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R E S E A R C H A R T I C L E Open Access
Diabetes related knowledge, self-care
behaviours and adherence to medications
among diabetic patients in Southwest
Ethiopia: a cross-sectional survey
Tefera Kassahun
1
, Hailay Gesesew
2,3*
, Lillian Mwanri
3
and Tesfahun Eshetie
4
Abstract
Background: The provision of health education involving self-care and good adherence to medications has been
acknowledged to be a cost effective strategy for improving quality of life of diabetes patients. We assessed levels of
knowledge about type 2 diabetes mellitus (T2DM), self-care behaviours and adherence to medication among DM
patients.
Methods: A facility based cross-sectional survey of 325 adults with T2DM patients attending Jimma University
Teaching Hospital, Southwest Ethiopia was conducted. We used diabetes Knowledge Test, Expanded Version of the
Summary of Diabetes Self-Care Activities and Morisky 8-Item medication adherence as tools to measure diabetic
knowledge, self-care behaviours and adherence to medications respectively. Multinomial logistic regression analyses
were used to assess the independent predictors of diabetes knowledge and adherence to medications. The binary
logistic regression was applied for self-care behaviours.
Results: 309 respondents were included in the survey. Of all the respondents, 44.9 %, 20.1 % and 34.9 % had low,
medium and high level diabetic knowledge respectively. High level of diabetic knowledge was the reference group.
Being illiterate (AOR = 3.1, 95%CI: 1.03-9.3), having BMI <18 kg/m
2
(AOR = 6.4, 95%CI: 1.2-34.9) and duration of DM <
5 years (AOR = 4.2, 95%CI: 1.9-9.5) were significantly associated with low level of diabetic knowledge. T2DM patients who
practiced good self-care (AOR = 0.5, 95%CI: 0.3-0.9) werelesslikelytohavelowknowledge.DurationofDM<5years
(AOR = 9.8, 95%CI: 3.2-30.2) was significantly associated with medium level of diabetic knowledge. 157(50.8 %) patients
had poor self-care behaviour and this was associated with level of education and adherence to medication. The
proportions of patients with low, medium and high adherence to medication were 24.9 %, 37.9 % and 37.2 %
respectively. Being a merchant, having medium level of diabetic knowledge and having good glycemic control level
were associated with low adherence to medications.
Conclusions: Significant number of DM patients had low level of knowledge, poor self-care behaviours and low level of
adherence to medications. These findings call for the need of integrated interventional management on diabetic
knowledge, self-care behaviours and adherence to medications. To ensure effective T2DM management, a strategic
approach that improves health literacy could be a cross cutting intervention.
Keywords: Knowledge, Self-care behaviour, Adherence, Type 2 diabetes mellitus, Ethiopia
* Correspondence: hailushepi@gmail.com
2
Department of Epidemiology, College of Health Sciences, Jimma University,
Jimma, Ethiopia
3
Discipline of Public Health, Faculty of Medicine, Nursing and Health
Sciences, Flinders University, Adelaide, Australia
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kassahun et al. BMC Endocrine Disorders (2016) 16:28
DOI 10.1186/s12902-016-0114-x
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
Diabetes mellitus (DM) is a serious global public
health problem, an illness that kills silently. In 2013,
it was reported that DM killed 4.6 million people,
[1] with further 387 million reported to being af-
fected in 2014 [2]. More than 77 % of morbidity [2]
and 88 % of mortality [3] resulting from DM oc-
curred in low- and middle- income countries in
2012. Diabetes mellitus scourge is expected to affect
nearly 592 million people globally by the end of
2035 [2]. In Ethiopia, the prevalence of diabetes was
3.5 % in 2011, [4] and the extrapolated prevalence in
2013 was 4.36 % [2]. Reportedly, 34,262 patients out
of 1.8 million DM cases died in Ethiopia in 2013 [2].
It is also known that a large number of people re-
main undiagnosed, with estimated number of un-
diagnosed cases reported to be 1.39 million people
in 2013 [2]. Type 2 diabetes mellitus (T2DM) is the
most common form of DM worldwide, accounting
for more than 90 % of cases [1].
Diabetes is a chronic disease significantly affecting the
quality of life of affected populations and can lead to
poor health outcomes of individuals, families and com-
munities [5]. Its impact affects social and economic out-
comes, [6] including costing millions of health care
budgets of nations [2] across the world [7]. The risk fac-
tors for DM include raised blood pressure, tobacco use,
alcohol consumption, physical inactivity, poor dietary
patterns and overweight [8, 9]. Poor adherence to medi-
cations and poor self-care behaviours have also been re-
ported to be barriers for effective management of DM
complications [8, 10]. Most of the risk factors of DM
and its complications are modifiable. Self-management
strategies such as self-monitoring of blood glucose, diet-
ary restrictions, regular foot care and ophthalmic exami-
nations have all been shown to markedly reduce the
incidence and progression of DM complications,[1] and
these can be achieved by patients themselves via effect-
ive education and enhanced knowledge [11].
Evidences from earlier studies have supported the no-
tion that having good knowledge and education have in-
fluence to good care and can reduce DM complications
significantly [12, 13]. Knowledge not only enhances the
self-care behaviours [1], but it enables DM patients to
adhere to their treatment effectively. It has also been
noted that age, lack of resources and perceived side ef-
fects have significant association with poor adherence to
medication [8].
