individuals affecting their work, relationships, leisure time, social activities, and quality of lives [25,26,27]. Nevertheless, reviews pointed out the need to further understand demographic and sociocultural determinants affecting the distress and coping among South Asian individuals [21, 24, 25]. Better understanding of psychological distress and coping and associated determinants can provide valuable insights into developing targeted psychosocial and person-centered coping programs and interventions. Therefore, this study was conducted to address this gap and gather preliminary understanding of the sociocultural determinants which may affect distress and coping among Pakistani South Asian individuals.

2 Purpose

To determine the levels of psychological distress and coping among South Asian individuals with chronic illness and identify sociocultural determinants affecting distress and coping.

3 Methods

A cross-sectional design was used. The data were collected at two large tertiary care hospitals in Khyber Pakhtunkhwa, Pakistan. The tertiary care hospitals had a capacity of around 1000 beds in the inpatient units and around 20 outpatient units.

3.1 Sample and sampling

All patients admitted in the inpatient settings and attending outpatient clinics were included. The inclusion criteria were: age 18 or above, having visited the inpatient or outpatient setting at least two times and being diagnosed with a chronic illness by a health care professional. Chronic illness was defined as illness lasting for more than 6 months. The researchers chose at least two times visitation as the criterion because the first visit may denote that the participants visited the doctor for health assessment or examination. However, more frequent visitation to seek health advise may denote that patients were experiencing chronic issues. This justification was based on researchers’ clinical experiences of working in these settings. Convenience sampling was used because random sampling was not possible due to lack of sampling frame; as patients were recruited from both outpatient and inpatient settings and it is difficult to discern the number and schedule of patients. The sample size was calculated using the formula (n = z2 [p × q]/d2), where n = sample size, p = estimated proportion of the primary study variable was approximate (50%), q = 1 − p (50%), and d = margin of error (5%). The estimated sample size was 384.

3.2 Data collection instruments

Demographic information was collected using a questionnaire consisting of 13 items about age, gender, ethnicity, language, socioeconomic status, educational status, type of family, primary decision maker in the family, type of community, type of chronic illness, number of years living with chronic illness, and type of health care services utilized. These determinants were selected based on existing research on determinants affecting individuals with chronic illnesses in Pakistan [8, 28,29,30,31,32,33,34].

Psychological distress was measured using the Hospital Anxiety and Depression Scale (HADS). Zingmond and Snaith [31] developed HADS to measure psychological distress. While the major constructs are anxiety and depression, the scale has been widely used for measuring psychological distress in various studies [14, 15, 18]. HADS comprises two subscales and 14 items; the Hospital Anxiety and Depression Scale Depression Subscale [HADS-D]) subscale and the Hospital Anxiety and Depression Scale Anxiety Subscale [HADS-A]). A three-point Likert scale is used as the response set: ‘0’ (not at all) to ‘3’ (most of the time). Items 1, 3, 5, 7, 9, 11, and 13 are designed to assess HADS-A and items 2, 4, 6, 8, 10, 12, and 14 to determine HADS-D. The total HADS-A and HADS-D score is the sum of all the items. During scoring seven items are reversed scored (3, 5, 11, 13, 6, 8, 10). The total score for the subscales ranges from 0 to 21 (0–7 = normal, 8–10 = borderline, and 11–21 = either anxious or depressed). In the original scale, Zingmond and Snaith assessed the internal consistency of both anxiety and depression subscales using Spearman’ correlations. The correlations for the items of HADS-A ranged from + 0.76 to + 0.41 (p < 0.01), and for HADS-D ranged from + 0.60 to + 0.30 (p < 0.02). The Urdu-translated HADS developed by Lodhi et al. [32] was used in this study. The Cronbach’s alpha for the anxiety subscale was 0.82, and 0.64 for the depression subscale. The overall alpha was 0.84.

