Prevalence and barriers to accessing depression care among people living with HIV in Juja Sub-County,Kiambu County,Kenya

Abstract

Background

Depression care in Kenya has limited access due to a shortage of specialists and inadequate finances for mental health services. This study aimed to determine the prevalence and barriers to accessing depression care among people living with HIV in Juja Sub-County, Kiambu County, Kenya.

Methods

A quantitative analytical cross-sectional study was used. A total of 329 people living with HIV receiving comprehensive care at six public health facilities in Juja Sub-County were selected using a stratified sampling technique. Data were collected using interviewer-administered questionnaires after obtaining informed consent from each study participant. The prevalence of depression was assessed using the PHQ-9 scale. Data analysis was done using R. Descriptive statistics were computed. An ordinal logistic regression model was used to determine the factors associated with the prevalence of depression. A binary logistic regression model was used to determine the individual barriers to accessing depression care. Adjusted odds ratios (AORs) with a 95% confidence interval were used to measure the association, and a p-value of less than 0.05 was considered statistically significant.

Results

The prevalence of depression was 27.4% (95% CI: 22.7–32.6), and 84.4% of affected participants did not access depression care. Depression was significantly associated with poor adherence to HIV medication (AOR = 2.42; 95% CI: 1.53–3.85), poor social support (AOR = 0.57, 95% CI: 0.33–0.99), and high perceived HIV stigma (AOR = 1.85; 95% CI: 1.18–2.93). Additionally, poor adherence to HIV medication (AOR = 0.21; 95% CI: 0.04–0.76) emerged as a significant barrier to accessing depression care.

Conclusions

A severe treatment gap exists, with 84.4% of depressed people living with HIV not receiving needed care despite a high depression prevalence of 27.4%.

Keywords

depression HIV/AIDS mental healthcare access treatment barriers Kenya

Introduction

Human Immunodeficiency Virus (HIV) and acquired immunodeficiency syndrome (AIDS) are among the most serious global health problems today. Since it was discovered in the 1980s, HIV/AIDS has affected more than 91.4 million people. About 40.8 million people were living with the virus as of 2024.¹ While antiretroviral therapy (ART) has turned HIV from a deadly infection into a long-term but manageable illness, caring for people with HIV/AIDS aims not only at ensuring viral suppression but also at mental health and psychosocial well-being. Depression is a mental illness in which a person experiences feelings of sadness and loss of interest in activities, and is unable to perform routine everyday tasks for at least 2 weeks.² The intersection of HIV/AIDS and mental health is a critical and relatively under-researched area of public health, and there are several serious issues here, especially in resource-constrained settings.³ One study found depression prevalence to be around three times higher among people with HIV/AIDS than in the general population, with prevalence reported at 50.5% and 17.5% respectively.⁴ The prevalence among HIV/AIDS patients ranges from 20% to 40% worldwide.⁵ One study reported a prevalence of 31% (95% CI: 28%–34%)⁶ and another study reported a prevalence of 32.2%.⁷

The prevalence in Sub-Saharan Africa ranges from 6% to 59%.⁸ In another study, the prevalence rate varies between 9.3% and 60%, with a total estimated prevalence of 33.1%.⁹ A more recent cross-sectional study reported a prevalence rate of 31%.¹⁰ A 2022 study of HIV/AIDS patients in Ghana also indicated a prevalence of 28.6%.¹¹ A prevalence of 32.2 was reported in one study in Kenya.¹² Equally, a study carried out at Mama Lucy Hospital in Nairobi recorded a prevalence of 35.6%.¹³

It is essential to diagnose and manage cases of depression at an early stage.¹⁴ Individual and health system barriers hinder mental health care among patients with HIV/AIDS.¹⁵ The issue of access to mental health care among HIV/AIDS patients is a timely challenge to be handled to enhance health outcomes.¹⁶ Among HIV patients with depression, 82% do not receive any treatment, 93 % do not receive adequate treatment, and 95 % do not achieve remission from depression.¹⁷ Also, another study reported that only 9% of HIV patients with depression receive sufficient care.¹⁸

Depression among people with HIV is a significant public health issue with far-reaching implications. Scientifically, this co-morbidity results in a synergistic relationship in which depression enhances the progression of the HIV disease by compromising immune system functioning, whereas HIV-related inflammation and neurological changes worsen the symptoms of depression.¹⁹ From a clinical perspective, untreated depression is associated with poor adherence to ART, elevated viral levels, accelerated CD4 + cell depletion, and increased mortality rates among people living with HIV.²⁰ The health impact is both significant and disproportionately high among HIV patients since depression promotes transmission of HIV due to increased risks and non-adherence to treatment.²⁰ The consequences of this dual burden in resource-limited environments, such as Kenya, where mental health access is suboptimal and HIV prevalence is high at 3.7% among adults,²¹ jeopardize the health and well-being of individuals, and are detrimental to national HIV control efforts.