Knowledge, self-care behaviours and adherence to
medications in diabetes could be helpful for early case
detection, prevention, and minimization of complica-
tions, and improvements of the quality of life of affected
individuals. Previous studies have reported poor health
outcomes to be associated within insufficient knowledge,
poor self-care behaviours and adherence to medications
among diabetic patients [14–16]. There is insufficient
work regarding knowledge, self-care behaviours and ad-
herence to medications related to DM in Ethiopia. Stud-
ies to provide evidences for these factors are noteworthy
for prevention and control of diabetes and other non-
communicable diseases (NCDs), and to inform policies
and strategies in such resource meager countries. We
assessed the levels of knowledge, self-care behaviours
and adherence to medications about diabetes mellitus
among diabetic adult patients in Ethiopia.
Methods
Study design, settings and participants
A facility based cross-sectional study was carried out
in diabetic clinic at Jimma University Teaching Hos-
pital (JUTH), Southwest Ethiopia between February
and April 2014. We followed Drug Administration
and Control Authority of Ethiopia guidelines [17] for
diagnosis and classification of DM. These guidelines
are similar to the criteria developed by International
Diabetes Federation (IDF) [18]. The study was con-
ducted among T2DM adult patients (≥18 years) who
were for at least four visits.
This work was conducted alongside our previously pub-
lished paper on glycemic control [19]. Originally, the pro-
ject had four outcomes: glycemic control, knowledge, self-
care behavior and medication adherence. The sample size
calculation considering all outcomes and ‘glycemic control’
gave us the maximum sample size that helped us to look at
variousfactorsthataffecttheoutcomes.Thesamplesize
was calculated via OpenEpi 2.3 software using a single
population proportion calculation formula using the follow-
ing assumptions: 58.2 % proportion [8], 95 % confidence
level, 5 % margin of error and 10 % non-response rate.
Considering a correction formula, the total calculated sam-
ple yielded 325. Using sampling frame of DM records, sim-
ple random sampling technique was used to recruit the
study participants (Fig. 1).
Dependent variables
Diabetes knowledge, self-care behaviours and adherence
to medications were the dependent variables. Diabetes
Knowledge was measured using The Diabetes Know-
ledge Test (DKT) [20, 21]. The DKT was developed and
tested for reliability and validity by the University of
Michigan scholars and was adapted for the Ethiopian
context. The DKT is a 23-item multiple-choice test de-
signed to assess knowledge about diet, exercise, blood
glucose levels and testing and self-care activities. Each
item had three or four multiple choices with only one
correct answer. The first 14 items were designed for all
adults with diabetes, while items 15–23 apply only to
those using insulin [21]. Scores on the DKT were
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 2 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
computed for each participant. The score was deter-
mined by dividing the number of correct answers by the
total number of questions (23 questions for patients tak-
ing insulin and 14 for those receiving oral hypoglycemic
agents). Scores ≥75 %, 74-60 % and ≤59 %, respectively,
were labeled as high, medium and low knowledge on
diabetes [20]. Internal consistency of the tools for know-
ledge was measured by Cronbach’s alpha and was ad-
equate (Cronbach’s alpha =0.78).
Self-care behaviours were assessed using Expanded
Version of the Summary of Diabetes Self-Care Activities
(SDSCA) [22]. The SDSCA was originally developed
from “The Summary of Diabetes Self-Care Activities
Measure”that resulted from seven studies carried out by
scholars from Oregon Research Institute, United States.
The tools were adapted for the Ethiopian Context. We
adapted some foodstuffs, for instance high fat diet food
types, and terminologies mentioned in the original ver-
sion to fit Ethiopian context. Each scale measured fre-
quency of self-care activity in the last 7 days for the
following aspects of the diabetes regimen: general diet,
foot-care, exercise and medication taking. The score was
presented in terms of mean number of days for each
self-care behaviours, which was calculated by summation
of number of days of self-care practice divided by total
number of patients. The overall mean score was calcu-
lated by summation of the mean score for diet, foot-
care, exercise and medication taking divided by the sum
of number of questions under each scale. After calculat-
ing an overall mean score, it was classified as having
good self-care behaviour if the patient scored ≥3orpoor
self-care behaviour if the patient scored <3.
Adherence to medications was measured using Mor-
isky scale [23] that was prepared from “Concurrent and
predictive validity of a self-reported measures of adher-
ence to medications”. The tool was originally developed
by scholars from the University of California, University
of Texas and John Hopkins Research institute, but again
adapted for Ethiopian use. It consisted of 8-item ques-
tionnaire with yes or no responses coded respectively as
1 or 0. But for one question, the score was given in-
versely. The total score of adherence to medications was
classified in to low adherence if the score was >2,
medium adherence if between 1 and 2, and high adher-
ence if 0.
Independent variables
The explanatory variables included: socio-demographic
and economic data (age, sex, education level, marital sta-
tus, occupation, income, ethnicity and religion), history
of smoking, history of alcohol consumption, family his-
tory of DM, duration of therapy, body mass index (BMI)
and glycemic control level. Level of education was classi-
fied as illiterate (couldn’t read and write), primary (re-
ceived education up to class eight), and secondary and
above (received education class from grade nine and
above).
History of smoking and history of alcohol consump-
tion has been assessed as during lifetime. We asked par-
ticipants to report their lifetime experience of cigarette
smoking and alcohol drinking. We recorded their re-
sponse as smoker, non-smoker and ex-smoker for
cigarette smoking whereas for alcohol drinking we asked
their status as drinker or non- drinker. For statistical
Fig. 1 Summary of flowchart record selection, 2014
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 3 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
analysis, we further aggregated their responses to yes or
no binary responses. Family history of DM was mea-
sured if any family member (mother or father) had DM.
Anthropometric measurements were used to assess the
body mass index (BMI). Glycemic level was coded as
poor or good. Poor glycemic control was defined if fast-
ing blood glucose (FBG) level was above 130 mg/dl. Pa-
tients FBG reading for at least four months were
recorded and the mean blood glucose level computed
[24].
