|Year : 2017 | Volume
| Issue : 1 | Page : 48-55
Predictors of quality of life in patients with diabetes mellitus in two tertiary health institutions in Ghana and Nigeria
Grace K Ababio1, Samuel Bosomprah2, Adesola Olumide3, Nicholas Aperkor4, Chris Aimakhu5, Audrey Oteng-Yeboah6, Joan Agama7, William F Chaplin8, Kola S Okuyemi9, Albert G.B. Amoah10, Gbenga Ogedegbe11
1 Department of Medical Biochemistry, University of Ghana School of Biomedical and Allied Health Sciences, Legon, Accra, Ghana
2 Department of Biostatistics, University of Ghana, School of Public Health, Legon, Accra, Ghana
3 Department of Child Health, University College Hospital, Ibadan, Nigeria
4 Department of Surgery, Korle-Bu Teaching Hospital, Accra, Ghana
5 Department of Obstetrics and Gynecology, University College Hospital, Ibadan, Nigeria
6 Department of Anesthesia, Korle-Bu Teaching Hospital, Accra, Ghana
7 Department of Medicine and Therapeutics, KBTH; Joan Agama – Deceased 8th March 2014, Ghana
8 Department of Population Psychology, St. John's University, Jamaica; School of Medicine, New York state University, USA
9 Department of Family Medicine, University of Minnesota, Minneapolis, MN, USA
10 Diabetes Clinic, Korle-Bu Teaching Hospital, Accra, Ghana
11 School of Medicine, New York state University, USA
|Date of Web Publication||9-May-2017|
Grace K Ababio
Department of Medical Biochemistry, University of Ghana School of Biomedical and Allied Health Sciences, P. O. Box 143, Accra
Source of Support: None, Conflict of Interest: None
Background: Patients with chronic diseases such as Type 2 diabetes mellitus (DM) usually have a relatively poor quality of life (QoL), because the cost of care (living expenses and health) or diet restrictions are heavily felt by these patients, and this is of a public health concern. However, limited data on DM QoL exist in Ghana and Nigeria. This makes it imperative for data to be collated in that regard. Materials and Methods: We adopted the Strengthening The reporting of observational studies in epidemiology (STROBE) consensus checklist to survey the patients with DM seen at the diabetic clinic at the Department of Medicine of the Korle-Bu Teaching Hospital and University College Hospital, Ibadan, Nigeria. Patients with Type 2 DM aged 40 years and older were recruited by using systematic random sampling method. The World Health Organization Quality of Life-BREF, diabetes empowerment scale, and DM knowledge scale were used to assess QoL, patient empowerment, and knowledge of DM, respectively. The predictors of QoL were determined using multiple linear regression analyses. Results: A total of 198 patients in Ghana and 203 patients in Nigeria completed the survey, with female-to-male ratio being 3:1 and 2:1, respectively. The overall QoL in both countries was relatively low: 56.19 ± 8.23 in Ghana and 64.34 ± 7.34 in Nigeria. In Ghana, significant correlates of higher scores on the QoL scale were medication adherence (P = 0.02) and employment status (P = 0.02). Among patients in Nigeria, employment status (P = 0.02) and DM empowerment (0.03) were significant predictors of QoL in patients with DM. Conclusion: Our study revealed an association between a number of psychosocial factors and QoL among patients with DM in Ghana and Nigeria.
