Background

Several global interventions to improve health outcomes have been implemented over the last two decades. The Sustainable Development Goals (SDGs) promulgated in 2015, for example, seek to ensure that developing countries around the world can achieve 17 development-oriented goals by 2030 [1]. Goal three aims to ensure healthy lives and promote well-being for all people of all ages, and target 3.4 specifically aims to reduce premature mortality from noncommunicable diseases by one-third by 2030 [1].

Despite efforts by various countries to meet the SDG targets, chronic illness remains a major global health issue, with the number of people affected steadily increasing [2]. About 41 million people worldwide die of CNCDs each year. This represents 71% of all deaths. Most of those deaths were caused by just four CNCDs: cardiovascular diseases (17.9 million deaths), cancer (9 million deaths), chronic respiratory diseases (3.8 million deaths), and diabetes (1.6 million deaths) [3]. With the obvious chronic nature of these non-communicable diseases, it is evident that CNCDs have become the heaviest burden to healthcare systems globally [4, 5].

CNCDs are the main causes of death in low- and middle-income countries (LMIC) [6], and they come at a high cost that goes beyond health, trapping people in poverty, denying them a dignified life, undermining labour productivity, and jeopardising economic growth [7]. CNCDs are also becoming a problem via widening gaps in opportunity, wealth, and power. Their effect on low- and lower-middle-income countries is a significant barrier. In all nations, the poorest and most vulnerable communities are the most at risk and have the least access to the services needed to detect and treat CNCDs [8].

Over the previous two decades, disability and death rates from chronic diseases such as diabetes, hypertension, and stroke have increased in many Sub-Saharan Africa (SSA) countries [9, 10]. In the same manner, Ghana’s position is no different. The WHO reported that CNCDs account for around 43% of all deaths in Ghana. Cardiovascular illnesses represented 19%; malignancies, 5%; chronic respiratory diseases, 3%; and other CNCDs, 13%. Ghana similarly reported a 21% risk of premature mortality due to CNCDs [8].

These records inherently support the interest in chronically ill patients’ health-related quality of life (HRQoL) as a key health outcome indicator, with a growing body of literature over the past few years [11]. This is because of the absence of an adequate cure for several CNCDs [12]. Empirical evidence has proven that there has been a significant decrease in HRQoL in patients with CNCDs and indicated HRQoL as an important predictor of morbidity and mortality [13, 14].

Findings from a study conducted in three European countries (Italy, Spain, and Greece) revealed that the quality of life (QoL) worsens for physically inactive people and those with multiple CNCDs. The same study also posited that there is a direct association between QoL and health perception [15].

On the other hand, a Chinese study that sought to measure the QoL among hypertensive patients found the perceived economic burden caused by hypertension to be the most common factor impacting the patient’s HRQoL. They also found that females were more susceptible to suffering from a decreased QoL as a result [16] An Ethiopian study that measured the HRQoL among diabetic patients found a high overall HRQoL score with social domains having a higher mean score. It also found a statistically significant association between educational status, marital status, occupation, duration of diabetes as well as diabetes-related complications and HRQoL [17]. Another study by Abegaz et al. [18] in Ethiopia which measured HRQoL among cancer patients revealed that HRQoL of cancer patients in Ethiopia is comparatively low. The study cited the unmet needs of cancer patients and the level of satisfaction with the overall care to be influencing the extent of HRQoL. The previous literature has shown that numerous elements might impact an individual’s HRQoL while suffering from CNCDs, yet HRQoL can also be an independent factor that impacts other areas of an individual’s life. Although there is a growing number of pieces of literature that seek to investigate the HRQoL of individuals, there remains a paucity in the literature on the HRQoL of persons living with CNCDs. Hence, this study sought to investigate the HRQoL of adults living with CNCDs and the factors influencing it. The findings could positively inform policy decisions on the management of CNCDs in Ghana and beyond.