Interviews
Six face-to-face interviews were conducted by six regis-
tered nurses and one public health officer to solicit
socio-demographic and economic data, medication ad-
herence, diabetic knowledge and self-care behavior of
participants. The interviews were conducted in a quiet
room at the DM clinic where patients came for follow
up checkup. Validated and structured interviewer rater
questionnaires were used for data collection. The Mor-
isky medication adherence scale questionnaires was used
to solicit participants’adherence to taking medications,
whereas Expanded Version of the Summary of Diabetes
Self-Care Activities (SDSCA) Self-care behaviours ques-
tionnaire were used to assess participants self-care be-
havior adherence. The Diabetes Knowledge Test (DKT)
tool, a 23 multiple-choice question test was used to
measure participants’diabetes knowledge. The tools
were firstly designed in English and then translated in
Amharic and Afan Oromo (local languages), and again
back translated into English by experts who had similar
experiences (Additional file 1). Questionnaires were pre-
tested with diabetic patients in another nearby hospital
at Jimma and necessary modifications were made.
Statistical analysis
Descriptive statistics included mean, median, standard
deviations, and range values for continuous data; per-
centage and frequency tables for categorical data. We
used multinomial logistic regression to analyze factors
that were associated with diabetes knowledge and adher-
ence to medications. To assess factors associated with
self-care behaviours, binary logistic regression analysis
was used. For both multinomial and binary logistic re-
gression analyses, bivariate and multiple regression as-
sessment was conducted to check the existence of crude
association and select the candidate variables (P< 0.25
was considered).
We checked multi-collinearity among selected inde-
pendent variables via variance inflation factor (VIF) and
none was found. P-value < = 0.05 was considered as a
cut off point for statistical significance in the final
model. Fitness of goodness of the final model was
checked by Hosmer and Lemeshow and was found fit.
The Data were summarized using odds ratio (OR) and
95 % confidence interval. The analyses were conducted
in Statistical Package for the Social Sciences (SPSS) ver-
sion 22.0 for mackintosh.
Results
Socio-demographic and clinical characteristics of
respondents
Three hundred and twenty five (325) DM patients were
considered eligible, but 16 were excluded because their
charts were not available (Fig. 1). In total, 309 (95 %) pa-
tients were included in the analysis.
Table 1 shows demographic characteristics of the re-
spondents. Males were over-represented (61.8 %) and al-
most two fifth (36.9 %) of the respondents represented
the age group 51–60 years. Nearly half (46.6 %) of the
respondents followed Muslim religion and four out of
five (81.2 %) respondents were married. Two fifth
Table 1 Frequency distributions of socio-demographic
characteristics of T2DM patients on follow up at JUTH, 2014
Socio-demographic
characteristics (n= 309)
Categories n (%)
Sex Male 189 (61.8)
Female 120 (38.2)
Age <40 years
40-60 years
> = 60 years
Missing
12 (3.9)
188 (60.8)
87 (28.2)
22 (7.1)
Marital status Married 251 (81.3)
Single 21 (6.8)
Widow/er 37 (11.9)
Ethnicity Oromo 170 (55.0)
Amhara 78 (25.2)
Keficho 21 (6.8)
Gurage 10 (3.2)
Dawero 8 (2.6)
Yem 8 (2.6)
Other
a
14 (4.5)
Religion Muslim 144 (46.6)
Orthodox
Protestant
Others
b
138 (44.7)
23 (7.4)
4 (1.3)
Level of education Illiterate
Primary
2
0
and above
109 (35.3)
112 (36.3)
88 (28.4)
Occupation Employed 72 (23.3)
Unemployed 78 (25.2)
Merchant 29 (9.4)
Farmer 94 (30.4)
Daily labor 36 (11.7)
a
Tigre, wolayita,
b
Catholic, Jehovah witness
T2DM type 2 diabetes mellitus, JUTH Jimma University Teaching Hospital
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 4 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(36.2 %) respondents attained grades 1–8, and 30.4 % of
respondents were farmers.
Level of knowledge on diabetes
Table 2 shows the distribution of subjects’knowledge of
diabetes by demographic and clinical characteristics.
Among the patients, 44.9 %, 20.1 % and 34.9 % had low,
medium and high level of knowledge of diabetes respect-
ively. For respondents who had low level of knowledge
of diabetes, 30.9 % had low adherence to medications,
56.1 % had poor self-care behaviours and 28.1 % had
poor glycemic control level. Similarly, among those who
had medium knowledge level, 33.9 % had medium ad-
herence to medications level, 41.9 % had poor self-care
behaviour and 38.7 % had poor glycemic control. Those
respondents who had high knowledge level of DM, 25 %
had low level of adherence to medications, 43.5 % had
poor self-care behaviour and 25 % had poor glycemic
control level.
Table 2 also presents results of the multinomial logis-
tic regression analysis of factors associated with know-
ledge of diabetes. High level of knowledge of diabetes
was the reference group. Illiterate respondents compared
to those who attained higher secondary education were
highly likely (AOR = 3.1, 95%CI: 1.03-9.3) to have low
level of knowledge. The relative probability of having a
low level of knowledge among respondents who had
BMI below 18 kg/m
2
was significantly higher (AOR =
6.4, 95%CI: 1.2-34.9) than those who had 30 kg/m
2
and
above. Duration of DM of less than five (<5) years was
associated with both low level of knowledge (AOR = 4.2,
95%CI: 1.9-9.5) and medium level of knowledge of dia-
betes (AOR = 9.8, 95%CI: 3.2-30.2).
Self-care behaviours toward diabetes
The overall prevalence of poor self-care behaviours to-
ward DM was 49.1 % (95%CI: 43.5-54.7 %). Poor self-
care behaviours was statistically different by level of edu-
cation, family history of DM, adherence to medications,
having glucometer at home and history of alcohol con-
sumption. Table 3 presents binary logistic regression re-
sults of factors independently associated with poor self-
care behaviours.
