Determinants of HIV Testing Among Men Who Have Sex with Men in Ghana: Insights from the Ghana Men’s Study II

Abstract

Despite notable progress in HIV prevention and treatment, men who have sex with men (MSM) continue to bear a disproportionate burden of HIV, particularly in sub-Saharan Africa, where systemic barriers restrict access to HIV testing. This study draws on data from the 2017 Ghana Men’s Study II (GMS II), to examine the socio-demographic, behavioural, and structural factors influencing HIV testing among MSM. The Ghana Men’s Study II dataset, involving 4095 MSM, was de-identified and analysed using STATA (software version 17). Before the analysis, missing information for categorical variables were treated using the mode imputation technique. Chi-square test was done to describe relevant characteristics of the study population, such as socio-demographic/socio-economic variables and behavioural practices. Multivariable logistic regression analysis was performed for variables with p < 0.05 to determine significant predictors of HIV testing among MSM. All the statistical analyses were performed at a 95% confidence interval, with significant differences at p < 0.05. In multivariable logistic regression analysis, age 25–34 (AOR: 1.43; 95% CI: 1.18–1.74, p < 0.001), having a senior high school education (AOR: 1.69; 95% CI: 1.02–2.80, p = 0.040), tertiary education (AOR: 2.03; 95% CI: 1.17–3.55, p = 0.012), being a light drinker of alcohol (AOR: 1.28; 95% CI: 1.04–1.58, p = 0.020), and having a comprehensive knowledge of HIV (AOR: 1.50; 95% CI: 1.26–1.78, p < 0.001) had higher odds for HIV testing. Other factors such as being a Muslim (AOR: 0.69; 95% CI: 0.54–0.90, p = 0.005) and sold sex to other males (AOR: 0.67; 95% CI: 0.50–0.90, p = 0.007) were also positively associated with HIV testing among Ghanaian MSM. The findings revealed a number of socio-demographic and behavioural factors associated with HIV testing among the MSM population in Ghana.

1. Introduction

Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS) remain critical global health issues, particularly affecting marginalised populations such as men who have sex with men (MSM) [1]. HIV prevalence among MSM in Ghana remains critically high. A report from the Ghana Men’s Study I conducted in 2011 documented a 17.5% prevalence of HIV among MSM, significantly higher than the 1.7% prevalence observed in the general population [2]. In 2017, the Ghana Men’s Study II again recorded a marginal increase in the HIV prevalence (18.1%) among Ghanaian MSM, which was higher than the 1.68% in the general population. According to the UNAIDS 2025 report, the Ghana Men’s Study III, which was conducted in 2023, revealed a 26.1% of HIV among the MSM population as against 1.53% among the general populace. This stark disparity highlights the pressing need to confront the unique challenges that MSM face in accessing HIV testing and care, particularly in sub-Saharan Africa, where the epidemic is most severe and systemic barriers continue to limit health equity [3].

Several socio-demographic factors significantly influence the decision to undergo HIV testing among MSM [4,5]. Age, education, and socioeconomic status are critical determinants of healthcare access and testing behaviour [5]. For instance, individuals from lower socioeconomic backgrounds often face systemic barriers to accessing healthcare services, including HIV testing [6,7]. Behavioural factors, such as sexual practices and perceived risk, also play a role. In this regard, many MSM engage in high-risk behaviours, including inconsistent condom use and transactional sex, which heighten their vulnerability to HIV [8]. Misconceptions about HIV transmission and a lack of awareness regarding testing services further contribute to low testing rates within this population [9,10].

Structural factors, particularly stigma and discrimination, are significant barriers that prevent MSM from accessing HIV testing and healthcare services [11]. In conservative societies like Ghana, the stigma associated with same-sex relationships often leads to fear of negative social consequences, which can deter individuals from seeking necessary healthcare [12,13]. Discriminatory attitudes among healthcare providers exacerbate this issue, creating an environment of mistrust that discourages MSM from utilising testing services [14]. The limited availability of MSM-specific outreach programmes further compounds these challenges, as generalised public health campaigns often fail to address the unique needs of this population [7,15].

Enhancing HIV testing uptake among MSM in Ghana requires a nuanced understanding of the intersecting socio-demographic, behavioural, and structural factors that shape their health-seeking behaviour. Evidence suggests that targeted interventions, which are informed by the lived experiences and specific challenges faced by MSM, can significantly improve testing rates [5,16]. Strategies such as peer-led education and community-based outreach initiatives have demonstrated effectiveness in raising awareness, reducing stigma, and encouraging greater engagement with testing services [5,17]. Moreover, cultivating a healthcare environment that is inclusive and responsive to the needs of MSM can help counteract the negative impacts of stigma and discrimination, ultimately contributing to better health outcomes for this vulnerable population [12,14,18,19].

In Ghana, MSM are disproportionately affected by HIV, yet there is scant evidence highlighting barriers to HIV prevention services, including testing. Hence, it is imperative to retrospectively investigate key factors influencing MSM’s HIV testing decisions from one of the country’s largest surveys among MSM. This study draws on data from the 2017 Ghana Men’s Study II (GMS II) to examine the socio-demographic, behavioural, and structural factors influencing HIV testing among MSM. The objective was to gain a comprehensive understanding of the patterns, disparities, and determinants of HIV testing within this key population, which remains disproportionately affected by the HIV epidemic. By analysing a nationally representative dataset, the study aimed to uncover both barriers that hinder testing—such as stigma, limited access to services, and socio-economic constraints—and facilitators that promote engagement, including peer support, education, and health literacy.

Through this nuanced exploration, the research sought to generate evidence that can inform the design and implementation of context-specific interventions aimed at improving HIV testing coverage among MSM. Insights gained from the analysis are intended to support policy development and public health programming that are more responsive to the unique needs and lived experiences of MSM in Ghana. Ultimately, the study contributes to national and regional efforts to strengthen HIV prevention and care strategies, reduce new infections, and promote health equity among vulnerable and marginalised groups.

2. Materials and Methods

2.1. Study Design and Study Setting

This study conducted a retrospective analysis of the GMS II dataset to examine the extent of HIV testing uptake and to investigate the socio-demographic, behavioural, and structural factors influencing testing among MSM in Ghana. The Ghana Men’s Study II, an Integrated Bio-Behavioral Surveillance Survey (IBBSS) and Mapping and Population Size Estimation (MPSE) Survey, was conducted amongst men who have sex with men (MSM) in 2017 across the ten regions of Ghana as shown in Figure 1 [20]. At the time, Ghana was structured into 10 administrative regions, though it has since been reorganised into 16 regions and 261 districts. This study was implemented by the Ghana AIDS Commission (GAC), a supra-ministerial and multi-sectoral coordinating body established under the authority of the President of the Republic of Ghana. The GAC serves as the lead national institution responsible for the strategic coordination, policy guidance, and oversight of HIV, tuberculosis (TB), and sexually transmitted infection (STI) interventions across the country. Its mandate focuses on ensuring an inclusive and effective national response, particularly for key and vulnerable populations such as men who have sex with men (MSM), transgender persons, adolescents, young women, sex workers, and other high-risk groups disproportionately affected by the HIV epidemic.

