Predictors of HIV status disclosure among PLHIV in the Volta region of Ghana: the disclosure conundrum and its policy implications.

doi:10.21203/rs.3.rs-3231475/v1

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Predictors of HIV status disclosure among PLHIV in the Volta region of Ghana: the disclosure conundrum and its policy implications.

https://doi.org/10.21203/rs.3.rs-3231475/v1

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Background

Globally, over 40 million lives have been claimed by HIV/AIDS. In Ghana, more than 350,000 people are currently living with HIV with 66% of them being females; out of this number, 71% know their status and 99% of the people diagnosed with HIV in Ghana received sustained antiretroviral therapy (ART), and 79% on ART have achieved viral suppression. Non-disclosure of HIV status is a major barrier to HIV/AIDS eradication; yet, little is known on the determinants of HIV status disclosure among the adult population of people living with HIV (PLHIV) in Ghana.

Objective

Determine the predictors of HIV status disclosure among the adult population of PLHIV and stimulate national policy discourse on support systems for self-disclosure in Ghana.

Methods

The study is a descriptive cross-sectional design among PLHIV (n = 181) in a tertiary hospital in the Volta region of Ghana. Bivariate analysis with Pearson Chi-square and Fisher’s Exact tests were run alongside stepwise forward logistic regression to determine factors associated with HIV status disclosure among PLHIV at 95% confidence level.

Results

HIV status disclosure was reported by 50% of the respondents; nearly 65% disclosed their status to non-family members. HIV status disclosure was significantly associated with marital status (p = 0.000), level of education (p = 0.035), divorce due to HIV (p = 0.000), monthly income (p = 0.019), occupation (p = 0.010) and partner being HIV positive. The odds of disclosing HIV status was higher among single/never married persons (OR = 0.012; p-value = 0.017, 95% [CI = 0.000 0.447), persons of Catholic denomination (OR = 0.060; p-value = 0.057, 95% [CI = 0.003 1.089), persons with monthly income of approximately (USD 44–88) (OR = 0.068; p-value = 0.011, 95% [CI = 0.009 0.543]) and persons adherent to ART (OR = 6.161; p-value = 0.058, 95% [CI = 0.937 40.490]).

Conclusions

HIV status disclosure is low in Ghana due to socio-economic and religious factors. There is the need for a national debate on policy guidelines for HIV status disclosure for PLHIV; targeted support systems for vulnerable groups within PLHIV would also help ameliorate their predicaments.

HIV/AIDS

status disclosure

antiretroviral therapy

people living with HIV

policy

LMICs

Volta Region

Ghana

According to the World Health Organization (WHO) (2023) latest update, over 40 million lives have so far been claimed by HIV/AIDS globally out of over 75 million infected persons. At the end of 2022 an estimated 39 million people were living with HIV and two thirds of those infected lived in the WHO-African region (WHO, 2023). Per the WHO 2025 target, 95% of all people living with HIV (PLHIV) should have a diagnosis, 95% of those diagnosed should be taking lifesaving antiretroviral treatment (ART) and 95% of PLHIV on treatment achieving a suppressed viral load.

However, the current global statistics stand at 86%, 76% and 71%, respectively. In Ghana, over 350,000 people are currently living with HIV with 66% of them being females (Ghana AIDS Commission, 2023); out of this number, 71% know their status and 99% of the people diagnosed with HIV in Ghana received sustained ART, and 79% on ART have achieved viral suppression (GAC, 2023). Notwithstanding the Government of Ghana (GoG) cumulative expenditure of over USD 500 million between 2017 and 2021 on HIV/AIDS, attainment of the SDG 3.3 target on HIV by Ghana remains a mirage, largely due to stigma and low tendency of HIV status disclosure, especially to sexual partners (Bajunirwe et al, 2018; Corless et al, 2017; Kim et al, 2018; Jewell et al, 2020).

In 2016, Ghana implemented the WHO roadmap, “90-90-90: Treatment for All” policy with the goal of achieving the 90-90-90 target by 2020 (GAC, 2019). Similarly, the “Test All” policy was initiated to improve universal access to quality HIV testing and treatment (GAC, 2019). These initiatives contributed to the increase in HIV testing, from 1 million in 2016 to 1.2 million in 2017.

The most effective way to reduce new infections and promote health for PLHIV is administration and strict adherence to ART, yet non-disclosure as a function of societal sigma continues to impede efforts towards eradication of the pandemic, especially in resource constrained countries in Africa, including Ghana.

Studies on HIV status disclosure show disclosure outcomes can be complex and could result in isolation and diminished social support by partners if not well managed and supported (Kimberly et al, 1995). Indeed, a recent study in Ghana found that ART adherence rate was paradoxically higher among “status non-disclosed” group than “status disclosed” group (Nutor et al, 2023).

Over the years, substantial number of studies have investigated outcomes of HIV status disclosure or otherwise in many African countries including Ghana (Agyarko-Poku et al, 2023; Kabriku et al, 2023; Obiri-Yeboah et al, 2016; Agbeko et al, 2022). However, little is known on the determinants of HIV status disclosure among the adult population of PLHIV, especially in the Volta region of Ghana where HIV/AIDS remains an important disease of public health concern. According the 2019 sentinel survey in Ghana, the Volta was among the top 11 regions with high HIV prevalence in the country, recording 1.5% and Ho (the study setting) recorded 1.8% higher than the regional average (NACP, 2019). This study therefore sought to investigate the predictors of HIV status disclosure among the adult population of PLHIV to inform national policy discourse on support systems for self-disclosure as a conduit for HIV/AIDS control in low-and-middle-income countries (LMICs) in Africa.

