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IJERPHInternational Journal of Environmental Research and Public Health
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19 October 2026

13 Pages

HIV Status and Non-Communicable Diseases in Kenya: Sex-Stratified Findings from the Kenya Demographic and Health Survey 2022

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Centre for Clinical Research, Kenya Medical Research Institute, Nairobi P.O. Box 54840-00200, Kenya
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Development Hub, Health Policy Division, Nairobi P.O. Box 75673-00200, Kenya
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Department of Medicine, University of California, San Francisco, CA 94158, USA
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Institute for Global Health Sciences, University of California, San Francisco, CA 94158, USA
Int. J. Environ. Res. Public Health2026, 23(10), 1329;https://doi.org/10.3390/ijerph23101329
This article belongs to the Special Issue Non-Communicable Diseases: Epidemiology, Determinants, and Prevention Strategies for the Global Burden of Disease
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Highlights

Public health relevance—How does this work relate to a public health issue?
  • People living with HIV experience a higher burden of selected noncommunicable disease risk factors and conditions than HIV-negative populations.
  • Tobacco and alcohol use, hypertension, depression, and selected cancers contribute to this burden, with important differences by sex.
Public health significance—Why is this work of significance to public health?
  • The findings demonstrate the growing overlap between HIV and noncommunicable diseases in Kenya.
  • Sex-specific patterns identify population groups that may benefit from more targeted prevention, screening, and treatment.
Public health implications—What are the key implications or messages for practitioners, policy makers, and/or researchers in public health?
  • Noncommunicable disease prevention, screening, and management should be strengthened within routine HIV care.
  • Integrated services should use sex-specific approaches to address differences in risk factors and disease burden.

Abstract

The dual burden of HIV and non-communicable diseases (NCDs) presents a significant health challenge in low- and middle-income countries, but limited evidence exists on their intersection. This study is a cross-sectional, secondary data analysis of the nationally representative Kenya Demographic and Health Survey (KDHS) evaluating the burden and multimorbidity of NCDs and mental health conditions by HIV status among women aged 15–49 years and men aged 15–54 years. Using survey-weighted logistic regression, we estimated crude and adjusted odds ratios (aOR), controlling for age group, place of residence, educational level, marital status, and county. Depression prevalence was significantly elevated among women living with HIV (9.8% vs. 3.0%; aOR = 2.70, p = 0.001) and higher but not statistically significant among HIV-positive men (7.6% vs. 2.6%; crude OR = 3.10, p = 0.005; aOR = 2.21, p = 0.069). HIV infection was independently associated with higher odds of anxiety in both men (aOR = 3.21, p = 0.047) and women (aOR = 2.74, p = 0.017). Crude hypertension was elevated in HIV-positive men (8.7% vs. 4.2%, p = 0.039) but was no longer significant after sociodemographic adjustment (aOR = 1.44, p = 0.390). Cervical cancer risk was higher but not statistically significant (aOR = 4.21, p = 0.053). Adults living with HIV in Kenya experience a disproportionate burden of common mental disorders (depression and anxiety) and heightened cervical cancer risk. Integrating routine mental health screening and cervical cancer surveillance directly into HIV primary care platforms is vital to address sex-specific chronic disease disparities.