Respondents with lower educational level were likely
to have poor self-care behaviour than those with higher
secondary education (AOR = 3.1, 95%CI: 1.7, 5.8). Hav-
ing previous family history of DM was found to be pro-
tective against poor self-care behaviours (AOR = 0.5,
9%CI: 0.3-0.9). Compared to respondents with low level
of adherence to medications, those with medium level
were 60 % less (AOR = 0.4, 95%CI: 0.3-0.8) likely to have
poor self-care behaviours. Interestingly, respondents
with glucometers at home were 2.5 times more likely to
have poor self-care behaviours than those who did not
have (AOR = 2.5, 95%CI: 1.1-5.8) glucometers at home.
Compared to respondents who did not drink alcohol,
those who had history of alcohol consumption were
highly likely to have poor self-care behaviours (AOR =
4.6, 95%CI: 1.3-15.7).
Level of anti-diabetic adherence to medications
Table 4 presents levels of adherence to medications by
demographic and clinical characteristics. Different levels
of adherence to medications were as follows: 24.9 % had
low, 37.9 % had medium and 37.2 % had high level of
adherence to medications. Of respondents with low level
of adherence to medications, 55.8 % had low level of
knowledge of diabetes, 63.6 % had poor self-care behav-
iours and 16.9 % had poor glycemic control level. Simi-
larly, among those who had medium level of adherence
to medications, 17.9 % had medium level of knowledge
on diabetes, 41.9 % had poor self-care behaviours and
21.4 % had poor glycemic control. Those respondents
who had high level of adherence to medications, 35.7 %
had high level of diabetes knowledge, 53.9 % had good
self-care behaviours and 54.8 % had good glycemic con-
trol level.
Table 4 presents results of the multinomial logistic re-
gression analysis of factors associated with adherence to
medications. High level of adherence to medications was
the reference group. Farmers compared to daily
labourers were highly likely (AOR = 6.8, 95%CI: 1.6-28.8)
to have low level of adherence to medications. Respon-
dents with medium level of knowledge of diabetes were
80 % less (AOR = 0.2, 95%CI: 0.1-0.6) likely to have low
level of adherence to medications. The relative probabil-
ity of having a low level of adherence to medications
among respondents who had good glycaemic control
level was higher (AOR = 3.3, 95%CI: 1.5-7.2) than those
who had poor glycaemic control level. The relative prob-
ability of having a medium level of adherence to medica-
tions among respondents who had BMI between 18 and
25 kg/m
2
was significantly higher (AOR = 3.4, 95%CI:
1.2-9.9) than those who had 30 kg/m
2
and above. Good
glycaemic control level was significantly associated with
medium level of adherence to medications (AOR = 2.8,
95%CI: 1.5-5.3).
Discussion
Diabetes is a chronic condition with many complica-
tions, and its management would need sufficient levels
of knowledge, self-care behaviours and adherence to
medications [17, 24]. For effective management and in
order to have good glycemic control, patients need to
have adequate levels of knowledge of diabetes regarding
self-care, a concept that can foster adherence to medica-
tions, good dietary pattern and physical activity [11, 25].
Very few studies have addressed the importance of
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 5 of 11
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Table 2 Multinomial logistic regression analyses findings of factors associated with knowledge on diabetes among T2DM patients
JUTH, 2014
Variable Knowledge, n (%) n= 309 Low versus High Medium versus High
Low (n= 139) Medium (n= 62) High (n= 108) COR (95%CI) AOR (95%CI) COR (95%CI) AOR (95%CI)
Sex Male 89 (64) 38 (61.3) 62 (57.4) 1.3 (0.8, 2.2) - 1.2 (0.6, 2.2) -
Female 50 (36) 24 (38.7) 46 (42.6) 1 - 1 -
Age <40 years 5 (3.8) 2 (3.4) 5 (5.1) 0.5 (0.1, 1.9) - 0.5 (0.1, 3.1) -
40-60 years 80 (61.1) 39 (66.1) 69 (70.4) 0.6 (0.3, 1.1) - 0.8 (0.4, 1.6) -
>60 years
Missing
46 (35.1)
8
18 (30.5)
4
24 (24.5)
10
1- 1 -
Marital status Married 111 (79.9) 52 (83.9) 88 (81.5) 1.1 (0.5, 2.4) 0.9 (0.3, 2.3) 1.2 (0.5, 3.1) 0.7 (0.2, 2.4)