Study Design

The study is an observational descriptive cross-sectional design that employed survey approach to ascertain status disclosure and the predictors among PLHIV.

Setting

The study was conducted in the ART clinic of a tertiary referral hospital in Ho Municipal of the Volta region of Ghana. The Volta region is one of 16 administrative regions in Ghana and represents 6% of the estimated population of Ghana (UN Ghana, 2019). The region lies at the east coast of Ghana and shares boundaries with Togo to the east, Gulf of Guinea to the south, Eastern region to the west and Oti region to the north. The selected tertiary hospital is the only tertiary level health facility in the region with the highest number of ART clinic attendance covering large catchment area within the region, neighboring regions and some parts of Togo.

Study population

The study population was adult PLHIV aged 18 years and above who have ever attended the ART clinic at least once. As at 2017 the HIV prevalence rate among people aged 15–45 years in the Volta region was 1.6% (GAC, 2019). Likewise, the HIV population in the Volta region is estimated to be 26,416, representing 8.4% of the national HIV population of 313,063. In the same year, unfortunately 1,324 deaths from HIV/AIDS were recorded in the Volta region alone, representing 4.2% of the total deaths in Ghana (GAC, 2019).

Sampling procedure

The sampling strategy was a census of adult PLHIV enrolled in ART and regularly attended ART clinic of the tertiary referral hospital. Purposive sampling of the tertiary hospital gives a wholistic picture of ART services in the Volta region since most cases are referred to this facility within and outside the region. PLHIV who attended 100% of clinic visits and those who missed > 50% of their appointments in the past 6–12 months were identified by the ART clinic nurse-manager and recruited into the study after obtaining voluntary consent to participate; subsequently, eligible respondents were contacted via phone and scheduled for interviews at agreed venues deemed most confidential.

Sample size determination

Statistically, 60 patients per group (i.e. low and higher ART clinic attendant groups) was assumed to give the researchers the requisite statistical power, assuming a large effect size difference of 1.0 standard deviation units between the high and low ART clinic attendants groups. Study target population was the cumulative ART clinic attendance at the study health facility in the last six (6) months (N = 120).

Subsequently, a conservative α = 0.01 adjusted from .05 was used to account for between 5–10 potential comparisons using mean differences (t-tests) or categorical differences (Chi-square) in individual and community factors associated with ART service utilization. In a linear regression model to account for the variance in ART clinic attendance rates, we simulated a standard normal distribution and 100–120 patients (about 10 cases per variable) is assumed to provide adequate power to achieve statistical significance (α = 0.05) with 5–10 variables accounting for > 13% of the variance (Cohen’s medium effect size R²) in clinic attendance rates (Sawilowsky, 2009). Following the power calculation of the study, an over-sampling 61 respondents (representing 50%) was done to account for possible non-response or attrition due to the sensitive nature of the study. Thus, a final census of 181 participants was done for the study. Details of sample size and power calculations are published by the authors in Nutor et al (2023).

Inclusion criteria

PLHIV ≥ 18 years and enrolled in ART for at least 6 months, and identified by clinic staff as either a high or low clinic attendant were eligible for inclusion. Also, only persons who voluntarily consented to participate in the study were recruited into the study.

Exclusion criteria

PLHIV who were seriously ill and hospitalized in the last one month were excluded from participating. Likewise, minors were excluded and PLHIV who did not access care at the ART clinic of the study facility were exempted from the study.

Participants’ recruitment procedure

PLHIVs who met the inclusion criteria, with either 100% or < 50% clinic attendance were first identified and recruited by the study site nurse who was acquainted with the research protocol. Next, trained research assistant were blinded to the ART attendance history of the consented participants and interviewed. Next, snowball approach was used to recruit PLHIVs who were not regular attendants at the ART through the high attendance group who were more regular at the ART clinic.

Data Collection and Measures

An in-person survey was conducted among 181 PLHIV identified by clinic staff as either high rate (100%) or low rate (< 50%) clinic attendees. For optimal privacy, data collection in the form of one-on-one interviews were done in a private room in turns at a convenient location for the participants. Each interview lasted approximately 45 minutes. Convenient location is operationalized in this context to mean a place where there was sufficient privacy and the interviewee not exposed to public or third parties. Data collection was between 14th – 30th June, 2021.

Instruments of Data Collection

A structured validated questionnaire was developed and piloted for the survey. The tool comprised of eight (8) major sections namely: Demographics and Medical History; Depression; Internalized Stigma of AIDS Tool (ISAT); Attitudes toward ART; Perceived Beliefs about ART; ART Adherence, HIV status self-disclosure, and ART Services Utilization. For the purposes of this paper, only variables on the socio-demographic, economic, ART adherence, service utilization and status disclosure are analyzed and reported. Relevant sections of the survey tool used in this paper are explained below:

Demographics and Medical History

Age (continuous), sex (dichotomized), occupation (categorical), education (categorical), religion (categorical), and type of residence (dichotomized), urban) documented based on the Demographic Health Survey (ZDHS, 2013). Data on health insurance, monthly income, subjective self-wealth, housing and food security, and means of transportation to the ART clinic as well as estimated distance and cost of transportation was also collected. Additionally, date of HIV diagnosis, start date of ART, and co-morbid conditions (e.g., hypertension, obesity, diabetes, cancer, tuberculosis, COVID infection status) were recorded.

