Introduction
The widespread availability of antiretroviral therapy (ART) has led to considerable strides in the management of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome in recent years. ART has not only increased the life expectancy of people living with HIV (PLHIV) but has also improved their overall quality of life.1,2 Despite these developments, the prevalence of mental health disorders among PLHIV continues to be a significant concern.3 Over 300 million people worldwide suffer from depression, making it one of the major causes of disability-adjusted life years and a major contributor to the burden of mental health disorders.4,5 Depression can lead to chronic feelings of sadness, loss of interest or desire for normal activities, changes in appetite, disruption in sleep patterns, fatigue, and reduced ability to concentrate or make decisions. These symptoms can significantly impede a person’s ability to work, perform daily tasks, and maintain social relationships.4,6
Depression has been reported to occur at high rates among individuals with chronic medical conditions, such as HIV.3,7 Among PLHIV, depressive symptoms may be exacerbated by the intricate interaction of the psychosocial impacts of HIV diagnosis, the stigma associated with HIV infection, and the neuropathological effects of the virus. Depression may also lead to poor medication adherence among PLHIV, which could potentially result in virologic failure and an increased risk of HIV transmission.7 Analyzing the demographics of PLHIV may provide a better understanding of and improved management for depression.8,9 It also helps healthcare providers, researchers, and policymakers develop more targeted interventions and efficient support systems for this group.10 In recent years, Plateau State has consistently been among the fifteen states with the highest HIV prevalence in the country.11 This study was designed to investigate the socio-demographic factors associated with depression in Jos, the cosmopolitan administrative capital of Plateau State, Nigeria.
Materials and methods
Study area
This study was conducted among PLHIV receiving care at the ART clinic of Bingham University Teaching Hospital, a tertiary health institution in Jos, Plateau State, North-Central Nigeria. The state has an estimated HIV prevalence of 1.5% and has been consistently listed among Nigerian states with the highest HIV prevalence in recent years.11 Compared to other major cities in Nigeria, Jos has a temperate climate that contributes to its cosmopolitan nature, with average monthly temperatures ranging from 21°C to 25°C. The city is surrounded by several smaller agrarian communities that produce and supply a wide variety of fruits, vegetables, and crops, making Jos a hub of commercial agricultural activities, not only for Plateau State residents but also for people from neighboring states in North-Central Nigeria.12
Study design and source population
A cross-sectional, hospital-based study design was used for this study. The source population comprised adult PLHIV (defined in this study as PLHIV aged ≥18 years) accessing healthcare at the ART clinic of Bingham University Teaching Hospital, Jos, Nigeria, from September to December 2022, which included approximately 4,500 patients.
Study population
The study population comprised adult PLHIV accessing healthcare at the ART clinic of Bingham University Teaching Hospital, Jos, Nigeria, who had been on ART for at least six months with adequate follow-up. In this study, adequate follow-up refers to ongoing medical care and support to ensure effective management of HIV, which includes adherence counseling and support, as well as assessment of clinical, immunological, and virological responses to treatment.
Inclusion and exclusion criteria
All adult PLHIV receiving treatment at the ART clinic of Bingham University Teaching Hospital, who had been on ART for at least six months with follow-up, and who consented to the study, were included. The first six months after initiation of ART are critical for a patient’s physical and mental well-being. Patients who did not consent to the study and those who were critically ill or pregnant were excluded.
Variables
The dependent variable was depression, while the independent variables were socio-demographic factors, which included gender, age, marital status, employment status, income level, and educational status. Socio-demographic factors were assessed based on different categories. For instance, marital status was categorized as divorced, widowed, married, and single (i.e., not previously or currently married). Categories of other independent variables are listed in Table 1.
