Challenges in using cardiovascular medications in Sub-Saharan Africa
Berhe, Derbew Fikadu
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6|
Impact of Adverse
Drug Events and
Treatment Satisfaction
on Patient Adherence
with Antihypertensive
Medication — A Study in
Ambulatory Patients
Derbew Fikadu Berhe Katja Taxis
Flora M Haaijer-Ruskamp Afework Mulugeta
Yewondwossen Tadesse Mengistu Johannes G M Burgerhof Peter GM Mol
British Journal of Clinical
Pharmacology 2017; 83(9):2107–2117. doi: 10.1111/bcp.13312
Aim: The aim of the present study was to evaluate the impact of adverse drug events (ADEs) and treatment satisfaction on antihypertensive medication adherence.
Methods: A cross-sectional study was conducted in six public hospitals in Ethiopia. We included adult ambulatory patients on antihypertensive med-ication. Adherence was measured using the 8-point Morisky Medication Adherence Scale (MMAS-8), which categorizes as low (0–5), medium (6–7), and high (= 8) adherence. Treatment satisfaction was measured using the Treatment Satisfaction Questionnaire for Medication (TSQM) version 1.4, which included questions about ADEs. Data were analysed using generalized ordered logistic regression at 95% confidence intervals (CIs).
Results: We included 925 out of 968 patients. Overall, 42% of patients scored low, 37% medium, and 21% high adherence. Satisfaction with treatment was low, with a mean (standard deviation) TSQM score for global satisfaction was 51 (14). A total of 193 (21%) patients experienced 421 ADEs — mainly dyspeptic symptoms (12%), headache (11%), and cough (11%). Experiencing more ADEs reduced the odds of being adherent [low vs. medium/high: odds ratio (OR) OR
1
0.77 (95% CI 0.67, 89)], and (low/medium vs. high: OR2
0.55 (0.41, 0.73)]. Being more satisfied increased the odds of being adherent [low vs. medium/high: OR1
1.02 (95% CI 1.01, 1.03)]. Taking medication > 1 year [OR1 = 2
0.60 (95% CI 0.43, 0.83)], and calcium channel blockers [OR1 = 2
0.71 (0.54, 0.92)] decreased the odds of being adherent.Conclusions: Only one in five patients reported perfect (high) adherence to their antihypertensive treatment regimen. Experiencing ADEs and being dissatisfied with treatment were associated with lower adherence. In addition to addressing treatment satisfaction and drug safety in first-world countries, these should also be addressed in resource-poor settings, within patient con-sultations, to enhance adherence.
6
Background |Background
Adherence to antihypertensive medication can significantly reduce hypertension associated cardiovascular morbidity and mortality [1]. Factors affecting adherence include patient characteristics, socioeco-nomic status, therapy condition, and health system/health care team [2]. Overall, experiencing adverse drug events (ADEs) is one of the important factors for patient medication adherence [3]. Furthermore, low treatment satisfaction is a major concern for adherence, particu-larly in patients with chronic diseases. Treatment satisfaction provides an understanding of a patient’s perspective on his/her treatment [4, 5]. The concept of treatment satisfaction gained growing importance over the past three decades [4–7]. However, there are few studies that tried to assess the relationship between antihypertensive medication adher-ence, ADEs and treatment satisfaction [4, 7], and in particular, this has not been studied in Sub-Saharan Africa. Factors which are particularly relevant in this context include the need to continue treatment, despite experiencing (mild) side effects, because of limited availability of alter-native drugs or higher cost of alteralter-native medication; low literacy; poor social support and lack of access to healthcare and continuity of care [8]. The prevalence of hypertension in African countries, including Ethi-opia, is increasing [9, 10], and mortality rates of cardiovascular diseases in low and middle-income countries is much higher than in high in-come countries [11]. Understanding potential determinants — treat-ment satisfaction and ADEs — for medication adherence could be used to design programs to improve treatment outcomes. Therefore, the aim of this study was to: (i) assess the level of antihypertensive medication adherence; and (ii) evaluate the impact of experiencing ADEs related to antihypertensive medication and treatment satisfaction on adherence in Ethiopia.
Methods
Study design and setting
In this cross-sectional study, we included two specialized referral hos-pitals (Tikur Anbessa and St. Paul’s) in Addis Ababa, the capital city of Ethiopia, and four general hospitals in Ethiopia (Yekatit 12 Hospital in Addis Ababa, and Lemlem Karl hospital in Maychew, Mekelle Hospital in Mekelle, and St. Mary Hospital in Axum; all in Tigray regional state).
Study population
Patients were recruited in the waiting areas from hypertension out-patient clinics of our six study hospitals. We used a consecutive sam-pling technique, whereby patients were approached successively and recruited until the required sample size was achieved at each hospital. Inclusion criteria included adult hypertensive patients (≥ 18 years) who had received at least one antihypertensive medication prescription from the same hospital previously, as reported by the patient and/or recorded in their pocket-size appointment card (these data were veri-fied with the patient medical record) and who gave informed consent. Patients were excluded if their medical records were unavailable or in-complete, if they had indicated that they were hypertensive but proved to be not hypertensive after review of the medication record, and if they were not able to complete the Morisky Medication Adherence Scale (MMAS-8) questionnaire.