Attitudes toward ART

This section comprised of 11-item scale developed from the Theory of Planned Behavior (TPB) (Ajzen, 1985; Ajzen, 2002). It has 5-point Likert scales (1 = “strongly disagree” to 5 = “strongly agree”) and the scores ranges from 11 to 55. Attitude is considered ‘positive’ if the score is ≥ the median, and ‘negative’ if the score is < the median.

Perceived Beliefs about ART

This 4-item scale was also adapted from TPB (Ajzen, 1985; Ajzen, 2002). It has 5-point Likert scales (1 = “strongly disagree” to 5 = “strongly agree”) and scores range from 4 to 20. Belief is ‘positive’ if the score is ≥ the median and ‘negative’ if the score is < the median.

ART Adherence

Self-report adherence was estimated with the Center for Support Evaluation (CASE), which combines difficulty taking medications on time, average number of days per week at least one dose was missed, and last time at least one dose was missed (Mannheimer et al, 2006). Scores range from 3–16, and an index score of 10 or less indicates suboptimal adherence.

ART Services Utilization

This section comprised of five-point Likert scale for PLHIV to rate the quality of their ART services. Data on date of ART initiation, duration of ART, and comorbidities was collected. During the past year, number of out-patient department visits, number of ART clinic visits for routine monitoring, and number of hospital in-patient admissions for opportunistic infections or COVID-19 was also recorded. Finally, under this section, data on the number of diagnostic tests, number of antiretroviral drugs and non-antiretroviral drugs prescribed during the past year was collected.

Data analysis

The data collected was analysed with the STATA statistical analysis software (version 16.0) (StataCorp LLC 4905 Lakeway Drive College Station, Texas 77845 USA, 800-STATA-PC). All data sets were coded to anonymize the identify of respondents. Descriptive statistics were estimated in frequencies and percentages for categorical variables, and means/standard deviations for continuous variables. Cross-tabulation comparison of background information of respondents was done using Pearson Chi-square and Fisher’s Exact tests as appropriate.

Moreover, as part of regression modeling, pair-wise correlation test was conducted to ascertain the paired associations between HIV status disclosure tendencies and socio-demographic, economic, and ART adherence at 95% confidence level (see Supplementary File 1). Finally, step-wise forward logistic regression test was run to test the predictors of HIV status disclosure among PLHIV. The regression model was run in a forward fashion, beginning with an empty model and explanatory variables loaded progressively. Regression models’ output were reported in odds ratio and variables dropped automatically because of estimability. Multicollinearity diagnostics was conducted on all explanatory variables and those with Variance Inflation Factors (VIFs) above 10.0 were excluded from the regression models.

Outcome variable of interest

Main predictive variable in the step-wise logistic region was HIV status disclosure (binary outcome, yes = 1, no = 0).

Explanatory variables and co-variates

Model 1 (socio-demographic determinants): marital status (categorical), level of education (categorical), and divorced due to HIV status (categorical). Co-variates in the model were gender (male = 1, female = 0), residence (rural = 1, urban = 2, peri-urban = 3) and religion (protestant/Pentecostal = 1, catholic = 2, traditional/other = 3).

Model 1 equation: logit(p) = log(p/(1-p))= β0 + β1*marriage + β2*education + β3*divorce + β4*gender + β5*residence + β6*religion

Model 2 (economic determinants): monthly income (< GHC500 = 1, GHC501-1000 = 2, GHC1001-2000 = 3; ≥GHC2001 = 4) and occupation (categorical) as explanatory variables. Co-variables were perceived economic status (student = 1, private business = 2, government = 3, housewife = 4, farmer = 5, other = 6) and employed (yes = 1, no = 0).

Model 2 equation: logit(p) = log(p/(1-p))= β0 + β1*income + β2*occupation + β3*economy + β4*employment.

Model 3 (service utilization determinants): ART adherence (binary, yes = 1, no = 0). Co-variates are experience of ARVs side effect (yes = 1, no = 0), fear of side ARVs effects (yes = 1, no = 0) and place of ARVs refil (ART clinic = 1, health centre = 2).

Model 3 equation: logit(p) = log(p/(1-p))= β0 + β1*adherence + β2*side_effect + β3*ARVs_fear + β4*ARVs_refil.

Validity and reliability of test instruments

All survey tools were validated and piloted with five respondents outside the study sites to avoid sensitizing participants to the tool ahead of the actual data collection. There was also an expert panel review of the tools guided by relevant literature. Computation of internal consistency reliability of the Likert’s scale items was done with a Cronbach’s alpha test and the mean scale reliability was > 0.70.

Data quality assurance

Survey data was entered into REDCap® by trained research assistants. Completed questionnaires were later stored in a locked box until data entry was completed and checked. Data management was further handled using University of California San Francisco (UCSF) and University of Health and Allied Sciences (UHAS) standard operating procedures for human research.

Reporting guideline

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used in writing this manuscript (see Supplementary File 2).