Although depression among people with HIV is well-documented globally, there are substantial knowledge gaps on access to depression care and related factors, especially in sub-Saharan Africa. Available studies are primarily centered on estimating depression prevalence rather than investigating care-seeking behavior and individual-level factors that might contribute to depression. Moreover, the data regarding the percentage of people with HIV with depressive disorders and who seek mental health services is limited, leaving a significant gap in the knowledge base necessary in planning the delivery of mental health services. This paper helps fill these knowledge gaps by offering a localized study covering Juja Sub-County, where local evidence is critical to policy interventions. Identifying depression prevalence and patterns of care among people with HIV is essential to the successful design of intervention strategies in the Kenyan healthcare system. Moreover, determining individual depression risk factors will guide the risk stratification and specific screening in HIV medical facilities. The results will be vital towards incorporating mental health screening and referral systems into the already established HIV care programs, maximizing resources allocated to mental health care, and formulating specific interventions aimed at high-risk groups. This evidence will help support Kenya in its bid to deliver comprehensive HIV care that equally addresses physical and mental health needs.

This research aims to: (1) determine the prevalence of depression among people with HIV; (2) assess the proportion of patients diagnosed with depression who have accessed depression care services; (3) identify individual factors associated with the prevalence of depression; and (4) determine patient-related barriers to accessing depression care among people with HIV. The results of the study will be used to provide evidence-based guidelines to enhance access to depression care and design specific interventions targeting patients with HIV who are at increased risk of developing depression.

Methods

Study setting

The study was carried out in Juja sub-county, Kiambu County, Kenya. Juja Sub-County is an electoral constituency in Kiambu County, Kenya. It covers a constituency area of approximately 326.60 km².²² Juja town is about 30 km north of Nairobi, between Thika and Ruiru. The study included six public healthcare facilities within the sub-county (Gachororo Health Centre, Kalimoni Mission Hospital, JKUAT Hospital, Juja Farm Health Center, Hamundia Health Centre, and GSU Dispensary). Gachororo Health Centre is located approximately 2 km southeast of Juja town, off Gachororo Road, near the main gate of JKUAT. Kalimoni Mission Hospital is situated along Kalimoni Road, approximately 1.5 km southwest of Juja town, near the Kalimoni Catholic Parish. JKUAT Hospital is located inside JKUAT’s main campus, approximately 1 km from Juja town, accessible via the university’s main access road off Thika Superhighway. Juja Farm Health Centre is positioned along Juja Farm Road, approximately 10 km east of Juja town centre, just past the Juja Farm Trading Centre. Hamundia Health Centre is in Theta (Off Nairobi Thika Road – Murera Sisal Stage – About 6 km Near Kwihota Primary School). GSU Dispensary is found within the GSU Training School compound, approximately 2.5 km northwest of Juja town, accessible via Kenyatta Road.

Study design

This is a quantitative analytical cross-sectional study. The design was chosen because it is the most appropriate to achieve the study objectives. The design was selected because it is suitable for determining the prevalence of a disease at a specified time, associated factors, and barriers within a specific population.^23–25

Study population

The study population was people with HIV attending Comprehensive Care Centres (CCCs) of government health facilities located in Juja Sub-County. Participants were outpatients seen at routine clinic appointments (usually every 3 months) for follow-up and refills of antiretroviral therapy. The target population was 1986 HIV/AIDS patients enrolled in CCCs in the sub-county. The sample size of 329 was determined using Cochran’s (1977) formula using the prevalence of depression among HIV/AIDS (35.6%).¹³ The formula n₀= (1.96)² 0.356 (0.644)/(0.05)² = 352.2. Finite population correction was done: n = 352/[1 + {(352 – 1)/1986}], resulting in a sample of 299. A 10% (30 participants) non-response allowance was applied, giving 329. Inclusion criteria: All patients with HIV/AIDS aged 18 years and older receiving comprehensive care at the selected public health facilities in Juja Sub-County who consented to participate. The exclusion criteria were people with acute or chronic conditions that would prevent them from participating in a study effectively, guaranteeing that participants could interact meaningfully with the data collection tools and give credible answers.

