Socio-demographic and biological profiles of people living with HIV/AIDS without antiretroviral treatment: a cross-sectional study conducted in two health centers in the commune of Adjamé, Côte d´Ivoire

¹Laboratory of Biology and Medical Research, National Institute of Public Health, Abidjan, Côte d’Ivoire, ²Laboratory of Biology and Health, UFR Biosciences, Félix Houphouët-Boigny University, Abidjan, Côte d’Ivoire

^&Corresponding author
Aïssé Florence Judith Trébissou, Laboratory of Biology and Medical Research, National Institute of Public Health, Abidjan, Côte d’Ivoire

Introduction    

Human immunodeficiency virus (HIV) infection is a public health problem. In 2023, approximately 39.9 million people were living with HIV worldwide, with 1.3 million new infections and 630,000 deaths recorded [1]. Despite significant progress in prevention, diagnosis, and treatment, the epidemic persists, particularly in resource-limited countries. Sub-Saharan Africa remains the most affected region, with a high concentration of HIV-related morbidity and mortality [1]. In Côte d'Ivoire, the HIV epidemic remains a concern, with a prevalence of 1.82% in 2022 [2].

HIV, a retrovirus of the lentivirus genus, causes progressive degradation of the immune system by primarily targeting CD4+ T lymphocytes, the central cells of the adaptive immune response [3]. The virus leads to immunosuppression in infected individuals if left untreated, resulting in opportunistic infections and cancers, characteristic of acquired immunodeficiency syndrome (AIDS). Viral load, which corresponds to the amount of HIV RNA circulating in the plasma, is the main marker for monitoring treatment. It allows for the assessment of the effectiveness of antiretroviral therapy, the monitoring of the infection's progression, and the estimation of the risk of transmission. The therapeutic goal is to achieve an undetectable viral load, generally less than 20 copies/mL, within six months of starting treatment. Despite the considerable progress made with combination antiretroviral therapies, the UNAIDS 95-95-95 targets remain insufficient to be met in several resource-limited countries. In Côte d’Ivoire, 73% of people living with HIV (PLHIV) receive antiretroviral therapy, but only 64% have a suppressed viral load [4].

In addition to viral load, biological assessment also relies on immunological and hematological parameters, including CD4+ T-cell count and complete blood count, abnormalities of which often reflect disease progression [5,6]. Despite the importance of these biological indicators, data on the characteristics of PLHIV not receiving antiretroviral therapy remain limited in Côte d’Ivoire [7-9]. A better understanding of their sociodemographic and biological profile is essential to guide care strategies and improve patient follow-up. The general objective of our study is to describe the sociodemographic and biological profile of people living with HIV/AIDS not receiving antiretroviral therapy, followed in the commune of Adjamé, Côte d’Ivoire.

Methods     

Study design: a cross-sectional study was conducted to gain knowledge of the sociodemographic and biological profile of people living with HIV who are not on antiretroviral treatment in Côte d'Ivoire.

Context and study population: Côte d'Ivoire is in West Africa and borders Guinea to the west, Liberia to the southwest, Mali to the northwest, Burkina Faso to the northeast, Ghana to the east, and the Atlantic Ocean to the south. Côte d'Ivoire covers an area of 322,462 km². The population was estimated at 29,389,150 in 2021 [10]. The country is subdivided into 111 departments, grouped into 31 regions and 14 autonomous districts. Among its districts is the autonomous district of Abidjan, which comprises 13 communes: 10 within the city of Abidjan and 3 in surrounding areas. The commune of Adjamé, one of the 10 communes of Abidjan, was the site of our study. The study took place from April 3 to May 30, 2025, at the General Hospital and the Tuberculosis Center in Adjamé. These two centers provide care for patients living with HIV/AIDS. The study population consisted of newly diagnosed HIV-positive patients who were not receiving antiretroviral therapy. They were all 17 years of age or older. Pregnant women were excluded from the study.

Variables: the variables included sociodemographic data such as age, sex, occupation, education level, and marital status, as well as plasma viral load, CD4+ T-cell count, and complete blood count. The dependent variable was viral load.

Data resources and measurement

Data collection tools: a questionnaire containing information such as age, sex, occupation, marital status, education level, place of residence, and clinical data was used to collect sociodemographic and clinical data.