The Brief COPE scale was used to determine the coping strategies. Carver [33] originally developed the scale and reported that the alpha reliabilities ranged from 0.50 to 0.90. The Urdu version of the scale was used [34]. The Urdu version of the scale comprised of three second-order factors with a total of 25 items compared to original scale which included 14 subscales and 28-items [33]. Systematic review of Brief COPE scale has demonstrated that both versions of the scale offer parsimonious way to measure coping strategies and researchers should explicitly state the version used [35]. The Cronbach’s alpha coefficient for the subscales was: problem-focused coping (0.77), avoidance coping (0.67), and emotion-focused coping (0.66). Problem-focused coping measures individuals’ abilities about active coping, informational support, planning, and positive reframing. Avoidance coping is characterized by self-distraction, denial, substance use, and behavioural disengagement. Emotion-focused coping determines individuals’ ability to vent and use emotional support, humour, acceptance, self-blame, and religion to cope. A four-point Likert-type scale is used for scoring, where 0 = I usually don’t do this at all, 1 = I usually do this a little bit, 2 = I usually do this a medium amount, 3 = I usually do this a lot. A composite score is not computed. Instead, each subscale is scored separately. There is no maximum or minimum score; higher scores demonstrate greater and low scores demonstrate less utilization of each coping strategy.

3.3 Data collection process

Data were collected from November 2021 until August 2022. Recruitment strategies included posters and outreach to patient support groups in the hospitals. The researchers dropped off the printed scales at the inpatient and outpatient units of the hospitals, and the nurses in the unit invited patients to complete the printed scales. The researchers visited the hospitals biweekly to collect the completed scales. Before data collection, the participants received complete written and verbal study information from the nurses. The nurses who helped in data collection received training in data collection.

3.4 Data analysis

The data were analyzed using IBM SPSS (Version 26.0). Descriptive statistics were calculated for the items. Independent T-test and One-Way ANOVA were used to examine the differences in mean scores, and correlations were used to determine the relationship between demographic and sociocultural variables and distress and coping strategies. The influence of sociocultural determinants on depression, anxiety, and coping scores was assessed by performing three-step hierarchical regression analysis. Three models were tested for each variable. The first model included demographic and sociocultural variables (i.e., age, gender, ethnicity, type of residence, family type, socioeconomic status, education level, and primary decision maker). The second model included all the demographic and sociocultural variables and disease related factors (i.e., type of chronic disease, years of living with the disease, number of family caregivers, and type of health care services utilized). The third model included all of the chosen demographic, sociocultural, disease related variables combined with two smoking and addiction. The decision to include the variables in this sequence was informed by previous qualitative and quantitative research [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. The p-value of ≤ 0.20 was chosen as a threshold of the inclusion and retention of the determinant in the regression model. The overall goodness-of-fit of each regression model was determined from the adjusted R2 value. The results of regression were reported as B and β coefficients with corresponding p-values, and R2 change.

3.5 Ethical considerations

This study was conducted in accordance with relevant guidelines and regulations.

Ethical approval for the study was obtained from the Institutional Review Board of Saidu Medical College, Swat, Pakistan (Approval#18-ERB/2021). All research participants provided a written informed consent for participation. The permission to use the data collection instruments was obtained from the scale developers.

4 Findings

4.1 Demographic information

In total, 69.8% of the patients were male (n = 268), and 30.2% were female (n = 116), with a mean age of (52.49 ± 15.18). Most of the participants lived in rural communities (n = 296, 77.1%) and identify their ethnicity as Pashtoon (n = 363, 94.5%). Most of the patients identified their socioeconomic status as middle class (Rs. 2253–3808 income/month) (n = 113, 29.4%), upper middle class (Rs. 3808–7769 income/month) (n = 98, 25.5%), and lower middle class (Rs. 1166–2253 income/month) (n = 97, 25.3%). Most of the patients were uneducated (n = 177, 46.1%) or had primary level education (Grade 5) (n = 111, 28.9%), followed by grade eight (n = 50, 13%) and high school (n = 24, 6.3%). Most participants lived in joint families (n = 339, 88%). Men were identified as the primary decision-makers in the household (n = 346, 90.1%).

