Factors predicting antiretroviral medication adherence among people living with HIV in Asunafo South District, Ghana. A cross-sectional study

doi:10.21203/rs.3.rs-5313445/v1

Background

Comorbidities among HIV positive individuals may have an impact on adherence to antiretroviral medication. Antiretroviral therapy's effectiveness is impacted by poor adherence, which also hastens the spread of the virus and increases medication resistance. This study was set to determine the level of adherence to antiretroviral treatment and identify the factors which predict ART medication adherence among people living with HIV who are on ART in Asunafo South District, Ghana.

Methods

Retrospective records review and analytical cross-sectional study designs were used. Systematic random sampling was used to collect the data. The data collection was conducted between 5th July 2023 and 28th July 2023. The literature for this study was reviewed and completed from 20th January 2023 and 28th April 2023. Descriptive statistics were performed. Bivariate analysis was done, where variables with p-values < 0.1 were employed in multivariate logistic regression analysis. Independent factors having p-values < 0.05 in multivariate logistic regression analysis were considered significantly associated with adherence. Correlation and regression analyses were performed on how ART adherence was related with viral load and CD4 cell counts. Lastly, direct and indirect relationships was done using structural equation modeling. The analysis was performed using Stata version 17.0.

Results

The study revealed a suboptimal ART adherence of 23.1%. Comorbidities such as hypertension, hepatitis B and C, psychiatric disorders, substance abuse, and pneumonia were found. The presence of hepatitis B and C, and ART site were significantly associated with ART adherence [(χ² = 5.21 (1); p = 0.022; site (χ² = 39.70 (1); p < 0.001) respectively)]. Younger patients (18–29 years and 30–39 years old) and increase in educational level of respondents were significantly more likely to influence adherence to treatment compared to older, less educated respondents. Males with higher education (senior high school and tertiary) were more likely to predict adherence to antiretroviral therapy. Respondents with two or more comorbidities were more likely to achieve optimal adherence, and a positive correlation was found between ART adherence and CD4 cell counts.

Conclusions

The study revealed that ART site, age, education, and gender, contributed significantly to influencing ART adherence in the Asunafo South District.

Antiretroviral

medication

Comorbidity

Adherence

HIV

Asunafo South District

A variety of comorbidities unrelated to Human Immunodeficiency Virus (HIV) are now experienced by people living with HIV. Furthermore, people living with HIV frequently have comorbidities, irrespective of their immunological state. The primary factor influencing the clinical results of antiretroviral therapy (ART) is adherence [1]. Therefore, maintaining compliance with antiretroviral medication is crucial to reducing viral replication and enhancing prognosis in HIV patients [2, 3].

Some factors reported to be influencing ART adherence in Ghana include education level, income, duration on ART, regimen type, and missed doses [4]. In Peru, a cross-sectional study conducted by Leyva-Moral and colleagues [5] reported that 58.3% of their respondents were not consistently adherent to ART, with concurrent tuberculosis being one of the significant risk factors for non-adherence. On the other hand, Cuenca and friends noted that 86.2% of ART adherents showed statistically significant relationships among patients with more than two chronic conditions in Spain [6]. This study therefore hypothesized that there is a significant association between comorbidities and adherence to ART among people living with HIV (Ha₁).

The presence of comorbidities is associated with a decrease in ART adherence [7]. For instance, In Nigeria, it was noted that poor adherence was predicted by low CD4 levels, anxiety, and depression [8]. According to Adeoti and colleagues, individuals with depression had 0.2 times worse likelihood of sticking to their ART regimens than patients without depression. The issue of comorbidity and ART adherence among HIV and AIDS patients in Ghana is therefore important. Hence, it was hypothesized in this study that there is a significant direct relationship between comorbidities and medication adherence (Ha₂). Moreover, in the United States, Tchakoute and colleagues [9] indicated that adherence was linked to increased CD4 counts and decreased viral load levels. It was also hypothesized that adherence to ART has indirect effect on CD4 cell count and direct effect on viral load (Ha₃).

This study aimed to determine the factors influencing adherence in the study area among patients receiving ART and the relationship between comorbidity and ART adherence.

The conceptual framework of this study suggests that ART adherence is influenced by several factors, including sociodemographic characteristics, comorbidities, and biological markers. Comorbidities in HIV patients, such as hypertension, pneumonia, and mental health issues, can be shaped by these sociodemographic and biological factors. Additionally, sociodemographic characteristics may affect biological markers like viral load and CD4 counts. Proper adherence to ART is crucial for maintaining stable biological function and preventing the progression of HIV and related comorbidities [10].