Single 12 (8.6) 3 (4.8) 6 (5.5) 1.8 (0.5, 5.9) 1.6 (0.4, 7) 1 (0.2, 5.2) 0.6 (0.1, 3.9)
Widow/er 16 (11.5) 7 (11.3) 14 (13) 1 1 1 1
Level of Education Illiterate 60 (43.2) 18 (29) 31 (28.7) 2.6 (1.3, 4.9)* 3.1 (1.03, 9.3)* 0.9 (0.4, 2.04) 0.4 (0.1, 1.7)
Primary 51 (36.7) 21 (33.9) 40 (37) 1.6 (0.9, 3.2) 1.9 (0.8, 4.9) 0.8 (0.4, 1.8) 0.4 (0.1, 1.3)
2
0
and above 28 (20.1) 23 (37.1) 37 (34.3) 1 1 1 1
Occupation Employed 28 (20.1) 16 (25.8) 28 (25.9) 0.8 (0.3, 1.8) 0.6 (0.2, 1.8) 2 (0.6, 7.1) 1.4 (0.3, 6.2)
Unemployed 31 (22.3) 18 (29) 29 (26.9) 0.8 (0.3, 1.9) 0.5 (0.2, 1.4) 2.1 (0.6, 7.6) 3.3 (0.7, 16.1)
Merchant 14 (10.1) 6 (9.7) 9 (8.3) 1.2 (0.4, 3.6) 0.7 (0.2, 2.7) 2.3 (0.5, 10.6) 3.8 (0.7, 22.2)
Farmer 48 (34.5) 18 (29) 28 (25.9) 1.3 (0.6, 3.09) 0.5 (0.2, 1.6) 2.3 (0.6, 7.9) 2.4 (0.5, 12.1)
Daily labor 18 (12.9) 4 (6.5) 14 (13) 1 1 1 1
Family/social support Yes 85 (61.2) 29 (46.8) 61 (56.5) 1.2 (0.7, 2) 0.9 (0.5, 1.7) 0.6 (0.4, 1.3) 0.6 (0.3,1.3)
No 54 (38.8) 33 (53.2) 47 (43.5) 1 1 1 1
DM Family history Yes 38 (27.3) 9 (14.5) 29 (26.9) 1.03 (0.6, 1.8) 1.3 (0.6, 2.5) 0.5 (0.2, 1.06) 0.5 (0.2, 1.4)
No 101 (72.7) 53 (85.5) 79 (73.1) 1 1 1 1
Glucometer at home Yes 12 (8.6) 3 (4.8) 15 (13.9) 0.6 (0.3, 1.3) 0.5 (0.2, 1.3) 0.3 (0.09, 1.1) 0.3 (0.07, 1.3)
No 127 (91.4) 59 (95.2) 93 (86.1) 1 1 1 1
Cigarette smoking Yes 10 (7.2) 5 (8.1) 7 (6.5) 1.1 (0.4, 3) - 1.2 (0.4, 4) -
No 129 (92.8) 57 (91.9) 101 (93.5) 1 - 1 -
Alcohol drinking Yes
No
7 (5)
132 (95)
3 (4.8)
59 (95.2)
7 (6.5)
101 (93.5)
0.8 (0.3, 2.2)
1
-
-
0.7 (0.2, 2.9)
1
-
-
Khat chewing status Yes 32 (23) 19 (30.6) 26 (24.1) 0.9 (0.5, 1.7) - 1.4 (0.7, 2.8) -
No 107 (77) 43 (69.4) 82 (75.9) 1 - 1 -
BMI in Kg/m
2
<18 11 (7.9) 3 (4.8) 3 (2.8) 6.9 (1.5, 32)* 6.4 (1.2, 34.9)* 2.5 (0.4, 16) 3.5 (0.4, 30.1)
18-25 78 (56.1) 33 (53.2) 50 (46.3) 2.9 (1.2, 7.4)* 2.5 (0.9, 7) 1.6 (0.6, 4.7) 2.6 (0.8, 8.9)
25-30 42 (30.2) 20 (32.3) 40 (37) 1.9 (0.8, 5.2) 2.4 (0.8, 6.9) 1.3 (0.4, 3.7) 2.09 (0.6, 7.4)
> = 30 8 (5.8) 6 (9.7) 15 (13.9) 1 1 1 1
Self-care behaviour Good 61 (43.9) 36 (58.1) 61 (56.5) 0.6 (0.3, 1) 0.5 (0.3, 0.9)* 1.1 (0.6, 2) 0.9 (0.4, 2.09)
Poor 78 (56.1) 26 (41.9) 47 (43.5) 1 1 1 1
Duration of DM in year <5 73 (52.5) 41 (66.1) 31 (28.7) 4.7 (2.3, 9.5)* 4.2 (1.9, 9.5)* 9.5 (3.3, 27.1)* 9.8 (3.2, 30.2)*
5-10 48 (34.5) 16 (25.8) 41 (38) 2.3 (1.2, 4.7)* 1.8 (0.8, 3.9) 2.8 (0.9, 8.4) 2.5 (0.8, 8.1)
>10 18 (13) 5 (8.1) 36 (33.3 1 1 1 1
Glycemic control level Good 100 (71.9) 38 (61.3) 81 (75) 1.2 (0.7, 2) - 1.9 (0.9, 3.7) 0.9 (0.4, 2.09)
Poor 39 (28.1) 24 (38.7) 27 (25) 1 - 1 1
* Statistically significant at P-value < = 0.05
T2DM type 2 diabetes Mellitus, JUTH Jimma University Teaching Hospital
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 6 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
knowledge, self-care behaviours and adherence to medi-
cations among diabetic patients in resource poor coun-
tries including Ethiopia [8, 15].
The findings of the current study conform with results
from Asian and African studies that have revealed low
level of knowledge of diabetes among participants, who
also demonstrated to have poor self-care behaviours and
poor adherence to medications [14, 16, 26–28]. How-
ever, studies from elsewhere have also reported contrast-
ing findings [25, 29]. We argue that, these differences in
findings could be due to differences in study populations
as wells as the type of tools used to measures these out-
comes. Consistent with the current study’s findings,
knowledge has a significant effect on self-care behav-
iours and adherence to medications [30, 31]. As such,
these findings inform of the necessity to have consistent
diabetic education to address issues related to self-care
behaviours and adherence to medications as both are
the most cost effective management strategies for DM
complications [32]. As the diabetic education alone
would not be sufficient for sustained control, compre-
hensive and effective strategies comprising actions to en-
force self-care behaviours such as good dietary patterns,
regular physical activity, self-glycemic control and foot
care [33] should be designed.
