Influence of Primary Healthcare Accessibility on Adherence to Prescribed Medication Among Geriatric Patients with Chronic Diseases in Central Uganda: A Cross-Sectional Study
HassanKasujja1,2,3,4✉Email
RoseClarkeNanyonga1
AgnesAgwang1
HarunaMuwonge3,4
JPWaswa5
ZziwaSwaibu2,6
MustafaSsaka1,2
AbdulMagalaSsekandi3,4
AgnesNamaganda3,4
RoselyneAkugizibwe4
GeoffreyKaujju1
ReginaNdagire1
FionaAtim1
1Clarke International UniversityP.O. Box 7782KampalaUganda
2Habib Medical SchoolIslamic University in UgandaP.O. BOX 2555MbaleUganda
3School of Biomedical Sciences, College of Health SciencesMakerere UniversityP.O. Box 7072KampalaUganda
4Translational Research Initiative for Biomedical Science Excellence (TRIBE) Research group, College of Health SciencesMakerere UniversityP.O. Box 7072KampalaUganda
5Infectious Diseases InstituteMakerere UniversityP.O. Box 7072KampalaUganda
6Department of Public Health, Faculty of Health SciencesMountains of the Moon UniversityFort PortalUganda
Hassan Kasujja1,2,3,4, Rose Clarke Nanyonga1, Agnes Agwang1, Haruna Muwonge3,4, JP Waswa5, Zziwa Swaibu2,6, Mustafa Ssaka1,2, Abdul Magala Ssekandi3,4, Agnes Namaganda3,4, Roselyne Akugizibwe4, Geoffrey Kaujju1, Regina Ndagire1, Fiona Atim1.
Affiliations:
1 Clarke International University, P.O. Box 7782, Kampala, Uganda
2 Habib Medical School, Islamic University in Uganda, P.O. BOX 2555, Mbale, Uganda
3 School of Biomedical Sciences, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda
4 Translational Research Initiative for Biomedical Science Excellence (TRIBE) Research group, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda
5 Infectious Diseases Institute, Makerere University, P.O. Box 7072, Kampala, Uganda
6 Department of Public Health, Faculty of Health Sciences, Mountains of the Moon University, Fort Portal, Uganda
Corresponding author:
Hassan Kasujja1,2,3,4
Email: hsnkasujja@gmail.com
Abstract
Background
Poor adherence to prescribed medication is a critical public health concern, particularly for geriatric patients with chronic illnesses in African countries, where less than 50% adhere to treatment. Primary healthcare (PHC) is essential in managing chronic diseases for this population, yet its accessibility’s impact on medication adherence remains underexplored. This study examined the influence of PHC accessibility on medication adherence among geriatric patients with chronic diseases in central Uganda.
Methods
A
This cross-sectional study, conducted from December 2024 to February 2025, involved patients aged 60 years and above with either hypertension, diabetes, or both, from two public PHC facilities in central Uganda. Using a pilot-tested questionnaire, self-reported medication adherence and PHC access (based on the PHC quality assessment tool and the World Health Organization PHC framework) were assessed. Descriptive and inferential statistics were used to analyze their relationship.
Results
Among 323 geriatric patients, only 23% adhered to prescribed medications. Univariate analysis indicated 28.2% faced challenges accessing the PHC facilities, 59.1% reported drug stockouts, 72.8% cited long waiting times, and 63.8% noted long review intervals as barriers. Multivariate binary logistic regression revealed medication stockouts (AOR: 4.42, 95% CI: 1.30-15.07, p = 0.018), waiting time (AOR: 6.68, 95% CI: 1.67–26.81, p = 0.007), and the duration between review dates (AOR: 4.17, 95% CI: 1.25–13.93, p = 0.020) as factors influencing adherence to prescribed medications:
Conclusion
There is suboptimal medication adherence among geriatric patients with chronic diseases in central Uganda. Targeted strategies to enhance drug availability are critical to improving adherence in this population.
Keywords:
Primary health care accessibility
geriatric patients
medication adherence
chronic diseases
appropriate medicine use
A
A
Background
Ageing is often associated with chronic illnesses, many of which require routine medication to enhance the quality of life for geriatric patients (1). Over 90% of individuals aged 60 and above live with at least one chronic illness, making regular medication use common among this population (2, 3). Medication adherence is a cornerstone for improving health outcomes in this population (4). However, the high burden of chronic medication use makes adherence to prescribed medications challenging (5). It is estimated that only 50% of geriatric patients worldwide adhere to prescribed medications (6). Similarly, in many African countries, less than 50% adhere to prescribed medications (7, 8). In Uganda, there is limited research on medication adherence among older persons; however, existing studies suggest that increasing age is associated with poorer adherence (9, 10). Additionally, many older persons frequently miss scheduled follow-up appointments, a factor known to contribute to non-adherence (11). Age-related factors, including cognitive impairment, impaired vision, and multiple comorbidities, further hinder medication adherence (12).