Socio-demographic and economic characteristics of respondents

Total of 181 participants were recruited for the study and successfully interviewed. Females dominated, representing 80% of the respondents while the average age was 47±12.6. Half of the respondents were urban residents while over 90% were Christians of varying denominations; more than 70% of the respondents either did not have formal education or had at most primary or secondary education. Out of the 44% of respondents who were employed, 40% were in private business employment with an average monthly income below GHC 500 (approximately USD 44.00); 69% of them perceived themselves as poor and not having enough in terms of their economic status; ownership of economic indicators like radio set, television set and access to electricity was high, ranging from 60 – 94% (see Table 1).

<Insert Table 1:Table 1: Demographic and socio-economic characteristics of respondents>

In terms of marital status most respondents (42%) were in a relationship and living with the partner; polygamous marital arrangement was minimal with barely 4% indicating they were in such marital arrangement. Over 90% of the respondents had at least a child with the average number of children per respondent being 3±1.67. Approximately 83% of the respondents indicated none of their children was HIV positive. As many as 42% of the respondents indicated their partner did not test positive for HIV and those whose partners were HIV positive, nearly 91% of them were on ART (see Table 1).

HIV status disclosure among PLHIV

Out of the 169 respondents who answered the question on status disclosure, 50% indicated they have not disclosed their HIV status to their partner while 50% of them said they did. As shown in Figure 1, follow-up questions on category of persons self-disclosures were made to, revealed that nearly 65% of them disclosed to non-family members followed by partner (50%) and sibling (29%). The least category of persons disclosures were made to are father (1.1%); aunt/uncle (3.3%); unspecific persons (5.5%); mother (17%), and children (18%) (see Figure 1).

<Insert Figure 1: Figure 1: Key outcome variables of interest (HIV status disclosure)>

Predictors of HIV status disclosure

Bivariate Pearson Chi-square test analysis of the association between economic and socio-demographics and HIV status disclosure show significant correlations with marital status (p=0.000); level of education (p=0.035) and divorce due to HIV (p=0.000). Other significant correlates of HIV status disclosure are monthly income (p=0.019); occupation (p=0.010) and partner being HIV positive (see Table 2).

<Insert Table 2: Bivariate analysis of background characteristics and HIV status disclosure>

As shown in Table 3, single/never married persons had a 1% likelihood increase in the odds of disclosing their HIV status (OR=0.012; p-value=0.017, 95% [CI = 0.000 0.447]), holding other marital status constant. Catholics, compared to other religious denominations had higher odds of disclosing their HIV status, after controlling for covariates (OR=0.060; p-value=0.057, 95% [CI = 0.003 1.089]). Even though the other predictors of HIV status disclosure were strong in terms of the odds ratio, they were not statistically significant (see Table 3).

<Insert Table 3: Stepwise logistic regression on socio-demographic predictors of HIV status disclosure>

Additionally, it was observed that persons in income brackets of GHC 501 – 1000 (USD 44 – 88) (OR=0.068; p-value=0.011, 95% [CI = 0.009 0.543]) and GHC 2001 – 3000 (USD 176 – 264) (OR=0.009; p-value=0.004, 95% [CI = 0.000 0.214]) had higher odds of self-disclosure, relative to those in the lower income brackets, holding the covariates constant.

Furthermore, respondents who said they were mainly housewives had a higher tendency of disclosing their HIV status than those in other occupational categories, holding other factors constant (OR=0.032; p-value=0.035, 95% [CI = 0.001 0.785]) (see Table 4).

<Insert Table 4: Stepwise logistic regression on economic predictors of HIV status disclosure>

Finally, it was discovered that being adherent to ART increased one’s odds of disclosing HIV status relative to ART non-adherents, holding other factors constant (OR=6.161; p-value=0.058, 95% [CI = 0.937 40.490]) (See Table 5).

<Insert Table 5: Table 5: Stepwise logistic regression on ARVs adherence and HIV status disclosure>

It was found that 70% of the PLHIV were females and their mean age was 47 years. Moreover, most of the PLHIV were within the low-income brackets who rated their present economic status as low/poor. These results are comparable with the national statistics on PLHIV in Ghana where 66% of PLHIV are women who are predominantly in the low-income group (GAC, 2023). Likewise, the findings resonate with previous studies in Ghana by Adams et al (2021) and Owusu et al (2020). These empirical evidences support the longstanding argument that the HIV/AIDS epidemic in LMICs has gender and socio-economic undertones which must be addressed wholistically with leadership from the relevant state institutions.

It was however, reassuring to observe that 83% of respondents indicated none of their children was HIV positive. This observation suggests, the Prevention of Mother to Child Transmission (PMTCT) of HIV policy could be making significant impact in reduction of new transmission of HIV. Ghana aims to eliminate new HIV infections among children by 2020 while ensuring that 1.6 million children have access to HIV treatment by 2018 (UNAIDS, 2019).

A rather interesting observation was that, 42% of the respondents indicated their partner did not test positive for HIV. This finding could be due to effective practice of safe sex or perhaps respondents did not truly know their partners status, yet gave a socially desirable response. Moreover, the finding that 91% of the PLHIV were on the ART corroborates the GAC (2023) statistics that 99% of PLHIV are on sustained ART. Nonetheless, a recent blog publication by Ofori (2023) challenges this percentage of ART enrolment given recent anecdotal and empirical reports of possible resurgence of HIV infections in the country (Boah et al, 2023).

In terms of the status disclosure to sexual partner, an equal proportion of 50% said they disclosed their status and another 50% did not, contrary to 79% disclosure rate among PLHIV in a study conducted in Ghana by Adam et al (2021). However, it must be clarified that Adam et al (2021) did not distill the responses to categories of family members disclosures were made to as examined in this paper.