Data collection procedures

Interviewer-administered questionnaires were conducted in English or Kiswahili. The questionnaire included questions on sociodemographic characteristics, depression, social support, HIV stigma, HIV medication adherence, tobacco, alcohol, prescription medication, and other substance use, and access to depression care. Depression was assessed using the Patient Health Questionnaire (PHQ-9). A score of 0–4 is classified as no depression, 5–9 as mild depression, 10–14 as moderate depression, 15–19 as moderately severe depression, and >= 20 as severe depression.²⁶ The overall prevalence of depression was estimated with the sum score >= 10, with a sensitivity and specificity of 88%.²⁷

Social support was measured using the Oslo 3-item social support scale (OSSS-3). A score of 3-8 is Poor Social Support, 9–11 Moderate Social Support, and 12–14 Strong Social Support, with a Cronbach’s alpha of 0.88.²⁸ HIV stigma was measured with a 12-item short version of the scale, with scores ranging from 12 to 48. A total score of 30 was used as a cut-off point to identify HIV stigma, with participants scoring 30 and higher as having “high perceived HIV stigma”. In contrast, a score of less than 30 was deemed to be “low perceived HIV stigma.” The Cronbach’s alpha of this scale is 0.7.²⁹ Adherence to HIV therapy was measured using the Morisky Medication Adherence Scale (MMAS-4). The MMAS-4 scale was dichotomized into High Adherence (score 0) and Low-Moderate Adherence (1 point or more).³⁰ The scale demonstrated acceptable internal consistency in this study (α = 0.74; 95% CI: 0.69–0.78), higher than the initially reported α of 0.61.³¹ The Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) scale was used to assess substance use. The TAPS scale has two sections; TAPS-1 has four items about substance use in the past 12 months in four categories, and TAPS-2, which rates the risk levels of substance use into three categories: No Use in Past 3 Months (sum score 0), Problem Use (sum score 1), and Higher Risk (sum score above 2).³² Access to depression care was assessed using a 3-item scale. The 3-item Access to Care scale, adapted from existing measures, demonstrated excellent internal consistency (α = 0.84; 95% CI: 0.80–0.86).

Data analyses

Data analysis was done using R. We considered all statistical tests significant with p < 0.05 with a 95% confidence interval. Descriptive statistics were used to determine the prevalence of depression and access to care. In determining the factors associated with the prevalence of depression, ordinal logistic regression was used. We used binary logistic regression to determine sociodemographic and psychosocial barriers to accessing depression care.

Ethical considerations

This study was conducted in accordance with ethical standards. A research permit was granted by the National Commission for Science, Technology & Innovation (NACOSTI) with license number- NACOST/P/24/37288. Ethical approval was sought from the institutional ethical review board. Permission was also sought from each facility before data was collected. Informed consent was obtained from the study participants. The participants were informed about the nature of the study and the data collection procedure. Each participant was handed a consent form, which was read and explained to them, and any clarifications needed were provided. Confidentiality was maintained during data collection. The filled consent forms were protected and were only accessible to the principal investigator. The research did not involve any invasive procedures. There were no direct benefits to the participants, which was well explained in the informed consent. Participants with moderate and severe depression were referred to a psychiatrist.

Results

Sociodemographic characteristics

329 people with HIV were interviewed over 8 weeks. The majority were between 35 and 44 years (31.9%). A significant proportion (94.8%) were Christians. On marital status, 33.1% are single, 45.0% are married, 1.5% are divorced, 9.1% are widowed, and 11.3% are separated. On employment status, 31.9% of the participants are employed, 33.4% are unemployed, 22.8% are self-employed, and 11.9% are students (Table 1).

Table 1.

Sociodemographic characteristics of the respondents.

Demographic statistics	Full sample
Demographic statistics	Frequency	Percent
Gender
Male	137	41.6
Female	192	58.4
Age
18–24	30	9.1
25–34	74	22.5
35–44	105	31.9
45–54	72	21.9
55–64	38	11.6
65 or over	10	3.0
Religion
Christian	312	94.8
Muslim	8	2.4
None	8	2.4
Traditional	1	0.3
Hindu	0	0
Marital status
Single	109	33.1
Married	148	45.0
Divorced	5	1.5
Widowed	30	9.1
Separated	37	11.3
Employment status
Employed	105	31.9
Unemployed	110	33.4
Self-employed	75	22.8
Student	39	11.9

Note: All variables are summarized as frequencies (n) and percent (%).