Limited access to healthcare services in many parts of Africa, particularly among geriatric patients, restricts access to medications and standard guidance (13). This stems from personal challenges in reaching healthcare facilities, limited availability of medications, the geographical location of health facilities, long waiting times, high patient-to-healthcare worker ratios, and stigma associated with old age, all of which can hinder medication adherence among geriatric patients (5, 14, 15) .
Many geriatric patients, who often have multiple comorbidities, have specific needs when accessing healthcare services (16). However, most PHC health facilities were originally designed to serve young populations, emphasizing curative care, which does not align with the needs of older patients (17). Similarly, several African countries report facing the same challenge, with PHC facilities often ill-equipped to meet the needs of geriatric patients (13). Although the World Health Organization (WHO) has advocated for age-friendly services to enhance healthcare accessibility for geriatric patients (18), in Uganda, where healthcare services are still inadequate and inaccessible for many, the situation is likely even more critical for this patient group (19).
A
Uganda’s PHC system is decentralized, with lower-level facilities, including health centers II, III, and IV, providing the services at the subnational level, and a referral network to the central level facilities (20, 21). In Uganda, many geriatric patients rely on PHC facilities for routine healthcare services. The system has been reported to have several limitations for service provision; however, data is limited on how it influences medication adherence among older persons (20, 21). This study set out to examine how access to healthcare services in PHC facilities affects medication adherence among patients aged 60 and above living with either diabetes, hypertension, or both. The findings will be crucial in informing interventions and policies geared towards enhancing healthcare access for the geriatric population.
Methods
Study design
This study employed a mixed-methods, cross-sectional design to assess adherence to prescribed medications among geriatric patients, combining quantitative data to measure adherence prevalence and associated factors with qualitative data to explore patients’ perceptions, facilitators, and barriers, aiming to provide a comprehensive understanding of adherence dynamics in a resource-limited setting.
Study sites and setting
The study was conducted at Wakiso Health Centre IV and Kasangati Health Centre IV, located in Wakiso District, on the outskirts of Uganda’s capital, Kampala, Central Uganda. These government-supported facilities provide general health services, including PHC, emergency maternal and neonatal care, outpatient and inpatient services, and specialized clinics for non-communicable diseases like hypertension and diabetes, serving diverse urban and suburban populations. Both facilities are critical in addressing Wakiso District’s health needs and supporting Kampala’s overburdened health system. Wakiso Health Centre IV receives approximately 47,768 outpatients annually, while Kasangati Health Centre IV receives 33,371. Both facilities handle an average of 40 patients per clinic day during the hypertension or diabetes clinic days (22).
Study population
The study comprised patients aged 60 years and above residing in Wakiso District, diagnosed with either hypertension, diabetes or both and receiving prescribed medications. Participants were at the hypertension and diabetes clinics at the study sites. All participants were required to have been on prescribed medication for at least six months and be able to provide informed consent independently. Critically ill patients, those unable to communicate independently due to severe cognitive or physical impairments, and individuals who refused to provide informed consent were excluded from the study.
Sample size and sampling procedure
The sample size was estimated using Kish & Leslie’s formula for cross-sectional studies (23). Based on local data suggesting a 30% non-adherence rate(24), a 95% confidence level, and a 5% margin of error, a sample of 323 participants was deemed sufficient. Attrition was not accounted for, as the cross-sectional design involved a single-point data collection during clinic visits, minimizing dropout risk. The healthcare facilities were purposely selected for their established hypertension and diabetes clinics. An equal number of patients were selected from each of the facilities since the number of patients attending the clinics in the facilities is almost the same. Participants were selected by consecutive sampling.
Data collection: From December 2024 to February 2025, the study recruited geriatric patients aged 60 years and above at the outpatient departments of the study sites during designated diabetes and hypertension clinic days. Recruitment began at 8:00 AM, with participants identified in collaboration with facility healthcare workers. A pre-tested, researcher-administered questionnaire, available in English and Luganda – the local language of the study area – was used to collect data through individual interviews on adherence to medication and PHC accessibility factors influencing adherence to the prescribed medication among the participants (See supplementary file 1 for the detailed questionnaire). The questionnaire was designed by the study team by incorporating indicators from the PHC quality assessment tool (25) and the WHO PHC framework (26) and adapting questions to local contexts. Before full-scale data collection, the questionnaire was pretested with 15 participants aged 60 and above at a healthcare facility to assess clarity, relevance, and feasibility. Based on feedback from the pretest, necessary modifications were made to improve the instrument.
To evaluate its reliability, Cronbach’s alpha was calculated. The sample size for this analysis was determined using the method described by Bujang et al. (27), setting the null hypothesis Cronbach’s alpha (CA₀) at 0.0 and the alternative hypothesis (CA₁) at 0.6. With 40 study variables, a minimum sample size of 19 was required. The final reliability analysis yielded a Cronbach’s alpha of 0.81, indicating strong internal consistency when compared with the PHC quality assessment tool.
To assess medication adherence, the participants were asked how many tablets they had been able to take since the previous facility visit. The percentage of tablets taken compared to the tablets the patient received was recorded as adherence. A patient was considered to have adhered to medication if they took at least 80% of the medication. To assess factors influencing adherence, the researchers interviewed patients as guided by the indicators in the questionnaire, with clinical data verified from medical records
All data collection tools were checked for completeness before leaving the field.