Data collection: patients who tested positive for HIV during a medical consultation were recruited after obtaining their written consent. After completing the questionnaire, a venous blood sample was taken from the antecubital fossa (elbow crease) and collected in two different EDTA tubes. The first tube was used for CD4+ T-cell count and complete blood count. At the same time, the second was transported one hour after the sample was taken to the Laboratory of Biology and Medical Research (LBMR) at the National Institute of Public Health, in a cooler containing ice pack, for viral load measurement. Once it arrived at the laboratory, the plasma was immediately obtained by centrifuging whole blood at 3000 revolutions per minute for 3 minutes. The decantation took place under a hood. LBMR is located 10 minutes from the sample collection centers. Master’s students in Functional and Molecular Biology at Félix Houphouët-Boigny University collected the data after intensive training. Sociodemographic, clinical, and biological data were entered into a password-protected Excel 365 database (Microsoft Corporation). Data confidentiality was maintained.

Sample size: the minimum sample size for the study population was calculated using the Swartz formula, resulting in 28 people living with HIV (PLHIV). Swartz's formula is equal to:

With p equal to the national prevalence of HIV in Côte d'Ivoire: 1.82%, z equals 1.96, which is the 95% confidence level, and m equals 0.05, which is the desired precision.

Data analysis: to obtain high-quality data, the researchers adopted a rigorous approach during data collection. Variables with missing data were excluded from the analysis. Excel 365 software was used for entering sociodemographic data, calculating means and standard deviations, and determining sample sizes. GraphPad Prism version 10 software (Dotmatics, USA) was used for statistical data analysis. One-Way ANOVA was used for quantitative variables. ANOVA was used for the statistical calculation of the 4 age groups at the same time. A p-value is considered significant when p < 0.05. The correlation between viral load and CD4+ T-cell count was established using Pearson's correlation coefficient. A correlation exists when p is less than 0.05, and r is between -1 and 1. The p-values correspond to an overall test covering all four groups.

Ethical considerations: this study was approved by the Local Ethics Committee of Biology and Health at the Biosciences Department of Félix Houphouët-Boigny University, Number 006-11/03/2025. All adults aged 18 and over were asked to provide informed consent and sign a consent form before participating in the study. For illiterate participants, the consent form was read and explained to them. The 17-year-olds were accompanied by their parents. The parents provided written informed consent for their child to participate in the study. The children consented to participate in the study by signing an acknowledgment form. The approval of the local ethics committee specifically covers the informed consent of minors. Interviews with children took place in a separate room or space to ensure the independence of their responses. No personally identifiable information was collected, and confidentiality was maintained. Electronic data were stored on a password-protected computer.

Results     

Socio-demographic analysis: a total of 40 HIV-positive patients not receiving antiretroviral treatment were included. There were 16 men (40%) and 24 women (60%), for a male-to-female ratio of 0.6. The mean age of the study population was 40.88 ± 10.91 years. The 39-48 age group was the most represented, accounting for 37.5% of HIV patients. HIV-1 was the only subtype represented in the study (Table 1). In the study population, traders were the most numerous, representing 45%. There were a higher proportion of illiterate individuals (37.5%), and single individuals represented 57.5% of the study population (Figure 1). Trade consisted of: shopkeepers, saleswomen, restaurant workers. Private sector: hotel workers, sanitation workers, cashiers, customer service representatives, sales agents. Qualified: agricultural engineer, entrepreneur, pastor. Manual and technical: driver, plumber, carpenter. Domestic workers: housekeeper. Unemployed: homeless. Student/Other: student, seamstress, dockworker.