The GAC study is supported through strong partnerships with international donors and development agencies. Notably, this initiative received substantial funding and technical assistance from the Global Fund to Fight AIDS, Tuberculosis and Malaria, as well as the United States President’s Emergency Plan for AIDS Relief (PEPFAR). This collaborative approach reflects a concerted effort to reduce HIV-related health disparities and to strengthen Ghana’s public health infrastructure. Through such partnerships, the GAC has expanded access to HIV prevention, testing, treatment, and care services, particularly for populations that remain underserved or marginalised within the national health system.

2.2. Sampling and Data Collection

The GMS II was designed to estimate sample sizes necessary for HIV surveillance, with the primary objective of monitoring significant changes in the epidemic between successive rounds of the IBBSS [20]. In alignment with GAC guidelines (2017), each study site was treated as a standalone survey location, with site-specific sample sizes calculated to detect local shifts in HIV prevalence over time. The study targeted MSM, defined as biologically male individuals who self-reported engaging in consensual sexual activity with another man within the preceding 12 months. For the purpose of this study, “consensual sexual activity” is defined as voluntary sexual engagement between partners, free from coercion, force, or manipulation. The survey included men who were biologically male, aged ≥18, had tested for HIV during the previous 12 months, and lived, worked, or socialised in any of the ten regions where the study was conducted. Transgender women were also considered for the study if they were biologically male and had tested for HIV within the last year.

The study utilised respondent-driven sampling (RDS), a method specifically designed to engage “hidden” or hard-to-reach populations, particularly where conventional sampling strategies are limited by the lack of a comprehensive sampling frame [20]. Rather than employing a predefined sampling list, participants were recruited through the social networks of previous respondents, allowing for broader access to MSM communities. To facilitate participation and recruitment, incentives were provided to respondents both for their involvement in the study and for successfully recruiting eligible peers. The target sample size was set at 500 MSM per region, a figure determined by multiple factors, including the projected duration of recruitment, available funding resources, the high-risk nature of the study population, and the need for robust statistical power to measure key behavioural indicators. This sample size enabled the detection of significant shifts in sexual behaviour, such as a 10% to 15% change in condom use during the last sexual encounter, and allowed for the estimation of HIV prevalence, which was projected at 34.3%.

In total, 4095 MSM were recruited for the GMS II, generating a robust sample size to produce precise estimates of key indicators and track changes in the HIV epidemic among MSM in Ghana. The survey was designed to collect data on various socio-demographic factors along with structural and behavioural elements. It also examined the frequency of HIV testing, the types of testing facilities utilised, and the reasons individuals choose to undergo HIV testing (see

Table 1. Summary of variables assessed from the 2017 GMS II dataset.

Variable Name	Variables	Variable Descriptions	Variable Recode
Dependent Variable (HIV Testing)
HIV testing	Whether the respondent has undergone HIV testing	(1) Yes (2) No (3) No response	(1) Yes (2) No
HIV counselling	Whether the respondent received HIV counselling	(1) Yes (2) No (3) No response	(1) Yes (2) No
HIV diagnosis	Whether the respondent was diagnosed with HIV	(1) Yes (2) No (3) No response	(1) Yes (2) No
Linkage to HIV medical care	Whether the respondent, if HIV positive, was linked to medical care	(1) Yes (2) No (3) No response	(1) Yes (2) No
HIV testing frequency	Number of HIV tests done	Integer	Integer
HIV testing facility types	Platform used for HIV testing	(1) HIV self-testing (2) Mobile testing (3) Community-based testing (4) Facility-based testing (5) Point-of-care testing	(1) HIV self-testing (2) Mobile testing (3) Community-based testing (4) Facility-based testing (5) Point-of-care testing
Reasons for HIV testing	Reasons for undergoing HIV testing	(1) Know my status (2) Exposed to risk (3) Healthcare worker Recommendations (4) Regular medical check-up (5) Other (please specify)	(1) Know my status (2) Exposed to risk (3) Healthcare worker Recommendations (4) Regular medical check-up (5) Other (please specify)
Independent Variables
Socio-demographic
Age	Respondent’s age	Integer	(1) ≥18 (2) 20–24 (3) 25–29 (4) 30–34 (5) 35–39 (6) 40–44 (7) 45–49 (8) 50–54 (9) 55–59 (10) ≥60
Education	The educational level of the respondents	(1) No formal education (2) Basic education (3) Secondary education (4) Tertiary education	(1) No formal education (2) Basic education (3) Secondary education (4) Tertiary education
Occupation	Respondents employment status	(1) Unemployed (2) Self-employed (3) Government-employed (4) Private-sector employed	(1) Unemployed (2) Self-employed (3) Government-employed (4) Private-sector employed
Steady partner	Whether the respondent has a steady partner	(1) Yes (2) No (3) No response	(1) Yes (2) No
Behavioural factors	Behaviours that affect HIV testing	(1) HIV risk perception (2) HIV knowledge and attitude (3) HIV testing history (4) Knowledge of HIV status (5) Sexual practices and partnerships (6) Alcohol use (7) Physical abuse	“(1) HIV risk perception” “(2) HIV knowledge and attitude” “(3) HIV testing history” “(4) Knowledge of HIV status” “(5) Sexual practices and partnerships” “(6) Alcohol use” “(7) Physical abuse”
Structural factors	Structural factors that influence HIV testing	(1) Stigma (2) Discrimination (3) Healthcare accessibility (4) Economic factors (5) Policy Legislation (6) Cultural and Societal norms (7) Geographical factors	(1) Stigma (2) Discrimination (3) Healthcare accessibility (4) Economic factors (5) Policy Legislation (6) Cultural and Societal norms (7) Geographical factors

Source: Adapted from the GMS II questionnaire.

). For more detailed information about the sample and methodology, earlier publications from the GAC since 2017 should be consulted [20].

2.3. Reliability and Validity

The RDS technique that was employed is a widely adopted form of snowball sampling method to recruit participants through various social networks of existing respondents rather than from a predefined sampling frame. This method enhanced the reliability and validity of the findings by reaching hidden or hard-to-reach populations [21]. The sample size calculations were also based on validated tools and principles that were employed for the GMS I, ensuring the precision and accuracy of estimates. Furthermore, the staggered approach to recruitment across various regions involved a substantial number of seeds to achieve a high response rate, which contributed to the representativeness and reliability of the data. By incorporating socio-demographic and behavioural data, the study provided a valid assessment of HIV testing uptake among MSM in Ghana, ensuring that the findings were both reliable and applicable to the target population.