Most of the patients noted having chronic respiratory diseases (n = 104, 27.1%), renal problems (n = 99, 25.8%), and cardiac problems (n = 58, 15.1%). Most of the participants were non-smokers (n = 175, 45.6%) and among smokers (n = 158, 41.1%) a majority of participants used chewing tobacco (n = 161, 41.9%). To manage chronic illnesses, most of the participants relied on family caregivers. Most participants had three or more family caregivers (n = 304, 79.2%). Most of the participants used public health care services (n = 321, 83.6%) followed by private (n = 50, 13%), and community care services (n = 13, 3.4%) (Table 1).

Table 1 Demographic data
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4.2 Psychological distress and coping strategies

The mean HADS-D score was 11.38 ± 2.53, and the HADS-A score was 13.42 ± 2.34, indicating high levels of depression and anxiety. The most commonly used coping strategies were problem-focused coping (15.96 ± 4.95), followed by emotion-focused coping (15.01 ± 2.33), and avoidance coping (13.89 ± 4.77). The most common problem-focused coping strategy was: I’ve been thinking hard about what steps to take (2.17 ± 0.82), and the common emotion-focused coping strategies were: I’ve been trying to find comfort in my religion or spiritual beliefs (2.72 ± 0.61), and I’ve been praying or meditating (2.72 ± 0.58) (Table 2).

Table 2 Comparison of depression, anxiety, and coping strategies based on type of chronic illnesses
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Group comparisons based on the type of chronic illness revealed that participants with musculoskeletal problems (12.68 ± 2.40) and chronic respiratory diseases (11.79 ± 2.55) reported higher levels of depression than those with cancers, cardiac problems, mental health problems, and endocrine issues. Participants with cardiac (13.84 ± 2.42) and musculoskeletal problems (13.73 ± 2.72) reported higher anxiety levels. In terms of coping strategies, participants with chronic respiratory problems (14.05 ± 4.22), cancers (15.53 ± 2.33), and renal problems (14.91 ± 4.32) mainly used coping avoidance strategies. Emotion-focused coping strategies were used frequently by participants with mental health problems (15.95 ± 2.37), and problem-focused coping strategies were used by those with cardiac problems (20.03 ± 3.60), cancers (19.66 ± 4.16), and endocrine problems (17.50 ± 3.33) (Table 3).

Table 3 Coping strategies
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5 Regression analysis

5.1 Depression and anxiety

The hierarchical analysis of sociocultural determinants of anxiety showed that model 1 accounted for variation in anxiety levels (R2 = 0.070, F(16, 367) = 0.663, p = 0.04) explaining 7% of the variance compared to model 2 (R2 = 0.116, F(16, 367) = 0.663, p = 0.061) and model 3 (R2 = 0.119, F(16, 367) = 0.663, p = 0.099) In model 1, the significant predictors included ethnicity and socioeconomic status (Table 4). The regression analysis of depression revealed that none of the three models showed significant variation in depression levels; model 1 (R2 = 0.028, F(16, 367) = 0.663, p = 0.205), model 2 (R2 = 0.084, F(32, 351) = 0.784, p = 0.205), and model 3 (R2 = 0.084, F(35,348) = 0.916, p = 0.205) (Table 5).

Table 4 Hierarchical regression analysis of anxiety
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Table 5 Hierarchical regression analysis of depression
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5.2 Coping strategies

For problem focused coping, model 1 explained 30.6% of the variance (R2 = 0.306, F(16, 366) = 10.077, p ≤ 0.001), model 2 explained 59.5% of the variance (R2 = 0.595, F(32, 350) = 16.062, p ≤ 0.001), and model 3 accounted for 63.6% of the variance (R2 = 0.636, F(35, 347) = 17.287, p ≤ 0.001). The significant predictors across all three models included age, type of residence, ethnicity, socioeconomic class, education level, type of chronic disease, number of family caregivers, smoking, and addiction (Table 6).