Respondents aged between 40 and 60 years were less
likely to have low knowledge level than those in age
group older than 60 years. This indicates that older
people were at a higher risk and thus there would be a
need to develop targeted programs to address inequity
that existed between age groups. A significant difference
in low level of knowledge of diabetes was observed
among illiterates than those who attained higher second-
ary education. This is not surprising as knowledge is
gained through education. This finding was consistent
with other studies from United Arab Emirates (UAE)
[16] and Bangladesh [14]. Lower education status could
end up with low self-management behaviours, lower
self-efficacy and lower continuity of care. Thus, as rec-
ommendations, measures to improve literacy level would
be cost effective to reduce diabetic morbidity and mor-
tality [12, 34]. Lower BMI and short duration of diabetes
(less than five years) were significantly associated with
level of knowledge and this was supported by findings
from Nigeria [35] to UAE [16].
Almost half (49.1 %) of the respondents had poor self-
care behaviours toward DM. Although these figures are
lower than the previously reported elsewhere including
in North Ethiopia (59 %) [15], East Ethiopia (60.8 %)
[31] and Kenya (63.2 %), [28], the magnitude of diabetes
in the current study society still denotes diabetes as a
significant public health problem. The hypothesise that
this variation could be due to the types instruments used
in different studies or duration of patients on treatment.
It well known that a well instituted diabetes self-care
plan lowers glycosylated hemoglobin levels- an indicator
that can be used to monitor diabetes [36].
Lower educational level and poor adherence to medi-
cations were among the predictors of poor self-care be-
haviours. This is similar to findings of the previous
studies [31, 37, 38], and shows how diabetes education
and its application is indispensable for diabetic manage-
ment [32, 33]. Having family history of DM was found
to be protective against poor self-care behaviours, a find-
ing which is not dissimilar with findings of the previous
studies [25, 39, 40]. It would be plausible to argue that
diabetic patients would share their knowledge, and expe-
riences with families members [25], information that
Table 3 Factors independently associated with poor self-care behaviours among T2DM patients JUTH, 2014
Variables Self-care behaviour (n= 309) COR (95%CI) AOR (95 %
CI)
Good n (%) Poor n (%)
Education Illiterate 18 (20.7) 69 (79.3) 2.7 (1.5, 4.9) 3.1(1.7, 5.8)*
Primary 10 (45.5) 12 (54.5) 1.8 (1, 3.1) 1.9(1.1, 3.6)*
2
0
and above 31 (27.7) 81 (72.3) 1 1
Family history Yes 31 (19.7) 45 (29.6) 0.5 (0.3, 0.9) 0.5 (0.3, 0.9)*
No 126 (80.3) 107 (70.4) 1 1
Adherence to medications High 62 (53.9) 53 (46.1) 0.5 (0.3, 0.9) 0.6 (0.3, 1.1)
Medium 68 (58.1) 49 (41.9) 0.4 (0.2, 0.7) 0.4 (0.3, 0.8)*
Low 28 (36.4) 49 (63.6) 1 1
Have glucometer at home Yes 12 (40) 18 (60) 1.7 (0.8, 3.6) 2.5 (1.1, 5.8)*
No 146 (52.3) 133 (47.7) 1 1
History of alcohol drinking Yes 4 (23.5) 13 (76.5) 3.6 (1.2, 11.4) 4.6 (1.3, 15.7)*
No 154 (52.7) 138 (47.3) 1 1
* Statistically significant at P-value < = 0.05
T2DM, type 2 diabetes Mellitus, JUTH Jimma University Teaching Hospital
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 7 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 4 Multinomial logistic regression analyses findings of factors associated with adherence to medications among T2DM patients
JUTH, 2014
Variable Adherence to medications, n (%) Low versus High Medium versus High
Low
(n= 77)
Medium
(n= 117)
High
(n= 115)
COR (95%CI) AOR (95%CI) COR (95%CI) AOR (95%CI)
Sex Male 52 (67.5) 70 (59.8) 67 (58.3) 1.5 (0.8, 2.7) 1.5 (0.6, 3.8) 1.1 (0.6, 1.8) 1.5 (0.7, 3.4)
Female 25 (32.5) 47 (40.2) 48 (41.7) 1 1 1 1
Age <40 years 3 (4.1) 7 (6.4) 2 (1.9) 1.8 (0.3, 11.7) - 4.6 (0.9, 23.9) -
40-60 years 42 (57.5) 77 (70) 69 (65.7) 0.7 (0.4, 1.4) - 1.5 (0.8, 2.7) -
>60 years
Missing
28 (38.4)
4
26 (23.6)
7
34 (32.4)
10
1- 1-