Figure 1: Conceptual framework indicating study variables of relationship between comorbidity and ART adherence among HIV patients.

Source

Modified from [11]

Methods

Study design

A retrospective records review and analytical cross-sectional study designs was used in the form of a check list and structured interviewer-administered questionnaire to collect the data on respondents.

Description of study area

The study was conducted in Asunafo South District in the Ahafo Region of Ghana. The district has a total population of 93,619, with 3,668 children under the age of one and 22,006 women of reproductive age. In terms of health service delivery, the district has twenty-two (22) health facilities. At the time of conducting this study, the district has two HIV clinics, anonymously labeled as A and B for the purpose of this study. The HIV clinics run five days a week and by the end of 2021, about 4,017 clients were active in care on ART. Nurses provide clinical care at the ART centres.

Study population

The study population included people living with HIV who have been on ART as of end of the end of December 2022.

Sample size determination

The Yamane formula for calculating sample sizes [12] was used. According to the Yamane’s formula, n= \(\:\frac{N}{1+N\left({e}^{2}\right)}\), where N = total population (population of persons living with HIV on ART in the Asunafo South District as of end of 2022 = 2,070), and e = margin error (0.05) because the formula is set for a minimum sample size at a 95% confidence level.

n= \(\:\frac{\text{2,070}}{1+\text{2,070}\left({0.05}^{2}\right)}\) = 335

Upon incorporating a 10% non-response rate, the total sample size for this study was 369.

Sampling Method

A systematic random sampling was used to select the participants in this study. This technique made it easier to choose participants from the population in a sequential manner using a periodic, set interval and a random beginning point.

The age of 18 and above was used. Persons who were living with HIV, but were not on ART or defaulted, and or has been enrolled into ART clinics later than 31st December 2022 were not included.

Data collection method

The sampling was done by selecting the first patient using simple random technique of Yes or No from the registers. At this stage, the first six respondents were considered, where one piece of paper was written with a Yes and the remaining five with No. The study investigators then asked six people who represented the first six clients in each register. The papers were then randomly displayed on a table and representative of each six clients were asked to pick the papers one after the other. The person who selected a Yes was traced to his or her match in the register and the corresponding patient was the starting point of the process. The rest of the respondents were drawn from the clinics registers according to a sample interval defined as 2,070/369 (every 6th patient in the registers as shown in Table 1) until the desired sample size was achieved. Any eligible patient who declined to participate or was not reachable was skipped to the person next to him or her in the registers.

Table 1

Summary of Sample size allocation by ART site
ART Clinic	Total Clients on ART	Sample size	Systematic sampling
A	1,336	\(\:\frac{\text{1,336}}{\text{2,070}}\times\:369\)= 238	\(\:\frac{1336}{238}=6th\:unit\)
B	734	\(\:\frac{734}{\text{2,070}}\times\:369\)=131	\(\:\frac{734}{131}=6th\:unit\)
Total	2,070	369	N/A

Data collection procedure

Biological information such the number of CD4 cells, viral load, and duration after infection (in months) were recorded. The viral load and the number of CD4 cells, were recorded from the medical records. The amount of time in months that passed after an HIV seropositivity diagnosis was used to determine the length of infection.

Three (3) data collection assistants with good knowledge in research data collection were recruited and trained on the objectives and aim of the study. Eligible HIV clients on ART were first contacted by the ART nurse via phone to explain the study prior to the conduct of the face-to-face structured interviews or telephone interviews for those who were not physically accessible but can be reached on phone and willing to participate in the study. The questionnaire was developed on KoboCollect data collection platform and used to collect the data. The data collection was conducted between 5th July 2023 and 28th July 2023. Following collection of written or verbal informed consent, data collectors used the structured questionnaire to interview respondents to collect the information. The literature for this study was reviewed and completed from 20th January 2023 and 28th April 2023.

Data collection tool

An interviewer-administered questionnaire was used to collect the data for this study. The questionnaire's initial section focused on sociodemographic information. This was followed by the number of CD4 cells, viral load, and duration after infection (in months) were noted. The Adherence Behaviours Questionnaire (VPAD-24) was used by adopting eighteen (18) questions to collect data on the factors in this study. The choice of 18 items used was based on their relation to the topic of this study, as adopted by [13] in their validated brief questionnaire for assessing psychological variables and medication-adherence behaviours among HIV-positive persons in Venezuela. Hence, the simplified version used here consisted of 18 items. Instead of employing a dichotomous scale to measure adherence, a continuous scale with categories for both optimal and suboptimal adherence was used.