Table 1Demographics of study participants
| Characteristics | Category | Frequency (%) |
|---|
| Gender | | |
| Male | 171 (28.9) |
| Female | 421 (71.1) |
| Total | 592 (100.0) |
| Age (years) | | |
| 18–35 | 96 (16.2) |
| 36–45 | 221 (37.3) |
| 46–55 | 159 (26.9) |
| 56–65 | 92 (15.5) |
| ≥66 | 24 (4.1) |
| Total | 592 (100.0) |
| Marital status | | |
| Single | 91 (15.4) |
| Married | 341 (57.6) |
| Widowed | 119 (20.1) |
| Divorced | 36 (6.1) |
| No response | 5 (0.8) |
| Total | 592 (100.0) |
| Employment status | | |
| Unemployed | 47 (7.9) |
| Self-employed | 303 (51.2) |
| Employed | 167 (28.2) |
| Retired | 71 (12.0) |
| No response | 4 (0.7) |
| Total | 592 (100.0) |
| Income level (Naira/month) | | |
| <50,000 | 339 (57.3) |
| 50,000–100,000 | 74 (12.5) |
| >100,000–200,000 | 33 (5.6) |
| >200,000 | 126 (21.3) |
| No response | 20 (3.4) |
| Total | 592 (100.0) |
| Educational status | | |
| No education | 49 (8.3) |
| Quranic | 19 (3.2) |
| Primary | 141 (23.8) |
| Secondary | 186 (31.4) |
| Tertiary | 194 (32.8) |
| No response | 3 (0.5) |
| Total | 592 (100.0) |
Operational definition of depression
Study participants who scored ≥5 on the Patient Health Questionnaire-9 (PHQ-9) were considered depressed, while those who scored <5 were considered not depressed.13,14
Sampling technique and sample size determination
Face-to-face interviews with PLHIV who visited the ART clinic during the study period were conducted by experienced ART clinic nurses, and eligible patients were recruited. The sample size was estimated using the Leslie-Kish formula for calculating sample size for cross-sectional studies, n = Z2 P(1-P)/d2, where n represents the minimum sample size.15 For this study, Z (z-score) and d (margin of error) were taken as 1.96 at a 95% confidence level and 0.05, respectively, while P (prevalence rate) was set at 50.0% based on previous studies in Nigeria and other parts of Africa.6,16,17 A minimum sample size of 384 was calculated, which was increased to 600 to boost the power of the study, with 592 individuals completing the questionnaire, giving a response rate of 98.7%. Study participants were chosen using a simple random sampling procedure. On ART clinic days, adult PLHIV were assigned numbers and a raffle of 35 to 40 numbers was drawn to select respondents. Selected respondents who provided consent were enrolled in the study. This process continued until the required sample size was attained.
Ethical statement
Ethical approval for this study was obtained from the Health Research Ethics Committee (HREC) of Bingham University Teaching Hospital, Jos, Nigeria, under approval number NHREC/21/05/2005/00950. Informed written consent was obtained from the participants before recruitment into the study. The study was conducted in accordance with the principles of the Declaration of Helsinki.
Data collection tools and procedure
The Patient Health Questionnaire-9 (PHQ-9) tool was used to assess depression. The PHQ-9 is a standardized and sensitive questionnaire that is commonly used among healthcare professionals to assess depression in patients.13,14 It is a nine-item depression screening tool that focuses on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition for major depressive disorder. The PHQ-9 has been used to provide valuable insights into the prevalence of depression. The questionnaire assesses how often the subjects had been disturbed by any of the nine items during the immediately preceding two weeks.18 Responses to each item were scored on a scale of zero to three, giving a total score ranging from zero to 27. While higher scores indicate greater depression severity, with 5, 10, 15, and 20 used as cut-offs for mild, moderate, moderately severe, and severe depression, respectively,18–20 a single cut-off score of ≥5 was used in this study. Study participants with a PHQ-9 score ≥5 were considered “depressed”, and those who scored <5 were considered “not depressed”. The PHQ-9 questionnaire was combined with a pretested sociodemographic questionnaire administered by experienced nurses and other trained personnel at the ART clinic.
Data quality control
Data collectors were trained in the use of data collection tools and the sampling technique. Data collection was duly supervised, and the collected data were regularly checked for accuracy and consistency.
Data management and analysis procedure
SPSS version 26 was used for cleaning and comprehensive analysis of the data collected. Categorical variables were measured using frequencies and percentages. The Chi-square test of association was used for preliminary assessment of the association between depression status and the demographics of study participants at α = 0.05. Univariate logistic regression analysis was used to obtain crude odds ratios (CORs) for the initial assessment of factors associated with depression. Variables with p-values less than 0.25 in the univariate logistic regression analysis were selected for multivariable binary logistic regression analysis.9,14,17
A stepwise, backward elimination procedure was used to remove non-significant variables from the model. Variables with p-values of less than 0.05 were considered statistically significant. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were calculated to determine associated factors. The multiple imputation method was used to account for missing data, which were found to be random. Results obtained after five iterations were pooled into one and used to complete missing data before statistical analyses. The variance inflation factor was used to check for multicollinearity, and none was found. The Hosmer–Lemeshow goodness-of-fit test was applied.9,14
Results
Socio-demographic characteristics of study participants and prevalence of depression
Five hundred and ninety-two participants were recruited. Most were aged 36–45 years (37.3%), males (71.1%), married (57.6%), and self-employed (51.2%). While about 80.0% had some type of employment, only about 27.0% were earning above ₦100,000 (about $130) monthly. More than 90.0% of the study participants also had some level of education.