It was discovered in this study that disclosures were predominantly made to non-family members (65%), followed by a partner (50%) and a sibling (29%). The least in the disclosure list were a father (1.1%), an aunt/uncle (3.3%); unspecific persons (5.5%); mother (17%), and children (18%). Similar findings were reported in studies carried out in Kenya (Mugo et al, 2023), Zimbabwe (Khan et al, 2023), South Africa (Mokgatle et al, 2023), Uganda (Kairania et al, 2023) and other settings in Africa (Mengesha et al, 2023).

Perhaps due to stigma, most respondents felt more secured with non-family members for the needed psycho-social support, as corroborated in a study by Mokgatle and Madiba (2023) in South Africa where almost half (45.7%) of the 670 respondents were unwilling to care for family members sick with AIDS, suggesting stigma and discrimination towards PLHIV. Lack of trust for family members by PLHIV could account for this perception. This feedback calls for follow-up qualitative studies to deeply explore why persons outside of the family circles are perceived to be safer agents for support after HIV status disclosure. Findings from this study could therefore guide in designing HIV status disclosure interventions and support systems for PLHIV. Traditionally, psycho-social support initiatives for PLHIV have been family-centred with the assumption PLHIV are more likely to be comfortable with family members in the context of status disclosure. Our evidence coupled with available literature suggests this longstanding notion might not always help PLHIV.

Predictors of HIV status disclosure was also explored and it was found that disclosure tendencies were significantly correlated with marital status, educational level, divorce status, monthly income, occupation and having an HIV positive partner. These findings are corroborated by similar studies in Ghana (Adam et al, 2021), other African countries (Tibebu et al, 2023; Mengesha et al, 2023; Tessema et al, 2023) and in Canada (Krüsi et al, 2023) where socio-economic factors were found be important correlates of HIV status disclosure. The empirical findings further buttress arguments in the literature (Tessema et al, 2023; Atanuriba et al, 2023; Cole et al, 2023) that HIV epidemiology and disease coping mechanisms have strong socio-economic and gender underpinnings which must be considered in designing interventions towards HIV/AIDS prevention and control.

A stepwise logistic regression results specifically revealed that religion (Catholic denomination), marital status (single/never married) and persons in the high-income bracket had a higher tendency to disclose their status, after controlling for covariates. A study by Khan et al (2023) on status disclosure in Zimbabwe concluded religion was a strong determinant of self-disclosure, similar to result of this study. Yang et al (2023) also alluded to the impact of religion on disclosure tendencies in their study among men-having-sex with men (MSMs) in South Carolina. Perhaps, the religious doctrines and orientations could explain these dynamics, particularly in Africa (Madlala & Khanyile, 2023; Adekola & Mothoagae, 2023; Dwamenah, 2023) and elsewhere (Kalita et al, 2023) where religion remains a strong determinant of health seeking behaviour.

Similarly, previous studies have alluded to the strong correlation between economic freedom/self-dependence and health seeking behaviour (Ma et al, 2023). HIV/AIDS disproportionally affects more women than men in many African countries including Ghana; on top of this burden is the low-economic freedom women have in the typical Ghanaian society (Ofei-Aboagye, 2000) which potentially culminates into the lack of courage by women to be the first to disclose HIV status to their partners (Poku et al, 2017; Agyarko-Poku et al, 2023). As demonstrated in this study, respondents in the high-income bracket (mostly men) were more likely to disclose their HIV status to their partner than their female counterparts as found in Poku et al (2017).

Financial insecurity and fear of divorce with its unpleasant consequences could explain why persons in low-income bracket are reluctant to be the first to disclose their HIV status. As part of the campaigns aimed at improving HIV status disclosure, it is imperative to include economic empowerment modules for PLHIV since poverty is a significant predictor of HIV status disclosure as demonstrated in this study and similar ones on Ghana (Ofei-Aboagye, 2000; Poku et al, 2017; Agyarko-Poku et al, 2023) and other settings (Tibebu et al, 2023; Mengesha et al, 2023; Tessema et al, 2023).

Another interesting observation was that, respondents who said they were mainly housewives were more likely to disclose their status to their partner. Even though there are no known direct comparative studies, the finding could be attributed to the fact that housewives in the Ghanaian context turn to have limited mobility in the marital arrangement which comes with higher level of trust, allegiance and dependance on the husband (Paulme, 2013). Moreover, perhaps these categories of women have greater assurance of support from the spouse so as to mitigate rejection after disclosure. Since these explanations are mainly academic conjectures, future exploratory studies into this relatively new dimension of HIV status disclosure could help properly understand this socio-cultural phenomenon.

Finally, respondents who were found to be adherent to ART also had higher odds of disclosing their HIV status to someone. Studies have showed that persons who are adherent to ART also turn have positive outlook of HIV/AIDS and are more likely to disclose their status to others (Nicholas et al, 2017; Riono & Muhaimin, 2021; Kairania et al, 2022). Studies have also indicated non-adherents of ART are sometimes in perpetual denial stage and not willing to seek treatment and social support in light of their condition (Nicholas et al, 2019; Dzansi et al, 2020). Based on these revelations, it is important efforts are intensified to ensure support systems (including stigma control) are given to PLHIV. These interventions will help PLHIV in the status disclose process and avert incidence of new transmissions. These efforts will cumulatively help promote attainment of the country’s 95-95-95 goal and eventual eradication of the HIV/AIDS epidemic. More studies are also encouraged in the area of HIV status disclosure and support systems, especially in resource constrained countries like Ghana.