2.4. Method of Data Analysis

2.4.1. Descriptive Statistics

An exploratory analysis was conducted to identify and address data discrepancies. As part of data cleaning, duplicate entries of healthcare centres (based on matching names and locations) were removed, and facilities marked as permanently closed in the registry were excluded from further analysis to ensure that only operational centres were included. Extreme outliers, which are values falling far outside the expected range and not consistent with clinical plausibility or known patterns, were treated as missing data. These were also excluded from the analysis. This approach was taken because such outliers were likely due to data entry errors or measurement inaccuracies, and retaining them would have introduced bias into the analysis. Responses such as “declined to answer/do not know” were removed from the analysis. Categorical variables with a missing response rate of less than 5% were treated using the mode imputation technique, where each missing response was addressed with the frequently occurring response for that particular variable, while missing categorical variables with a missing response rate of more than 5% were excluded from the analysis. To summarise the dataset, descriptive analysis was conducted, reporting frequencies and proportions aggregated by socio-demographic characteristics, clinical indicators, and test results.

2.4.2. Bivariate and Multivariable Logistic Regression

Bivariate logistic regression was employed to examine the relationship between a single independent variable (e.g., age, education, or structural factors that influence HIV testing) and a binary outcome (HIV testing: yes or no). This method identifies crude associations (unadjusted effects) and serves as an initial step in screening variables for potential inclusion in multivariable models. On the other hand, multivariable logistic regression analysis was employed to assess the impact of several independent variables on the outcome, while controlling for potential confounding factors like age and geographic location. This method allows for the identification of independent predictors of HIV testing by considering the relationships among the variables.

The robustness of the estimations was assessed through sensitivity analysis by systematically varying key model parameters and assumptions such as sample weights, inclusion criteria, and prevalence estimates to observe the extent to which these changes affected the study outcomes. This approach helped to identify the stability of the results under alternative scenarios and ensured the reliability of the conclusions drawn. Data were broken down by socio-demographic factors, including age, race, geographic location, and whether individuals were part of the general or key population. Statistical significance was set at α = 0.05, and all reported results include odds ratios (ORs) and their corresponding 95% confidence intervals (CIs), indicating the magnitude and precision of each association. The statistical analysis was conducted using STATA version 17 (College Station, TX, USA). Additionally, geospatial mapping was conducted to evaluate the accessibility of HIV testing services for MSM, with the primary objective of identifying underserved regions and informing the development of targeted outreach strategies.

3. Results

3.1. Socio-Demographic Characteristics of the Study Participants

A total of 4095 MSM participated in the survey. The majority of participants (81.5%) were between the ages of 25 and 34 years, while a small proportion (1.1%) fell within the 18 to 24 years age group. Additionally, 17.4% of the participants were aged 35 years and older. Over half (52.9%) of the MSM surveyed reported having completed secondary education. In addition, 27.3% had completed junior high school, while 11.9% had attained tertiary education or higher (see

Table 2. Socio-demographic characteristics of the study participants.

Characteristic	HIV Testing Status		Overall	p-Value 2
	MSM Who Tested for HIV	MSM Who Did Not Test for HIV	n = 4095 1
	n = 2116 1	n = 1979 1
Age in years				<0.001
18–24	36 (1.7%)	9 (0.5%)	45 (1.1%)
25–34	1602 (75.7%)	1735 (87.7%)	3337 (81.5%)
35+	478 (22.6%)	235 (11.8%)	713 (17.4%)
Educational level				<0.001
Less than primary	87 (4.1%)	95 (4.8%)	182 (4.4%)
Primary school	51 (2.4%)	92 (4.6%)	143 (3.5%)
Junior High school	503 (23.8%)	615 (31.1%)	1118 (27.3%)
Secondary school	1150 (54.3%)	1015 (51.3%)	2165 (52.9%)
Tertiary or higher	325 (15.4%)	162 (8.2%)	487 (11.9%)
Income status				<0.001
No income	702 (33.2%)	829 (41.9%)	1531 (37.4%)
Low income	1019 (48.2%)	917 (46.3%)	1936 (47.3%)
Middle income	181 (8.5%)	123 (6.2%)	304 (7.4%)
High income	214 (10.1%)	110 (5.6%)	324 (7.9%)
Marital status				0.002
Single/Never Married	1971 (93.1%)	1881 (95%)	3852 (94.1%)
Married/living with a woman	102 (4.8%)	82 (4.1%)	184 (4.5%)
Widowed/Divorced/Separated	43 (2.0%)	16 (0.8%)	59 (1.4%)
Living status of participant				<0.001
Alone	992 (46.9%)	774 (39.1%)	1766 (43.1%)
Female sexual partner	112 (5.3%)	84 (4.3%)	196 (4.8%)
Male Sexual partner	57 (2.7%)	41 (2.1%)	98 (2.4%)
With other relatives	221 (10.4%)	248 (12.5%)	469 (11.5%)
With parents and/or siblings	732 (34.6%)	824 (41.6%)	1556 (38.0%)
Other	2 (0.1%)	8 (0.4%)	10 (0.2%)
Employment status				<0.001
Unemployed	799 (37.8%)	936 (47.3%)	1735 (42.4%)
Informal	467 (22.1%)	443 (22.4%)	910 (22.2%)
Formal	522 (24.7%)	331 (16.7%)	853 (20.8%)
Sex worker	18 (0.9%)	14 (0.7%)	32 (0.8%)
Other	310 (14.5%)	255 (11.4%)	565 (13.8%)
Religious affiliation				<0.001
Christian	1538 (72.7%)	1307 (66.0%)	2845 (69.5%)
Muslim	214 (10.1%)	314 (15.9%)	528 (12.9%)
Traditional	48 (2.3%)	49 (2.5%)	97 (2.4%)
Other	248 (11.7%)	226 (11.4%)	474 (11.5%)
No religion	68 (3.2%)	83 (4.2%)	151 (3.7%)

¹ n (%), ² Pearson’s Chi-squared test.

). More than 80% of the participants were classified as having either no income (37.4%) or falling within the low-income bracket (47.3%). The overwhelming majority of the participants (94.1%) were single or never married, and 81.1% lived either alone (43.1%) or with parents and/or siblings (38.0%). Approximately 42.4% of the MSM surveyed were unemployed. In terms of religious affiliation, 69.9% identified as Christians, followed by 12.9% who identified as Muslims. Regarding sexual orientation, over 80% of the MSM were either bisexual (45.5%) or gay (43.2%). All the sociodemographic factors considered in the study were significantly associated with HIV testing status, with statistical significance determined at an alpha level of 5% (Table 2).

3.2. Behavioural Practices of the MSM Population in Ghana

In a survey of 4095 MSM, almost two-thirds (65.0%) of the MSM had knowledge of HIV and a positive attitude towards its prevention. Among the gay individuals, 43.5% reported having tested for HIV, as against 42.9% who had not taken the HIV test. Bisexual individuals who reported having tested for HIV were also 46.3%, with only 1.2% of transgender individuals having taken the HIV test. (see

Table 3. Behavioural practices of the MSM population in Ghana.