Theoretical issues

This study was underpinned by the Social Ecological Model (SEM), which was originally conceptualised to understand human development. Urie Bronfenbrenner, the main proponent of the SEM introduced this modelintroduced this model in 1979. Subsequently, numerous scholarly inquiries have embraced and adapted this model to delineate the diverse strata of influence within a specific milieu [19]. The SEM posits that individuals are subject to multifaceted influences at five distinct societal levels, thereby elucidating the complex interplay of factors shaping human behaviour and development within a given context.

In his initial theory, Bronfenbrenner postulated that the entire ecological system in which growth occurs needs to be considered to understand human development. In subsequent revisions, Bronfenbrenner acknowledged the relevance of biological and genetic aspects of the person in human development [20].

The ecological approach provides a useful framework for comprehending the various aspects that influence health and well-being. It is a model that can help provide a comprehensive view of the elements that influence certain health behaviours, including social determinants of health. The multiple levels of influence recognized under the social-ecological models include intrapersonal/individual factors, interpersonal factors, institutional or organizational factors, community factors and public policy factors.

Potential factors to influence health-related quality of life

In this study, intrapersonal factors, including specific intrinsic socio-demographic variables such as age, sex, and educational level, were systematically examined to gauge their predictive capacity concerning Health-Related Quality of Life (HRQoL) [21, 22]. Concurrently, interpersonal and community-level variables such as marital status, religious affiliations, and ethnic background were scrutinized. These variables were of particular interest due to their roles as conduits for social support, a factor posited to significantly impact HRQoL [23]. Additionally, the study delved into individuals’ diagnoses of select Chronic Non-Communicable Diseases (CNCDs), the duration of their conditions, and any comorbidities they might be managing. This comprehensive investigation was motivated by the understanding that these variables are likely influenced by one or more domains at various levels within the Social Ecological Model (SEM). To visually represent the intricate interplay of these factors, we developed Fig. 1, a conceptual framework aligned with the a priori hypotheses guiding our study.

Fig. 1
figure 1

Conceptual framework

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Materials and methods

Study site

The Ho Teaching Hospital (HTH), situated in the Ho Municipality of the Volta Region, is one of the latest teaching hospitals in Ghana. The hospital provides both out-patient and in-patient services where walk-in patients can receive general or specialized consultations, and prolonged treatment and observation respectively. The hospital has five clinical departments: internal medicine, surgery, obstetrics and gynaecology, child health, and public health. The HTH’s clinical services are broadly divided into two categories: general and specialist clinical services. General services include general surgery, urology, paediatrics, orthopaedics, etc., whereas specialist services are delivered by disease-/condition-specific clinics such as the diabetes clinic, fertility clinic, physiotherapy, anti-retroviral therapy clinic, and eye clinic.

The HTH has been chosen for this study because it has become one of the top hospitals in the country and receives both primary and referral cases from the entire Volta region and parts of the neighbouring regions in Ghana.

Data source and study design

This was a health facility-based study that utilized a quantitative descriptive cross-sectional design. Data obtained from respondents was used to determine the HRQoL of adults living with chronic non-communicable diseases. The primary target population of this study was adults diagnosed with hypertension, cancer (breast, cervical, prostate, or lung cancers), chronic kidney disease (CKD), stroke, or diabetes. Adults with any of the CNCDs of interest who visited the Ho Teaching Hospital’s disease-/condition-specific clinics, as well as chronic disease patients in in-patient care units, were included in the study. In contrast, adults with any of the CNCDs of interest who visited the hospital’s disease-/condition-specific clinics but were foreign nationals, severely or terminally ill and unable to effectively communicate were excluded from participating in this study. A probabilistic sampling method utilizing the simple random sampling technique was adopted in recruiting respondents for the study. We relied on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement in writing the manuscript [see Additional File 1] [24].

Sample size determination

In this study, a total of 432 adults living with CNCDs were included in the final analysis. This sample size was determined using the single proportion population formula by Cochran (1977): n= [z² p (1-p)]/d². The sample size was estimated based on 50% (0.5) prevalence estimates of CNCDs in Ghana as used by Osei et al. in their 2021 study [25].