We did not conduct a formal sample size calculation as we used available data from our other project (unpublished data). In that pro-ject, we aimed for a representative sample size of patients with hy-pertension and controlled blood pressure. This resulted in recruiting 984 patients equally distributed over the six hospitals based on an es-timated prevalence of 30% of patients with controlled blood pressure and 10% missing cases.
6
Methods |Data collection
Patients were interviewed before they went into the doctor’s consultation room. After the consultation was completed, patient medical records were reviewed to collect additional treatment parameters (prescribed medications and comorbid illnesses). The interviews were conducted by professional nurses or pharmacists, using a case-report form. The same staff reviewed the patient records. Data were collected between Febru-ary and August, 2015. DFB supervised data collection on site.
Outcome measure
Patients were interviewed about how they had adhered to their pre-scribed antihypertensive medication using the MMAS-8 [12]. Adher-ence was grouped into either of three ordinal categories based on eight questions; a score of 0 to 5 was considered as low adherence, a score of 6 to 7 as medium adherence, and a score of 8 as high adherence [12].
Main explanatory variables
Patients were also interviewed for their antihypertensive medication- related treatment satisfaction. Quintiles Inc., San Francisco, CA, US provided us with the 14-item Treatment Satisfaction Questionnaire for Medication (TSQM) version 1.4 [13]. The TSQM comprises four do-mains: (i) effectiveness (TSQM 1–3); (ii) side effect (TSQM 4, 5–8); (iii) convenience (TSQM 9–11); and (iv) global satisfaction (TSQM 12–14). The score in each domain ranges from 0 to 100, with higher score rep-resenting more satisfaction [13]. Patients who reported they did not ex-perience any ADE (‘no’ answer for TSQM 4) skipped TSQM questions 5 through 8, and were given a score of 100% for the side effect-related satisfaction domain. Participants who reported any antihypertensive medication-related ADE were asked in local vernacular which ADE they had experienced. In addition, patient records were reviewed for
any documented ADEs since their previous visit. For the analysis, we combined reported and documented ADEs.
For the purpose of the present study, both MMAS-8 and TSQM scales were translated into two Ethiopian languages (Amharic and Tigrigna), which are spoken in the catchment area of our study. The translation process followed a stepwise process with translation, back translation and piloting as described by Beaton et al [14]. Translations and tran-scripts of translation process were sent to Quintiles Inc., San Francisco, CA, USA (TSQM v.1.4) and Morisky (MMAS-8). The internal consist-ency of the questionnaire was assessed: MMAS-8 (8 item, Cronbach’s alpha 0.72), effectiveness (3 items, Cronbach’s alpha 0.88), side effects (4 items, Cronbach’s alpha 0.81), convenience (3 items, 0.79), and global satisfaction scale (3 items, Cronbach’s alpha 0.80).
Other explanatory variables
Other variables included were: (i) socio-demographics (age, gender, ed-ucational status, alcohol use, and smoking history); (ii) hospital type (specialized/general); (iii) comorbid cardiometabolic illnesses; and (iv) antihypertensive treatment characteristics {drug class prescribed [an-giotensin converting enzyme (ACE) inhibitor, beta blocker, calcium channel blocker, and diuretic], duration of antihypertensive treatment (≤ 1 year/> 1 year) and medication regimen complexity index}. A higher score indicates a more complex treatment regimen — i.e. with more drugs, frequent dosing schedule and/or more cumbersome administra-tion route [15].
Statistical analyses
For data entry, processing, and descriptive statistics, we used Microsoft access 10, and SPSS statistical software version 22.0 (SPSS, Inc., Chicago, IL, U. Stata version 13 (Stata Corp, College Station, TX, USA) was used for bi/multivariable generalized ordered logistic regression.
6
Methods |Owing to the ranked outcome (low, medium, and high adherence), we used generalized ordered logistic regression (gologit) with autofit (also called partial proportional odds) for assessing the association be-tween adherence and explanatory variables [16]. The gologit model with autofit is a hybrid of ordinal regression (same odds ratio across cate-gories) and the default gologit (different odds ratio across catecate-gories). If proportional odds assumption (Brant test) was violated, the analysis gave two odds ratios (OR
1
and OR2
) for an explanatory variable (Fig-ure 1). For variables that did not violate the assumption a single OR1
=2
was reported; i.e. OR1
= OR2
[16]. Bivariable gologit models were used for variable inclusion to the final multivariable model.We developed two statistical multivariable gologit models with autofit. In the primary model, we used two main explanatory variables (global satisfaction and number of ADEs per patient), and other potential de-terminants with p < 0.20 in the bivariable model is inclusion criteria of variables in the multivariable variable model. We included the global
Figure 1 Analysis of adherence measured on the eight-point Morisky Medica-tion Adherence Scale (MMAS-8), using generalized ordered logistic regression (gologit). The level of adherence was classified into low, medium and high adherence groups, based on MMAS-8 scores of 0–5; 6–7; and 8, respectively. Low vs. medium/high adherence (OR
1
) and low/medium vs. high adherence (OR2
). OR, odds ratio; OR1
= OR2
(OR1 = 2
) if the variable does not violate pro-portional odds assumption.satisfaction score as this captured the overall satisfaction of patients with their antihypertensive medication. In the secondary model, we evaluated the impact of the three TSQM domains (effectiveness, side effects, and convenience) on adherence adjusted for other potential de-terminants with p < 0.20 in the bivariable model for inclusion criteria of variables in the multivariable variable model. This secondary model did not include the variable ‘number of ADEs’ because of collinearity with the side effect related TSQM domain (r = −0.8). We have also tested for possible collinearity within each multivariable gologit model, and vari-ables were not strongly correlated. Statistical significance for the multi-variable model was set at p < 0.05.