Keywords: Adherence, diabetes, empowerment, knowledge, quality of life
|How to cite this article:|
Ababio GK, Bosomprah S, Olumide A, Aperkor N, Aimakhu C, Oteng-Yeboah A, Agama J, Chaplin WF, Okuyemi KS, Amoah AG, Ogedegbe G. Predictors of quality of life in patients with diabetes mellitus in two tertiary health institutions in Ghana and Nigeria. Niger Postgrad Med J 2017;24:48-55
|How to cite this URL:|
Ababio GK, Bosomprah S, Olumide A, Aperkor N, Aimakhu C, Oteng-Yeboah A, Agama J, Chaplin WF, Okuyemi KS, Amoah AG, Ogedegbe G. Predictors of quality of life in patients with diabetes mellitus in two tertiary health institutions in Ghana and Nigeria. Niger Postgrad Med J [serial online] 2017 [cited 2021 Dec 7];24:48-55. Available from: https://www.npmj.org/text.asp?2017/24/1/48/205977
| Introduction|| |
Diabetes mellitus (DM) is a chronic disease with an increasing prevalence worldwide. Estimates of the World Health Organization Global Burden of Disease data on DM suggest that DM was responsible for 1.4 million deaths in 2011. The International Diabetes Federation estimated that the prevalence of DM in Nigeria was 3.1%, whereas that of Ghana was 3.3%. Doctor-to-patient ratio is very low in both countries, that is, eight physicians per 100,000 patients in Ghana and 18.5 per 100,000 patients in Nigeria; hence, relying on physicians alone to manage and reduce diabetes-related complications is almost impossible. This makes some medical conditions or complications of patients especially undiagnosed DM to persist., Curative, medical approaches and compliance/adherence model of care had not been fully effective in diabetes care., For instance, someone with diabetes might encounter a number of factors that might influence dietary regimens, diabetes-related knowledge, treatment choices, communication with providers, exercise, and ability to adhere to recommended medication, and these need to be addressed to avoid patients’ dissatisfaction with treatment outcomes, for example, as seen in lower limb amputation. Globally, 70% of all the leg amputations occur in patients with diabetes with a leg being lost to diabetes every thirty seconds. Few compelling evidence also exist for an association between adverse health outcomes and quality of life (QoL) for persons with diabetes and other chronic conditions,, although the pathways through which QoL and health are related in persons with chronic illness are poorly understood.
QoL is defined by World Health Organization (WHO) as an individual’s perception of their position in life in the context of culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns. This broad range concept implied identifying psychosocial variables (e.g., patient empowerment, knowledge of DM, adherence to medication, family support) associated with QoL. Predictors of DM QoL in Ghana and Nigeria affect QoL through patient-related, disease-related, and provider-related factors, and these factors could be influenced by age, gender, body mass index (BMI), and other co-morbidities − such predictors and factors together constitute the conceptual framework in this study. Interventions based on a model that provides the baseline assessment of patients’ perceived psychosocial self-efficacy levels, glycemic index, and blood pressure management outcomes would help in diabetes self-management as well as provide an outcome measure to establish the effectiveness of such interventions.
Although DM is a chronic and potentially incapacitating disease, the likelihood of a patient developing complications (e.g., cardiovascular complications, cerebrovascular complications, retinopathy, nephropathy, neuropathy, and peripheral arterial diseases) can be significantly reduced if it is properly managed. However, a significant part of care for diabetes is conducted by the patient at home, for example, the patient has to regulate her/his food intake, monitor blood glucose levels, and take prescribed drugs at specific times. These instances, when not properly handled, could compromise the standards of DM management. The patients with noncompliant DM remain victims of socioenvironmental barriers and emotional distress.,, This has identified concerns about the role of health promotion among the patients with diabetes, of which empowering the patients with a sense of control of the disease is the central theme that the current, proposed study is advocating for. In addition to disease specific outcomes, health-related quality of life (HRQoL) is also an important factor for the patient with the possible hope of averting complications. Patients’ empowerment and knowledge of the disease are examples of HRQoL. This presumes that the patient has adequate knowledge of the disease, and she/he is able to take responsibility for the management of diabetes for its associated medication and diet while at home, that is, she/he possesses a considerable level of self-efficacy. Self-efficacy is the ability of a person to organize and execute certain behaviors that are necessary to produce given attainments. For DM, the given attainment is glycemic control. However, the self-management system involves one’s own thoughts, emotion, and behaviors, which enable her/him to attain the expected results or outcome. In this case, the person with DM is able to achieve glycemic control. This places an immense responsibility on the patient and for those who do not achieve glycemic control; it can eventually take its toll on them leading to a low QoL. As a result, desired health outcomes are not fully achieved leading to the development of complications and a further reduction in QoL.