Data collection and instrument

Structured interviewer-administered questionnaires were used in collecting data from respondents who met the eligibility criteria. Even though the data were collected at the hospital, the interviews were conducted at locations where others could not listen to the responses to the questions. The data collection instrument utilized consisted of two sections with the first section gathering respondents’ socio-demographic characteristics and the second section gathering data on respondents’ HRQoL [see Additional File 2]. The study adopted the EQ-5D-5L protocol from EuroQol to measure the HRQoL of patients. The questionnaire was mainly administered in English although a proportion was administered in the Ghanaian language (Ewe) local to the study setting. To ensure that the use of the local language did not affect the quality of data collected, the training organised for assistants also focused on interpretations of the instrument in the language to ensure that the right questions were asked. Before the main data collection, the instrument was pre-tested among 50 CNCD patients in the Volta Regional Hospital in the Volta Region of Ghana. The pre-testing helped to fine-tune the choices of words to use in the interpretation of the instrument in the local language.

Study variables

Outcome variable

HRQoL was the main outcome variable in this study. This was measured using the EQ-5D-5L protocol from EuroQol. The protocol measured five different dimensions of an individual’s health to posit the overall HRQoL. These dimensions included “Mobility”, “Self-care”, “Usual Activities”, “Pain/Discomfort”, and “Anxiety/Depression”. Each dimension, hence, had five levels of responses which measured respondents’ state or functionality across the dimensions. The responses according to the levels were “No problem”, “Slight problem”, “Moderate problem”, “Severe problem”, and “Extreme problem/Unable to do”. The validity and reliability of the EQ-5D-5L protocol are adequately established in other studies [26,27,28,29].

Explanatory variables

Age, sex, marital status, education, religion, ethnicity, diagnosed CNCD, diagnosis duration, comorbidity status, specific comorbidities, recommended behavioural/lifestyle changes: physical activity, dietary changes, smoke cessation, and alcohol intake moderation were the explanatory variables used for this study [see Additional File 3]. These variables were selected based on their relevance to the study and as found by other studies [25, 30].

Statistical analyses

Data analyses were done using Stata software version 17.0 (Stata Corporation, College Station, TX, USA). We adopted the Ugandan EQ-5D-5 L value set to compute the HRQoL index for this study [31]. Descriptive summary statistics were estimated for socio-demographic variables and EQ-5D-5L dimensions. As the EQ-5D-5L index was non-normally distributed, the differences between the index and explanatory variables were computed using the median, lower and upper quartiles. A Kruskal-Wallis test was performed to establish statistical significance between explanatory variables and the outcome variable. A quantile regression was performed to estimate the effect of the explanatory variables on the HRQoL index of respondents. A p-value of 0.05 or less was considered statistically significant in this study. Variables that showed significance (p < 0.05) in the bivariate quantile regression analysis were included in the multivariable quantile regression model in which statistical significance was considered at p < 0.05.

Results

Socio-demographic characteristics of the patients

Table 1 presents the background characteristics of thepatients. The mean age of patients was 59 years with a standard deviation of 11.31. About 48% were 60 years and above, and 52.5%, were females. Additionally, 69.7% of the study patients were married whereas 40.7% had a tertiary-level education. A total of 83.3% reported being Christians and 54.4% reported being Ewes. Amongst the five CNCDs of interest in this study, 43.5% of the total study population reported being diagnosed with hypertension. However, the results showed that 40.7% of the study respondents also reported living with the various conditions for a minimum of a year and a maximum of five years while only 38.7% of the study population reported living with at least one comorbidity. Furthermore, 99.1% of the study respondents reported being on behavioural and lifestyle treatment with 84.7%, 92.7%, and 16.5% respondents reporting being recommended dietary changes, physical exercise/activity, and alcohol intake cessation, respectively.

Table 1 Socio-demographic characteristics of respondents
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Summary statistics of respondents’ HRQoL index using median, lower and upper quartiles

Table 2 below provides the summary statistics of the five dimensions of the EQ-5D-5 L utilizing the medians of each dimension as well as the lower and upper quartiles to illustrate the distribution of the respondents’ health-related quality of life index.