1. Introduction

Non-communicable diseases (NCDs), including mental health conditions, are an increasingly significant contributor to global morbidity and mortality, especially in low- and middle-income countries (LMICs), where 73% of NCD deaths occur [1,2]. Epidemiological, demographic, socioeconomic, and health system-related factors drive this trend. Improvements in sanitation, immunization, and healthcare access have shifted the focus from infectious diseases to chronic conditions as the leading causes of death [3]. In addition, many LMICs are experiencing rapid population growth and rising life expectancy, resulting in more people living long enough to develop NCDs, which are more prevalent with aging [4,5]. Furthermore, urbanization and lifestyle changes in LMICs have introduced new behavioral risk factors, including decreased physical activity, increased consumption of energy-dense processed foods, and higher rates of tobacco and alcohol use [6,7,8]. However, many health systems in LMICs remain primarily focused on addressing infectious diseases, hence making them ill-equipped to manage NCDs effectively [9,10]. In sub-Saharan Africa (SSA), health systems continue to prioritize infectious diseases despite the rising prevalence of NCDs [11].
People living with HIV (PLHIV), especially in SSA and other LMICs, face a growing burden of NCDs due to increased life expectancy, long-term antiretroviral therapy, and HIV-related immune dysregulation [12,13]. While ART has significantly improved life expectancy and reduced HIV-related mortality, it is also associated with metabolic complications, including central adiposity, dyslipidemia, and insulin resistance [14]. Chronic immune activation and inflammation further elevate the risk for cardiovascular diseases and other NCDs [15]. This phenomenon, often referred to as “accelerated aging,” means PLHIV are experiencing age-related illnesses earlier than the general population [16]. Lower CD4 counts have also been linked with increased arterial stiffness and a heightened risk of hypertension [17]. Therefore, conditions such as hypertension, diabetes, dyslipidemia, depression, and obesity are increasingly prevalent among PLHIV, with pooled prevalence estimates in SSA as follows: hypertension (20.1%), diabetes (5.4%), hypercholesterolemia (21.3%), depression (30.4%), and overweight/obesity (32.2%) [12].
Kenyan data reflect these trends. In a cross-sectional study from Meru County, 55.4% of PLHIV had at least one comorbid condition, with dyslipidemia (21.2%) and hypertension (20.2%) being the most common [18]. A retrospective data analysis on a nationally representative study sample revealed that NCD comorbidity among PLHIV was present in 11.5% of the population. Among PLHIV with a recorded NCD, cardiovascular disease was the most frequently documented condition, affecting 88.9% [19]. Studies also indicate sex differences, with women more likely to present with obesity and diabetes [20,21]. At the same time, men tend to engage in high-risk behaviors such as tobacco and alcohol use more regularly [18,22].
The increasing burden of NCDs among PLHIV poses a significant challenge for health systems in Kenya and other LMICs [18]. Vertical programming and fragmented services hinder the potential for integrated care models that can effectively address both HIV and NCDs [9]. There is an urgent need to utilize existing HIV platforms to enhance NCD prevention, screening, and management. Task-shifting, community health worker models, and peer-led interventions that have been proven effective in HIV care could be adapted for the management of NCDs in this population [23].
However, inadequate surveillance systems and limited data on the intersection of HIV and NCDs hinder effective policymaking and resource allocation [24]. Addressing these gaps through research and data-driven planning is essential for developing targeted, context-specific interventions [25].
Despite growing recognition of the HIV-NCD syndemic, few studies in Kenya have directly compared NCD risk profiles between PLHIV and the general population using nationally representative data. This study aims to address this gap by analyzing differences in sociodemographic profiles and the burden of NCD risk factors among men and women living with HIV and their HIV-negative counterparts in Kenya.

2. Materials and Methods

2.1. Study Design and Data Source

A cross-sectional secondary analysis design was used to analyze data from the 2022 Kenya Demographic and Health Survey (KDHS) [26]. The KDHS is a nationally representative household survey that provides detailed information on demographics, reproductive health, child health, and key health behaviors, including tobacco use, as well as knowledge, attitudes, and behaviors related to HIV/AIDS. The 2022 KDHS was carried out by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other partners. Data collection was conducted from 17 February to 19 July 2022. All data management and statistical analyses were performed using Stata (Version 14.0, StataCorp LLC, College Station, TX, USA).

2.2. Study Setting and Population for Comparative Analysis

The 2022 KDHS surveyed 42,022 households throughout Kenya, including 16,611 in urban areas and 25,411 in rural areas. The overall response rate was 98%. The survey involved 14,818 men aged 15–54 years and 33,137 women aged 15–49 years. Of the 47,955 total survey respondents, HIV serotesting was administered to a nationally representative 50% household subsample per standard KDHS biomarker protocol. Within this subsample, 23,887 individuals (13,689 women and 10,068 men) completed testing with valid results [26].