Almost two-thirds (64.1%) of the participants had high medication adherence, with 35.9% having low-moderate adherence. 73.3% have poor social support, 26.7% have moderate social support, and none have strong social support. 54.1% have a high perceived HIV stigma. On tobacco use, 93.3% did not use in the past 3 months, 2.1% problem users, and 4.6% higher risk users. On alcohol use, 83.6% did not use in the past 3 months, 3.6% were categorized as problem users, and 12.8% as high-risk users. 95.7% did not use marijuana in the past 3 months, 1.5% are problem users, and 2.7% higher-risk users (Table 2).

Table 2.

Psychosocial, substance use, and medication adherence-related characteristics of the respondents.

Descriptive statistics	Full sample
Descriptive statistics	Frequency	Percent
HIV medication adherence
High adherence	211	64.1
Low-moderate adherence	118	35.9
Social support
Poor	241	73.3
Moderate	88	26.7
Strong	0	0
Perceived HIV stigma
Low perceived stigma	151	45.9
High perceived stigma	178	54.1
Tobacco use
No use in the past 3 months	307	93.3
Problem use	7	2.1
Higher risk	15	4.6
Alcohol use
No use in the past 3 months	275	83.6
Problem use	12	3.6
Higher risk	42	12.8
Marijuana (hash, weed) use
No use in the past 3 months	315	95.7
Problem use	5	1.5
Higher risk	9	2.7

Note. The variables are categorical. Summarized as frequencies (n) and percent (%).

Prevalence of depression

The prevalence of depression (PHQ-9 score ≥10) is 27.4% (95% CI: 22.7–32.6). The mean (SD) total score of PHQ-9 was 5.24 (6.09). The severity level distribution was as follows: none 60.2%, mild 12.5%, moderate 18.5%, moderately severe 5.2%, and severe 3.6% (Figure 1).

Figure 1.

Depression severity.

The proportion of those diagnosed with depression who accessed depression care services

The proportion of those diagnosed with depression and who have accessed depression care services is 15.6% (95% CI: 9.07%–25.06 %). Therefore, 84.4% of HIV/AIDS patients with depression did not access the care they needed (Figure 2).

Figure 2.

Proportion of access to depression care.

Factors influencing depression prevalence

Ordinal logistic regression revealed that the prevalence of depression among people with HIV is influenced by adherence to medication, social support, and perceived HIV stigma. Values with p-value <0.05 were considered significant. Patients with low to moderate adherence to medication were two times (AOR = 2.42; 95% CI: 1.53–3.85) more likely to have depression compared to those with high adherence. The odds of patients with moderate social support having depression were 43% (AOR = 0.57; 95% CI: 0.33–0.99) lower than those with poor social support. Patients with high perceived HIV stigma were two times (AOR = 1.85; 95% CI: 1.18–2.93) more likely to have depression than their counterparts (Table 3).

Table 3.

Factors influencing depression prevalence.

Predictor	Category	CORs (95% CI)	AORs (95% CI)	p-value
Adherence to HIV medication	Low-moderate	2.53(1.63,3.94)	2.42(1.53,3.85)	0.00015
Adherence to HIV medication	High	1	1	0.00015
Social support	Moderate	0.484(0.282,0.807)	0.57(0.33,0.99)	0.0483
Social support	Poor	1	1	0.0483
Perceived HIV stigma	High	1.757(1.14,2.727)	1.85(1.18,2.93)	0.0076
Perceived HIV stigma	Low	1	1	0.0076
Tobacco use	Higher risk users	1.778 (0.657,4.605)	1.90 (0.15,3.89)	0.2078
Tobacco use	Non-users	1	1	0.2078
Alcohol use	Higher risk users	0.979 (0.510,1.821)	0.69 (0.67,5.49)	0.3173
Alcohol use	Non-users	1	1	0.3173
Marijuana use	Higher risk users	1.284 (0.321,4.525)	1.41 (0.19,2.66)	0.6290
Marijuana use	Non-users	1	1	0.6290
Tobacco use	Problem users	1.036 (0.213,4.034)	0.81 (0.33 1.40)	0.7971
Tobacco use	Non-users	1	1	0.7971
Alcohol use	Problem users	0.888 (0.233,2.844)	0.78 (0.06,2.52)	0.6986
Alcohol use	Non-users	1	1	0.6986
Marijuana use	Problem users	0.700 (0.099,3.338)	0.46 (0.32,5.44)	0.3905
Marijuana use	Non-users	1	1	0.3905

NB: 1-Reference Group.