Limitations

The researchers acknowledge some limitations associated with the study, for which reason the results should be interpreted with caution.

First, the study was a descriptive cross-sectional design on personal views, perceptions and self-reported tendency for HIV status disclosure. This approach, without independent verification of responses, is subject to biases including socially desirable responses given the sensitive nature of the topic. However, the deployment of robust sampling techniques, test of internal reliability of the data collection instruments, and employment of rigorous statistical tests, guarantee the results are reliable and trustworthy.

Finally, the study was conducted in only one ART centre in a tertiary referral hospital in one out of the 16 administrative regions of Ghana. The findings might therefore not reflect the national character of PLHIV on status disclosure due to the striking socio-cultural and economic differences in the various regions of the country. Nonetheless, the results are sufficient guide for future researchers on the potential predictors of the HIV status disclosure in the country.

The following recommendations are proposed by the researchers, subject to the acknowledged status limitations:

First, there is the need for a national policy debate on piloting guidelines for HIV status disclosure for PLHIV. Even though guidelines exist on disclosure for minors, there is no tailored framework for self-disclosure among the adult population who record higher prevalence rates.

Secondly, deliberate campaigns on status disclosure should be intensified as part of the national strategy for HIV/AIDS control interventions in the country. Low disclosure rates are a threat to HIV prevention and control since these could contribute to new infections and reduce ART enrollment and retention in the country.

Finally, since HIV status disclosure has gender and socio-economic associations, there is the urgent need for the ministries of gender children and social protection, and ministry of health to initiative policy interventions seeking to support vulnerable groups within PLHIV with pre- and post-disclosure support systems to ameliorate their plight.

AIDS Acquired immune-deficiency syndrome

ART Antiretroviral therapy

COVID-19 Coronavirus disease 2009

GAC Ghana AIDS Commission

GHS Ghana Health Service

HIV Human immune virus

ISAT Internalized Stigma of AIDS Tool

LMICs Low- and middle-income countries

MoH Ministry of Health

NACP National AIDS control programme

PLHIV People living with HIV

PMTCT Prevention of Mother to Child Transmission

REC Research Ethics Committee

SDGs Sustainable development goals

TPB Theory of Planned Behavior

UCSF University of California San Francisco

UHAS University of Health and Allied Sciences

UN United Nations

UNAIDS United Nations AIDS

VIFs Variance Inflation Factors

ZDHS Zimbabwe Demographic Health Survey

Ethical Approval and Consent to Participate

All experimental protocols were approved by the Research Ethics Committee (REC) of the University of Health and Allied Sciences, Ghana (clearance number: UHAS-RECA.6 [1] 20–2) and the University of California San Francisco Institutional Review Board with reference number 20-32955. Written informed consent was obtained from all subjects before they were recruited into the study. Data set was coded to anonymize personal information of respondents.

Consent for publication

All authors of this manuscript have consented to publish this work

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests

Authors declare there is no conflict of interest

Funding

This project is funded by University of California San Francisco School of Nursing.

Authors' contributions

RKA provided conceptualization direction, analysis, review and supervision; collected data, wrote initial draft; field data collection, initial draft writing; review; resource mobilization

Acknowledgements

The researchers appreciate and acknowledge the support of the community health nurses who were involved in the data collection and all respondents who voluntarily participated in the study.

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Table 1: Demographic and socio-economic characteristics of respondents

Characteristics	Statistics
Socio-demographic variables
Age (n=170)		Mean= 46.61, SD=12.57; min= 18, max=85
Gender (n=126)	Freq. (f)	Percentage (%)
Male	25	19.84
Female	101	80.16
Place of residence (n=170)
Rural	36	21.18
Urban	86	50.59
Peri-urban	48	28.24
District (n=181)
Adaklu	4	2.21
Adaklu Waya	2	1.10
Agotime-Ziope	11	6.08
Ho	151	83.43
Ho west	10	5.52
Hohoe	1	0.55
Ketu-north	1	0.55
North Dayi	1	0.55
Religious affiliation (n=170)
Protestant/Pentecostal	97	57.06
Catholic	21	12.35
Traditional	1	0.59
Other Christians	51	30.00
Education (n=170)
None	18	10.65
Primary	82	48.52
Secondary	48	28.40
Diploma	4	2.37
Degree	13	7.69
Other	4	2.37
Marital status (n=169)
Widowed	37	21.89
Divorced	27	15.98
In relationship/living with a partner	71	42.01
In relationship/not living with a partner	22	13.02
Unmarried	12	7.10
Polygamous marriage (n=165)
Yes	6	3.64
No	159	96.36
Has children (n=170)
Yes	154	90.59
No	16	9.41
Number of children (n=158)		Mean= 2.85, SD=1.67; min= 1, max=12
HIV exposure proxies
Any of the children has HIV (n=151)
Yes	20	13.25
No	125	82.78
Don’t know	6	3.97
Partner has HIV (n=162)
Yes	43	26.54
No	68	41.98
Never tested/Don’t know	51	31.48
HIV partner on ART (n=42)
Yes	34	80.95
No	8	19.05
Economic status proxies
Occupation (n=168)
Student	5	2.98
Private business owner	67	39.88
Government/white collar job	10	5.95
Housewife	3	1.79
Farmer	27	16.07
Other	56	33.333
Employment status (n=169)
Employed	74	43.79
Unemployed	95	56.21
Monthly income (n=155)
Below 500 GHC	94	60.65
501 - 1,000 GHC	38	24.52
1,001 – 2,000 GHC	19	12.26
2,001 – 3,000 GHC	4	2.58
Perceived financial sufficiency (n=170)
Poor/not enough	117	68.82
Enough	53	31.18
Assets ownership (n=170)
Owns radio	111	65.29
Owns Television	101	59.76
Access to electricity	160	94.12
Economic status proxies (n=170)
Owns a car	16	9.41
Owns refrigerator	79	46.47
Access to potable water	143	84.12
Owns WC toilet	140	82.35
Owns solar electricity	3	1.76