Finally, respondents were interviewed on the experience of any additional disease (s) aside HIV. The confirmation of each of the comorbid conditions captured in the study instruments was scaled as one (1) for the laboratory or medically diagnosed disease (s) and zero (0) for negative laboratory test or medically confirmed negative of any of those disease (s).

Reliability and Validity of the Study Instrument

This research instrument was checked and validated by the researchers and corrections were included into the final draft of the research instrument. The investigators tested the reliability of the research instrument by using the 18 items of the adherence behaviour questions. The investigators also take into consideration gender equity and geographical background of individuals. A construct composite reliability co-efficient (Cronbach alpha) of 0.7 or above, for all the constructs were considered adequate [14] for this study and the values was obtained as 0.80.

Data analysis

In this study, comorbidity, and sociodemographic variables, along with medication adherence, were analyzed using bivariate analysis. The total adherence score was categorized into two levels: optimal adherence (≥ 85%) and suboptimal adherence (< 85%), based on the WHO’s classification system, where good adherence is defined as ≥ 95%, fair adherence as 85–94%, and poor adherence as < 85% [15].

Viral non-suppression in this study was defined as viral load greater than 1000 copies/mL, whereas viral load suppression was defined as viral load measurement of less than or equal to 1000 copies/mL [16].

Stata version 17.0 was used to carry out the analysis. Multivariate logistic regression was employed to identify significant predictors of adherence, with independent factors having p-values below 0.05 considered statistically significant. The odds ratio with a 95% confidence interval was used to assess these associations. Correlation and regression analyses were performed to evaluate the relationship between ART adherence, viral load, and CD4 cell counts. Structural equation modeling was then used to assess the direct and indirect relationships between the variables.

Socio-demographic characteristics and clinical information of study respondents

From Table 2, majority of the respondents were of age 38–47 years old (28.9%), followed by respondents who were 28–37 years old (26.7%). The mean age of the respondents was 43.4 years (SD = 13.6). Most respondents were females (61.2%), have secondary education level (28.4%), and were of Akan ethnicity (60.6%). Most respondents were identified as Christians (78.2%), were married (55.9%), and lived in rural areas (57.0%). Finally, most respondents had active health insurance (82.1%).

Table 2

Socio-demographic characteristics of HIV clients on ART in Asunafo South District
Variables	Frequency (n = 363)	Percentage (%)
Age
18–27	32	8.8
28–37	97	26.7
38–47	105	28.9
48–57	68	18.7
58–67	40	11.0
68–89	21	5.8
Mean (± SD)	43.4(± 13.6)
Minimum age	18
Maximum age	89
Sex
Female	222	61.2
Male	141	38.8
Highest level of formal education
None	47	13.0
Primary	99	27.3
Secondary	103	28.4
HND/Training college	59	16.3
University	55	15.2
Ethnicity
Akan	220	60.6
Dagomba/Dagao/Gonja	84	23.1
Ewe	30	8.3
Fante	23	6.3
Other	6	1.7
Religion
Christian	284	78.2
Islam	77	21.2
Traditionalist	2	0.6
Marital status
Divorced/Separated/Widowed	47	13.0
Married	203	55.9
Single	113	31.1
Occupation
Unemployed	70	19.3
Artisan	6	1.7
Dressmaker	39	10.7
Farmer	137	37.7
Government Worker	70	19.3
Other	41	11.3
Place of locality
Rural	207	57.0
Urban	156	43.0
Have active health Insurance
No	65	17.9
Yes	298	82.1

From Table 3, most respondents (71.9%) were infected for 37 months or more with HIV. The mean duration of infection in this study was 59.7 months, with standard deviation of 36.3, and the shortest infection lasting 6 months, while the longest lasted for 168 months at the time of conducting this study. Furthermore, most of the respondents (83.1%) had CD4 cell count of less than 200 cells/µL. Also, most respondents (93.7%) had achieved viral load suppression in this study.

Table 3

Clinical information of HIV clients on ART at Asunafo South District
Variables	Frequency (n = 363)		Percentage (%)
Duration (Months)
6–12 months	66		18.2
13–24	9		2.5
25–36	27		7.4
≥ 37	261		71.9
Mean duration of infection (± SD)	59.7(± 36.3)
Minimum duration of infection		6 months
Maximum duration of infection	168 months
CD4 cell count (cells/µL)
< 200	300		83.1
200–349	5		1.4
350–500	2		0.5
> 500	54		15.0
Viral load suppression
Viral Suppressed	340		93.7
Viral Non-suppressed	23		6.3

Association between comorbidities and adherence to antiretroviral treatment among people living with HIV and AIDS

The results in Fig. 2 showed that majority (76.9%) of the respondents had suboptimal ART adherence (< 85%) in this study.