Patient-related barriers to accessing depression care

Binary logistic regression revealed that adherence to HIV medication is significantly associated with access to depression care. The odds of accessing depression care among patients with low to moderate adherence to medication were 79% (AOR = 0.21; 95% CI: 0.04–0.76) lower than those of patients with high adherence (Table 4).

Table 4.

Patient-related barriers to accessing depression care.

Predictor	Category	CORs (95% CI)	AORs (95% CI)	p-value
Gender	Female	2.02 (0.62, 7.9)	2.22 (0.60,10.05)	0.2571
Gender	Male	1	1	0.2571
Adherence to HIV medication	Low-moderate	0.2 (0.04, 0.7)	0.21(0.04, 0.76)	0.0278
Adherence to HIV medication	High	1	1	0.0278
Social support	Moderate	0.52 (0.15, 2.11)	0.30 (0.06, 1.47)	0.1302
Social support	Poor	1	1	0.1302
Perceived HIV stigma	High	0.34 (0.07, 1.18)	0.26 (0.05, 1.07)	0.0863
Perceived HIV stigma	Low	1	1	0.0863

NB: 1-Reference Group.

Discussion

This study found that more than one in four people with HIV (27.4%) in Juja Sub-County report clinically significant depression, and almost one in ten (8.8%) report a moderately severe to severe depression, which requires immediate medical attention. This prevalence corresponds to the global estimates of 20–40%.³³ The findings are consistent with data in similar resource-limited settings, such as Ghana (28.6%)¹¹ and Iran (31%).⁶ Nevertheless, the rates are significantly lower compared to those reported in Ethiopia (52.4%) and among pregnant people with HIV (47.6%), indicating that the population and healthcare environment are essential factors contributing to the prevalence of depression.^26,34 The comparative lower prevalence compared to Ethiopian counterparts may be related to variations in healthcare integration, where Kenyan HIV care programs have gained increased incorporation of mental health screening as part of comprehensive care. By contrast, a higher prevalence rate than the cases in Nigeria (16.3%) may indicate different levels of stigma, developed social supports, or even access to higher treatment availability in other African settings.³⁵ These findings highlight the large but potentially treatable mental health burden among HIV patients in this setting, and underscore the importance of regular depression screening and mental health integration as part of existing HIV treatment services.

The fact that only 15.6% of patients with depression accessed care services indicates a huge treatment gap, with four-fifths still in need of treatment. The findings are in line with one study which reported that 82% of patients with depression received no treatment.¹⁷ Similarly, another study reported treatment rates of 7–28%,³⁶ and another reported that only 16.5% received adequate treatment.³⁷ The consistency among populations and health care contexts suggests systemic rather than local barriers. This treatment gap is likely due to interlocking factors, including HIV and mental health dual stigma, inadequate healthcare provider training in screening patients for depression, siloed care systems linking HIV and mental health, and patient reluctance to report psychological symptoms. The fact that this gap persists across studies in different settings indicates that despite increasing awareness of the co-occurrence of HIV and depression, knowledge translation to accessible, integrated care is a critical gap with profound implications for patient outcomes and HIV epidemic control.

This extremely high correlation between low-moderate medication adherence and prevalence of depression (AOR = 2.42; 95% CI: 1.53–3.85) is a phenomenon that has been well demonstrated in HIV care because of a bidirectional association. The finding aligns with other studies: (AOR = 1.5; 95% CI: 0.90–2.61),³⁸ (AOR = 2.84),³⁹ (AOR = 5.96; 95% CI: 1.74–20.52),⁴⁰ and (OR = 1.13; 95% CI: 1.02–1.26).⁴¹ The association is likely mediated through multiple pathways: lack of adherence can lead to viral replication, disease progression, and resulting psychological distress, and depression itself can lead to poor self-care behaviors and failure to adhere to antiretrovirals. This forms a cycle where depression affects adherence, which can cause treatment failure and worsening depression. The presence across widely varying populations indicates that this could be a deeply rooted issue in HIV care. These findings underscore the need for multifaceted interventions that address both mental health and adherence-related behaviors.