Characteristic	HIV Testing Status		Overall	p-Value 2
	MSM Who Tested for HIV	MSM Who Did Not Test for HIV	n = 4095 1
	n = 2104 1	n = 1991 1
HIV knowledge and attitude				<0.001
Yes	1464 (69.0%)	1192 (60.0%)	2656 (65.0%)
No	646 (31.0%)	793 (40.0%)	1439 (35.0%)
Sexual orientation				<0.001 *
Gay	920 (43.5%)	850 (42.9%)	1770 (43.2%)
Bisexual	980 (46.3%)	883 (44.6%)	1863 (45.5%)
Straight	190 (9.0%)	239 (12.1%)	429 (10.5%)
Transgender	26 (1.2%)	7 (0.4%)	33 (0.8%)
Alcohol use				<0.001
Abstainers	1492 (70.9%)	1565 (78.6%)	3057 (74.7%)
Low risk-Light drinker	484 (23.0%)	322 (16.1%)	806 (19.7%)
Moderate drinker	85 (4.1%)	67 (3.4%)	152 (3.7%)
High risk/Harmful Drinker	43 (2.0%)	37 (1.9%)	80 (1.9%)

¹ n (%), ² Pearson’s Chi-squared test; * Fisher’s exact test.

).

Additionally, about seventy-five percent (74.7%) of the surveyed population reported abstaining from alcohol, and nearly twenty percent (19.7%) classified themselves as low-risk light drinkers (Table 3).

3.3. Sexual Practices of the MSM Study Participants

Based on the sexual practices of the respondents, a small percentage (6.4%) reported experiencing abuse at least once in the past twelve months, which included being spat on, slapped, or forced to engage in sex. As shown in

Table 4. Sexual Practices of the MSM study participants.

Characteristic	HIV Testing Status		Overall	p-Value 2
	MSM Who Tested for HIV	MSM Who Did Not Test for HIV	n = 4095 1
	n = 2123 1	n = 1972 1
In the last 12 months, how many times have you been spat on, slapped or force you to have sex with				<0.001
No times, did not happen	1911 (90.0%)	1862 (94.4%)	3773 (92.1%)
One or more times	171 (8.1%)	90 (4.6%)	261 (6.4%)
Decline to answer	41 (1.9%)	20 (1.0%)	61 (1.5%)
During your first sexual encounter with another man, were you forced or coerced to have sex with this male partner?				<0.001
Yes	408 (19.2%)	370 (18.7%)	778 (19.1%)
No	1668 (78.6%)	1577 (80.0%)	3245 (79.2%)
Don’t know	32 (1.5%)	6 (0.3%)	38 (0.9%)
Decline to answer	15 (0.7%)	19 (1.0%)	34 (0.8%)
The last time you had sex with your main/regular male partner, did you use a condom?				<0.001
Yes	1328 (62.6%)	995 (50.5%)	2323 (56.7%)
No	480 (22.6%)	678 (34.3%)	1158 (28.3%)
I don’t have a male/regular partner	283 (13.3%)	278 (14.1%)	561 (13.7%)
Don’t know	21 (1.0%)	9 (0.5%)	30 (0.7%)
Decline to answer	11 (0.5%)	12 (0.6%)	23 (0.6%)
The last time you had sex with a man who you received money from in exchange for sex, did you use a condom?				<0.001 *
Yes	642 (30.2%)	478 (24.2%)	1120 (27.4%)
No	221 (10.4%)	345 (17.5%)	566 (13.8%)
I don’t have a man I received money from	1245 (58.7%)	1137 (57.7%)	2382 (58.1%)
Don’t know	0 (0.0%)	0 (0.0%)	0 (0.0%)
Decline to answer	15 (0.7%)	12 (0.6%)	27 (0.7%)
The last time you had sex with a man you gave money to in exchange for sex, did you use a condom?				<0.001 *
Yes	448 (21.1%)	275 (13.9%)	723 (17.7%)
No	190 (8.9%)	232 (11.8%)	422 (10.3%)
I don’t have a man I gave money	1460 (68.8%)	1453 (73.7%)	2913 (71.1%)
Don’t know	0 (0.0%)	0 (0.0%)	0 (0.0%)
Decline to answer	25 (1.2%)	12 (0.6%)	37 (0.9%)

¹ n (%), ² Pearson’s Chi-squared test; * Fisher’s exact test.

, nearly one-fifth of participants reported being coerced or forced into sex during their first sexual encounter with another man. More than half (56.7%) of MSM reported using a condom during their most recent sexual act with a regular male partner. Almost one-third (27.4%) of MSM who received payment for sex used a condom, while approximately 18% of MSM who paid for sex also used a condom. All the factors related to sexual practices were found to be associated with HIV testing status, and these associations were statistically significant at 5% significance level.

3.4. Structural and Clinical Characteristics of the MSM Study Participants

Table 5. Structural and clinical characteristics of the MSM study participants.

Characteristic	HIV Testing Status		Overall	p-Value 2
	MSM Who Tested for HIV	MSM Who Did Not Test for HIV	n = 4095 1
	n = 2129 1	n = 1966 1
Refused health service because of being MSM				<0.001
None	2057 (96.6%)	1934 (98.4%)	3991 (97.5%)
Discriminated	72 (3.4%)	32 (1.6%)	104 (2.5%)
Refused employment service because of being MSM				0.009
None	2044 (96.0%)	1916 (97.5%)	3960 (96.7%)
Discriminated	85 (4.0%)	50 (2.5%)	135 (3.3%)
Refused education service because of being MSM				0.001
None	2038 (96%)	1918 (98%)	3956 (97%)
Discriminated	91 (4.3%)	48 (2.4%)	139 (3.4%)
Refused religious/church service because of being MSM				<0.001
None	2059 (97%)	1941 (99%)	4000 (98%)
Discriminated	70 (3.3%)	25 (1.3%)	95 (2.3%)
Refused restaurant/bar service because of being MSM				<0.001
None	2049 (96%)	1937 (99%)	3986 (97%)
Discriminated	80 (3.8%)	29 (1.5%)	109 (2.7%)
Refused housing service because of being MSM				<0.001
None	2036 (96%)	1932 (98%)	3968 (97%)
Discriminated	93 (4.4%)	34 (1.7%)	127 (3.1%)
Refused Police service because of being MSM				<0.001
None	2063 (97%)	1940 (99%)	4003 (98%)
Discriminated	66 (3.1%)	26 (1.3%)	92 (2.2%)
Received HIV testing and counselling at health facility in the last 12 months				<0.001
Yes	1682 (80.0%)	31 (1.6%)	1713 (41.8%)
No	411 (19.5%)	137 (6.9%)	548 (13.4%)
Don’t know	8 (0.4%)	498 (25.0%)	506 (12.4%)
Decline to answer	3 (0.1%)	1325 (66.5%)	1328 (32.4%)

¹ n (%), ² Pearson’s Chi-squared test.

presents the findings from a survey conducted among MSM to explore the structural and clinical factors associated with HIV testing. The results indicated that nearly three percent of MSM reported being denied services, including healthcare (2.5%) and dining at restaurants or bars (2.7%). Additionally, just over three percent experienced discrimination in various areas: employment (3.3%), education (3.4%), and housing (3.1%). More than two percent encountered discrimination in religious or church settings (2.3%) and by law enforcement (2.2%). All the structural and clinical factors examined were significantly associated with HIV testing status at a statistical significance level of 5%.