Data analysis
Data was cleaned, entered into Microsoft Excel, and analyzed using SPSS (version 25). Descriptive statistics were used to summarize socio-demographic characteristics, PHC accessibility, adherence levels, and influencing factors. Pearson’s Chi-square tests were applied to examine relationships between categorical study variables, with an alpha of 0.05 to determine statistical significance. Multivariate binary logistic regression analysis was employed to assess the strength of associations between independent and dependent variables.
Results
Demographic characteristics of the study respondents
The demographic and clinical characteristics of the study participants are shown in Table 1. The mean age of the participants was 73 years, and the age group 71–80 years had the highest number of participants,126 (39.0%). The majority, 190 (58.8%), of the participants were female, and 139 (43.0%) resided in suburban areas. Most participants had received a formal education. The majority (59.8%) of the participants had only hypertension, with only 59 (18.3%) having both diabetes and hypertension.
Table 1
Demographic factors of the respondents, N = 323
Variable
Category
Frequency (n)
Percentage (%)
Age
60–70 years
99
30.7
71–80 years
126
39.0
81 years and above
98
30.3
Sex
Male
133
41.2
Female
190
58.8
Occupation
Business
64
19.8
Professional works
71
22.0
Unemployed
103
31.9
Housewife
25
7.7
Manual/unprofessional labor
60
18.6
Religion
Catholic
136
42.1
Muslim
56
17.3
Anglican
93
28.8
Pentecostal
7
2.2
Local traditions
31
9.6
Marital status
Single
81
25.1
Married/cohabiting
168
52.0
Widow/widower
53
16.4
Separated/divorced
21
6.5
Nature of residence
Urban
101
31.3
Rural
83
25.7
Suburban
139
43.0
Highest education
No formal education
13
4.0
Primary
97
30.0
Secondary
137
42.4
Technical/ Vocational
64
19.8
University
12
3.7
Patient’s disease
Hypertension
193
59.8
Diabetes
71
22.0
Both
59
18.3
Place Table 1 here
Adherence to medication
Among the 323 geriatric patients, only 74 (23%) adhered to prescribed medications, as shown in Fig. 1 below
Fig. 1
percentages of geriatric patients and their adherence to their medication
Click here to Correct
Place Fig. 1 here
Factors influencing adherence to prescribed medication
As shown in Table 2, univariate analysis revealed that 232 (71.8%) participants found healthcare facilities easily accessible, with 91 (28.2%) having challenges in accessing the PHC facilities. However, upon arrival, 191(59.1%) reported being unable to obtain prescribed medications from the facilities due to stockouts, which often affected their adherence. Additionally, many participants noted long waiting times at facilities, which were 4 hours on average, and extended intervals between review dates, which were usually 2 months.
Table 2
Univariate analysis of PHC accessibility factors influencing adherence to prescribed medications among older persons with diabetes and hypertension. (N = 323)
Variable
Category
Frequency
Percentage
Health facility accessibility
Yes
232
71.8
No
91
28.2
Failure to get medicines due to stockouts
Yes
191
59.1
No
132
40.9
Long waiting time
Yes
235
72.8
No
88
27.2
Long review intervals
Yes
206
63.8
No
117
36.2
Negative influence due to long review intervals
Forgetfulness
102
49
Disrupted work schedules
63
31
Interaction with other personal programs
41
20
Place Table 2 here
Bivariate analysis was conducted to assess factors influencing adherence to prescribed medications among older persons with diabetes and hypertension. As shown in Table 3, the social demographic characteristics of the respondents that were significantly associated with adherence to prescribed medications among older persons with diabetes and hypertension included; age (χ2 = 14.38, p = < 0.001), sex (χ2 = 15.16, p = < 0.001), marital status (χ2 = 27.23, p = < 0.001), nature of residence (χ2 = 11.50, p = 0.003), highest education level (χ2 = 19.93, p = 0.001) and patient’s disease (χ2 = 8.19, p = 0.017). However, occupation and religion weren’t significantly associated with adherence to prescribed medications among older persons with diabetes and hypertension. Furthermore, in Table 4, findings showed that inaccessibility to healthcare facilities reduced adherence to medications; however, the association was not significant. Additionally, the findings show that adherence to prescribed medication was significantly reduced by; failure to get medications due stock outs (χ2 = 64.24, p = 0.001), the lengthy waiting time at the facility that some time it made one leave the medicines and ended up missing taking medicine (χ2 = 38.40, p = 0.001), and the lengthy time differences between review dates (χ2 = 44.42, p = < 0.001).