Preliminary assessment of the association between sociodemographic factors and depression
Depression was more prevalent among PLHIV who were previously married (i.e., either “divorced” or “widowed”) than among those who were “married” or “single”, but it was less prevalent among participants with tertiary and secondary education compared to those with no education or with only primary education. A preliminary assessment of the association between depression status and the demographics of study participants using the chi-square test of association revealed that gender (χ2 = 4.66, p = 0.03), employment status (χ2 = 9.21, p = 0.03), income level (χ2 = 15.00, p = 0.00), and educational status (χ2 = 14.02, p = 0.01) were significantly associated with depression at α = 0.05. These results are shown in Table 2.
Table 2Preliminary assessment of association between depression status and demographics of study participants
| Characteristics and categories | Depression status among PLHIV
| Chi-square | p-value |
|---|
| Not depressed (%) | Depressed (%) |
|---|
| Gender | | | 4.66 | 0.03* |
| Male (n = 171) | 106 (62.0) | 65 (38.0) | | |
| Female (n = 421) | 220 (52.3) | 201 (47.7) | | |
| Total (N = 592) | 326 | 266 | | |
| Age (years) | | | 7.39 | 0.12 |
| 18–35 (n = 96) | 51 (53.1) | 45 (46.9) | | |
| 36–45 (n = 221) | 116 (52.5) | 105 (47.5) | | |
| 46–55 (n = 159) | 101 (63.5) | 58 (36.5) | | |
| 56–65 (n = 92) | 48 (52.2) | 44 (47.8) | | |
| ≥66 (n = 24) | 10 (41.7) | 14 (58.3) | | |
| Total (N = 592) | 326 | 266 | | |
| Marital status | | | 6.99 | 0.07 |
| Single (n = 92) | 51 (55.4) | 41 (44.6) | | |
| Married (n = 344) | 202 (58.7) | 142 (41.3) | | |
| Widowed (n = 120) | 58 (48.3) | 62 (51.7) | | |
| Divorced (n = 36) | 15 (41.7) | 21 (58.3) | | |
| Total (N = 592) | 326 | 266 | | |
| Employmentstatus | | | 9.21 | 0.03* |
| Employed (n = 168) | 107 (63.7) | 61 (36.3) | | |
| Self-employed (n = 305) | 154 (50.5) | 151 (49.5) | | |
| Unemployed (n = 47) | 29 (61.7) | 18 (38.3) | | |
| Retired (n = 72) | 36 (50.0) | 36 (50.0) | | |
| Total (N = 592) | 326 | 266 | | |
| Income level (Naira/month) | | | 15.00 | 0.00* |
| <50,000 (n = 350) | 183 (52.3) | 167 (47.7) | | |
| 50,000–100,000 (n = 77) | 52 (67.5) | 25 (32.5) | | |
| >100,000–200,000 (n = 34) | 26 (76.5) | 8 (23.5) | | |
| >200,000 (n = 131) | 65 (49.6) | 66 (50.4) | | |
| Total (N = 592) | 326 | 266 | | |
| Educational status | | | 14.02 | 0.01* |
| No education (n = 49) | 23 (46.9) | 26 (53.1) | | |
| Quranic (n = 19) | 14 (73.7) | 5 (26.3) | | |
| Primary (n = 142) | 66 (46.5) | 76 (53.5) | | |
| Secondary (n = 187) | 99 (52.9) | 88 (47.1) | | |
| Tertiary (n = 195 | 124 (63.6) | 71 (36.4) | | |
| Total (N = 592) | 326 | 266 | | |
Associated factors of depression among people living with HIV during the study
Univariate logistic regression analysis revealed that at least 50.0% of the categories under five of the six predictor variables were significantly associated with depression compared to their respective reference categories. The significantly associated categories were “male” (COR = 0.67; 95%CI: 0.47–0.96) under “gender”; “single” (COR = 0.57; 95%CI: 0.26–1.24) and “married” (COR = 0.50; 95%CI: 0.25–1.01) under “marital status”; “employed” (COR = 0.58; 95%CI: 0.33–1.01) and “unemployed” (COR = 0.62; 95%CI: 0.29–1.31) under “employment status”; “₦50,000–100,000” (COR = 0.47; 95%CI: 0.25–0.85) and “>₦100,000–200,000” (COR = 0.32; 95%CI: 0.14–0.77) under “income level”; and “no education” (COR = 0.34; 95%CI: 0.11–1.09) and “secondary” (COR = 0.51; 95%CI: 0.27–0.96) under “educational status”. The association between the gender category “male” (AOR = 0.62; 95%CI: 0.42–0.92) and the income level categories “₦50,000–100,000” ($65–130) (AOR = 0.49; 95% CI: 0.27–0.91) and “>₦100,000–200,000”(>$130–260) (AOR = 0.33; 95% CI: 0.13–0.77) with depression remained significant after adjusting for age, employment status, and