Ethical considerations
This study was approved by Ethiopian Health Research Ethical Review Committees of (i) the College of Health Sciences, Mekelle University, (ii) St Paul’s Hospital Millennium Medical College, and (iii) the De-partment of Internal Medicine, School of Medicine, College of Health Sciences, Addis Ababa University. All participants gave informed con-sent. Considering the expected low literacy rate, data collectors read the consent statement to the patients and then marked a potential partici-pant’s consent (yes/no) on the case-report form.
Results
We approached 968 patients, of whom eight refused to participate, six were not hypertensive, 18 had unavailable or incomplete medication records, and 11 did not complete the MMAS-8, Figure 2. Our analysis thus included 925 patients.
The mean age was 57 [standard deviation (SD) 14] years and 570 (63%) were females. One third of study participants had at least one comorbid illness (n = 319). The most common comorbid illness was diabetes mellitus (n = 229), and 66 (7%) patients had two or more comorbidities (Table 1).
6
Results |Overall, 334 (38%) patients had their BP controlled (< 140/90 mm Hg). Commonly prescribed antihypertensive medications were ACE inhibi-tors 509 (55%), diuretics 505 (55%), calcium channel blockers 451 (49%), and beta blockers 174 (19%). Most commonly prescribed drugs within each group were enalapril (n = 507), hydrochlorothiazide (n = 438), nifedipine (n = 402), and atenolol (n = 151). The median of regimen complexity index was 6 (inter quartile range: 4–9). The highest regimen complexity indices were observed in patients with diabetes who were us-ing multiple drugs includus-ing injectable insulin.
Based on MMAS-8, 391 (42%) of the study participants scored low, 340 (37%) medium and 194 (21%) high adherence to their antihypertensive Figure 2. Case inclusion flow chart for analysis.
Ta ble 1: D et er min an ts f or a nt ih yp er ten siv e m edic at io n ad her en ce; b iva ria ble g en era lize d o rder ed log ist ic r eg res sio n m ode l Ad her en ce (MMA S-8) s co re Bi va ria ble m ode l a t 95% CI Ch arac ter ist ics To ta l Low (0-5) M edi um (6-7) Hi gh (=8) OR
1
OR1
=2
OR2
p-va lue(s) Ad her en ce s co re 391 (42) 340 (37) 194 (21) D emo gr ap hi cs (y es vs. n o [r ef ] )b A ge (m ea n, S D) 57 (14) 57 (14) 57 (19) 56 (12) 1.00 [0.99; 1.01] 0.41 Fem ale (vs. m ale [r ef ]) (n, %) 570 (63) 234 (61) 220 (66) 116 (60) 1.04 [0.81; 1.34] 0.74 Sm ok in g hi sto ry (n, %) 60 (7) 29 (8) 25 (7) 6 (5) 0.69 [0.41; 1.09] 0.10 A lco ho l u se (n, %) 398 (44) 191 (50) 128 (39) 79 (41) 0.72 [0.57; 0.93] 0.01 M ar rie d (n, %) 585 (64) 253 (66) 208 (62) 124 (65) 0.94 [0.73; 1.21] 0.64 Ed uc at io n (n, %) P rim ar y o r n o f or m al e duc at io n 583 (65) 252 (66) 201 (61) 130 (69) Ref S eco nd ar y e duc at io n 142 (16) 60 (16) 60 (18) 22 (12) 0.90 [0.64; 1.26] 0.54 C ol leg e/U ni ver sit y 175 (19) 70 (18) 68 (21) 37 (20) 1.07 [0.78; 1.46] 0.69 H os pi ta l t yp e G en era l H os pi ta ls 632 (68) 255 (40) 240 (38) 137(22) Ref S pe ci alize d H os pi ta ls 293 (32) 136 (46) 100 (34) 57 (20) 0.81 [0.62; 1.05] 0.12 Tr ea tme nt s at isf ac tio n (T SQ M) a (m ea n, S D) Eff ec tiv en es s 64 (17) 62 (18) 66 (17) 66 (18) 1.01 [1.00; 1.02] < 0.01 Side eff ec t 88 (26) 84 (29) 89 (25) 95 (19) 1.01 [1.01; 1.02] < 0.01 C on venien ce 64 (14) 60 (16) 65(13) 70 (11) 1.04 [1.03; 1.05] < 0.01 G lo