Table 6 Hierarchical regression analysis of problem focused coping
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For emotion focused coping, model 1 explained 27.6% of the variance (R2 = 0.276, F(16, 366) = 8.705, p ≤ 0.001), model 2 explained 42.0% of the variance (R2 = 0.420, F(32, 350) = 7.908, p ≤ 0.001), and model 3 accounted for 46.1% of the variance (R2 = 0.461, F(35, 347) = 8.474, p ≤ 0.001). The significant predictors across all three models included type of residence, ethnicity, socioeconomic class, family type, education level, type of chronic disease, years of living with chronic disease, type of health care services, number of family caregivers, smoking, and addiction (Table 7).

Table 7 Hierarchical regression analysis of emotion focused coping
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For avoidance coping, model 1 explained 16.6% of the variance (R2 = 0.166, F(16, 363) = 4.526, p ≤ 0.001), model 2 explained 64.8% of the variance (R2 = 0.648, F(32, 347) = 19.986, p ≤ 0.001), and model 3 accounted for 66% of the variance (R2 = 0.660, F(35, 344) = 19.100, p ≤ 0.001). The significant predictors across all three models included age, gender, type of residence, socioeconomic class, family type, education level, type of chronic disease, type of health care services, number of family caregivers, and addiction (Table 8).

Table 8 Hierarchical regression analysis of avoidance coping
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6 Discussion

This study found that individuals with high levels of psychological distress utilized avoidance, emotion, and problem-focused coping strategies regularly and relied on physical and spiritual ways of tackling their distress. High levels of depression and anxiety levels were determined among individuals with all types of chronic illnesses. The highest levels were reported in those with musculoskeletal problems, chronic respiratory disorders, and cardiac problems. Consistent with previous studies, the individuals in this study had higher levels of psychological distress compared to individuals with cancer (HADS-D: 4.34 ± 3.92; HADS-A:6.76 ± 4.31) [14], COPD of GOLD3 + stage (HADS-D: 4.50 ± 3.97; HADS-A: 4.25 ± 3.45) [15], endometriosis [HADS-D (7.5 ± 3) and HADS-D (6.1 ± 3.5)] [18], and fibromyalgia syndrome (HADS-D: 9.00; HADS-A: 9.00) and rheumatoid disease (HADS-D: 7.00; HADS-D: 8.00) [20]. There are two likeliest explanations for higher levels reported in this study. First, the data were collected during the COVID-19 pandemic, which could have increased the levels of distress in individuals due to the inability to self-manage or access care at acute and community care centers. Second, the sociocultural context, such as limited resources, limited access to health care, inadequate emphasis on mental health, poverty, family burden, limited faith in psychological therapy, religious fatalism, and living conditions, could have aggravated the levels. In terms of patient related factors, the burden of chronic disease, overreliance and often co-dependence on family members can contribute to higher levels of stress [8, 28, 29]. Although this explanation is not well-supported by this study, previous studies from Pakistan demonstrate that such factors can lead to distress and suffering [8, 36, 37]. Health professionals can collaborate with community-based patient support organizations to design and evaluate interventions to address distress and improve coping among these individuals.

Most individuals used problem-focused coping and emotion-focused coping strategies compared to avoidance coping strategies. However, differences were noted in individuals with different chronic diseases and their preference for certain types of coping strategies based on the nature of the disease. Problem-focused coping is peculiar to active coping, using informational support, planning, and positive reframing. In contrast, emotion-focused coping is characterized by the ability to vent and use emotional support, humour, acceptance, self-blame, and religion to cope. The use of these coping strategies is consistent with the sociocultural context because previous research demonstrated that in Pakistani culture there is mostly a greater emphasis on learning to live with the disease without burdening others and relying on religion and faith to combat the disease [8, 38]. Zeb et al. [8] reported that many people with COPD consider it the devil’s work and frequently use faith healing and complementary medicines for self-care and cure. Family members and communities are also very involved in the chronic disease management of their relatives and neighbours in rural and semi-urban areas [8, 38]. Most of the individuals in this study have many similar characteristics, further supporting the use of problem-focused and emotion-focused strategies. However, further research is needed to better understand why South Asian individuals with certain diseases preferred one or the other type of coping strategies.