Table 2 Summary statistics of the EQ-5D-5 L using median, lower and upper quartiles
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Distribution of levels of perceived problems based on EQ-5D-5L dimension

Table 3 presents the distribution of levels of the perceived problem by respondents based on the dimensions of the EQ-5D-5L. It was found that the majority of respondents reported having “slight problem” and “moderate problem” across the five dimensions. The “anxiety/depression” and “usual activities” saw 2.3% and 1.8% of respondents reporting extreme problems.

Table 3 Distribution across EQ-5D-5L dimensions
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Table 4 shows the distribution of perceived problem levels across the EQ-5D-5L dimensions based on two levels of problem categorization: “No problem” and “Some problem.” More than half of the respondents reported having a problem in at least one of the five dimensions. The most problems were reported in the dimensions “Anxiety/Depression” (94.4%) and “Pain/Discomfort” (91.4%).

Table 4 Distribution of levels of perceived problems using two levels of categorization
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Figure 2 shows the distribution of perceived problem levels across the EQ-5D-5L dimensions based on two levels of problem categorization: “No problem” and “Some problem”, as recommended by the EuroQol Research Foundation [23]. More than half of the respondents reported having a problem in at least one of the five dimensions. The most problems were reported in the dimensions “Anxiety/Depression” (94.4%) and “Pain/Discomfort” (91.4%).

Fig. 2
figure 2

Distribution of perceived problems according to dimensions

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Predictors of HRQoL

Quantile regression of health-related quality of life

Table 5 presents the quantile regression analysis performed on respondents’ background characteristics and HRQoL. The results show that respondents who were divorced/separated (aOR=-0.52, 95% CI=-0.71, -0.33, p = < 0.001) were less likely to report a high index for HRQoL. However, our result posited that respondents diagnosed with CKD (aOR = 0.26, 95% CI = 0.10, 0.42, p = 0.001), diabetes (aOR = 0.28, 95% CI = 0.11, 0.45, p = 0.001), hypertension (aOR = 0.35, 95% CI = 0.19, 0.50, p = < 0.001) and stroke (aOR = 0.26, 95% CI = 0.11, 0.40, p = 0.001) were all more likely to report a high index for HRQoL compared to respondents diagnosed with cancer but respondents who reported living with other comorbidities (aOR =-0.95, 95% CI=-0.15, -0.04, p = 0.001) were less likely to report a high index for HRQoL. Additionally, we found that respondents who reported being recommended a change in dietary habits (aOR = 0.22, 95% CI = 0.13, 0.31, p = < 0.001) were more likely to report a high index for HRQoL.

Table 5 Quantile regression model on the predictors of health-related quality of life
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Discussion

We examined the health-related quality of life of adults living with CNCDs in the Ho Municipality. We found that the majority of our respondents reported having problems with all the dimensions of the EQ-5D-5L scale. The most affected dimension was the Anxiety/Depression (94%) dimension whereas the least affected was the Selfcare (59%) dimension. The results of the study also postulated that the marital status of respondents, their level of education, the specific CNCD they had been diagnosed with, their comorbidity status, and their dietary change behaviour predicted their level of HRQoL. The high levels of problems reported in this study are worrying as they may have negative implications on the general well-being of adults living with CNCDs as well as the achievement of the sustainable development goals (SDGs), specifically target 3.4 that aims to reduce premature mortality from non-communicable diseases by one-third by 2030.

In the current study, 63.7% of our respondents reported some problem in at least one of the dimensions measured. This proportion was similar to that found in another study which reported 64% of their respondents having problems in at least one of the dimensions [33]. In our study, the most affected dimension of HRQoL was the Anxiety/Depression dimension followed by the Pain/Discomfort dimension. 94% and 91% of our respondents reported some problems with these dimensions, respectively. This finding was consistent with the findings of other studies albeit with varying proportions [33,34,35,36]. The difference in proportions could likely be attributed to the distinct socio-cultural environments. However, it is worth noting that, of all these studies the Anxiety/Depression and Pain/Discomfort dimensions recur as the top two dimensions with the most problems reported. This underscores the need to incorporate mental health services and pain management into various CNCD treatments.