ba l (o vera ll) s at isfac tio n 51 (14) 49 (14) 53(13) 52 (13) 1.02 [1.01; 1.03] 1.01 [1.00; 1.02] < 0.01/0.13 Adv ers e dr ug e ve nt (AD E) N um ber o f AD Es (m ea n, S D) 0.5 (1) 0.7(1.2) 0.4 (1.0) 0.1 (0.0) 0.72 [0.63; 0.82] 0.51 [0.38; 0.69] < 0.01/< 0.01 Dis eas es (y es vs. n o [r ef ]) b (n, %) Di ab et es 229 (25) 98 (25) 93 (27) 38 (20) 0.88 [0.66; 1.15] 0.35 Di sli pidemi a 58 (6) 27 (7) 22 (6) 9 (5) 0.79 [0.48; 1.29] 0.34 H ea rt fa ilur e o r p re vio us m yo ca rdi al infa rc tio n 74 (8) 36 (9) 24 (7) 14 (7) 0.78 [0.50; 1.23] 0.30 Ren al di se as e 24 (3) 13 (3) 7 (2) 4 (17) 0.64 [0.29; 1.41] 0.276
Results | Ad her en ce (MMA S-8) s co re Bi va ria ble m ode l a t 95% CI Ch arac ter ist ics To ta l Low (0-5) M edi um (6-7) Hi gh (=8) OR1
OR1
=2
OR2
p-va lue(s) Tr ea tme nt cha rac te ris tics ( yes vs. n o [r ef ]) b (n, %) ACE in hi bi to rs 509 (55) 207 (53) 197 (58) 105 (54) 1.09 [0.85; 1.38] 0.50 Bet a b lo ck er s 174 (19) 79 (20) 68 (20) 27 (14) 0.79 [0.58; 1.07] 0.13 Ca lci um c ha nn el b lo ck er s 451 (49) 213 (54) 164 (48) 74 (38) 0.64 [0.50; 0.82] < 0.01 Di ur et ics 505 (55) 213 (54) 187 (55) 105 (54) 1.00 [0.78; 1.27] 0.98 Reg im en co m plexi ty I ndex (m edi an, I Q R) 6 (4; 9) 6 (4 ;10) 6 (4 ;9) 5 (3; 8) 0.96 [0.93; 0.98] < 0.01 D ura tio n o f t hera py (n, %) < 1 y ea r 173 (20) 56 (15) 66 (21) 51 (28) Ref > 1 y ea r 699 (80) 312 (85) 254 (79) 133 (72) 0.58 [0.43; 0.79] < 0.01 A s g lo ba l s at isfac tio n a nd AD Es v io la te d t he p ro po rt io na l o dd s a ss um pt io n, O R1
≠ OR2
va lues a re p res en te d s ep ara te ly. O dd s ra tio 1 (O R1
) f or lo w vs. m edi um a nd hig h ad her en ce; o dd s ra tio 2 (O R2
) f or lo w a nd m edi um vs. hig h ad her en ce . F or a ll o th er va ria bles, O R1
= OR2
, a nd t hes e a re p res en te d in t he co lumn O R1 =
2
. ACE, a ng io ten sin-co nv er tin g enzy m e; AD E, ad ver se dr ug e ven t, CI, co nfiden ce in ter va l; g olog it, g en era lize d o rder ed log ist ic r eg res sio n; I Q R, in ter qu ar tile ra ng e; MMA S, eig ht-p oin t M or isk y M edic at io n A dh er en ce S ca le; S D , s ta nd ar d de vi at io n; T SQ M, T re at m en t Sa tisfac tio n Q ues tio nn air e f or M edic at io n. aTh e s co re in e ac h T SQ M do m ain ra ng es f ro m 0 t o 100, w ith hig her s co re r ep res en tin g m or e s at isfac tio n. bD et er min an ts f or w hic h t her e w er e o nl y t w o c at eg or ies o nl y t he ‘ yes ’ c at eg or y i s p res en te d, e .g . t he sm ok er s in o ur s tud y p op ul at io n. Th os e t ha t did b elo ng ed t o t he ‘n o’ c at eg or y, h er e t he n on sm ok er s a re t he r ef er en ce [r ef ] g ro up f or t he a na lys es.medication (Table 1). The overall median adherence score was 6 (inter quartile range: 4; 7).
We identified 193 (21%) patients experiencing a total of 421 ADEs. Up to six ADEs were observed per patient (Supporting Information Table S1). The majority of ADEs (n = 402) experienced by patients (n = 182) were captured during the interviews. Only 26 patient medi-cal records had documented ADEs, for 15 of these charts, these ADEs were also reported by patients during their interviews. The most com-monly reported ADEs were dyspeptic symptoms [52 (12%)], headache [47 (11%)], cough [46 (11%)], fatigue/weakness [39 (9%)], leg swelling [27 (6%)], and palpitation or other cardiac problems [23 (5%)] (Sup-porting Information Table S2).