3.5. Associated Factors of HIV Testing Among MSM in Ghana

The analysis of factors associated with HIV testing among men who have sex with men (MSM) in Ghana revealed a number of statistically significant and policy-relevant findings. Age emerged as a key socio-demographic determinant of testing behaviour. MSM aged 25 to 34 years were significantly more likely to have ever tested for HIV compared to those aged 18 to 24 years (Adjusted Odds Ratio [AOR]: 1.43; 95% Confidence Interval [CI]: 1.18–1.74; p < 0.001), indicating a possible increase in risk perception or health-seeking behaviour among those in their mid-twenties to early thirties. However, the association was not statistically significant for those aged 35 years and above after adjusting for other covariates (AOR: 1.36; 95% CI: 0.82–2.27; p = 0.238), suggesting that older age alone does not independently predict higher testing rates in this population.

Educational attainment was another strong predictor of HIV testing. MSM with tertiary or higher education had significantly increased odds of having tested for HIV compared to those with less than primary education (AOR: 2.03; 95% CI: 1.17–3.55; p = 0.012). Similarly, individuals with senior high school education were more likely to have tested (AOR: 1.69; 95% CI: 1.02–2.80; p = 0.040). These results reinforce existing evidence that education enhances health literacy, risk perception, and engagement with preventive services. In contrast, marital status did not show any statistically significant association with HIV testing in the adjusted model, indicating that relationship status may not be a key factor in testing decisions among MSM in this context.

Religious affiliation, however, demonstrated a notable influence. Muslim MSM were significantly less likely to have tested for HIV compared to their Christian counterparts (AOR: 0.69; 95% CI: 0.54–0.90; p = 0.005), suggesting that religious and cultural contexts may shape attitudes toward HIV and healthcare utilisation. No statistically significant associations were observed among participants who identified with traditional religions, other faiths, or reported no religious affiliation.

In terms of sexual behaviour, the nature of sexual roles also affected testing behaviour. MSM who reported engaging exclusively in insertive anal sex had significantly lower odds of testing compared to those with a versatile role (AOR: 0.75; 95% CI: 0.62–0.91; p = 0.004), possibly due to a perceived lower risk of acquiring HIV. This highlights the role of risk perception in influencing engagement with testing services. Transactional sex was another influential factor: MSM who did not sell sex had significantly lower odds of testing compared to those who did engage in sex work (AOR: 0.67; 95% CI: 0.50–0.90; p = 0.007). This may reflect greater exposure to targeted interventions or health programmes among individuals involved in commercial sex.

Alcohol consumption also showed interesting patterns. Light alcohol drinkers were significantly more likely to have tested for HIV than those who abstained (AOR: 1.28; 95% CI: 1.04–1.58; p = 0.020). However, no significant associations were observed for moderate or heavy drinkers, suggesting a complex and potentially non-linear relationship between substance use and health-seeking behaviours that warrants further investigation.

HIV-related knowledge was a strong and consistent predictor of HIV testing. Participants who demonstrated awareness and understanding of HIV were significantly more likely to have been tested compared to those lacking such knowledge (AOR: 1.50; 95% CI: 1.26–1.78; p < 0.001). This finding reinforces the importance of health education and information dissemination in promoting testing behaviour among MSM. Additionally, condom use during the last sexual encounter with a regular male partner was positively associated with HIV testing. Those who did not use condoms were significantly less likely to have been tested (AOR: 0.57; 95% CI: 0.47–0.70; p < 0.001), suggesting that safer sex practices may go hand-in-hand with greater engagement in HIV prevention services.

Previous HIV test results played a significant role in shaping future testing behaviour. MSM who had previously tested positive for HIV were markedly less likely to engage in further testing compared to those who received a negative result (AOR: 0.40; 95% CI: 0.31–0.51; p < 0.001). This pattern points to a potential gap in follow-up engagement and continuity of care among individuals already diagnosed with HIV. It underscores the need for interventions that support ongoing engagement with HIV services—not only for those at risk, but also for those already living with the virus. These findings offer critical insights into the multifaceted determinants of HIV testing among MSM in Ghana. They highlight opportunities for targeted interventions that address both individual and structural barriers to testing and suggest pathways for strengthening HIV prevention and care strategies within this key population (

Table 6. Factors associated with HIV testing among MSM in Ghana.