Descriptive analysis: at the CD4+ T lymphocyte level, the results showed that the 29-38 age group exhibited severe immunodeficiency (152.63 ± 105.51 cells/µL). In contrast, the 39-48 and 49-57 age groups exhibited advanced immunodeficiency (234.66 ± 195.36 cells/µL and 298.71 ± 420.24 cells/µL, respectively) (Figure 2). Normal CD4+ T-cell counts: 400-1750 cells/µL. Non-significant immunodeficiency = 500 cells/µL; moderate immunodeficiency = 499-350 cells/µL; advanced immunodeficiency = 349-200 cells/µL; severe immunodeficiency = < 200 cells/µL. Significant = p-value < 0.05. The mean viral load was highest in the 39-48 age group (6.03 ± 5.10 log copies/ml). There was no significant difference between the different age groups (p = 0.359) (Figure 3). A correlation between the load and CD4+ T lymphocytes was calculated between the different age groups. The results are: age range 17-28 (r = 0.223 and p-value = 0.856); age range 29-38 (r = 0,465 and p =0,127); age range 39-48 (r= 0.071 and p-value = 0.815; age range 49-57 (r= -0.144 and p-value = 0.757). According to Pearson's test, a correlation exists when r is between -1 and 1, and p is less than 0.05. Therefore, there is no correlation between viral load and CD4 T cell count. Undetectable viral load (log): < 1.30 copies/mL. Suppressed viral load (log): 1.30 – 2.99 copies/mL, significant = p-value < 0.05. Women had a mean hemoglobin level of 10.5 ± 2.16 g/L compared to 10.5 ± 2.36 g/L in men (p = 0.918), indicating moderate anemia in both groups. Hematocrit, MCH, and MCHC values were below normal (Table 2) in both groups, reflecting normocytic hypochromic anemia.

Discussion     

The objective of our study was to describe the socio-demographic and biological profiles of people living with HIV (PLHIV) not receiving antiretroviral treatment in the Adjamé district. The study data revealed a female predominance with a sex ratio of 0.6. This distribution was consistent with UNAIDS estimates, according to which 62% of new infections occur in women [1]. Women are more exposed to HIV due to the fragility of the vaginal mucosa, which facilitates viral absorption, and the higher viral load in semen, increasing the risk of infection during unprotected sex. This vulnerability has been exacerbated by social factors such as gender-based violence (rape), stigmatization (poverty, illiteracy), and discrimination. Furthermore, women are more likely to visit hospitals, thus increasing their chances of early detection of infection. Our results corroborate those of [11], who highlighted the predominance of women with 53.2%.

The 39-48 age group was the most represented, with an average age of 40.88 years. Chronic HIV infection in infected adults was diagnosed at a later age. Sexually active adults, particularly those aged 35-49, were the most affected in our country, with an estimated prevalence of 3.9% [12]. Most of our patients were illiterate (37.5%), highlighting the impact of social factors on vulnerability to HIV. Lack of education is linked to an increased risk of infection due to limited access to information, preventive measures, and healthcare services. Illiteracy remains a significant problem in vulnerable regions of West Africa, affecting women who often face precarious living conditions. However, a survey conducted by [13] showed that school-aged people were the most infected. The investigation showed that 57.5% of patients were single. This status is often associated with risky sexual practices such as multiple partners and unprotected sex, exposing them to infections such as STIs (AIDS, syphilis, etc.). A study in Guinea indicated a predominance of single, widowed, or divorced individuals at 62% [14].

The most represented profession was commerce (45%). This could be explained by the study site, which was near the largest market in West Africa (the Adjamé Forum). Our results contradict those of [13], who showed that unemployed patients were the most affected (55.6%), followed by employed individuals (20.1%) in Burkina Faso. Regarding the type of HIV, it should be noted that HIV-1 was the most prevalent, accounting for 100% of PLHIV. A similar result to our study was reported by [15] in Mali, with 95.1% of cases [16] recorded a high predominance of HIV type 1, at 100%, in Guinea. It can be said that HIV-1 is the most widespread in West Africa, particularly in Côte d'Ivoire, and the most transmissible. The CD4+ T-cell count in the 17-28 age group (602 ± 321 cells/µL) indicated relatively intact immune function, although the viral load was very high (6.03 ± 5 log copies/mL). The presence of a high CD4+ T-cell count and a high viral load in young people aged 17-28 years could be due to a new infection. Indeed, when a person newly contracts HIV, usually in the first few weeks, viral replication is explosive; HIV multiplies massively and rapidly in the patient's body, resulting in a very high viral load (hundreds or millions of virus copies per mL of blood). There is no correlation between viral load and CD4 T lymphocytes.