Depression and anxiety levels were associated and influenced by years of living with a chronic illness and ethnicity. However, almost all of the sociocultural variables influenced the coping strategies of individuals. Individuals in rural areas and living in joint families mainly use emotion and problem-based coping strategies, which are explained by the sense of community and family cohesion and collaborative work to manage chronic illness [22, 38]. Individuals with women as primary decision-makers in their families used more avoidance-focused coping compared to those participants who had men as the primary decision-makers. Avoidance coping is characterized by self-distraction, denial, substance use, and behavioural disengagement. It is plausible to argue that men with chronic illnesses may find it difficult to accept women’s leadership due to the male-dominant culture. Hence, these relied more on avoidance-type coping strategies due to hegemonic masculinity.

7 Implications for research and practice

They study findings can be useful for developing optimized and person-centered programs and interventions to address psychological distress and promote coping behaviours among individuals with chronic illness. Mental health is often an underemphasized issue in South Asian culture and speaking about mental health is often considered shameful [39, 40]. Therefore, it is important that community and public mental health programs and rehabilitation programs are initiated to address the distress and promote healthy coping among those living with chronic illnesses. Mental health practitioners can work together with these patients in the acute care settings or prepare them for healthy coping at the time of discharge. Future research can focus on developing and evaluating the effectiveness of hospital and community based mental health programs. Further research is also warranted to examine the impact of sociocultural determinants as mediating or moderating factors as well as more intersectional and longitudinal analysis of variables such sex, gender, multiple ethnicities, socioeconomic status, family system on phycological distress and coping. Several above listed sociocultural determinants such as religious fatalism, limited belief on psychological interventions, and family process and functioning were not examined in this study because of the lack of valid and reliable data collection instruments in the local language. Further qualitative and quantitative research may focus on deeper exploration of these factors, developing data collection instruments, and combining these factors with other determinants to examine differences in coping and distress.

8 Limitations

Our sample comprised participants from Northern Pakistan. Therefore, given the cultural difference, the findings may not be generalizable to other developed countries. Further studies are needed in other regions of Pakistan and South Asian countries to generate additional insights into the distress and coping behaviours among South Asian individuals. Nevertheless, this study offers insights into the role of sociocultural factors in contributing to distress and coping abilities of similar minority populations residing in Western countries. Our definition of socioeconomic status was restricted to the monthly household income of the participants. However, socioeconomic status can also be influenced by other factors such as transportation, distance from healthcare facilities, and the number of household members. Future studies should consider more broader definition of socioeconomic status. Additional limitations include convenience sample, self-selection bias, and possible social desirability bias which could affect the statistical generalizability of the findings. Therefore, further longitudinal studies with random samples may be useful to determine changes in psychological distress and coping behaviours of South Asian individuals.

9 Conclusions

Higher levels of psychological distress in individuals with chronic illnesses underscore the importance of monitoring and implementing family and community-based support approaches. Immediate programs should be implemented to provide adequate mental health and counselling resources to individuals with chronic illnesses to combat their distress, depression, and anxiety. Policies should be developed to implement mental health programs in community settings involving a wide range of healthcare professionals. Varied use of distinct coping strategies to manage psychological distress was influenced by the type of chronic illness, living conditions, educational level, years of living with a chronic illness, family dynamics, and available support systems. Future cross-cultural research is critical in developing a better understanding of sociocultural determinants affecting psychological distress and coping of individuals with chronic illness.