Marital status Married 66 (85.7) 95 (81.2) 90 (78.3) 1.7 (0.6, 4.3) - 1.2 (0.6, 2.6) -
Single 4 (5.2) 8 (6.8) 9 (7.8) 1.02 (0.2, 4.4) - 1.02 (0.3, 3.4) -
Widow/er 7 (9.1) 14 (12) 16 (13.9) 1 - 1 -
Level of Education Illiterate 29 (37.7) 47 (40.2) 33 (28.7) 1.9 (0.9, 4.1) 2.03 (0.6, 6.8) 1.9 (0.9, 3.6) 1.01 (0.4, 2.9)
Primary 30 (39) 40 (34.2) 42 (36.5) 1.6 (0.8, 3.3) 1.3 (0.5, 3.8) 1.3 (0.7, 3.3) 0.7 (0.3, 1.7)
2
0
and above 18 (23.3) 30 (25.6) 40 (34.8) 1 1 1 1
Occupation Employed 20 (26) 25 (21.4) 27 (23.5) 2.3 (0.8, 6.5) 2.9 (0.9, 9.7) 2.9 (1.06, 7.9)* 3.1 (1.01, 9.7)*
Unemployed 15 (19.5) 33 (28.2) 30 (26.1) 1.6 (0.6, 4.5) 2.7 (0.7, 10.8) 3.5 (1.3, 9.3)* 8.2 (2.2, 30.4)*
Merchant 13 (16.9) 9 (7.7) 7 (6.1) 5.8 (1.7, 20.4)* 6.8 (1.6, 28.8)* 4 (1.09, 14.9)* 5.3 (1.2, 22.8)*
Farmer 22 (28.6) 43 (36.7) 29 (25.2) 2.4 (0.9, 6.6) 1.6 (0.4, 5.8) 4.7 (1.8, 12.3)* 3.8 (1.1, 12.8)*
Daily labor 7 (9) 7 (6) 22 (19.1) 1 1 1 1
Family/social support Yes 46 (59.7) 69 (59) 60 (52.2) 1.4 (0.8, 2.4) - 1.3 (0.8, 2.2) -
No 31 (40.3) 48 (41) 55 (47.8) 1 - 1 -
DM Family history Yes 18 (23.4) 34 (29.1) 24 (20.9) 1.2 (0.6, 2.3) 1.1 (0.5, 2.6) 1.6 (0.9, 2.8) 1.6 (0.8, 3.2)
No 59 (76.6) 83 (70.9) 91 (79.1) 1 1 1 1
Glucometer at home Yes 8 (10.4) 13 (11.1) 9 (7.8) 1.4 (0.5, 3.7) - 1.4 (0.6, 3.6) -
No 69 (89.6) 104 (88.9) 106 (92.2) 1 - 1 -
Cigarette smoking Yes 10 (13) 4 (3.4) 8 (7) 1.9 (0.8, 5.3) 2.3 (0.6, 8.6) 0.5 (0.1, 1.6) 0.4 (0.1, 1.6)
No 67 (87) 113 (96.6) 107 (93) 1 1 1 1
Alcohol drinking Yes
No
8 (10.4)
69 (89.6)
5 (4.3)
112 (95.7)
4 (3.5)
111 (96.5)
3.2 (0.9, 11.1)
1
2.1 (0.5, 9.2)
1
1.2 (0.3, 4.7)
1
1.8 (0.4, 7.9)
1
Khat chewing status Yes 18 (23.4) 38 (32.5) 21 (18.3) 1.4 (0.7, 2.8) 0.8 (0.4, 2.1) 2.2 (1.2, 3.9)* 1.9 (0.9, 3.9)
No 59 (76.6) 79 (67.5) 94 (81.7) 1 1 1 1
BMI in Kg/m
2
<18 7 (9.1) 6 (5.1) 4 (3.5) 5.6 (1.2, 27.4)* 4.9 (0.8, 31) 3 (0.7, 13.8) 2.9 (0.6, 16.3)
18-25 46 (59.7) 69 (59) 46 (40) 3.2 (1.1, 9.5)* 2.6 (0.7, 9.1) 3 (1.2, 7.6)* 3.4 (1.2, 9.9)*
25-30 19 (24.7) 34 (29.1) 49 (42.6) 1.2 (0.4, 3.9) 1.3 (0.3, 4.6) 1.4 (0.5, 3.6) 1.7 (0.6, 4.9)
> = 30 5 (6.5) 8 (6.8) 16 (13.9) 1 1 1 1
Knowledge High
Medium
27 (35.1)
7 (9.1)
40 (34.2)
21 (17.9)
41 (35.7)
34 (29.6)
1.6 (0.9, 3.1)
0.3 (0.1, 0.8)*
1.1 (0.5, 2.4)
0.2 (0.1, 0.6)*
1.4 (0.8, 2.6)
0.6 (0.3, 1.3)
1.4 (0.7, 2.9)
0.5 (0.2, 1.2)
Self-care Low 43 (55.8) 56 (47.9) 40 (34.8) 1 1 1 1
Good 28 (36.4) 68 (58.1) 62 (53.9) 0.5 (0.3, 0.9)* 0.7 (0.3, 1.3) 1.2 (0.7, 1.9) 1.7 (0.9, 3.1)
Poor 49 (63.6) 49 (41.9) 53 (46.1) 1 1 1 1
Duration of DM in year <5 43 (55.8) 54 (46.2) 48 (41.7) 1.7 (0.8, 3.7) 2.1 (0.8, 5.6) 1.2 (0.6, 2.5) 1.02 (0.4, 2.4)
5-10 21 (27.3) 41 (35) 43 (37.4) 0.9 (0.4, 2.2) 0.9 (0.4, 2.5) 1.04 (0.5, 2.1) 1.01 (0.4, 2.3)
>10 13 (16.9) 22 (18.8) 24 (20.9) 1 1 1 1
Glycemic control level Good 64 (83.1) 92 (78.6) 63 (54.8) 4.1 (2.02, 8.2)* 3.3 (1.5, 7.2)* 3.04 (1.7, 5.4)* 2.8 (1.5, 5.3)*
Poor 13 (16.9) 25 (21.4) 52 (45.2) 1 1 1 1
T2DM type 2 diabetes mellitus, JUTH Jimma University Teaching Hospital, COR crude odds ratio, AOR adjusted odds ratio
*Statistically significant at P-value < = 0.05
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
could be used by the newly diagnosed members to im-
prove their conditions through effective self-care
behaviours.
The current study also revealed that respondents who
had glucometer at home were 2.5 times more likely to
have poor self-care behaviours than those who did not.
This could be due to the fact that the majority (nearly
90 %) of the respondents had not been counseled on
how to measure self-glucose level using glucometer. Be-
sides, more than 90 % of respondents did not Self-
Monitoring Blood Glucose (SMBG) service at home ei-
ther due to lack of knowledge or scarcity of consum-
ables. Consistent with findings from California [41] to
United Kingdom [42], the relative probability of having
poor self-care behaviours among respondents who had
history of alcohol drinking was also significantly higher
than those who did not drink. This is a clue for the in-
clusion of brief interventions strategy for alcohol in dia-
betes care.