Data source: Field Data (2021); Legend: n (valid responses); ART (Anti-retroviral Therapy); HIV (Human Immune Virus); SD (Standard Deviation)

Table 2: Bivariate analysis of background characteristics and HIV status disclosure

Descriptives	Disclose status partner
Gender (n=122)	No (%)	Yes (%)	Total (%)			p-value
Male	13.43	27.27	19.67			0.056
Female	86.57	72.73	80.33
Total	100.00	100.00	100.00
Residence (n=169)						0.859
Rural	18.82	21.43			20.12
Urban	51.76	52.38			52.07
Peri-urban	29.41	26.19			27.81
Total	100.00	100.00			100.00
Marital status (n=168)						0.000*
Widowed	28.57	13.10		20.83
Divorced	26.19	7.14		16.67
In a relationship/living with partner	20.24	66.67		43.45
In a relationship/not living with partner	11.90	13.10		12.50
Single/never married/no current partner	13.10	0.00		6.55
Total	100.00	100.00		100.00
Religion (n=169)						0.323
Protestant/Pentecostal	62.35	51.19		56.80
Catholic	10.59	15.48		13.02
Traditional	1.18	0.00		0.59
Other	25.88	33.33		29.59
Total	62.35	51.19		56.80
Educational status (n=168)						0.035*
Never been to school	16.67	3.57		10.12
Primary	51.19	50.00		50.60
Secondary	19.05	34.52		26.79
Diploma	3.57	1.19		2.38
University degree/higher	7.14	8.33		7.74
Other	2.38	2.38		2.38
Total	100.00	100.00		100.00
Divorce due to HIV status (n=152)						0.000*
Yes	4.11	5.06		4.61
No	73.97	24.05		48.03
Living with partner	21.92	70.89		47.37
Total	100.00	100.00		100.00
Monthly income (n=154)						0.019*
Below GHC 500	69.86	51.85		60.39
GHC 501-1000	20.55	27.16		24.03
GHC 1001-2000	5.48	19.75		12.99
GHC 2001-3000	4.11	1.23		2.60
Total	100.00	100.00		100.00
Perceived economic status (n=169)						0.229
Poor/not enough	71.76	63.10		67.46
Enough	28.24	36.90		32.54
Total	100.00	100.00		100.00
Occupation (n=164)						0.010*
Student	4.71	0.00		2.38
Private business	32.94	49.40		41.07
Government/white collar	2.35	9.64		5.95
Housewife	1.18	2.41		1.79
Farmer	16.47	14.46		15.48
Other	42.35	24.10		33.33
Total	100.00	100.00		100.00
Employed (n=168)						0.121
Yes	60.71	48.81		54.76
No	39.29	51.19		45.24
Total	100.00	100.00		100.00
Partner HIV positive (n=162)						0.000*
Yes	6.25	48.78		27.78
No	43.75	37.80		40.74
Don’t know	50.00	13.41		31.48
Total	100.00	100.00		100.00
On ART (n=44)						0.084
Yes	50.00	15.00		18.18
No	50.00	85.00		81.82
Total	100.00	100.00		100.00

Source: Field Data (2021); Legend: *Pearson Chi-square test statistically significant at 95% confidence level

Table 3: Stepwise logistic regression on socio-demographic predictors of HIV status disclosure

HIV Status Disclosure	Odds Ratio	Std.Err.	z	P>z	[95%Conf.	Interval]
Marital status
Divorced	0.188	0.298	-1.060	0.291	0.008	4.188
Single/never married	0.012	0.022	-2.400	0.017	0.000	0.447

Religion
Catholic	0.060	0.089	-1.900	0.057	0.003	1.089
Other	0.195	0.217	-1.470	0.143	0.022	1.737

Educational level
Primary	3.961	4.768	1.140	0.253	0.374	41.921
Secondary	3.028	4.135	0.810	0.417	0.208	44.030
University degree/higher	1.279	2.110	0.150	0.882	0.050	32.475

Residential location
Urban	1.616	2.421	0.320	0.749	0.086	30.455
Peri-urban	0.809	1.240	-0.140	0.890	0.040	16.307

Gender
Female	0.418	0.560	-0.650	0.515	0.030	5.793

Divorce due to HIV
No	0.751	1.216	-0.180	0.860	0.031	17.936
Living with partner	0.449	0.939	-0.380	0.702	0.007	26.955

Number of children	0.961	0.334	-0.110	0.910	0.486	1.899
_cons	66.143	234.595	1.180	0.237	0.063	69107.760