educational status using multivariable binary logistic regression analysis. These results are shown in Tables 3 and 4. Male participants were 38% less likely to be depressed than female participants, while those who earned a monthly income from ₦50,000 to ₦200,000 ($65–260) also had significantly reduced odds of experiencing depression at α = 0.05 compared to those with a monthly income of >₦200,000 (>$260). Figure 1 depicts the patterns of depression among participants based on the three sociodemographic variables associated with depression during this study. It reveals that depression occurred more among females, self-employed and retired categories, and PLHIV with income below ₦50,000 ($65) and above ₦200,000 ($260).
Table 3Univariate logistic regression analysis for initial assessment of socio-demographic factors associated with depression among study participants
| Characteristics and categories | Depression status among PLHIV
| COR (95% CI) | p-value |
|---|
| Not depressed (n = 326) | Depressed (n = 266) |
|---|
| Gender | | | | |
| Male | 106 | 65 | 0.67 (0.47–0.96) | 0.03* |
| Female | 220 | 201 | I | |
| Age (years) | | | | |
| 18–35 | 51 | 45 | 0.63 (0.26–1.56) | 0.32 |
| 36–45 | 116 | 105 | 0.65 (0.28–1.52) | 0.32 |
| 46–55 | 101 | 58 | 0.41 (0.17–0.98) | 0.05* |
| 56–65 | 48 | 44 | 0.66 (0.26–1.63) | 0.36 |
| ≥66 | 10 | 14 | I | |
| Marital status | | | | |
| Single | 51 | 41 | 0.57 (0.26–1.24) | 0.16* |
| Married | 202 | 142 | 0.50 (0.25–1.01) | 0.05* |
| Widowed | 58 | 62 | 0.77 (0.36–1.64) | 0.50 |
| Divorced | 15 | 21 | I | |
| Employment status | | | | |
| Employed | 107 | 61 | 0.58 (0.33–1.01) | 0.05* |
| Self-employed | 154 | 151 | 0.99 (0.59–1.66) | 0.96 |
| Unemployed | 29 | 18 | 0.62 (0.29–1.31) | 0.21* |
| Retired | 36 | 36 | I | |
| Income level (Naira/month) | | | | |
| <50,000 | 183 | 167 | 0.91 (0.60–1.35) | 0.62 |
| 50,000–100,000 | 52 | 25 | 0.47 (0.25–0.85) | 0.01* |
| >100,000–200,000 | 26 | 8 | 0.32 (0.14–0.77) | 0.01* |
| >200,000 | 65 | 66 | I | |
| Educational status | | | | |
| No education | 23 | 26 | 0.34 (0.11–1.09) | 0.07* |
| Quranic | 14 | 5 | 1.00 (0.52–1.92) | 0.99 |
| Primary | 66 | 76 | 0.79 (0.42–1.49) | 0.47 |
| Secondary | 99 | 88 | 0.51 (0.27–0.96) | 0.04* |
| Tertiary | 124 | 71 | I | |
Table 4Binary logistic regression analysis for associated factors of depression among PLWH assessing ART at Bingham University Teaching Hospital, Jos, Nigeria
| Characteristics and categories | Depression status among PLHIV
| COR (95% CI) | AOR (95% CI) | p-value |
|---|
| Not depressed (n = 326) | Depressed (n = 266) |
|---|
| Gender | | | | | |
| Male (n = 171) | 106 (62.0) | 65 (38.0) | 0.67 (0.47–0.96) | 0.62 (0.42–0.92) | 0.02* |
| Female (n = 421) | 220 (52.3) | 201 (47.7) | I | I | |
| Employment status | | | | | |
| Employed (n = 168) | 107 (63.7) | 61 (36.3) | 0.58 (0.33–1.01) | 0.55 (0.31–0.97) | 0.04* |
| Self-employed (n = 305) | 154 (50.5) | 151 (49.5) | 0.99 (0.59–1.66) | 0.92 (0.54–1.55) | 0.74 |
| Unemployed (n = 47) | 29 (61.7) | 18 (38.3) | 0.62 (0.29–1.31) | 0.57 (0.27–1.21) | 0.14 |
| Retired (n = 72) | 36 (50.0) | 36 (50.0) | I | | |
| Income level (Naira/month) | | | | | |
| <50,000 (n = 350) | 183 (52.3) | 167 (47.7) | 0.91 (0.60–1.35) | 0.89 (0.59–1.34) | 0.60 |
| 50,000–100,000 (n = 77) | 52 (67.5) | 25 (32.5) | 0.47 (0.25–0.85) | 0.49 (0.27–0.91) | 0.02* |
| >100,000–200,000 (n = 34) | 26 (76.5) | 8 (23.5) | 0.32 (0.14–0.77) | 0.33 (0.13–0.77) | 0.01* |
| >200,000 (n = 131) | 65 (49.6) | 66 (50.4) | I | | |
Discussion