Table 3
Bivariate Analysis of demographic factors influencing adherence to prescribed medications among older persons with diabetes and hypertension. (n = 323)
Variable
Category
Adherence levels to Hypertension and diabetes Medications
χ2
P-value
Adhered
Never adhered
Age
60–70 years
31 (31.3%)
68 (68.7%)
14.38
< 0.001**
71–80 years
28 (28.6%)
70 (71.4%)
81 years and above
15 (11.9%)
111 (88.1%)
Sex
Male
16 (12.0%)
117 (88.0%)
15.16
< 0.001**
Female
58 (30.5%)
132 (69.5%)
Occupation
Business
14 (21.9%)
50 (78.1%)
0.52
0.971
Professional works
17 (23.9%)
54 (76.1%)
  
Unemployed
23 (22.3%)
80 (77.7%)
house wife
7 (28.0%)
18 (72.0%)
manual (unprofessional) labour
13 (21.7%)
47 (78.3%)
Religion
Catholic
33 (24.3%)
103 (75.7%)
1.66
0.798
Muslim
12 (21.4%)
44 (78.6%)
Anglican
18 (19.4%)
75 (80.6%)
Pentecostal
2 (28.6%)
5 (71.4%)
local traditions
9 (29.0%)
22 (71.0%)
Marital
Single
5 (6.2%)
76 (93.8%)
27.23
< 0.001**
Married/cohabiting
57 (33.9%)
111 (66.1%)
Widow/widower
7 (13.2%)
46 (86.8%)
separated/divorced
5 (23.8%)
16 (76.2%)
Nature of residence
urban
36 (33.6%)
71 (66.4%)
11.50
0.003**
Rural
11 (13.8%)
69 (86.3%)
Suburban
27 (19.9%)
109 (80.1%)
Highest education
No formal education
1 (9.1%)
10 (90.9%)
19.93
0.001**
Primary
11 (12.2%)
79 (87.8%)
Secondary
28 (20.9%)
106 (79.1%)
Technical/ Vocational
29 (39.7%)
44 (60.3%)
University
5 (33.3%)
10 (66.7%)
Patient disease
Hypertension
43 (23.2%)
142 (76.8%)
8.19
0.017**
Diabetes
8 (11.9%)
59 (88.1%)
Both
23 (32.4%)
48 (67.6%)
** Denotes significance at 95% CI
Table 4
Bivariate Analysis of PHC accessibility factors influencing adherence to prescribed medications among older persons with diabetes and hypertension. (n = 323)
Variable
Category
Adherence to Prescribed Medications
Chi-square (χ2)
p-value
Adherent n (%)
Non-adherent n (%)
Health facility accessibility
Yes
57 (24.6)
175 (75.4)
1.28
0.257
No
17 (18.7)
74 (81.3)
Failure to get medicines due to stockouts
Yes
14 (7.3)
177 (92.7)
64.24
< 0.001**
No
60 (45.5)
72 (54.5)
Long waiting time
Yes
33 (14.0)
202 (86.0)
38.40
< 0.001**
No
41 (46.6)
47 (53.4)
Long review intervals
Yes
23 (11.2)
183 (88.8)
44.42
< 0.001**
No
51 (43.6)
66 (56.4)
**Denotes significance at 95% CI
Place Table 3 here
Place Table 4 here
Multivariate binary logistic regression analysis of factors influencing adherence to prescribed medications among older persons with diabetes and hypertension
On multivariate binary logistic regression analysis, as shown in Table 5, findings show that respondents who were 71–80 years (AOR: 0.35, 95% CI: (0.15–0.78), p = 0.011) and 81 years and above (AOR: 0.30, 95% CI: (0.13–0.69), p = 0.005) were less likely to adhere to hypertension and diabetes medications as compared to respondents who were 60–70 years. Females were almost five times more likely to adhere to their medications (AOR: 4.71, 95% CI: (2.27–9.77), p = 0.001) as compared to males. Married and cohabiting patients were six times more likely to adhere to their medications (AOR: 6.36, 95% CI: (1.33–30.51), P = 0.021) as compared to single respondents. Respondents who stayed in rural areas were less likely to adhere to hypertension and diabetes medications (AOR: 0.21, 95% CI: (0.08–0.57), p = 0.002) as compared to urban dwellers. Additionally, respondents who suffered from both hypertension and diabetes were more likely to adhere to medications than patients who only had a single disease.