Among TSQM domains, the lowest score was for the global satisfac-tion domain was 51 (SD 14). The highest mean score for any domain was on side effect related satisfaction [88 (SD 26)]. This result was driven by the 732 (79%) patients who did not report any ADE and received a score of 100% for this domain. Considering only those patients who had re-ported ADE(s), the side effect related satisfaction score was 41 (SD 25). In the primary model (Table 2A), a one-point increase in the global satisfaction score was associated with an increased odds of having me-dium/high adherence scores {OR
1
1.02 [95% CI: 1.01; 1.03]}, but no significant difference for low/medium vs. high scores. Experiencing more ADEs significantly reduced the odds of being adherent [low vs. medium/high scores, OR1
0.77 (95% CI: 0.67; 0.89)], with the biggest effect for the low/medium vs. high adherence levels [OR2
0.55 (95% CI: 0.41; 0.73)]. Patients taking their medication for more than a year were less likely to have a higher level of adherence scores (OR1 = 2
0.60 [95% CI: 0.43; 0.83]). In addition, antihypertensive medication regimens con-taining calcium channel blockers decreased the odds of having higher adherence scores (OR1 = 2
0.71 [95% CI: 0.54; 0.92]). In the secondary multivariable model (Table 2B), higher TSQM convenience scores were associated with better adherence [low vs. medium/high: OR1
1.03 (95% CI: 1.02; 1.04) and low/medium vs. high scores OR2
1.05 (95% CI: 1.03; 1.06)]. Higher side effect satisfaction scores were associated with achieving higher adherence scores [OR1 = 2
1.01 [95% CI: 1.00; 1.01)].6
Results |Table 2A: Impact of ADE experience and global satisfaction (TSQM) on medi-cation adherence adjusted for other explanatory variables
Explanatory variable
Multivariable primary gologit model, 95% CI
OR
1
OR1 = 2
OR2
P-value(s)Global satisfaction a 1.02 [1.01; 1.03] 1.00 [0.99; 1.01] < 0.01/0.87
Number of ADE (per patient) 0.77 [0.67; 0.89] 0.55 [0.41; 0.73] < 0.01/ < 0.01
Regiment complexity index 0.99 [0.96; 1.02] 0.41
Duration of therapy
(> 1 years vs. ≤ 1 year [ref]) 0.60 [0.43; 0.83] < 0.01
Alcohol use (yes vs. no [ref]) b 0.77 [0.59;1.00] 0.05
Smoking (yes vs. no [ref]) b 0.73 [0.42; 1.28] 0.27
Specialized (versus general
[ref]) hospitals 0.90 [0.67; 1.23] 0.50
Beta blockers (yes vs. no [ref]) b 0.94 [0.65; 1.35] 0.74
Calcium channel blockers
(yes versus no [ref]) b 0.71 [0.54; 0.92] 0.01
Table 2B: Impact of TSQM subscales (effectiveness, side effect and conveni-ence) on medication adherence adjusted for other explanatory variables
Explanatory variable
Multivariable secondary gologit model, 95% CI
OR
1
OR1 = 2
OR2
P-value(s)Effectiveness a 1.00 [0.99; 1.01] 0.99 [0.98; 1.00] 0.61/0.05
Convenience a 1.03 [1.02; 1.04] 1.05 [1.03; 1.06] < 0.01/ < 0.01
Side effect a 1.01 [1.00; 1.01] 0.03
Regiment complexity index 1.00 [0.97; 1.03] 0.94
Duration of therapy
(> 1 years vs. ≤ 1 year [ref]) 0.65 [0.47; 0.90] 0.01
Alcohol use (yes versus no [ref]) b 0.75 [0.57; 0.97] 0.03
Smoking (yes versus no [ref]) b 0.76 [0.43; 1.34] 0.35
Specialized (versus general
[ref]) hospitals 0.89 [0.65; 1.21] 0.45
Beta blockers (yes vs. no [ref]) b 0.94 [0.65; 1.21] 0.72
Calcium channel blockers
(yes versus no [ref]) b 0.71 [0.54; 0.93] 0.01
As global satisfaction, Number of ADEs (per patient) ADEs, effectiveness, and convenience violated
the proportional odds assumption, OR
1
≠ OR2
andvalues are presented separately. Odds ratio 1 (OR
1
) for low vs. medium and high adherence; OddsRatio 2 (OR
2
) for low and medium vs. high adherence. For all other variables, OR1
= OR2
andthese are presented in the column OR
1 = 2
. Variables with P < 0.20 in the bivariable model(Ta-ble 1) were included in these multivariable models (Table 2A and B). ADE, adverse drug event; CI, confidence interval; gologit, generalized ordered logistic regression; TSQM, Treatment Satisfaction Questionnaire for Medication.
a The score in each TSQM domain ranges from 0 to 100, with higher scores representing more
satisfaction.
b Determinants for which there were only two categories only the ‘yes’ category is presented, e.g.
the smokers in our study population. Those that did belonged to the ‘no’ category, here the non- smokers are the reference [ref] group for the analyses.
Similar to the primary model (Table 2A), patients with more than one year of treatment duration [OR
1 = 2
0.65 (95% CI: 0.47; 0.90)], and with a treatment regimen containing calcium channel blockers [OR1 = 2
0.71 [95% CI: 0.54; 0.93)], had significantly decreased odds for higher levels of adherence. Alcohol use was significantly associated with lower levels of adherence [OR1 = 2
0.75 (95% CI: 0.57; 0.97); see Table 2B], while in the primary model (Table 2A) this effect just failed to achieve statistical significance.Discussion
This was one of the few studies from Sub-Saharan Africa investigating the association between ADEs, treatment satisfaction, and adherence to antihypertensive medication. The study indicated that adherence with antihypertensive medication was high for 21%, medium for 37% and low for 42% of ambulatory patients from six Ethiopian hospitals. Across the different adherence categories, lower adherence was associated with experiencing more ADEs and not being satisfied with treatment. We also found that adherence was negatively affected by treatment regimens containing calcium channel blockers, taking medication for longer than a year and by regular or occasional alcohol use.