This study reported that one fourth and two fifth of
the respondents had low and medium level of adherence
to diabetic medications respectively, the findings which
were lower than a study from France [10]. The differ-
ences could be explained due to financial problem, man-
agement of side effect of the drugs, health care
providers’approach during diabetic education and coun-
seling, and general quality of care for diabetic services in
Ethiopia [8]. Furthermore, individuals with low socio-
economic status have limited access to education, infor-
mation and transportation, which are necessary drivers
to required necessary services including medications.
In the current study, farmers compared to daily la-
bours were high likely to have lower level of adherence
to medications. This could be related with level of edu-
cation as it was obvious that the majority of the farmers
in this study were illiterate compared to daily labourers.
In addition, other plausible factors including poor access
to health care and allocating less time for self-care man-
agement could be barriers to adherence to medications.
In contrast to other studies, [13, 43–45], findings from
this study revealed that good glycaemic control level was
significantly associated with low and medium level of ad-
herence to medications. We hypothesise that these find-
ings could be related to protection provided by
participants life styles including being laborers where
they would endure significant incidental physical activity
and having dietary patterns that are plant based. How-
ever, these findings need further exploration.
This study had several limitations which were worth
noting. The institutional based nature of the study might
not infer for other diabetic patients. Similarly, the nature
of cross-sectional study design does not indicate tem-
poral relationship or causality. Self-report of adherence
to medications could also be affected by recall bias.
Moreover, selection bias could also have been intro-
duced because patients who are under regular follow-up
by the university clinic are likely to be receiving better
care and support than those in the lower level clinics.
We were unable to use HbA1c, a more accurate than
FBG measurement to evaluate glycemic control due to
inaccessibility and high cost of the measurement in our
country. It is not routinely done as part of the standard
care in the study hospital. This limitation is not only af-
fecting our study, but also is a significant challenge for
diabetes control in the country as a whole without ac-
cess to Hb1Ac measurement. Finally, a psychometric
study for the three tools: The Diabetes Knowledge Test
(DKT), Expanded Version of the Summary of Diabetes
Self-Care Activities (SDSCA) and Morisky scale was not
conducted.
Conclusion
In summary, the findings from the current study re-
vealed that a significant number of diabetic patients had
low level of knowledge, poor self-care behaviours and
low level of adherence to medications. These findings
suggest the need to work on integrated interventional
management on diabetic knowledge, self-care behaviours
and adherence to medications. Education, awareness cre-
ation and implementation of good self-care behaviours
could be improved as cross cutting interventions. We
recommend the inclusion of brief interventions strategy
for alcohol in diabetes care. It has been reported that
brief interventions to reduce at-risk drinking has the po-
tential to improve diabetic medication adherence and
treatment outcome [46]. We also recommend further
population based research to explore specific factors such
as the association found in this study that indicated that
low level of adherence to medications was associated with
good control of glycaemia. Where possible, psychometric
studies should be conducted as tools to assess diabetic
knowledge, self-care behavior and medication adherence.
Additional file
Additional file 1: Tools for assessing diabetes related knowledge, self-
care behaviours and adherence to medications among diabetic patients
in Southwest Ethiopia, 2014. (DOCX 110 kb)
Abbreviations
BMI, body mass index; DM, diabetes mellitus; DKT, diabetes knowledge test;
FBG, fasting blood glucose; HbA1c, glycosylated hemoglobin; IDFA,
International Diabetes Federation Atlas; JUTH, Jimma University Teaching
Hospital; OHA, oral hypoglycemic agent; SMBG, self-monitoring blood glucose;
SDSCA, summary of diabetes self-care activities; T2DM, type-2 diabetes mellitus
Acknowledgments
The authors are grateful to the respondents and data collectors. This
research was funded by Jimma University.
Kassahun et al. BMC Endocrine Disorders (2016) 16:28 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Funding
This research was funded by Jimma University and was received by Hailay
Gesesew. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The dataset supporting the conclusions of this article is included within the
article.
Authors’contributions
TK involved in designing of the study, data collection, data analysis, drafting
and critically reviewing the manuscript. Likewise, HG, LM and TE involved in
designing of the study, analysis of the data and critically reviewing the
manuscript. All authors read and approved the final manuscript.
Authors’information
TK is a clinical pharmacist in Dilchora Hospital of Ethiopia. HG is a lecturer of
Epidemiology in college of Health Sciences of Jimma University and PhD
fellow in Flinders University. LM is a senior lecturer and course coordinator in
Faculty of Medicine, Nursing and Health Sciences at Flinders University. TE is
associate professor of Clinical Pharmacy in college of Health Sciences of
Jimma University. All authors are currently staff members in their respective
department.
Competing interests
The authors declare that they have no competing interest.
Consent for publication
Not Applicable.
Ethics approval and consent
Informed consent was obtained from study participants before the
commencement of each interview, and no personal identification was
registered. There was no any financial compensation or provision for the
study participants. The permission to conduct the study was obtained from
JUTH and the study was approved by institutional review board (IRB) of
college of health sciences at Jimma University, Southwest Ethiopia.
Author details
1
Dilchora Hospital, Diredawa, East Ethiopia.
2
Department of Epidemiology,
College of Health Sciences, Jimma University, Jimma, Ethiopia.
3
Discipline of
Public Health, Faculty of Medicine, Nursing and Health Sciences, Flinders
University, Adelaide, Australia.
4
Department of Clinical Pharmacy, College of
Health Sciences, Jimma University, Jimma, Ethiopia.
Received: 7 January 2016 Accepted: 26 May 2016
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