Notwithstanding the added input, some studies have reported a low QoL in patients with DM.,, However, most were restricted to data from a single site, thus, limiting generalizability of the findings. Furthermore, none of the studies explored the association between patient factors such as patients’ knowledge of the disease or perception of their self-efficacy with respect to expected home care of DM, and their QoL as stated in this current study. It has been noted that material and social deprivation are also thought as being directly related to incidence and prevalence of the disease and inversely related to health status., In the current study, two sites were purposively chosen, Ghana and Nigeria. These sites were English-speaking countries with similar economic backgrounds, which were also closer in proximity and very much convenient for the investigators for joint capacity building and comparative study. Unlike Nigeria, health spending in Ghana is high. This indicates the complex nature of QoL and how disease situations would be, for example, how DM influences QoL in these two countries creates a subject of concern, but limited data exist. In addition, being faced with the issue of long-term complications of DM not being consistent with QoL, patient satisfaction and desired outcome are always not adequate. A new innovative approach comprises patients’ empowerment and knowledge of the disease, which are a measure of psychosocial variables, for example, choices, control, and consequences, and these are more applicable for the prevention of the disease. Evidence suggests psychosocial variables to be good predictors of QoL. One notable question could persist though: what are the predictors of DM QoL in Ghana and Nigeria? There is yet only one study on socioeconomic status on DM burden of foot ulcers existing in Nigeria but none in Ghana. Data are, therefore, needed to be collated in that regard to help guide prevention and clinical care efforts, and this is the focus of the current study.
To assess QoL of patients with diabetes and to identify the predictors of good QoL among the patients with DM in the leading tertiary hospitals in Ghana and Nigeria.
| Materials and Methods|| |
A cross-sectional study using patients with DM attending the outpatient DM clinic of the Department of Medicine of the Korle-Bu Teaching Hospital, Accra, Ghana and University College Hospital (UCH), Ibadan, Nigeria, was conducted from January to December 2014. The study design was chosen to identify predictors or correlates of QoL.
The patients with Type 2 DM aged 40 years and above (because the disease often occurs in middle-aged people) and who have been diagnosed at least a year prior to the study, were recruited by using systematic random sampling after ethical approval (MS-Et/M.10-P 3.2/2013-2014). The eligible participants provided informed consent (written) and were interviewed in their native language.
The patients with Type 2 DM aged 40 years and above, diagnosed for more than a year, were recruited by using systematic random sampling method, with sampling fraction being ¼.
The patients who did not consent or those with other co-morbid conditions not directly related to diabetes and/or patients diagnosed less than a year to commencement of study were excluded from the study.
A total of 458 participants were recruited, with 232 participants from Ghana and 226 participants from Nigeria with the assumption being that the DM population in either country was randomly ordered. We also assumed that the mean QoL score (%) in Ghana and Nigeria was 50 (SD = 15.5). We, therefore, required a total of 132 participants (in each country) to detect a decrease in the QoL score to 32 at an 80% power by using two-sided two-sample t test at 5% level of significance. This estimate was adjusted for 10% nonresponse rate. At recruitment, each participant completed a questionnaire, after an ethical approval has been obtained in both countries. Two-hundred and three participants from Nigeria participated or completed the study to the latter in the study, while one hundred and ninety-eight participants from Ghana complied leaving 34 Ghanaian participants and 23 Nigerian participants failing to fully complete all fields of the study.
Samples for fasting blood sugar levels and glycated hemoglobin in DM were obtained and analyzed in line with the standard protocol obtainable in the laboratories in both teaching hospitals.
To determine predictors of QoL in the patients with DM Type 2, patient factors (e.g., sociodemographic characteristics − employment status, empowerment score, BMI, DM knowledge score, and medication adherence score), disease related factors (e.g., glycemic control, presence of co-morbidities, and duration of disease), and provider related factors (e.g., enrolment in health insurance plan) were assessed with the appropriate questionnaire. Since there were no such data on the instruments’ validity in both countries, we performed pilot testing and field testing. After pilot testing, the instrument on 20 patients with DM, interviewers were debriefed to look for patterns in the feedback. The feedback data were then used to revise the way the instrument or questionnaire was administered. Then a field testing of the revised version executed before the actual data collection began.