The level of adherence we found fell within the wide range (15% to 93%) of non-adherence reported in low- and middle-income countries [17]. Only 21% of patients had high levels of adherence. This is a serious concern as poor adherence may negatively affect the benefit of antihy-pertensive agents on cardiovascular outcomes [1, 2], and implies a waste of scarce resources. The proportion of patients with high adherence in our study lies between studies performed in Ghana (7%) and Nigeria (8%), and those in Palestine (36%), China (52%), and France (50%) [7, 18–21]. These studies also used MMAS-8 separating levels similarly into
low/ medium and high adherence.
Patients who experience ADEs from their medication are more likely to be non-adherent, and to have poor outcome [3]. In our study, 21% of patients had experienced ADEs, and these were indeed strong predictors
6
Discussion |for poor medication adherence. It has been reported that up to 20% of all outpatients experience ADEs to any prescribed medication; this rein-forces the need to pay attention to ADEs in follow up consultations [22]. Our findings show that treatment satisfaction is an important predic-tor of antihypertensive medication adherence in an Ethiopian ambula-tory patient population. It confirms the association reported between adherence and satisfaction in general [10], and specifically between antihypertensive medication adherence and treatment satisfaction, as reported in two earlier studies from Palestine and Lebanon [4, 7]. The satisfaction scores of the TSQM domains — effectiveness, side effect and convenience — are similar to those cited in the afore-mentioned Pales-tinian study, which also reported domain specific scores [7]; however, global satisfaction in the latter study was higher than in the present study. Patients in our study rated this domain the lowest of all TSQM domains. Even though global satisfaction in the TSQM focuses on medication- related treatment satisfaction, which was emphasized by our interview-ers, the low score may mean that our patients interpreted this question as scoring the overall healthcare service.
Our study indicates that patients treated for more than a year require attention, as they had lower adherence scores than patients in their first year of treatment. It has been reported elsewhere that up to 50% of pa-tients become non-adherent during their first year of treatment [2, 23], but also adherence problems with longer treatment durations have been reported before [2]. This suggests that patients require attention and need to be motivated to continue treatment as prescribed — not just at start of treatment but, throughout the whole period of medication use. Calcium channel blocker use was associated with poor medication adherence. In our setting, the limited availability of alternative agents may have contributed to this effect. Mainly nifedipine (90% of all pre-scribed calcium channel blockers) was prepre-scribed, although safety and once daily dose make amlodipine preferable over nifedipine [24]. Fi-nally, we found that alcohol use had a significant effect on adherence in the secondary model. This is in line with a systematic review that showed a generally negative impact of alcohol use in patients with chronic illnesses [25]. However, three studies in that review included
patients with hypertension, and from these only one showed a negative association of alcohol use and adherence [25].
What are practical implications of our study? As highlighted above, poor adherence threatens the potential cardiovascular benefit of treat-ment with antihypertensive medication. This may result in more strokes, myocardial infarctions, and cardiovascular mortality [1, 2]. Moreover, in developing countries antihypertensive treatment has been found to be among the most cost-effective measures to reduce cardiovascular mortality and morbidity, [26, 27]. In our study, we identified signifi-cant determinants of adherence that are modifiable and can be im-proved through interventional strategies — i.e medication adherence can be improved by addressing treatment satisfaction and management of ADEs. Strategies may include patient education and counseling — e.g. involving family members, actively involving patients in decision making, training healthcare providers in patient counseling and ADE management [28]. This may be challenging in Ethiopia due to limited number of physicians, like in many other developing countries [29]. In-creasing the role of nurses and pharmacists in patient counseling may be a solution. This would also need more inter-professional communi-cation, to enhance prescribers’ knowledge about patients’ satisfaction and level of adherence. In Europe, this has been suggested in the latest ESH/ESC hypertension guideline as a “team based care for hyperten-sion’’ concept [30]. Optimizing the dose and dosing schedule may partly ameliorate experienced ADEs when there are limited treatment options. Given the lack of available studies showing that interventions to im-prove treatment satisfaction imim-prove adherence [4, 31], further research is required in what may be the most effective intervention strategies.
Strengths and limitations
Papers reporting adherence, as measured with MMAS-8, are often di-chotomized at the cost of losing information, and there is inconsistency in the selection of the most appropriate cut-off value defining good adherence [4, 17, 32]. We tried to maximize the methodological rigor
6
Conclusion |of our paper by categorizing adherence into three levels of adherence measure, proposed by Morisky, and used gologit to allow statistical analyses on an ordinal outcome [12, 16].