Figure 2: Level of adherence to antiretroviral treatment among HIV clients in Asunafo South District

In Fig. 3, the most common self-reported comorbidities were hypertension and hepatitis B and C (11.0% each). These were followed by psychiatric disorders like depression and anxiety (8.3%), substance abuse (4.4%), and pneumonia (3.0%).

Figure 3: Proportion of selected self-reported comorbidities among HIV clients in Asunafo South District

The results in Fig. 4 indicated that the majority (72.7%) of the respondents did not report any form of comorbidity. However, some 16.8% and 9.4% of the respondent self-reported that they had one comorbidity and two comorbidities respectively.

Figure 4: Level of categories of self-reported comorbidities among HIV clients in Asunafo South District

The findings in Table 4 demonstrated a statistically significant association between ART adherence status and the presence of hepatitis B and C (χ² = 5.21 (1); p = 0.022). Specifically, lower adherence to ART was more prevalent among those with Hepatitis B and C (17.9%).

Table 4

Univariate analysis of self-reported comorbidities with adherence to antiretroviral treatment among HIV clients in Asunafo South District
	ART adherence status
Variables	Optimal (n = 84)	Sub-optimal (n = 279)	Chi-square (df)	P-value
Diabetes mellitus			0.91 (1)	0.340
Yes	0 (0.0)	3 (1.1)
No	84 (100.0)	276 (98.9)
Hypertension			0.01 (1)	0.919
Yes	9 (10.7)	31 (11.1)
No	75 (89.3)	248 (88.9)
Pneumonia			0.11 (1)	0.741
Yes	3 (3.6)	8 (2.9)
No	81 (96.4)	271 (97.1)
Hepatitis B and C			5.21 (1)	0.022
Yes	15 (17.9)	25 (9.0)
No	69 (82.1)	254 (91.0)
Psychiatric disorders (depression, anxiety, etc)			3.36 (1)	0.067
Yes	11 (12.1)	19 (6.8)
No	73 (86.9)	260 (93.2)
Renal diseases			0.91 (1)	0.340
Yes	0 (0.0)	3 (1.1)
No	84 (100.0)	276 (98.9)
Substance dependence and abuse			0.62 (1)	0.431
Yes	5 (6.0)	11 (3.9)
No	79 (94.0)	268 (96.1)
Number of comorbidities			3.74 (2)	0.154
None	55 (65.5)	209 (74.9)
One	16 (19.1)	45 (16.1)
Two or more	13 (15.5)	25 (9.0)
df = degree of freedom

Factors which predict ART medication adherence among people living with HIV who were on ART.

The results in Table 5 showed a strong association between ART site and ART adherence (χ² = 39.70 (1); p < 0.001). ART site A had a higher percentage of optimal adherence (92.9%) compared to site B (7.1%). In addition, a significant association was found between ART adherence status and the highest level of formal education (χ² = 10.80 (4); p = 0.029).