The focus of this study was to analyze and identify sociodemographic factors associated with depression among PLHIV on ART in the study area. The high prevalence of depression [44.9%] found in this study is similar to findings from related studies among PLHIV in different parts of Africa,14,16,21,22 as well as in other parts of the world.9 In view of the 34.17% global average prevalence of depressive symptoms reported in a recent systematic review,23 the prevalence of 44.9% obtained in this study is considered considerably high. A recent study also reported a very high prevalence of depression (80.0%) among PLHIV in the Asia–Pacific region.24 While the estimation of depression prevalence may have been influenced by factors such as sampling technique, sample size, and data collection tools, and while some studies have reported relatively lower prevalence rates of depression among PLHIV,6,25 the widespread high prevalence of depression among adult PLHIV in recent years highlights the need for improved diagnosis and better management of mental health issues in this vulnerable population.
In this study, gender was found to be a significant factor associated with depression. The AOR of 0.62 for males implies that, after controlling for other covariates, male participants had 0.62 times the odds of having depression compared to female participants. This indicates that gender was significantly associated with depression among the study participants at α = 0.05, with males (AOR = 0.62, p = 0.03) being 38% less likely to have depression compared to females. This result contrasts with a report from Ethiopia, where male PLHIV had higher odds of depression,14 but it aligns with findings from other studies where being female was more associated with depression among PLHIV.9,16,26 Factors such as differences in culture and ethnicity may influence how depressive tendencies vary by gender. However, while this study was conducted in a cosmopolitan area and included participants from different cultural backgrounds, an important limitation was that ethnicity was not included as a covariate in our model. Future studies could explore the interaction between depression and gender more thoroughly by including ethnicity or race as a predictor variable.
Age, marital status, and educational attainment have been linked to depression in PLHIV in some earlier studies.6,9 However, these variables did not significantly affect depressive tendencies in the present study, which is similar to some reports, such as that by Obadeji and colleagues, where marital status was not associated with depression among PLHIV.26 While unemployment was previously reported to be associated with depression among PLHIV in Nigeria,16 in the present study, being unemployed or self-employed was not found to be significantly associated with depression. However, our results revealed that PLHIV who were formally employed, either by the government or the private sector, had significantly lower odds of depression (AOR = 0.55, p = 0.04) at α = 0.05 compared to the “retired” group. In other words, study participants who had formal employment had 0.55 times lower odds of experiencing depression compared to those who were retired, making them 45% less likely to experience depression than the retired group. This finding is similar to that of Ware and colleagues, where being employed was associated with lower odds of depression among PLHIV.27
Jos is home to various ethnic groups and people of different religious backgrounds and is considered one of the major cosmopolitan cities in Nigeria. It shares common challenges with other cosmopolitan areas globally, such as cultural tensions, high cost of living, traffic congestion, gentrification leading to overcrowding in low-cost areas, high crime rates, and poor social integration and inclusivity.28,29 In the present study, results indicated that individuals with a monthly income of ₦50,000–100,000 ($65–130) (AOR = 0.49) and >₦100,000–200,000 (>$130–260) (AOR = 0.33) had statistically significant reduced odds of depression at α = 0.05compared to the reference category (monthly income of >₦200,000 (about $260). However, the category with a monthly income of <₦50,000 ($65) did not have statistically significant odds of depression compared to the reference category at α = 0.05.