A limitation of our study was the validity of the tools. We followed a standard stepwise translation procedure but this did not include a cog-nitive validation step. Nevertheless, both TSQM and MMAS-8 captured differences between study participants. Even though most of the ADEs reported were typical for antihypertensive medication, a formal causal-ity assessment of the ADEs was beyond the scope of this study. Finally, the TSQM domain of effectiveness was not associated with adherence. The initially asymptomatic nature of hypertension probably made it difficult for ambulatory hypertensive patients to judge the effectiveness of therapy, unless they measure their own BP and have good clinical knowledge about hypertension. This was unlikely in our study setting, where most of the participants had a low level of education. This may have explained why effectiveness was not a significant determinant for better adherence scores. Our nonrandom consecutive sampling tech-nique may have introduced some bias. For practical reasons, we chose to recruit hypertensive patients attending hypertension clinics. These clinics invited patients for routine visits on specific days. On these days, the large majority of hypertensive patients were actually recruited into our study. Our sample is therefore typical for the included hospitals. The mean age of patients in our study (57-years) compares well with a re-cently published paper in Southwest Ethiopia (54-years), however our study included more women (63% vs. 46% respectively 46%) [33].
Conclusion
Only one in five patients reported good adherence to their antihyper-tensive treatment regimen. Experiencing ADEs and being dissatisfied about treatment were associated with lower adherence. Specific medi-cations especially calcium channel blockers, longer treatment duration and patients using alcohol were also associated with lower adherence. In addition to addressing treatment satisfaction and drug safety in
first-world countries, these should also be addressed in resource-poor settings, within patient consultations, to enhance adherence.
Acknowledgments
Authors wish to thank study participants, data collectors, and study hospital administrators who contributed to this study. The study was part of a PhD project funded by NUFFIC (Netherlands Organization for International Cooperation in Higher Education).
Competing of interest: There are no competing interests to declare.
Contribution
D.F. Berhe, K. Taxis and P. Mol designed and performed the research, ana-lyzed, interpreted the data and wrote the manuscript. Flora M Haaijer- Ruskamp, Afework Mulugeta, Yewondwossen Tadesse Mengistu were involved in designing and manuscript writing, while J. GM Burgerhof was involved on the analysis and writing.
References
(1) Corrao G, Parodi A, Nicotra F, Zambon A, Merlino L, Cesana G, Mancia G. Better com-pliance to antihypertensive medications reduces cardiovascular risk. J Hypertens 2011; 29:610–8.
(2) Sabaté E. Adherence to long-term therapies: evidence for action. Geneva: World Health Organization; 2003.
(3) Leporini C, De Sarro G, Russo E. Adherence to therapy and adverse drug reactions: is there a link? Expert Opin Drug Saf 2014; 13:41–55.
(4) Saarti S, Hajj A, Karam L, Jabbour H, Sarkis A, El Osta N, Khabbaz LR. Association be-tween adherence, treatment satisfaction and illness perception in hypertensive patients.
6
References |(5) Revicki DA. Patient assessment of treatment satisfaction: methods and practical issues. Gut. 2004;53: iv40–4.
(6) Barbosa CD, Balp MM, Kulich K, Germain N, Rofail D. A literature review to explore the link between treatment satisfaction and adherence, compliance, and persistence.
Pa-tient Prefer Adherence 2012; 6:39–48.
(7) Sa’ed HZ, Al-Jabi SW, Sweileh WM, Morisky DE. Relationship of treatment satisfaction to medication adherence: findings from a cross-sectional survey among hypertensive patients in Palestine. Health Qual Life Outcomes 2013; 11:191.
(8) Bowry AD, Shrank WH, Lee JL, Stedman M, Choudhry NK. A systematic review of adherence to cardiovascular medications in resource-limited settings. J Gen Intern Med 2011; 26:1479–91.
(9) Kayima J, Wanyenze RK, Katamba A, Leontsini E, Nuwaha F. Hypertension awareness, treatment and control in Africa: a systematic review. BMC Cardiovasc Disord 2013; 13:54. (10) Kibret KT, Mesfin YM. Prevalence of hypertension in Ethiopia: a systematic meta-
analysis. Public Health Rev 2015; 36:1.
(11) World Health Organization. A global brief on hypertension: Silent killer, global pub-lic health crisis. 2013. URL: http://apps.who.int/iris/bitstream/10665/79059/1/WHO_ DCO_WHD_2013.2_eng.pdf [accessed on 2013-07-17]
(12) Morisky DE, Ang A, Krousel‐Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens 2008; 10:348–54. (13) Atkinson MJ, Sinha A, Hass SL, Colman SS, Kumar RN, Brod M, Rowland CR.
Valida-tion of a general measure of treatment satisfacValida-tion, the Treatment SatisfacValida-tion QuesValida-tion- Question-naire for Medication (TSQM), using a national panel study of chronic disease. These seeking information regarding or permission to use the TSQM are directed to Quintiles, Inc. at www.quintiles.com/TSQM or TSQM@quintiles.com. Health Qual Life Outcomes 2004; 2:12.
(14) Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine 2000; 25:3186–91.
(15) George J, Phun YT, Bailey MJ, Kong DC, Stewart K. Development and validation of the medication regimen complexity index. Ann Pharmacother 2004; 38:1369–76.
(16) Williams R. Generalized ordered logit/partial proportional odds models for ordinal de-pendent variables. Stata Journal 2006; 6:58–82.
(17) Nielsen JØ, Shrestha AD, Neupane D, Kallestrup P. Non-adherence to anti-hypertensive medication in low-and middle-income countries: a systematic review and meta-analysis of 92 443 subjects. J Hum Hypertens 2017; 31:14–21.