Table 5

Univariate analysis of factors associated with adherence to antiretroviral treatment.
	ART adherence status
Variables	Optimal (n = 84)	Sub-optimal (n = 279)	Chi-square (df)	P-value
ART site			39.70 (1)	0.000
A	78 (92.9)	154 (55.2)
B	6 (7.1)	125 (44.8)
Duration (Months)			1.15 (3)	0.766
6–12 months	49 (17.6)	17 (3.6)
13–24	6 (2.2)	3 (3.6)
25–36	20 (7.2)	7 (8.3)
≥ 37	204 (73.1)	5 (67.9)
Age			8.10 (5)	0.151
18–27	12 (32.1)	20 (7.2)
28–37	27 (32.1)	70 (25.1)
38–47	19 (22.6)	86 (30.8)
48–57	13 (15.5)	55 (19.7)
58–67	7 (8.3)	33 (11.8)
68–89	6 (7.1)	15 (5.4)
Sex			0.12 (1)	0.726
Female	50 (59.5)	172 (61.7)
Male	34 (40.5)	107 (38.3)
Highest level of formal education			10.80 (4)	0.029
None	8 (9.5)	39 (14.0)
Primary	13 (15.5)	86 (30.8)
JHS/SHS/Vocational	30 (35.7)	73 (26.2)
HND/Training college	17 (20.2)	42 (15.0)
University	16 (19.1)	39 (14.0)
Ethnicity*				0.766
Akan	53 (63.1)	167 (59.9)
Dagomba/Dagao/Gonja	19 (22.6)	65 (23.3)
Ewe	7 (8.3)	23 (8.2)
Fante	3 (3.6)	20 (7.2)
Other (Specify)	2 (2.4)	4 (1.4)
Religion			4.04 (1)	0.133
Christian	60 (71.4)	224 (80.3)
Islam	24 (28.6)	53 (19.0)
Traditionalist	0 (0.0)	2 (0.7)
Marital status			0.99 (2)	0.609
Divorced/Separated/Widowed	12 (14.3)	35 (12.5)
Married	43 (51.2)	160 (57.4)
Single	29 (34.5)	84 (30.1)
Occupation			5.20 (2)	0.074
Unemployed	19 (22.6)	51 (18.3)
Not formally employed	43 (51.2)	180 (64.5)
Formally employed	22 (26.2)	48 (17.2)
Place of locality			1.52 (1)	0.219
Rural	43 (51.2)	164 (58.8)
Urban	41 (48.8)	115 (41.2)
Have active health Insurance			0.10 (1)	0.756
No	16 (19.1)	49 (17.6)
Yes	68 (80.9)	230 (82.4)
Self-reported Comorbidity status			2.90 (1)	0.089
No comorbidity reported	55 (65.5)	209 (74.9)
Comorbidity reported	29 (34.5)	70 (25.1)
* Means Fishers exact test performed on variable

From Table 6, the only ART site A had significantly higher odds of influencing optimal ART adherence compared to those at B (aOR = 14.98, p = 0.000).

Table 6

a: Selected socio-demographic and clinical factors which influenced ART adherence to among HIV clients in Asunafo South District
	ART adherence status		Odd ratio
Variables	Optimal (n = 84)	Sub-optimal (n = 279)	cOR (95%CI)p-value	aOR(95%CI)p-value
ART site
A	78 (92.9)	154 (55.2)	10.55(4.45–25.02)0.000	14.98(5.54–40.51)0.000
B	6 (7.1)	125 (44.8)	Reference	Reference
Duration (Months)
6–12 months	49 (17.6)	17 (3.6)	Reference	Reference
13–24	6 (2.2)	3 (3.6)	1.44(0.32–6.40)0.631	1.93(0.37–10.05)0.433
25–36	20 (7.2)	7 (8.3)	1.0(0.36–2.80)0.987	3.03(0.93–9.90)0.066
≥ 37	204 (73.1)	5 (67.9)	0.81(0.43–1.50)0.497	1.93(0.97–3.84)0.061
Age
18–27	12 (32.1)	20 (7.2)	Reference	Reference
28–37	27 (32.1)	70 (25.1)	0.64(0.28–1.49)0.304	0.72(0.27–1.92)0.509
38–47	19 (22.6)	86 (30.8)	0.37(0.15–0.88)0.025	0.31(0.09-1.00)0.051
48–57	13 (15.5)	55 (19.7)	0.39(0.15–1.01)0.051	0.30 − 0.08)1.13)0.075
58–67	7 (8.3)	33 (11.8)	0.35(0.12–1.05)0.060	0.45-0.10-2.05)0.304
68–89	6 (7.1)	15 (5.4)	0.67(0.20–2.18)0.503	0.99(0.19–5.23)0.987
Sex
Female	50 (59.5)	172 (61.7)	0.91(0.56–1.51)0.726	1.07(0.59–1.95)0.824
Male	34 (40.5)	107 (38.3)	Reference	Reference
Highest level of formal education
None	8 (9.5)	39 (14.0)	Reference	Reference
Primary	13 (15.5)	86 (30.8)	0.74(0.28–1.82)0.532	1.22(0.41–3.66)0.722
Secondary	30 (35.7)	73 (26.2)	2.00(0.84–4.79)0.118	1.71(0.58–5.03)0.329
HND/Training college	17 (20.2)	42 (15.0)	1.97(0.77–5.09)0.159	0.91(0.29–2.89)0.872
University	16 (19.1)	39 (14.0)	2.00(0.77–5.21)0.156	1.07(0.34–3.31)0.908
cOR = crude odd ratio; aOR = adjusted odd ratio