Table 5
Multivariate binary logistic regression analysis of factors influencing adherence to prescribed medications among older persons with diabetes and hypertension, n = 323
Variable
Category
COR (95%CI)
AOR (95%CI)
P-value
Individual Factors
   
Age
60–70 years
1
1
 
71–80 years
0.30 (0.15–0.59)
0.35 (0.15–0.78)
0.011**
81 years and above
0.34 (0.17–0.68)
0.30 (0.13–0.69)
0.005**
Sex
Male
1
1
 
Female
3.21 (1.75–5.90)
4.71 (2.27–9.77)
< 0.001**
Marital
Single
1
1
 
Married/cohabiting
4.75 (1.23–18.36)
6.36(1.33–30.51)
0.021**
Widow/widower
0.61 (0.21–1.75)
0.39 (0.11–1.36)
0.140
separated/divorced
2.05 (0.57–7.39)
1.20 (0.47–8.45)
0.348
Nature of residence
urban
1
1
 
Rural
0.49 (0.27–0.87)
0.21 (0.08–0.57)
0.002**
Suburban
1.55 (0.72–3.33)
0.41 (0.09–1.80)
0.235
Highest education
No formal education
1
1
 
Primary
5.00 (0.49–50.83)
7.25(0.50–05.93)
0.148
Secondary
3.59 (1.03–12.47)
4.95(1.05–23.29)
0.043**
Technical/ Vocational
1.89 (0.60–5.99)
0.92 (0.25–3.41)
0.905
University
0.76 (0.24–2.45)
0.95 (0.22–4.01)
0.941
Patients’ disease
Hypertension
1
1
 
Diabetes
1.58 (0.87–2.89)
2.39 (1.02–5.58)
0.045**
Both
3.53 (1.45–8.61)
6.25(1.36–28.67)
0.019**
**Denotes significance at 95% CI
Place Table 5 here
Furthermore, multivariate binary logistic regression analysis, as shown in Table 6, revealed that respondents who reported that the time interval between review dates did not negatively affect adherence were four times more likely to adhere to hypertension and diabetes medications (AOR: 4.17, 95% CI: 1.25–13.93, p = 0.020) compared to those who indicated it did. Similarly, respondents who never experienced medication stockouts at health facilities were four times more likely to adhere to their medications (AOR: 4.42, 95% CI: 1.30-15.07, p = 0.018) compared to those who occasionally faced stockouts and, consequently, struggled with adherence. Additionally, respondents who reported that waiting times at facilities were not excessively long, thus not causing them to leave without their medications or miss doses, were seven times more likely to adhere to their medications (AOR: 6.68, 95% CI: 1.67–26.81, p = 0.007) compared to those who found waiting times excessively long, leading to missed doses and non-adherence.
Table 6
Multivariate binary logistic regression analysis of PHC accessibility factors influencing adherence to prescribed medications among older persons with diabetes and hypertension. (N = 323)
Variable
Category
COR (95%CI)
AOR (95%CI)
p-value
Individual Factors
   
Long review intervals
Yes
1
1
 
No
0.16 (0.09–0.29)
4.17 (1.25–13.93)
0.020**
Failure to get medicines due to stockouts
Yes
1
1
 
No
0.09 (0.05–0.18)
4.42 (1.30-15.07)
0.018**
Long waiting time
Yes
1
1
 
No
0.19 (0.11–0.33)
6.68 (1.67–26.81)
0.007**
**Denotes significance at 95% CI
Place Table 6 here
Discussion
In our study, we defined adherence as taking at least 80% of the prescribed medication, a threshold widely used in adherence studies as the minimum level for effective treatment (28, 29). Our findings show that only 23% of geriatric patients adhered to their medication, aligning closely with a review reporting 20–30% adherence among older persons with hypertension in selected European countries (1). Consistent with our findings, that study noted poor adherence among patients newly initiated on medication or older patients, making the low adherence in geriatric patients as they age, unsurprising. Our reported adherence prevalence is slightly below the pooled prevalence for Africa of 37.5%(8), possibly due to Uganda’s specific challenges, such as a poor supply chain limiting access to medicines compared to other countries (30).
Our results showed that various demographic factors significantly influenced adherence to medication. Our findings showed that as patients become older, their adherence to medication reduces, a similar finding in other studies. This can be attributed to age-related conditions that could minimize adherence, including reduced cognitive ability that comes with forgetfulness, poor vision and hearing, among others (12, 31). The findings further showed that the females were more adherent than males, similar to what many other studies have reported (14, 32). In many studies, females have been shown to have better health-seeking behavior than males. (33, 34). For this reason, it is no surprise that women take their medication better as prescribed compared to men. Our results also showed that participants with partners were more adherent to medications, which is not different from other studies (11, 35). Partners play a key role in caring for each other and thus improving the medication adherence of their family members (36). In one study, a diabetic patient was quoted, “I don’t think I’d be alive truly if it weren’t for my husband. The wonderful part about that is it’s great to have that support when you don’t feel good” (37). The lower adherence among the people who stayed in rural areas is a similar finding in related studies in Africa and elsewhere (3840). These studies have listed some factors behind the low adherence, including patient delays in accessing their respective healthcare facilities, the poor accessibility to healthcare facilities and the poor economic status.
Our findings also showed that the higher the formal education, the higher the adherence, which is similar to other studies, in which adherence levels were found to be directly proportional to education levels (31, 32). This is expected since education increases patients’ knowledge about the medicines, which improves medication adherence (32). Studies in Uganda and elsewhere have shown that patients with a good understanding of their diseases and medication, and patients with high literacy levels, adhere to medication better than their counterparts (9, 41). Contrary to other studies, our findings show that participants with multiple illnesses were more adherent than those with one illness (1, 42). However, this may be because individuals with multiple chronic conditions may be highly motivated to manage their health because of the fear of being held down by one of the illnesses, hence demonstrating good adherence. Delays in taking diabetic and hypertensive medications instantly make the patients feel unwell, and thus, they are pushed to take their medication. In an attempt to take medications for one of the diseases, the patients are also compelled to take medication for the other disease.