Note: _cons estimates baseline odds. NOTE: Ten variables and 15 obs. dropped because of estimability

Logistic regression model statistics

Number of obs = 86
LR chi2(13) = 18.35
Prob > chi2 = 0.1447
Log likelihood = -23.710869

Pseudo R2 = 0.2790

Forward step-wise logistic regression (begin with empty model)
p = 0.0023 < 0.9900 adding single/never married
p = 0.0792 < 0.9900 adding catholic
p = 0.2105 < 0.9900 adding other religion
p = 0.1608 < 0.9900 adding divorced
p = 0.4167 < 0.9900 adding primary education
p = 0.2298 < 0.9900 adding secondary education
p = 0.5371 < 0.9900 adding urban residence
p = 0.5158 < 0.9900 adding female
p = 0.6945 < 0.9900 adding living with partner
p = 0.8016 < 0.9900 adding not divorced due to HIV
p = 0.8794 < 0.9900 adding university education
p = 0.9012 < 0.9900 adding peri-urban residence
p = 0.9095 < 0.9900 adding number of children

Table 4: Stepwise logistic regression on economic predictors of HIV status disclosure

HIV Status Disclosure	Odds Ratio	Std.Err.	z	P>z	[95%Conf.	Interval]
Monthly income
GHC 501-1000	0.068	0.072	-2.540	0.011	0.009	0.543
GHC 1001-2000	0.208	0.286	-1.140	0.254	0.014	3.090
GHC 2001-3000	0.009	0.015	-2.920	0.004	0.000	0.214

Ownership of fridge
Yes	2.576	2.030	1.200	0.230	0.549	12.074

Financial status
Enough	4.459	3.880	1.720	0.086	0.810	24.539

Owns radio
Yes	2.973	2.236	1.450	0.147	0.681	12.980

Occupation
Private business	1.402	0.983	0.480	0.630	0.355	5.543
Government	1.304	2.229	0.160	0.876	0.046	37.175
Housewife	0.032	0.053	-2.110	0.035	0.001	0.785
Farmer	0.630	0.700	-0.420	0.678	0.071	5.556

Employed
No	0.323	0.225	-1.620	0.105	0.083	1.265

Owns television
Yes	2.792	2.281	1.260	0.209	0.563	13.846

Owns toilet
Yes	2.358	2.374	0.850	0.394	0.328	16.967
_cons	1.724	1.812	0.520	0.604	0.220	13.517

Note: _cons estimates baseline odds. NOTE: Fifteen variables and 32 obs. dropped because of estimability

Logistic regression model statistics

Number of obs = 120
LR chi2(13) = 21.72
Prob > chi2 = 0.0599
Log likelihood = -39.865878

Pseudo R2 = 0.2141

Forward step-wise logistic regression (begin with empty model)
p = 0.0432 < 0.9900 adding 4.q21
p = 0.0588 < 0.9900 adding 1.q27
p = 0.1682 < 0.9900 adding 2.q21
p = 0.1804 < 0.9900 adding 2.q22
p = 0.2127 < 0.9900 adding 1.q23
p = 0.1529 < 0.9900 adding 4.q18
p = 0.1258 < 0.9900 adding 1.q20
p = 0.2801 < 0.9900 adding 1.q24
p = 0.2355 < 0.9900 adding 3.q21
p = 0.2942 < 0.9900 adding 1.q29
p = 0.5302 < 0.9900 adding 2.q18
p = 0.6722 < 0.9900 adding 6.q18
p = 0.8765 < 0.9900 adding 3.q18

Table 5: Stepwise logistic regression on ARVs adherence and HIV status disclosure

HIV Status Disclosure	Odds Ratio	Std.Err.	z	P>z	[95%Conf.	Interval]
ART adherence
Yes	6.161	5.918	1.890	0.058	0.937	40.490

ARVs side effect
Yes	2.488	1.909	1.190	0.235	0.553	11.191

Fear of side effects
No	2.314	1.795	1.080	0.279	0.506	10.586
Sometimes	1.607	2.102	0.360	0.717	0.124	20.855

Place of ARVs refill
ART centre	1.585	0.829	0.880	0.378	0.569	4.418
_cons	0.181	0.245	-1.260	0.207	0.013	2.576

Note: _cons estimates baseline odds. NOTE: Seven variables and 4 obs. dropped because of estimability

Logistic regression model statistics

Number of obs = 164
LR chi2(5) = 6.64
Prob > chi2 = 0.2486
Log likelihood = -59.435264

Pseudo R2 = 0.0529

Forward step-wise logistic regression (begin with empty model)
p = 0.0921 < 0.9900 adding no knowledge of ARVs take
p = 0.1203 < 0.9900 adding no experiences of ARVs side effect
p = 0.2483 < 0.9900 adding no fear of ARVs
p = 0.3652 < 0.9900 adding takes ARVs at ART centre
p = 0.7167 < 0.9900 adding sometimes fear ARVs

No competing interests reported.

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Predictors of HIV status disclosure among PLHIV in the Volta region of Ghana: the disclosure conundrum and its policy implications.

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Abstract

Background

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Background

Methods

Study Design

Setting

Study population

Sampling procedure

Sample size determination

Inclusion criteria

Exclusion criteria

Participants’ recruitment procedure

Data Collection and Measures

Instruments of Data Collection

Data analysis

Outcome variable of interest

Explanatory variables and co-variates

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