Table 6

b: Selected socio-demographic and clinical factors which influenced ART adherence to among HIV clients in Asunafo South District
	ART adherence status		Odd ratio
Variables	Optimal (n = 84)	Sub-optimal (n = 279)	cOR (95%CI)p-value	aOR(95%CI)p-value
Ethnicity
Akan	53 (63.1)	167 (59.9)	0.63(0.11–3.56)0.606	1.24(0.21–7.36)0.815
Dagomba/Dagao/Gonja	19 (22.6)	65 (23.3)	0.58(0.10–3.44)0.553	1.53(0.24–9.73)0.650
Ewe	7 (8.3)	23 (8.2)	0.61(0.09–4.06)0.608	2.09(0.27–15.84)0.477
Fante	3 (3.6)	20 (7.2)	0.30(0.04–2.42)0.258	1.07(0.11–10.13)0.952
Other (Specify)	2 (2.4)	4 (1.4)	Reference	Reference
Marital status
Divorced/Separated/Widowed	12 (14.3)	35 (12.5)	0.99(0.46–2.17)0.986	1.04(0.32–3.42)0.947
Married	43 (51.2)	160 (57.4)	0.78(0.45–1.34)0.363	1.21(0.57–2.57)0.629
Single	29 (34.5)	84 (30.1)	Reference	Reference
Occupation
Unemployed	19 (22.6)	51 (18.3)	Reference	Reference
Not formally employed	43 (51.2)	180 (64.5)	0.64(0.34–1.20)0.162	1.67(0.78–3.56)0.187
Formally employed	22 (26.2)	48 (17.2)	1.23(0.59–2.55)0.578	1.93(0.79–4.74)0.150
Place of locality
Rural	43 (51.2)	164 (58.8)	Reference	Reference
Urban	41 (48.8)	115 (41.2)	1.36(0.83–2.22)0.219	1.35(0.76–2.38)0.308
Have active health Insurance
No	16 (19.1)	49 (17.6)	Reference	Reference
Yes	68 (80.9)	230 (82.4)	0.91(0.48–1.69)0.756	0.61(0.29–1.29)0.195
Self-reported Comorbidity status
No comorbidity reported	55 (65.5)	209 (74.9)	0.64(0.38–1.07)0.090	0.97(0.54–1.74)0.911
Comorbidity reported	29 (34.5)	70 (25.1)	Reference	Reference
cOR = crude odd ratio; aOR = adjusted odd ratio

The results in Fig. 5 also showed that younger patients (18–29 years and 30–39 years old) and those with increased educational level were significantly more likely to adhere to treatment compared to older, less educated respondents. In addition, older respondents (40 + years) were less likely to adhere to ART as their level of education increases, compared to patients less than 40 years. However, this difference was not statistically significant (p > 0.05), because ART adherence varied widely among those with the medication.

Figure 5: Adherence prediction using a combined effect of age and education level of HIV clients in Asunafo South District

From Fig. 6, male respondents and increase in educational level up to secondary level were significantly more likely to predict adherence to ART compared to females with the same level of education. However, there was an observed statistically significant decrease in both males and females’ adherence to ART, as respondents attained tertiary education (p < 0.05).

Figure 6: Adherence prediction using a combined effect of sex and education level of HIV clients in Asunafo South District

In Fig. 7, being a male still had the chance of predicting optimal adherence to ART treatment. This was higher among divorced, separated, or widowed respondents, compared to respondents who were single. These differences were statistically significant because among those with different marital statuses, the likelihood of optimal adherence varied narrowly, with no probabilities of equaling that of respondents with different marital status.

Figure 7: Adherence prediction using a combined effect of sex and marital status of HIV clients in Asunafo South District

Finally, the likelihood of optimal adherence varied based on the number of self-reported comorbidities (Fig. 8). Generally, respondents who did not report any comorbidity were significantly less likely to predict ART adherence (margins: 0.21). However, respondents who reported two or more comorbidities were significantly more likely to predict high optimal ART adherence (margins: 0.31).

Figure 8: Adherence prediction using the effect of self-reported comorbidities among HIV clients in Asunafo South District

Relationship between ART medication adherence on viral load and CD4 cell counts.

In Table 7, it was found that a positive relationship existed between ART adherence and CD4 cell counts (r = 0.135, p < 0.05). Additionally, a significant negative relationship was observed between comorbidity and period (duration) of infection (r = -0.131, p < 0.05), while viral load and CD4 cell counts were positively correlated (r = 0.612, p < 0.001).