Our findings indicate that many patients in Uganda miss medications due to drug stock-outs in government healthcare facilities. This challenge is corroborated by other researchers documenting frequent stock-outs in the country compared to many African countries, reporting that over 84% of Uganda’s healthcare facilities report stockouts of essential medicines in a year (30). In cases where health supply chain systems are lacking, and patients fail to access medicines as planned, with poor refill tendencies, there is reduced medication adherence (11, 43). Consequently, it is unsurprising that Uganda’s medication adherence prevalence is lower than the pooled African average and that reported in other studies (4, 79, 44). This may be because we examined adherence to medications for both hypertension and diabetes, whereas most studies focused on adherence to medication for a single condition.
Our findings show that accessibility to PHC facilities in Central Uganda remains a challenge for geriatric patients with chronic illness, negatively impacting medication adherence due to missed appointments. Participants highlighted issues such as long waiting times at facilities, limited medication availability, and transportation difficulties, particularly for older patients and rural residents. Other studies highlight similar gaps in Uganda’s PHC delivery for non-communicable disease management (45, 46). In our study, patients reported that the long intervals between review dates reduced their medication adherence, suggesting that a one-month interval would be more appropriate. In Uganda, many healthcare facilities schedule longer review periods to manage high patient loads due to the elevated patient-to-healthcare worker ratio (47, 48). Such elevated ratios also explain the long waiting hours at the facilities. Health care workers who actively plan and communicate medication schedules, enhance patient follow-up, and shorten intervals between hospital visits were found to improve adherence (11, 31, 35). However, some studies indicate that rigid routines may bore patients, potentially resulting in poor adherence. This underscores the importance of fostering strong healthcare worker-patient relationships to facilitate communication and identify optimal strategies for improving adherence (35).
Conclusion
Medication adherence was low among geriatric patients with hypertension, diabetes, or both, with only 23% taking at least 80% of their prescribed medication. Contributing factors included drug stock-outs, long waiting times at healthcare facilities, and extended intervals between review dates. Targeted strategies to improve drug availability are critical for enhancing adherence.
Recommendations
Based on the study’s findings regarding challenges faced by geriatric patients, we recommend several measures. Healthcare workers should be supported to optimize their duty schedules to effectively manage the patient loads. Additionally, community health workers, where available, can support older persons by assisting with medication access and adherence. Many of these patients reported missing medication because they regard the health facilities as inaccessible, bridging the facility accessibility gap by community health workers might be helpful. Healthcare workers should enhance patient education on medication adherence and the risks of non-adherence during patient reviews. Finally, the government and health service providers should integrate geriatric-friendly services to enhance accessibility for older persons, addressing the complications associated with ageing.
Study limitations
The study had several limitations. First, the questionnaire included questions requiring participants to remember past events, which could have introduced recall bias. To mitigate this, some questions were repeated to ensure consistency in responses, and discrepancies were discussed with participants to confirm accurate answers. Second, the study only included patients who visited the healthcare facility during data collection, potentially excluding those with poor adherence who did not attend. This may have skewed the sample towards patients with better medication adherence, limiting insights among non-attending patients.
List of abbreviations:
CI
Confidence interval
PHC
Primary Healthcare
Declarations
Ethics approval and consent to participate:
A
The research was approved by the Scientific Research Committee and the Research Ethics Committee of Clarke International University, Kampala, with approval number CLARKE-2024-1189. Administrative approval was obtained from the respective facility administrators where the data were collected.
A
The researcher informed all participants about the study’s purpose, and written informed consent was obtained before enrolment. Participation study was voluntary; participants were informed of their rights and welfare and freedom to withdraw at any time. Interviews were conducted to ensure confidentiality. The research was conducted in compliance with the Helsinki Declaration
Clinical Trial Number
Not applicable
Consent for publication:
Not applicable
A
Data Availability
The datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request
Competing interests:
The authors declare that they have no competing interests
A
Funding:
No funding was received for this work
A
A
Author Contribution
HK and AF conceptualized the study, HK collected the data, and wrote the first draft of the manuscript; GK performed statistical analysis; AF supervised the work, RCN, AA, HM, JPW, AN, AMS, RA, SZ, MS, GK, RN, and AF reviewed the manuscript.
A
Acknowledgement
A
The authors thank the facility administrators who authorized the study in the respective facilities. The authors thank healthcare workers in the respective departments and clinics where the participants were recruited for their cooperation and support in conducting this study.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
References
1.
Burnier M, Polychronopoulou E, Wuerzner G. Hypertension and drug adherence in the elderly. Front Cardiovasc Med. 2020;7:49.
2.
National Council on Aging. Get the Facts on Healthy Aging [Internet]. 2025 [cited 2025 Jun 25]. p. 1. Available from: https://www.ncoa.org/article/get-the-facts-on-healthy-aging/
3.
Lim YX, Yap XY, Domingo RVC, Tan YQ, Tan PC, Wu XV. Perspectives of Older Adults on Self-Care of Chronic Diseases Utilizing Online Health Resources: An Exploratory Descriptive Qualitative Study. Nurs Health Sci. 2025;27(1):e70056.
4.
Krass I, Schieback P, Dhippayom T. The significance of compliance and persistence in the treatment of diabetes, hypertension and dyslipidaemia: A review. 32, Diabetic Medicine. 2015. pp. 725–37.