These results suggest that study participants with a monthly income below ₦50,000 (<$65) did not have a statistically significant difference in their likelihood of depression compared to those with a monthly income above ₦200,000 (>$260) (the reference group with an odds ratio = 1). This finding is similar to a previous study in Nigeria where low income was associated with depressive symptoms among PLHIV.16 However, study participants earning a moderate income [₦50,000–100,000 ($65–130) per month] to moderately high income [₦100,000–200,000 (>$130–260) per month] had a significantly reduced risk of depression at α = 0.05 compared to those with a monthly income >₦200,000 (>$260). The odds of depression for these income categories suggest that a moderate to moderately high monthly income was associated with a lower likelihood of depression. The observed reduced odds of depression among these two income categories in the cosmopolitan city of Jos may have been influenced by factors such as lower financial responsibility, better job satisfaction, job security, and lifestyle differences. These factors could be included as covariates in future studies to obtain more robust findings. Additionally, the impact of internalized HIV stigma—which involves the acceptance of negative societal attitudes about HIV, leading to self-disparagement, derogatory self-evaluations, and feelings of rejection—could be explored in future longitudinal studies to assess its role in the occurrence of depressive symptoms in the study area.30,31
Study limitations
Limitations of the study include its inability to confirm causality, as it was designed as a cross-sectional study. The findings only highlight associations between certain socio-demographic variables and depression among PLHIV in the study area. Depression can also be influenced by factors not accounted for in the model used for this study, such as social support, job satisfaction, and lifestyle differences. Interviewer-administered questionnaires, while advantageous, can also be susceptible to various biases. Despite efforts to minimize such biases through interviewer training, the use of standardized survey tools, and data quality checks, we acknowledge that some biases may not have been completely eliminated. Additionally, the relatively small sample sizes in some categories of predictor variables might have introduced bias and limited the generalizability of our findings. Further research with larger and more diverse samples is recommended to validate these results and enhance their applicability.
Future directions
Future research should include longitudinal studies to determine causal relationships between sociodemographic factors and depression among this vulnerable population. Studies may also incorporate factors such as social support, access to healthcare, lifestyle differences, job satisfaction, job security, and ethnicity as predictor variables. Given the recognized role of internalized HIV stigma as a significant risk factor for depression among PLHIV, future studies should explore its development and impact on depressive symptoms across different age, gender, educational, and occupational categories of PLHIV, especially in low- and middle-income countries, using various network analysis models. Additionally, considering the potential emergence and re-emergence of infectious diseases, predictive models could be developed to assess the impacts of future epidemics and pandemics on the occurrence of depressive symptoms among PLHIV.
Conclusions
The findings revealed a high prevalence of depression, indicating a need for improved management of mental health issues among PLHIV on ART in the study area. Being male, officially employed, and earning a moderate monthly income of ₦50,000–200,000 (>$65–260) were significantly associated with reduced odds of depression. These findings may inform policy and intervention strategies for addressing mental health issues among PLHIV on ART. PLHIV categories used as reference groups in the present study, as well as other categories with comparable odds for depression, may benefit from targeted mental health support systems. Promoting mental health and well-being for everyone is crucial, regardless of their specific mental health condition, as prevention and early intervention can reduce suffering and improve overall quality of life.
Declarations
Acknowledgement
The authors would like to thank Miss Ronke Ajayi, Madam Scholar, and Miss Miriam Silas for assisting in collecting and compiling the data.
Data sharing statement
The data used to support the findings of this study are available upon reasonable request from the corresponding author at [email protected].
Ethical statement
Ethical approval for this study was obtained from the Health Research Ethics Committee (HREC) of Bingham University Teaching Hospital, Jos, Nigeria, under approval number NHREC/21/05/2005/00950. Informed written consent was obtained from the participants before recruitment into the study. The study was conducted in accordance with the principles of the Declaration of Helsinki.
Funding
This study did not receive any external grant or funding. The authors did not receive any financial gain as a result of this study.
Conflict of interest
The authors have no conflict of interest related to this publication.
Authors’ contributions
Study design and supervision (JO, OAA, PE, AAGC), data collection (JO, PE, GOS, AE, AAGC), literature search (OAA, GOS, AE), analysis and interpretation of data (OAA, AAGC), and preparation of original and revised manuscripts (OAA). All authors have made significant contributions to this study and have approved the final manuscript.