Our study found that the marital status of respondents has statistical significance with their HRQoL. We found that divorced/separated patients were less likely to report a high index of HRQoL when compared to patients who had never married. This finding was inversely reiterated by those of Han et al. [37] and Zhu et al. [38] which found that married individuals had a higher HRQoL. This was attributed to the fact that married or partnered individuals are likely to benefit from the companionship of their spouses or partners. This companionship is most likely to be lacking in the case of divorced/separated individuals.

Additionally, we found in our study that respondents’ HRQoL was predicted by their level of education. We found that respondents who reported having only primary-level education were less likely to report a high index for HRQoL than those with no formal education. This finding was inversely corroborated by the findings of other studies which postulated that the higher an individual’s level of education the higher their HRQoL is likely to be [39,40,41,42]. This could likely be attributed to the fact that individuals gain more health awareness with increasing levels of education which allows them to be more health conscious.

On the other hand, we observed that respondents’ specific CNCD diagnosis impacts their HRQoL. Respondents diagnosed of CKD, diabetes and hypertension were more likely to report a high HRQoL index as compared to those diagnosed with cancers. These findings were consistent with those by Tannor et al. [43] and Liang et al. [44] which similarly posit that the specific diagnosis of patients was impactful on their HRQoL. This may be attributed to the fact that many of these individuals are unable to reconcile with the reality of their diagnosis how they would invariably be on treatment for the rest of their life and how they may have to make drastic lifestyle adjustments to their new realities. All these point to the lack of health system support which could have seamlessly assisted these individuals with making these transitions. This, similarly, indicates the varying impact different CNCDs may have on an individual’s HRQoL which would require a disease-specific approach in management.

However, we found that respondents who reported living with comorbidities were less likely to report a high index for HRQoL. This finding was consistent with those of other studies which reported the negative impact of comorbidities on an individual’s HRQoL [45,46]. This underscores the need for a robust treatment regimen which accommodates other comorbidities of patients aside from their primary diagnosis to provide wholistic care which could improve patients’ HRQoL.

Furthermore, we discovered that respondents who were on behavioural/lifestyle treatment especially those who were recommended dietary change by their physician and nurses were more likely to report a high HRQoL index. This was in line with the findings of Qin et al. [47] which revealed in their study that respondents who consumed fast food, and sugar-sweetened beverages regularly were more likely to report a lower HRQoL. This highlights the importance of the role of dieticians and dietary education in the management of CNCDs.

Strengths and limitations

In this study, we provided important insights into the impact of CNCDs on the quality of life of affected individuals, which was lacking in the status quo. By examining multiple dimensions of health, including physical, mental, and social well-being, the study identifies areas where interventions can be targeted to improve the quality of life of individuals with CNCDs. In addition, our use of quantile regression ensured that we robustly established the relationships that exist between our outcome and explanatory variables. However, a limitation of the study is that we relied on self-reported data, which may be subject to recall bias and social desirability bias. Additionally, the use of a simple random sampling technique could potentially influence the interpretation of our results. Despite these limitations, the study can provide valuable information for healthcare providers and policymakers to help improve the lives of those living with chronic non-communicable diseases.

Conclusion

This study examined the HRQoL of persons living with CNCDs and found that more than half of the study respondents reported some problem in at least one of the HRQoL dimensions. With the “Anxiety/Depression” and “Pain/Discomfort” dimensions having the highest proportions of problems reported. Marital and comorbidity status as well as education were predictors of low HRQoL index. The above-stated phenomena could have negative effects on the treatment outcomes of persons living with CNCDs such as worsening of their conditions which could further perpetuate the high levels of morbidity and mortality due to CNCDs in Ghana. These have the potential to further undermine interventions put in place to reduce the incidence of CNCD mortality and morbidity as well as undercut Ghana’s attempt to achieve target 3.4 of the Sustainable Development Goals (SDGs) which seeks to reduce by one-third premature mortality from NCDs through prevention and treatment by 2030.