5.
Odubanjo OA, Tipping B, Greenstein LS. Medication adherence in geriatric patients attending medical outpatient department. South Afr Fam Pract. 2024;66(1):6011.
6.
Marcum ZA, Hanlon JT, Murray MD. Improving medication adherence and health outcomes in older adults: an evidence-based review of randomized controlled trials. Drugs Aging. 2017;34(3):191–201.
7.
Macquart de Terline D, Kane A, Kramoh KE, Ali Toure I, Mipinda JB, Diop IB, et al. Factors associated with poor adherence to medication among hypertensive patients in twelve low and middle income Sub-Saharan countries. PLoS ONE. 2019;14(7):e0219266.
8.
Abegaz TM, Shehab A, Gebreyohannes EA, Bhagavathula AS, Elnour AA. Nonadherence to antihypertensive drugs a systematic review and meta-analysis. 96, Medicine (United States). 2017.
9.
Bagonza J, Rutebemberwa E, Bazeyo W. Adherence to anti diabetic medication among patients with diabetes in eastern Uganda; a cross sectional study. BMC Health Serv Res. 2015;15(1).
10.
Faisal K, Tusiimire J, Yadesa TM. Prevalence and factors associated with non-adherence to antidiabetic medication among patients at Mbarara Regional Referral Hospital, Mbarara, Uganda. Patient Prefer Adherence. 2022;479–91.
11.
Najjuma JN, Brennaman L, Nabirye RC, Ssedyabane F, Maling S, Bajunirwe F, et al. Adherence to antihypertensive medication: An interview analysis of southwest ugandan patients’ perspectives. Ann Glob Heal. 2020;86(1):1–11.
12.
Cárdenas-Valladolid J, Martín-Madrazo C, Salinero-Fort MA, De-Santa Pau EC, Abnades-Herranz JC, De Burgos-Lunar C. Prevalence of adherence to treatment in homebound elderly people in primary health care: A descriptive, cross-sectional, multicentre study. Drugs Aging. 2010;27(8):641–51.
13.
Mussie KM, Setchell J, Elger BS, Kaba M, Memirie ST, Wangmo T. Care of older persons in Eastern Africa: A scoping review of ethical issues. Front Public Heal. 2022;10:923097.
14.
Yap AF, Thirumoorthy T, Kwan YH. Medication adherence in the elderly. J Clin Gerontol Geriatr. 2016;7(2):64–7.
15.
Yap AF, Thirumoorthy T, Kwan YH. Systematic review of the barriers affecting medication adherence in older adults. Geriatr Gerontol Int. 2016;16(10):1093–101.
16.
de Carvalho IA, Epping-Jordan J, Pot AM, Kelley E, Toro N, Thiyagarajan JA, et al. Organizing integrated health-care services to meet older people’s needs. Bull World Health Organ. 2017;95(11):756.
17.
Kwaitana D, Bates MJ, Msowoya E, van Breevoort D, Mildestvedt T, Meland E, et al. Primary health care challenges: insights from older people with multimorbidity in Malawi–a qualitative study. BMC Public Health. 2024;24(1):1434.
18.
Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, Grymowicz M. The World Health Organization (WHO) approach to healthy ageing. Maturitas. 2020;139:6–11.
19.
Guwatudde D, Mutungi G, Wesonga R, Kajjura R, Kasule H, Muwonge J, et al. The epidemiology of hypertension in Uganda: findings from the national non-communicable diseases risk factor survey. PLoS ONE. 2015;10(9):e0138991.
20.
Kim JH, Bell GA, Bitton A, Desai EV, Hirschhorn LR, Makumbi F, et al. Health facility management and primary health care performance in Uganda. BMC Health Serv Res. 2022;22(1):275.
21.
Sewankambo N, Kawoya I, Ethel N, Mijumbi-Deve R. Primary health care systems (PRIMASYS): case study from Uganda [Internet]. 2017. Available from: https://iris.who.int/handle/10665/341064
22.
Ministry of Health U. ANNUAL HEALTH SECTOR PERFORMANCE REPORT 2023/24 [Internet]. 2025. Available from: https://library.health.go.ug/monitoring-and-evaluation/annual-quarterly-performance-reports/annual-health-sector-performance-8
23.
KISH L. SAMPLING ORGANIZATIONS AND GROUPS OF UNEQUAL SIZES. Am Sociol Rev. 1965;30:564–72.
24.
Mugwano I, Kaddumukasa M, Mugenyi L, Kayima J, Ddumba E, Sajatovic M, et al. Poor drug adherence and lack of awareness of hypertension among hypertensive stroke patients in Kampala, Uganda: a cross sectional study. BMC Res Notes. 2016;9(1):1–8.
25.
Farrokhi P, Zarei E, Bagherzadeh R, Irannejad B, Hashjin AA. Development and validation of primary health care quality assessment tool. BMC Health Serv Res. 2023;23(1):1156.
26.
Organization WH, Fund UNC. Primary health care measurement framework and indicators: monitoring health systems through a primary health care lens. World Health Organization; 2021.
27.