Variables		Bivariate Analysis		Multivariable Analysis
	% (n)	Unadjusted Odds Ratio (95% CI)	p-Value	Adjusted Odds Ratio (95% CI)	p-Value
Age category, yes
18–24	64.39 (1779)	REF		REF
25–34	31.99(884)	1.93 (1.63–2.27)	<0.001	1.43 (1.18–1.74)	<0.001
35+	3.62 (100)	2.08 (1.36–3.18)	0.001	1.36 (0.82–2.27)	0.238
Educational level
Less than primary	2.71 (75)	REF		REF
Primary school	3.11 (86)	0.88 (0.47–1.66)	0.702	0.72 (0.37–1.41)	0.339
Junior High school	26.82 (741)	1.27 (0.78–2.06)	0.330	1.32 (0.79–2.19)	0.293
Senior High school	55.23 (1526)	1.77 (1.10–2.83)	0.018	1.69 (1.02–2.80)	0.040
Tertiary or higher	12.12 (335)	3.24 (1.94–5.43)	<0.001	2.03 (1.17–3.55)	0.012
Marital status
Married/living with a woman	4.63 (128)	REF		REF
Single/Never Married	94.03 (2598)	0.66 (0.46–0.95)	0.027	0.85 (0.56–1.29)	0.454
Widowed/Divorced/Separated	1.34 (37)	2.10 (0.89–4.98)	0.091	1.82 (0.72–4.58)	0.204
Employment
Unemployed	40.72 (1125)	REF		REF
Employed	44.55 (1231)	1.58 (1.34–1.86)	<0.001	1.12 (0.85–1.48)	0.415
Sex worker	14.73 (407)	1.31 (1.05–1.65)	0.019	1.07 (0.79–1.44)	0.679
Religion
Christianity	71.88 (1986)	REF		REF
Muslim	11.98 (331)	0.59 (0.47–0.75)	<0.001	0.69 (0.54–0.90)	0.005
Traditional	1.81 (50)	0.83 (0.48–1.46)	0.527	0.88 (0.48–1.64)	0.697
Other	11.00 (304)	0.84 (0.66–1.08)	0.171	0.89 (0.68–1.15)	0.360
No religion	3.33 (92)	0.65 (0.43–0.98)	0.042	0.80 (0.51–1.25)	0.323
Income status (GHS)
No Income	35.36 (977)	REF		REF
Low Income (1–599 cedis/month)	48.82 (1349)	1.31 (1.11–1.54)	0.002	1.14 (0.88–1.49)	0.319
Middle Income (600–999 cedis/month)	7.46 (206)	1.82 (1.34–2.48)	<0.001	1.12 (0.75–1.68)	0.576
High come (≥1000 cedis/month)	8.36 (231)	2.45 (1.80–3.33)	<0.001	1.42 (0.94–2.12)	0.093
Sexual orientation
Gay	40.83 (1228)	REF		REF
Bisexual	48.61 (1343)	0.99(0. 84–1.16)	0.883	1.05 (0.88–1.26)	0.580
Transgender	0.22 (6)	4.11 (0.48–35.30)	0.198	3.44 (0.36–33.19)	0.286
Other	10.35 (286)	0.75 (0.57–0.97)	0.027	0.87 (0.66–1.16)	0.352
Type of anal sexual intercourse
Versatile sex	29.93 (827)	REF		REF
Receptive sex	23.42 (647)	1.08 (0.88–1.33)	0.460	1.02 (0.81–1.29)	0.845
Insertive sex	46.65 (1286)	0.72 (0.60–0.86)	<0.001	0.75 (0.62–0.91)	0.004
Bought sex from a male in the past six months
Yes	19.00 (525)	REF		REF
No	10.13 (280)	0.42 (0.31–0.56)	<0.001	0.74 (0.52–1.05)	0.087
Do not buy sex	70.87 (1958)	0.60 (0.50–0.73)	<0.001	0.86 (0.67–1.09)	0.198
Sold sex to a male in the past six months
Yes	29.64 (819)	REF		REF
No	13.86 (383)	0.44 (0.34–0.57)	<0.001	0.67 (0.50–0.90)	0.007
Do not sell sex	56.50 (1561)	0.80 (0.68–0.96)	0.013	0.83 (0.68–1.01)	0.068
Alcohol intake
Abstainers	74.05 (2046)	REF		REF
Light drinkers	20.05 (554)	1.54 (1.27–1.87)	<0.001	1.28 (1.04–1.58)	0.020
Moderate drinkers	4.02 (111)	1.14 (0.78–1.68)	0.499	1.07 (0.71–1.63)	0.735
Heavy drinkers	1.88 (52)	1.28 (0.73–2.23)	0.391	1.18 (0.65–2.17)	0.583
Knowledge of HIV
No	33.26 (919)	REF		REF
Yes	66.74 (1844)	1.46 (1.24–1.71)	<0.001	1.50 (1.26–1.78)	<0.001
Condom use at last sex with regular/main male partner
Yes	60.98 (1685)	REF		REF
No	28.59 (790)	0.48 (0.40–0.57)	<0.001	0.57 (0.47–0.70)	<0.001
Do not have a main/regular male partner	10.42 (288)	0.86 (0.67–1.10)	0.233	0.88 (0.67–1.16)	0.370
HIV test results
Negative	83.24 (2300)	REF		REF
Positive	16.76 (463)	0.33 (0.26–0.41)	<0.001	0.40 (0.31–0.51)	<0.001
Number of times forced to have sex within the past 12 months
No history of forced sex	97.14 (2684)	REF
One or more times	2.86 (79)	1.57 (0.98–2.50)	0.059

).

4. Discussion

Understanding the factors influencing HIV testing among MSM is essential for improving prevention and intervention strategies. Socio-demographic characteristics such as age, education, income level, and marital status significantly shape testing behaviours, with younger, less educated, and lower-income individuals often exhibiting lower testing rates. Behavioural factors, including HIV knowledge, perceived risk, and sexual practices, also play a crucial role, as misconceptions about risk and experiences of stigma can deter testing. Additionally, structural barriers, such as discrimination in healthcare settings, further limit access to testing services. This section explores the interplay of these factors and their implications for public health interventions aimed at increasing HIV testing uptake among MSM.

4.1. Socio-Demographic Factors Associated with HIV Testing Among MSM

Understanding the socio-demographic factors associated with HIV testing among MSM is critical to informing effective public health strategies aimed at reducing HIV transmission and improving prevention outcomes. In the present study involving 4095 MSM across Ghana, the majority of participants were between the ages of 25 and 34 years, with a significant proportion reporting unemployment and low income. These findings mirror broader trends observed in the literature, which consistently highlight the influence of age, educational attainment, and socio-economic status on HIV testing behaviour within key populations.

Age has been widely recognised as a key determinant of HIV testing, with younger MSM often less likely to access testing services compared to their older counterparts [22,23]. The predominance of participants in the 25–34 age category in this study reinforces this observation and suggests a transitional stage in the life course where individuals may become more aware of health risks and seek preventive care more actively. Tailoring testing interventions to address the unique needs of both younger and older MSM could enhance overall testing coverage and improve early diagnosis rates.

Educational attainment also emerged as a critical factor. MSM with secondary or tertiary education were significantly more likely to engage in HIV testing, supporting previous research that links higher education levels with increased health literacy, better risk perception, and proactive health-seeking behaviours [24,25]. In this study, over half of the participants had completed at least secondary education, indicating that education-based interventions could serve as a powerful lever to improve testing uptake among MSM. Educational initiatives that incorporate accurate information about HIV transmission, prevention, and treatment may help empower MSM to make informed health decisions.

Economic factors further compounded disparities in testing behaviour. The results showed that more than 80% of participants were either unemployed or belonged to the low-income bracket, echoing findings from earlier studies, which suggest that financial instability can significantly hinder access to healthcare services, including HIV testing [26,27]. Unstable income often limits access to transport, time off work, or health insurance, thereby creating practical and psychological barriers to seeking testing services. These constraints may lead to delayed diagnosis, lower engagement with HIV services, and an increased risk of onward transmission. As such, programmes aiming to increase testing among MSM must consider integrating economic support components or offering free, community-based services to reduce cost-related barriers.

Marital status also revealed insights into the social contexts influencing testing. The majority of participants identified as single or never married. This demographic profile may affect the extent of social support available to MSM, which in turn impacts health-seeking behaviours. Research suggests that supportive social networks can positively influence HIV testing uptake, especially among single individuals who may lack the structural and emotional support typically provided by a spouse or family [28]. However, the stigma associated with being MSM—particularly in conservative cultural and social environments—can lead to social isolation and internalised fear, further deterring individuals from accessing HIV testing services [29].