(18) Kretchy IA, Owusu-Daaku FT, Danquah S. Locus of control and anti-hypertensive med-ication adherence in Ghana. Pan Afr Med J 2014;17 Suppl 1:13.
(19) Okwuonu CG, Ojimadu NE, Okaka EI, Akemokwe FM. Patient-related barriers to hy-pertension control in a Nigerian population. Int J Gen Med 2014; 7:345–53.
(20) Yue Z, Bin W, Weilin Q, Aifang Y. Effect of medication adherence on blood pressure control and risk factors for antihypertensive medication adherence. J Eval Clin Pract 2015; 21:166–72.
(21) Korb‐Savoldelli V, Gillaizeau F, Pouchot J, Lenain E, Postel‐Vinay N, Plouin PF, Durieux P, Sabatier B. Validation of a French Version of the 8‐Item Morisky Medication Adher-ence Scale in Hypertensive Adults. J Clin Hypertens 2012; 14:429–34.
(22) Tache SV, Sonnichsen A, Ashcroft DM. Prevalence of adverse drug events in ambulatory care: a systematic review. Ann Pharmacother 2011; 45:977–89.
(23) Jin J, Sklar GE, Oh VM, Li SC. Factors affecting therapeutic compliance: A review from the patient’s perspective. Ther Clin Risk Manag 2008; 4:269–86.
(24) de Champlain J, Karas M, Nguyen P, Cartier P, Wistaff R, Toal CB, Nadeau R, Larochelle P. Different effects of nifedipine and amlodipine on circulating catecholamine levels in essential hypertensive patients. J Hypertens 1998;16: 1357–69.
(25) Grodensky CA, Golin CE, Ochtera RD, Turner BJ. Systematic review: effect of alcohol intake on adherence to outpatient medication regimens for chronic diseases. J Stud
Al-cohol Drugs 2012; 73:899–910.
(26) Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, Chalmers J, Rodgers A, Rahimi K. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet 2016; 387:957–67.
(27) Ortegón M, Lim S, Chisholm D, Mendis S. Cost effectiveness of strategies to combat cardiovascular disease, diabetes, and tobacco use in sub-Saharan Africa and South-East Asia: mathematical modelling study. BMJ 2012 ;344: e607.
(28) Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005; 353:487–97. (29) World Health Organization. Global health workforce shortage to reach 12.9 million
in coming decades. 2014/02/24. http://www.who.int/mediacentre/news/releases/2013/ health-workforce-shortage/en 2013.
(30) Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M, Christiaens T, Cifk-ova R, De Backer G, Dominiczak A, Galderisi M. 2013 ESH/ESC guidelines for the management of arterial hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European So-ciety of Cardiology (ESC). Blood Press 2013; 22:193–278.
6
Supplement Tables |(31) Chiolero A, Burnier M, Santschi V. Improving treatment satisfaction to increase adher-ence. J Hum Hypertens 2016; 30:295–6.
(32) Oliveira-Filho AD, Barreto-Filho JA, Neves SJ, Lyra Junior DP. Association between the 8-item Morisky Medication Adherence Scale (MMAS-8) and blood pressure control.
Arq Bras Cardiol 2012; 99:649–58.
(33) Asgedom SW, Gudina EK, Desse TA. Assessment of Blood Pressure Control among Hypertensive Patients in Southwest Ethiopia. PloS One 2016;11: e0166432.
Supplement Tables
Supporting Information Table S1: Number of ADEs per patient
Characteristics
Adherence (MMAS-8)
Total Low (0–5) Medium (6–7) High (= 8)
Number of ADEs per patient (n, %)
No ADEs 732 (79) 280 (38) 277 (38) 175 (24) One ADE 75 (8) 38 (51) 23 (31) 14 (19) Two ADEs 49 (5) 28 (57) 16 (33) 5 (10) Three ADEs 41 (4) 25 (61) 16 (39) 0 Four ADEs 19 (2) 13 (68) 6 (32) 0 Five ADEs 5 (0.5) 4 (80) 1 (20) 0 Six ADEs 4 (0.4) 3 (75) 1 (25) 0
Supporting Information Table S2: Type of ADEs per patient (n = 421)
Characteristics
Adherence (MMAS-8) **
Total* Low (0–5) Medium (6–7) High (= 8)
Type of ADE (n = 421); (n, %) Dyspeptic symptoms 52 (12) 34 (65) 17 (33) 1 (2) Headache 47 (11) 30 (64) 14 (30) 3 (6) Cough 46 (11) 25 (54) 17 (37) 4 (9) Fatigue/ weakness 39 (9) 25 (64) 13 (33) 1 (3) Leg swelling 27 (6) 12(44) 12 (44) 3 (11)
Palpitation, or other cardiac problems 23 (5) 10 (43) 11 (48) 2 (9)
Vision disturbance or other eye problems 18 (4) 14 (78) 4 (22) 0
Vertigo 8 (2) 6 (75) 2(25) 0
Nausea/vomiting 6 (1) 5 (83) 1(17) 0
Other ADEs 155 (37) 98 (63) 47 (30) 10 (7)
ADEs reported by more than five patients are presented and rest under “Other ADEs’’ *percentage of total number of ADEs are presented within the column.