Data on demographics and selected factors (knowledge of DM, adherence to medications, DM empowerment) previously reported to be associated with QoL were collected with the aid of an interviewer-administered questionnaire. Weights and heights were measured (according to standard guidelines) and computed for BMI. At risk for overweight was defined using the international BMI cutoff points by the WHO’s International Obesity Task Force. BMI and glycemic index were also obtained.
The knowledge of DM was assessed using the DM knowledge scale having 24 questions relating to definitions, causes, symptoms, and complications of DM. DM knowledge was scored using one point for each correct response. Diabetes empowerment five scale was used to assess patients’ awareness of the importance of adherence to DM (self) care, their attitudes and ability to self-manage diabetes. The World Health Organization Quality of Life-BREF (WHOQOL-BREF) scale, was used to assess patients’ QoL. Patients’ practices were assessed using questions on self-care, dietary modification, compliance with medications (medication adherence), weight control, self-monitoring of blood sugar, and regular follow-up. The DM knowledge scale and diabetes empowerment scale have been validated,, in many countries in sub-Saharan Africa and were used in this study as well. Quartiles were then used to assess DM knowledge score, DM empowerment score, and medication adherence.
The outcome of interest was QoL calculated as the total scores on 25-point items on the scale of 1–5 divided by the total obtainable score expressed as a percentage. The questions were adopted from the WHOQOL-BREF tool.
Patient factors (employment status, empowerment score, BMI, DM knowledge score, and medication adherence score), disease factors (glycemic control, presence of co-morbidities, and duration of disease), provider-related factors [National Health Insurance Scheme (NHIS)].
Knowledge of DM was measured based on 24 questions. Correct answers fetched one point and incorrect zero. Total score was computed for each participant. Both the medication adherence scale and empowerment scale consisted of eight items, and each was measured on 5-point Likert scale.
Analysis of variance (ANOVA) was used to test for difference in mean QoL score among potential predictors, for example, economic status. Scores were calculated for some predictors in a similar way and categorized using the median score as cutoff. These included DM patient’s knowledge score, empowerment score and medication adherence – which were represented as quartiles. In all these calculations, negative responses were converted into a positive scale by subtracting each item score from 6 (because the scale was 1–5). Linear regression model was used to investigate the predictors of QoL. Variables that showed evidence of association with mean QoL score in the ANOVA were candidate for inclusion in the multivariable regression model. The analyses were performed using Stata 13 for MP power command (StataCorp, College Station, Texas, USA).
| Results|| |
The survey was successfully completed by 198 patients in Ghana [Table 1], while 203 patients in Nigeria completed [Table 1] and [Table 2] with response rates being 85.0 and 89.8% in Ghana and Nigeria, respectively. The female-to-male ratio was 3:1 in Ghana and 2:1 in Nigeria. This formed our valid subjects and data analysis was duly performed on them [Table 1],[Table 2],[Table 3],[Table 4]. The general QoL in both countries was low: 66.14 ± 9.99 in Ghana and 68.78 ± 7.86 in Nigeria. The quartiles in [Table 1] were exploratory and, therefore, depicted statistical findings between mean QoL and characteristics of DM in the respective country. There was also a significant relationship between medication adherence and employment status and QoL in Ghana, while DM empowerment and employment status were significant predictors of QoL [Table 4] in Nigeria. However, there were no differences in both countries in QoL scores when sorted by sex categories, age categories, BMI categories, fasting plasma glucose (FPG) categories, and glycated hemoglobin (HbAic) categories [Supplementary Table S1]. As shown in [Table 1] and [Table 3], participants from both countries had comparable age and BMI. A 96.9% Ghanaian patients with DM patronized NHIS unlike their Nigerian counterparts [Table 1] and [Table 2]. With regard to education, 51.5% of Ghanaian patients with DM reported completing primary education, while 37.0% Nigerian counterparts successfully completed tertiary education [Table 2]. The unemployed and employed percentages in the Ghanaian subjects were 41.7 and 58.3%, respectively, while that for Nigeria were 27.8 and 72.2%, respectively. When ANOVA was performed on the entire clinical data, FPG, HbAic, duration of treatment, systolic blood pressure, and heart rate indicated strong evidence against the null hypothesis. However, t-test analysis stratified by gender in [Table 3] showed the actual trend of statistical significance.