Table 7

Strength and direction of the relationship between medication adherence, period of HIV infection, viral load, and CD4 cell counts, and self-reported comorbidities
Variables	ART adherence	Period of infection	Viral load	CD4 cell counts	Comorbidity
ART adherence	1.000
Duration of infection	-0.044	1.000
Viral load	0.072	-0.025	1.000
CD4 cell counts	0.135*	-0.041	0.612***	1.000
Comorbidity	-0.089	0.131*	-0.044	-0.044	1.000
=p < 0.05; **=p < 0.001

In Table 8, the duration of infection among respondents was negatively related with ART adherence (β = -0.011, p = 0.579, 95% CI [-0.048, 0.027]). Similarly, viral load showed a negative relationship with ART adherence, which was not significant with adherence (β = -0.037, p = 0.748, 95% CI [-0.262, 0.188]). In contrast, CD4 cell counts had a significant positive relationship with ART adherence (β = 0.056, p = 0.031, 95% CI [0.005, 0.107]).

Table 8

Nature of the relationship between ART adherence and period of HIV infection, viral load, and CD4 cell counts, and self-reported comorbidities
Variables	Coefficient (β)	p-value	[95% confidence interval]
Duration of infection	-0.011	0.579	-0.048	0.027
Viral load	-0.037	0.748	-0.262	0.188
CD4 cell counts	0.056	0.031	0.005	0.107
Comorbidity	-0.078	0.122	-0.177	0.021

Notably, from Fig. 9, a significantly low positive relationship existed between comorbidity (β = 0.070, p = 0.038, 95% CI [0.004, 0.135]), education (β = 0.041, p = 0.023, 95% CI [0.006, 0.076]), and adherence within the structural category. This result indicated that the hypothesis (H_a2) that there was direct significant relationship between comorbidities and medication adherence was true. In the viral load category, there was no significant relationship between adherence and viral load, while in the CD4 cells count category, there was a strong positive relationship between viral load and CD4 cell count (β = 2.698, p < 0.001, 95% CI [2.339, 3.058]). This result also led to the acceptance of the third hypothesis (H_a3) that adherence to ART has indirect effect on CD4 cell count and no direct effect on viral load.

Figure 9: Structural equation analyses of ART adherence among 363 persons living with HIV in Asunafo South District

Encouraging optimal adherence is crucial in preventing the emergence and transmission of drug-resistant HIV. The suboptimal adherence to ART is reported to be the most common cause of treatment failure [17]. This study found that a little above one-fifth (23.1%) of the respondents were optimally adhering to ART medication. This implied that this proportion of respondents had consumed or likely to consume all medications as prescribed by their service providers. The public health implication of this finding is a cause for concern, as suboptimal adherence (76.9%) to ART can have significant consequences for both individual and public health [18], including effective management and suppressing the HIV virus [19]. This finding did not support what was found in Peru, that 58.3% of their respondents were not consistently adherent to ART [5]. Other findings which contradicted this current study’s finding revealed in the Upper West Region of Ghana, that the overall lifetime adherence to ART was 62.2% [20] and in northwest Ethiopia, that the overall rate of adherence to ART was 88.2% [21].

This study found a statistically significant association between ART adherence and the presence of hepatitis B and C. Respondents co-infected with these conditions were less likely to adhere to ART compared to those without co-infection. This is consistent with previous studies [26, 27, 28], which also noted that co-infected individuals face greater challenges in adhering to ART. In contrast, no significant association was found between other comorbidities, such as type 2 diabetes mellitus, pneumonia, or substance abuse, and ART adherence. This finding contradicts studies conducted in Tamale, Ghana [25], where a high level of these comorbidities influenced adherence. Furthermore, respondents who reported two or more comorbidities were statistically significantly more likely to predict high optimal adherence to ART in this study.

The current study also found significant demographic predictors of ART adherence, including age and education. Older individuals (36–63 years) were significantly more likely to adhere to ART compared to younger respondents, supporting the idea that older age predicts better adherence. However, this result contrasts with studies from Cameroon [26] and the UAE [27], which suggested that older age did not correlate strongly with adherence. Additionally, higher education levels predicted better adherence, particularly among younger individuals (≤ 39 years). This aligns with findings from Tanzania [28] but contradicted previous studies in which education level had a negligible impact on adherence [29].

Gender differences in adherence were not statistically significant, aligning with a study from Malawi [19]. However, males with higher education were more likely to adhere to ART, a finding not observed in similar study finding from Cameroon [26]. These disparities highlight the need for interventions such as providing educational and socioeconomic support to younger and less educated individuals. However, an observed statistically significant decrease in adherence to ART was found in both males and females, as respondents attained tertiary education. The finding suggests a potential disconnect between higher educational attainment and optimal adherence to ART, challenging the assumption that increased education universally correlates with better health outcomes [33,34]. This may indicate that the complexities of ART adherence extend beyond educational levels.