The findings indicate a significant relationship between the prevalence of depression and social support. According to our study, individuals who have moderate social support are 43% less likely to experience higher levels of depression as compared to those with poor social support (AOR = 0.57; 95% CI: 0.33–0.99). The findings agree with one study that reported that people with HIV with poor social support are 2.5 times more likely to experience depressive symptoms as compared to those with good social support.²⁸ Another study reported that participants who had poor support were 2.53 times more likely to experience depression than those with strong social support (OR = 2.53; 95% CI: 1.47–4.35).⁴² The mechanical linkage between the two is that social support provides emotional protection against HIV-related stress and stigma, facilitates coping behaviors, and can provide instrumental support in health-care access and medication adherence. Furthermore, positive relationships could negate the isolation and prejudice often encountered by patients, thus making them less vulnerable to depression. The replicability of these protective effects across studies indicates that social support interventions have the potential to be cost-effective add-ons to clinical depression treatment, with the positive impact on depression, self-efficacy, and resilience likely to improve both mental health and HIV care outcomes.

High perceived stigma was significantly associated with the prevalence of depression, with AOR = 1.85; 95% CI: 1.18–2.93. This means that individuals with high perceived stigma are 1.85 times more likely to experience higher levels of depression severity as compared to those with low perceived stigma. The findings align with a study conducted in Ethiopia, which reported AOR = 2.79; 95% CI: 1.84–4.23.⁴² Similarly, another study found that patients experiencing high perceived stigma are 2.43 times likely to have depression compared to those with low perceived stigma; OR = 2.43; 95% CI: 1.13–5.21.⁴⁰ Stigma probably worsens depression by bringing out internalized shame, social isolation, and disclosure fear, which adds to the mental burdens brought about by HIV.

Low to moderate adherence to medication emerged as a barrier to depression care access (AOR = 0.21; 95% CI: 0.04–0.76), reducing odds by 79%. These findings align with one study that reported that low adherence to ART significantly impacts a person’s willingness and capability to access health services.⁴³ The association reflects a cascade in which medication non-adherence signifies disengagement with care, and resulting missed chances of mental health screening and referral eventually culminate in untreated depression, contributing to further adverse HIV and mental health impacts.

Limitations

The study’s cross-sectional design limits the establishment of causality between identified risk factors and depression. The study focused on depression among people living HIV, excluding other psychiatric conditions such as anxiety disorders, adjustment disorders, psychosis, personality disorders, and cognitive impairments.

Conclusion

The study identified a severe treatment gap, with 84.4% of depressed patients not receiving needed mental health care despite a high depression prevalence of 27.4%. Factors contributing to depression in patients include poor adherence to medications, insufficient social support systems, and high perceived stigma, while medication non-adherence was identified as the primary barrier to accessing depression care services. The trends observed are consistent with those found worldwide, indicating that population-level interventions that simultaneously address stigma reduction, increase social support, and improve adherence could significantly reduce the burden of depression in this high-risk group. These findings highlight a critical need to develop integrated models of HIV-mental health that consider clinical, psychosocial, and structural barriers simultaneously. Policy efforts should aim at educating medical professionals in HIV-mental health co-competencies and developing referral processes that mitigate the obstacles to accessing depression care. Future studies should explore the extent to which community-based interventions may focus on social support networks and establish the longitudinal associations between adherence patterns and mental health service use to guide intervention efforts.

Footnotes

Acknowledgement

We thank Kiambu County for allowing us to conduct the study in Juja Sub-County, the facilities in Juja Sub-County for their permission to collect data, and the study participants for their time and contributions.

ORCID iD

Naomi Kemboi

Consent for publication

All participants provided consent for the publication of anonymized data derived from the study.

Author contributions

Kemboi N was responsible for research procedures and manuscript writing. Mwangi E and Kigundu A were the research supervisors.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Human and animal welfare statement

This study was conducted with approval from the relevant institutional or national research ethics committee and in accordance with the 1964 Helsinki Declaration and its subsequent amendments. The study obtained ethics clearance from the JKUAT Institutional Ethics Review Committee, reference number (JKU/ISERC/02317/1343). A research permit was granted by the National Commission for Science, Technology & Innovation (NACOSTI) with license number- NACOSTI/P/24/37288. Permission was sought from Kiambu County, reference: KIAMBU/HRDU/AUTHO/KEMBOI N. A. All participants gave written informed consent before the study. No animals were involved in this study.

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