|Table 1: Sociodemographic characteristics of DM patients from both countries|
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|Table 3: Mean QoL score and characteristics of diabetes mellitus patients in the study|
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|Table 4: Predictors of quality of life among diabetes mellitus patients in Ghana and Nigeria|
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| Discussion|| |
We sought to assess the QoL of patients with Type 2 diabetes and to identify the predictors of good QoL among patients with DM in Ghana and Nigeria. QoL was generally low in both countries by demographics. Each predicted score of QoL had a possible value in medication adherence score, employment status, and DM empowerment score. The significant relationship of QoL with patients’ DM empowerment score could possibly imply that a well-informed patient with DM would be able to self-manage the disease.
The measurements of QoL were based on individual perceptions on goals, expectations, standards, and concerns with regard to their DM management. These measures were more of reaction time, which might include error counts. From statistics, measures of reaction time with low scores indicated more ability. Therefore, in this study, there was a positive association between QoL and the psychosocial parameters investigated as opposed to measures based on the number of items answered correctly in DM knowledge scale. In this case (DM knowledge scale), a high score indicated more ability to answer correctly. The measure of spread chosen (quartile) also gave an idea of how well the mean represented each predicted data set (i.e., medication adherence score, DM empowerment, and employment status) of QoL and this was much less affected by outliers or skewed data. These results, therefore, indicated a relationship between the QoL of diabetic patients and the aforementioned variables and, therefore, pointed out that QoL of patients with diabetes could be affected by the disease. Our current findings supported the work endeavored by several others.
Nevertheless, glycemic control had been an important marker for diabetes control. However, studies employing generic measures have reported null findings with QoL,,,,,,, and this seemed consistent with the current study.
Lower socioeconomic status as measured by education and employment (in this study) had been associated with poorer glycemic control elsewhere, in diabetes. This, therefore, suggested that the function of glycemic control in discriminating whether subjects engaged in good self-management that possibly led to a better QoL might be questionable due to the interplay of environmental influences.
Our stance, therefore, was that socioeconomic factors and or geographical barriers might influence QoL of a patient with DM. One unique example was seen in the percentage ratings of employment statuses of patients with DM in both countries in the current study. Notwithstanding the added input, some studies have reported a low QoL in patients with DM., As poor QoL is being associated with lower monthly income,,, economic factors also seem to remain barriers to adequate diabetes care delivery, for example, the patient copayments and low-income persons seemed sensitive to cost-sharing issues as suggested by Brown et al. Even though Nigeria (most populace) seemed to have a wealthy economy, wealth distribution does not seem to be evenly. The cost of living in Nigeria seemed high, but with a low health spending on average. Unlike Nigeria, health spending in Ghana was high.The trend observed during sampling depicted NHIS to cover the cost of care for DM in Ghana unlike Nigeria, where most federal civil servants were on alternate insurance schemes that catered for their health. NHIS registration did not seem to be universally compulsory in Nigeria. Cost of care was also relatively high in the tertiary hospital (UCH) in Nigeria, so those with employment who could afford were seen in hospital unlike Ghana that cost of care was solely borne by NHIS.
The low doctor:patient ratio observed during sampling in both countries (which seemed to be consistent with literature) could perhaps, if not possible, account for short consultation times and limited or no time for educating a patient on the disease.,
The major limitation was our cross-sectional design. In addition, direct comparisons with other studies were difficult, because there had been no previous studies conducted on QoL of patients with DM in Ghana. However, the study performed in Nigeria on leg ulcers had findings slightly different from ours. Life events, which could possibly affect QoL of patients with DM, were never left out.
| Conclusion|| |
A well-informed patient with diabetes had a better QoL. There was also an association between a number of psychosocial factors and QoL among patients with DM in Ghana and Nigeria.
This work was supported by funding from the NIH/FIC (D43TW009140). The authors duly acknowledge the KBTH, UCH, CaRT faculty, diabetic patients, and patronage of research assistants (Emmanuel Abindau and Gideon Acheampong) of the Univ. of Ghana Medical School and staff of National Diabetic and Chronic Research Laboratory, Korle-Bu Teaching Hospital, Accra.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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