The low CD4+ T-cell counts in the 29-38 age group reflected severe immunodeficiency (152.6 ± 105.5 cells/µL). This corresponded to the critical phase of the infection, or the WHO clinical stage of AIDS. Viremia was also high (6.00 ± 5.5 log copies/mL). This is explained by the fact that the immune system was no longer able to compensate for the loss of CD4+ T cells, and the response became impaired. This stage was dominated by chronic immune activation depleting CD4+ T cells, massive apoptosis due to a bystander effect, systemic inflammation sustained by IL-6 and TNF-α, and mitochondrial oxidative stress that impairs cell regeneration. Furthermore, a Senegalese study led by [17] reported a low mean CD4+ T cell count (inferior at 200 cells/µL). This low CD4+ count indicated late detection of HIV infection and suggested the stage of AIDS.

CD4 T-cell counts of 234.66 ± 195.36 cells/µL in the 39-48 age group and 298.7 ± 420.2 cells/µL in the 49-57 age group indicated advanced immunosuppression, respectively. Viremia levels in these age groups were 6.03 ± 5.10 log copies/mL and 5.65 ± 4.90 log copies/mL, reflecting intense active viral replication. Undetectable viral loads (< 1.3 log copies/mL) were observed. These results could be explained by recent HIV infection. Hemoglobin levels were below normal in both women (10.5 ± 2.16 g/dL) and men (10.53 ± 2.36 g/dL). Red blood cell indices (MCV ≈ 80 fL, MCH ≈ 25 pg, MCHC ≈ 30 g/dL) were not within physiological ranges, indicating the presence of normocytic hypochromic anemia, specific to chronic infections such as HIV. The mean white blood cell count was within the normal range in women and men (5.2 ± 2.13 x 10³/mm³ and 6.6 ± 4.20 x 10³/mm³, respectively). Contrary to this finding, the study by [18] presented mild normocytic normochromic anemia. Bone marrow suppression, i.e., the destruction of hematopoietic cells by HIV, is one of the most frequent causes of anemia in treatment-naïve people living with HIV [5], conducted a study in which leukopenia was present in participants at clinical stage 4.

Mean platelet counts were normal in both men (268.87 ± 143.88 x 10³/mm³) and women (268.87 ± 143.88 x 10³/mm³). Consequently, there were no cases of thrombocytopenia. Our results corroborate those of [15], who found normal platelet counts in 82.9% of their PLHIV. However, one case of thrombocytopenia was observed, representing 2.4%.

The study's limitations were primarily the small sample size of 40 patients. HIV prevalence in Côte d'Ivoire fell to 1.82% in 2022. Newly diagnosed HIV cases were very rare at both sample collection sites. Consequently, the results were analyzed with a small sample size, preventing us from drawing any firm conclusions. The cross-sectional design of the study could prevent any causal inferences. The absence of WHO clinical classification data.

Conclusion     

Our study showed a predominance of young, single women, advanced immunosuppression in early and middle adulthood, and a high rate of illiteracy. All participants had normocytic hypochromic anemia, characteristic of untreated chronic infections. These results confirm the importance of early detection, rapid access to antiretroviral treatment, and regular follow-up of people living with HIV to improve the effectiveness of care and reduce the risk of complications related to the disease.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Aïssé Trébissou participated in the conception of the subject, the production and the writing of the manuscript. Fatoumata Diabaté participated in the completion of the study. Ella Brou provided the topic and participated in conducting the study. Rolland Kipré participated in the design and supervision of the study. All the authors read and approved the final version of this manuscript.

Acknowledgments     

We would like to thank Mrs. Gnonhouri for allowing us to have the patients for this study.

Tables and figures     

Table 1: distribution of HIV-positive patients without antiretroviral treatment according to age group and sex (N = 40)

Table 2: distribution of complete blood counts of PLHIV not on antiretroviral therapy by sex (N=40)

Figure 1: distribution of HIV-positive patients not receiving antiretroviral treatment according to their professional status (A), education level (B) and marital status (C) (N = 40)

Figure 2: CD4+ T-cell counts in PLHIV not receiving antiretroviral therapy, distributed by age group

Figure 3: viral load values of PLHIV not receiving antiretroviral treatment, distributed according to age groups

References