Religious affiliation was another important socio-demographic dimension. A substantial proportion of participants identified as Christian, which reflects the general religious landscape of Ghana. Previous studies have shown that religious beliefs can shape attitudes toward sexuality, health, and HIV testing [30]. While faith communities can be sources of support, they can also perpetuate stigma, especially when dominant doctrines are intolerant of same-sex relationships. The influence of religious teachings and norms may discourage open discussions about sexual health and reduce the likelihood of testing, particularly among those who fear judgement or exclusion.

Taken together, these socio-demographic insights underscore the complexity of factors influencing HIV testing behaviours among MSM in Ghana. They point to the need for multifaceted, culturally sensitive interventions that address the intersecting roles of age, education, income, marital status, and religion. By tailoring HIV prevention and testing strategies to accommodate these contextual realities, public health efforts can more effectively engage MSM, reduce testing disparities, and contribute to the broader goal of ending the HIV epidemic.

4.2. Bahavioural Factors Affecting HIV Testing Among MSM

The behavioural factors associated with HIV testing among MSM are multifaceted, encompassing knowledge, attitudes, sexual practices, and structural barriers. A significant proportion of MSM (95.1%) in a recent survey reported awareness of HIV/AIDS, yet only 28.9% knew their HIV status, highlighting a gap between awareness and testing behaviour [31]. This discrepancy is further illustrated by the finding that nearly half (49.0%) of the MSM surveyed believed they were not at risk for HIV infection, which aligns with previous studies indicating that perceived low risk is a major barrier to HIV testing [32,33]. The relationship between knowledge and testing behaviour is critical. As such, higher levels of HIV knowledge are associated with increased willingness to undergo testing, as noted in studies that emphasise the importance of targeted education for MSM [34].

Sexual practices also play a crucial role in influencing HIV testing behaviours. The survey revealed that a significant number of MSM engaged in risky sexual behaviours, with only 56.7% reporting condom use during their last sexual encounter with a regular male partner [31]. This is consistent with findings from other studies that show a correlation between unprotected anal intercourse and increased HIV risk, underscoring the need for comprehensive sexual health education [35,36]. Furthermore, experiences of sexual coercion were reported by a notable minority of participants, which may further complicate their willingness to seek testing [33]. The stigma surrounding HIV and the fear of discrimination in healthcare settings can deter MSM from getting tested, as evidenced by research indicating that negative social experiences significantly influence testing behaviours [10,37].

Structural and clinical factors also significantly impact HIV testing among MSM. The survey indicated that a small percentage of MSM faced discrimination in various services, including healthcare, which can create barriers to accessing testing [31]. Previous studies have shown that structural interventions, such as increasing the availability of testing sites and ensuring confidentiality, are essential for improving testing rates among MSM [38,39]. Moreover, the role of peer support and community-based organisations has been highlighted as a facilitator for HIV testing, as they can provide a more comfortable environment for MSM to access services without fear of stigma [39,40]. Addressing these factors through targeted education, supportive community interventions, and structural changes in healthcare delivery is crucial for improving testing rates and ultimately reducing HIV transmission among this vulnerable population.

4.3. Relationships Between Factors Influencing HIV Testing Among MSM in Ghana

Understanding the multifactorial determinants of HIV testing among MSM in Ghana is essential for developing effective strategies to address the disproportionately high burden of HIV in this key population. This study presents a comprehensive analysis of socio-demographic, behavioural, and structural factors associated with HIV testing uptake among MSM, using data from a nationally representative sample. The findings underscore the interplay of age, education, religion, sexual behaviour, and health knowledge in shaping testing behaviours, and point to critical gaps and opportunities for targeted intervention.

Age emerged as a significant determinant of HIV testing, with MSM aged 25 to 34 years exhibiting significantly higher odds of testing compared to their younger counterparts aged 18 to 24 years (AOR: 1.43; 95% CI: 1.18–1.74; p < 0.001). This pattern may reflect greater risk perception, increased sexual activity, or more exposure to health promotion interventions in the 25–34 age bracket. Younger MSM often face unique challenges such as limited access to health information, financial dependence, and heightened fear of disclosure, all of which can deter engagement with health services [41]. Interestingly, the study found no statistically significant increase in testing among MSM aged 35 years and above (AOR: 1.36; 95% CI: 0.82–2.27; p = 0.238), suggesting that older age does not necessarily correlate with better health-seeking behaviour. This nonlinear association points to the need for age-specific interventions that address both the hesitancy of younger individuals and the potential complacency or systemic barriers faced by older adults.

Education was one of the most powerful predictors of HIV testing. MSM with tertiary-level education were more than twice as likely to have been tested compared to those with less than primary education (AOR: 2.03; 95% CI: 1.17–3.55; p = 0.012). Those with senior high school education also had significantly higher odds of testing (AOR: 1.69; 95% CI: 1.02–2.80; p = 0.040). These findings align with global literature demonstrating that higher educational attainment enhances health literacy, increases awareness of HIV transmission routes, and reduces susceptibility to misinformation [24,42,43]. Educated MSM may also have greater access to health services, increased self-efficacy, and more social capital to navigate systems of care. These insights underscore the need for integrating HIV education into school curricula and designing accessible community-based awareness programmes for low-literacy populations.

Religious affiliation significantly influenced HIV testing behaviours. Muslim MSM were notably less likely to test than their Christian counterparts (AOR: 0.69; 95% CI: 0.54–0.90; p = 0.005). This disparity likely reflects sociocultural and religious norms that shape perceptions of sexuality and health. In many conservative religious communities, homosexuality is stigmatised, often viewed as immoral or deviant. This internalised stigma, coupled with fear of social ostracism or divine condemnation, may prevent Muslim MSM from disclosing their sexuality or seeking HIV-related services [41]. On the other hand, Christian institutions in Ghana—while also conservative—may offer more engagement through faith-based health services, particularly those partnered with NGOs. These results suggest that religious leaders and organisations should be engaged in sensitization efforts to reduce stigma and promote inclusive messaging around sexual health and HIV testing.

Sexual behaviour, especially sexual positioning, influenced HIV testing decisions. MSM who engaged exclusively in insertive anal sex had significantly lower odds of testing compared to those who assumed versatile sexual roles (AOR: 0.75; 95% CI: 0.62–0.91; p = 0.004). This difference likely stems from lower perceived risk, as insertive partners are generally at reduced biological risk of HIV acquisition compared to receptive partners. However, the risk is not eliminated, and false confidence may lead to reduced testing. This finding highlights the importance of targeted health communication that explains the relative risks of different sexual roles and encourages routine testing regardless of sexual positioning [43].

Engagement in sex work appeared to correlate with higher HIV testing rates. MSM who did not sell sex were less likely to test than those who did (AOR: 0.67; 95% CI: 0.50–0.90; p = 0.007). MSM sex workers may have better access to health services through targeted outreach programmes, NGO support, or peer networks. Additionally, financial dependence on sex work may increase awareness of risk and the perceived importance of maintaining one’s health. This finding suggests that health systems should expand the reach of successful sex worker-focused interventions to MSM outside the transactional sex sphere, who may lack similar access to supportive services.