The present study found a significant positive relationship between ART adherence and CD4 cell counts. This result is consistent with a study in the Upper West Region of Ghana [32], which also demonstrated that ART adherence leads to improved CD4 cell counts. However, no significant direct relationship was found between ART adherence and viral load. This finding contradicts previous studies in Mexico [11], which reported a direct effect of adherence on viral suppression. Despite this, adherence indirectly influenced the positive relationship between viral load and CD4 cell counts, indicating that while adherence may not directly reduce viral load, it plays a crucial role in improving immune function.

This study was limited by the fact that adherence and comorbidities were measured through self-reports, routine and random pill counts, and assessment of medical records. These methods were not full proof, and actual adherence could be lesser than what was found. However, due to feasibility issues, it was only possible to use a combination of these methods to minimize error.

Co-infections with hepatitis B and C significantly hinder ART adherence. Demographic factors, such as ART site, age and education, were significant predictors of ART adherence. Older individuals and those with higher levels of education were more likely to adhere to their medication regimens, although adherence was lower among older individuals with increased education, suggesting the influence of outdated beliefs or treatment fatigue. Gender differences in adherence were not significant, though males with higher education were more likely to adhere. The study further established a positive relationship between ART adherence and CD4 cell counts, affirming the critical role of consistent adherence in improving immunological outcomes. Nevertheless, adherence indirectly supported better immune function through its effect on CD4 counts.

Since adherence rates were influenced by variables such as ART site, age, and education, further research should explore these associations in diverse settings to develop context-specific interventions that address both clinical and social determinants of health.

AIDS   Acquired Immune Deficiency Syndrome
ART   Antiretroviral Therapy
ARV      Antiretroviral
CD4   Cluster of differentiation 4
CI   Confidence Intervals

cART combined antiretroviral therapy.

DHIMS2 District Health Information Management System 2

GHS Ghana Health Service

GSS Ghana Statistical Service

HIV Human Immunodeficiency Virus

LMICs Low-and middle-income countries

NHIS National health insurance
PLHIV   People Living With HIV/AIDS
WHO   World Health Organization
UNAIDS   Joint United Nations Program on HIV/AIDS

UNICEF United Nation Children Fund

Ethics approval and consent to participate

The Ghana Health Service Ethics Review Committee was consulted for ethical approval (GHS-ERC 045/05/23), which was used to request approval from the various facilities' in-charges as well as the Asunafo South District Health Directorate. Participants' informed consent was obtained before participation. The absence of any identifier(s) during data collection and the use of password-protected computers were part of the assurance given to the respondents that the information they submitted would be kept private and anonymous. Participants did not receive any form of compensation for the purpose of being enrolled in this study.

Consent for publication

Respondents were made aware from the initial stage that the findings of this study would be published in a reputable scientific journal.

Competing interests

The authors declare that there are no competing interests in conducting this study.

Authors’ contributions

RK conceptualized the study and planned its conduct with MAGH and EA. RK, MAGH and EA wrote the introduction, reviewed the literature, and wrote the methodology together. RK sought ethical clearance from the Ghana Health Service Research Ethics Committee. RK collected the data, and carried out the analysis with MAGH, and EA. The reporting of the work was done by RK, MAGH and EA. The discussion and final version of the manuscript was written, reviewed, and accepted by all authors.

RK accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Acknowledgements

We are grateful to the Asunafo South District Health Directorate for granting us the permission to conduct this study. We also thank the facilities’ In-charges and ART nurses for their assistants in getting the needed study respondents. To our research data collection assistants, our research team, and especially the participants, we say thank you.

Funding

The authors did not receive any funding support to conduct this study.

Availability data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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No competing interests reported.

Factors predicting antiretroviral medication adherence among people living with HIV in Asunafo South District, Ghana. A cross-sectional study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Conceptual Framework

Methods

Study design

Description of study area

Study population

Sample size determination

Sampling Method

Data collection method

Data collection procedure

Data collection tool

Reliability and Validity of the Study Instrument

Data analysis

Results

Socio-demographic characteristics and clinical information of study respondents

Association between comorbidities and adherence to antiretroviral treatment among people living with HIV and AIDS

Discussion

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Authors’ contributions

Acknowledgements

Funding

Availability data and materials

References

Additional Declarations

Status:

Version 1