Bujang MA, Omar ED, Foo DHP, Hon YK. Sample size determination for conducting a pilot study to assess reliability of a questionnaire. Restor Dent Endod. 2024;49(1).
28.
Baumgartner PC, Haynes RB, Hersberger KE, Arnet I. A systematic review of medication adherence thresholds dependent of clinical outcomes. 9, Frontiers in Pharmacology. 2018.
29.
Lam WY, Fresco P. Medication adherence measures: an overview. Biomed Res Int. 2015;2015(1):217047.
30.
Lugada E, Ochola I, Kirunda A, Sembatya M, Mwebaze S, Olowo M, et al. Health supply chain system in Uganda: assessment of status and of performance of health facilities. J Pharm Policy Pract. 2022;15(1):58.
31.
Smaje A, Weston-Clark M, Raj R, Orlu M, Davis D, Rawle M. Factors associated with medication adherence in older patients: A systematic review. Aging Med. 2018;1(3):254–66.
32.
Jin HK, Kim YH, Rhie SJ. Factors affecting medication adherence in elderly people. Patient Prefer Adherence. 2016;10:2117–25.
33.
Rata Mohan DS, Jawahir S, Manual A, Abdul Mutalib NE, Mohd Noh SN, Ab Rahim I, et al. Gender differences in health-seeking behaviour: insights from the National Health and Morbidity Survey 2019. BMC Health Serv Res. 2025;25(1):900.
34.
Thompson AE, Anisimowicz Y, Miedema B, Hogg W, Wodchis WP, Aubrey-Bassler K. The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study. BMC Fam Pract. 2016;17(1):38.
35.
Gow K, Rashidi A, Whithead L. Factors influencing medication adherence among adults living with diabetes and comorbidities: a qualitative systematic review. Curr Diab Rep. 2024;24(2):19–25.
36.
Liu J, Yu Y, Yan S, Zeng Y, Su S, He T, et al. Risk factors for self-reported medication adherence in community-dwelling older patients with multimorbidity and polypharmacy: a multicenter cross-sectional study. BMC Geriatr. 2023;23(1):75.
37.
Shirazian S, Crnosija N, Weinger K, Jacobson AM, Park J, Tanenbaum ML, et al. The self-management experience of patients with type 2 diabetes and chronic kidney disease: a qualitative study. Chronic Illn. 2016;12(1):18–28.
38.
Pan J, Yu H, Hu B, Li Q. Urban-rural difference in treatment adherence of Chinese hypertensive patients. Patient Prefer Adherence. 2022;2125–33.
39.
Njohjam MN, Falonne NT, Ngoule MO. Barriers to medication adherence for secondary stroke prevention in rural communities in Cameroon: a qualitative study. BMC Prim Care. 2025;26(1):125.
40.
Arbuckle C, Tomaszewski D, Aronson BD, Brown L, Schommer J, Morisky D, et al. Evaluating factors impacting medication adherence among rural, urban, and suburban populations. J Rural Heal. 2018;34(4):339–46.
41.
Singh S, Acharya SD, Kamath A, Ullal SD, Urval RP. Health literacy status and understanding of the prescription instructions in diabetic patients. J Diabetes Res. 2018;2018.
42.
Allaham KK, Feyasa MB, Govender RD, Musa AMA, AlKaabi AJ, ElBarazi I et al. Medication adherence among patients with multimorbidity in the United Arab Emirates. Patient Prefer Adherence. 2022;1187–200.
43.
Zullig LL, Gellad WF, Moaddeb J, Crowley MJ, Shrank W, Granger BB et al. Improving diabetes medication adherence: Successful, scalable interventions. Vol. 9, Patient Preference and Adherence. 2015. pp. 139–49.
44.
McGovern A, Tippu Z, Hinton W, Munro N, Whyte M, de Lusignan S. Comparison of medication adherence and persistence in type 2 diabetes: A systematic review and meta-analysis. Diabetes, Obes Metab. 2018;20(4):1040–3.
45.
Olds PK, Nuwagaba G, Obwoya PS, Nuwagira E, Haberer JE, Okello S. Patient-provider experiences with chronic non-communicable disease care during COVID-19 lockdowns in rural Uganda: A qualitative analysis. PLoS ONE. 2023;18(12):e0295596.
46.
Rogers HE, Akiteng AR, Mutungi G, Ettinger AS, Schwartz JI. Capacity of Ugandan public sector health facilities to prevent and control non-communicable diseases: an assessment based upon WHO-PEN standards. BMC Health Serv Res. 2018;18(1):606.
47.
Twineamatsiko A, Mugenyi N, Kuteesa YN, Livingstone ED. Factors associated with retention of health workers in remote public health centers in Northern Uganda: a cross-sectional study. Hum Resour Health. 2023;21(1):83.
48.
Ajari EE, Ojilong D. Assessment of the preparedness of the Ugandan health care system to tackle more COVID-19 cases. J Glob Health. 2020;10(2).
Total words in MS: 4251
Total words in Title: 22
Total words in Abstract: 245
Total Keyword count: 5
Total Images in MS: 1
Total Tables in MS: 6
Total Reference count: 48