Alcohol consumption revealed a nuanced relationship with HIV testing. Light alcohol users had significantly higher odds of testing compared to abstainers (AOR: 1.28; 95% CI: 1.04–1.58; p = 0.020). While heavy alcohol use is often associated with risky sexual behaviour and poor health-seeking, light drinking may reflect greater social integration and exposure to health messaging in social settings. However, no significant association was observed for moderate or heavy drinkers, suggesting that excessive alcohol use may dampen health engagement or impair decision-making, pointing to the need for harm-reduction approaches integrated into HIV prevention.

HIV-related knowledge was a strong facilitator of testing behaviour. Participants with accurate knowledge about HIV transmission, prevention, and treatment had significantly higher odds of undergoing testing (AOR: 1.50; 95% CI: 1.26–1.78; p < 0.001). This supports a wealth of evidence showing that informed individuals are more likely to engage in health-promoting behaviours, including regular testing, condom use, and PrEP adherence [44,45]. In contrast, lack of knowledge perpetuates myths, stigma, and fear, which are key barriers to testing [46]. Thus, investments in comprehensive, MSM-sensitive health education are crucial to scaling up HIV testing in this population.

An unexpected finding was the inverse relationship between condom use and HIV testing. Participants who reported using condoms during their last sexual encounter were less likely to test for HIV (AOR: 0.57; 95% CI: 0.47–0.70; p < 0.001). This may indicate a false sense of security among those who perceive consistent condom use as a substitute for testing. While condoms are effective in reducing transmission, no prevention method is foolproof, and routine testing remains essential. This insight calls for reinforcing dual prevention messaging—encouraging both condom use and regular testing regardless of perceived protection.

Prior HIV test results significantly influenced subsequent testing behaviour. MSM who had previously tested positive were substantially less likely to test again compared to those with negative results (AOR: 0.40; 95% CI: 0.31–0.51; p < 0.001). This finding reveals a potential gap in retention and continuity of care for people living with HIV. Barriers such as fear of disclosure, stigma, mental health challenges, or dissatisfaction with services may lead to disengagement. It underscores the urgent need for supportive interventions such as peer navigation, ongoing counselling, and patient-centred care models to ensure sustained engagement with HIV services [44,45].

4.4. Strengths and Limitations of the Study

This study presents several notable strengths that bolster its credibility, relevance, and overall contribution to the field of HIV prevention and sexual health research among MSM in sub-Saharan Africa. One of the most significant strengths is the utilisation of data from the GMS II, a nationally representative and methodologically rigorous dataset. The GMS II captures a comprehensive range of socio-demographic, behavioural, and structural indicators pertinent to the lived experiences of MSM, a population that remains severely underrepresented in public health research within the region. The breadth and quality of the dataset allow for in-depth analysis of complex interrelated factors influencing HIV testing behaviour.

A further methodological strength lies in the study’s use of RDS, a technique specifically designed to reach “hidden” and marginalised populations. In contexts such as Ghana, where same-sex sexual relationships are criminalised and highly stigmatised, traditional sampling methods often fail to capture the realities of MSM communities. RDS helps to overcome these barriers by leveraging peer networks for participant recruitment, thereby improving the inclusivity and representativeness of the sample. This approach enhances the external validity of the findings and ensures that the voices of individuals who are often excluded from mainstream health surveys are meaningfully included.

In addition, the study adopts a theoretically grounded and multi-method approach, combining quantitative secondary data analysis with qualitative insights drawn from programme implementers and field practitioners. This integration of methods enhances the interpretative richness of the findings, facilitating a more nuanced understanding of the issues at hand. By situating statistical trends within real-world operational contexts, the study not only strengthens the validity of its conclusions but also improves their practical applicability. Such a comprehensive design increases the study’s relevance to both academic audiences and policy stakeholders.

Importantly, the study examines HIV testing uptake through a multidimensional lens, acknowledging both individual-level determinants (such as age, education, and sexual behaviour) and broader structural influences (such as religious affiliation, stigma, and economic precarity). This holistic framing aligns with current public health thinking, which recognises that health behaviours are shaped not only by personal choices but also by socio-political and cultural environments. By incorporating this layered perspective, the study contributes meaningfully to the literature on structural vulnerability and health equity. It also provides concrete, context-sensitive recommendations that can be used to inform programme design, advocacy efforts, and national policy formulation aimed at improving HIV service delivery for MSM.

Nonetheless, the study is not without limitations. First, it relies heavily on self-reported data, which may be subject to recall bias and social desirability bias, particularly given the sensitive nature of the topics discussed, such as sexual behaviour, HIV status, and engagement with health services. Participants may underreport high-risk behaviours or overreport desirable practices such as condom use and testing frequency. While this is a common limitation in behavioural research, it must be considered when interpreting the results.

Secondly, the dataset includes only biologically male individuals who self-identified as MSM and consented to participate. As a result, it may not fully capture the experiences of transgender, non-binary, or gender-diverse persons who also engage in same-sex sexual activity but may not identify with the MSM label or may be less willing to disclose their identity in a hostile legal and social climate. This limits the scope of the findings and highlights the need for more inclusive research frameworks that address the full spectrum of gender and sexual diversity.

Thirdly, the associations do not imply causality due to the cross-sectional study design employed by the GAC in completing the original survey. Hence, future studies should apply longitudinal designs to establish strong associations between the explanatory and outcome variables.

Finally, although the study’s findings are highly relevant within the Ghanaian context, their generalisability to other countries in sub-Saharan Africa may be limited. Legal frameworks, cultural norms, health system capacities, and the visibility of LGBTQ+ communities vary considerably across the region. As such, caution must be exercised in extrapolating these findings to other settings without accounting for contextual differences.

5. Conclusions

This study highlights critical socio-demographic, behavioural, and structural factors influencing HIV testing among MSM in Ghana. Despite relatively high awareness of HIV, significant gaps persist in testing uptake, with only 51.6% of participants having tested within the last year and 28.9% knowing their status. Many of the findings, for example, age, education, religion, transactional sex, alcohol use, condom use, and comprehensive knowledge of HIV, are significant predictors of HIV testing among MSM. Alarmingly, 31.5% of participants who tested were HIV-positive, underscoring the urgent need for targeted interventions. Structural barriers such as stigma and discrimination affect MSM in terms of accessing healthcare, which in turn affects HIV testing efforts. Behavioural gaps, such as low condom use in transactional sex and misperceptions of HIV risk, exacerbate vulnerabilities within this population. These findings underscore the need for comprehensive, context-specific strategies to increase testing rates. Efforts should focus on reducing stigma, addressing structural barriers, and enhancing targeted education and outreach programmes. Expanding access to community-based testing and supportive counselling services is vital to bridge the gaps in testing uptake and ensure timely linkage to care for MSM in Ghana.