2.3. Data Resource and Measurement of HIV Status and NCD Risk Factors

HIV status was determined through blood tests administered as part of the KDHS biomarker questionnaire. Participants were classified as either HIV-positive (PLHIV) or HIV-negative (general population comparison group). The KDHS estimated HIV prevalence at 1.29% among men and 2.41% among women in the surveyed population. NCD risk factors examined in this study included (but were not limited to) tobacco use, defined as current daily or non-daily use of any form of tobacco (binary: user vs. non-user). Alcohol use encompassed beverages such as beer, wine, spirits, and traditional brews, with at least one alcoholic drink consumed in the past month (binary: non-drinker vs. drinker). The KDHS also collected data on NCDs, such as hypertension, diabetes, heart disease, lung disease, arthritis, depression, anxiety, and specific cancers (breast and cervical), through self-reported diagnoses by a doctor or health worker [26]. Multimorbidity was defined as the concurrent presence of two or more chronic non-communicable conditions (among hypertension, diabetes, depressive symptoms, anxiety symptoms, and cancer). In addition to population-wide prevalence, we assessed multimorbidity within the subpopulation of adults presenting with at least one chronic condition, calculating the proportion living with two or more coexisting conditions, both overall and stratified by sex.

2.4. Measures for Sociodemographic and Risk Profile Comparison

Participants reported their age (15–19 years, 20–24 years, 25–29 years, 30–34 years, 35–39 years, 40–44 years, 45–49 years, 50–54 years), marital status (never married, living together, married, widowed/divorced/separated), and region; to account for sub-national geographic variation without running into the sparse-data issues common when analyzing rare conditions across 47 individual counties, we grouped counties into eight geographic regions: Coast, North Eastern, Eastern, Central, Rift Valley, Western, Nyanza, and Nairobi. This classification reflects Kenya’s historical administrative provinces and aligns with today’s regional economic and health partnerships, allowing us to capture broader geographic disparities while maintaining robust statistical models. Alongside geography, we assessed living standards using the standard DHS Wealth Index, which ranks households into five quintiles (poorest, poorer, middle, richer, richest) based on asset ownership, housing quality, and basic amenities as a proxy for overall socioeconomic status and healthcare access, residence (urban, rural), education (no education, primary, secondary, and higher education), occupation (unemployed, agriculture, service-manual, non-manual), and whether they received HIV treatment (yes, no). The KDHS defines unemployed as anyone who performed no paid or unpaid work in the past 12 months, encompassing both the economically unemployed and non-working populations, such as students, caregivers, retirees, and individuals with disabilities or illnesses.
In the KDHS 2022, non-communicable diseases (NCDs) and their risk factors were assessed using both self-reported history and standardized measurements. Depression and anxiety were captured through self-report questions where respondents were asked if they had ever been diagnosed with these conditions by a health professional. Hypertension was measured objectively: respondents aged 15 and above had three blood pressure readings taken at intervals, and hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or self-reported current use of antihypertensive medication [26].
For cancers, breast and cervical cancer were assessed by self-report of having ever been told by a doctor or other health care worker that the respondent had breast or cervical cancer, respectively. These measures reflect reported prior diagnoses, not clinically verified cancer. Diabetes was determined based on self-report of a prior diagnosis by a health professional [26]. Lifestyle risk factors included tobacco and alcohol use.

2.5. Statistical Analysis

We used descriptive statistics to describe the study sample, focusing on sociodemographic variables and the prevalence of NCDs and their risk factors.
All analyses accounted for the 2022 KDHS complex survey design, incorporating sampling weights, stratification, and clustering, and used Taylor series linearization to estimate standard errors and 95% confidence intervals (CIs). Bivariate comparisons between PLHIV and HIV-negative individuals were evaluated separately by sex using design-based Rao–Scott-corrected Pearson chi-square tests (F-transformed).
Multivariable survey logistic regression models were fitted separately for men and women across binary NCD outcomes: hypertension, diabetes, depression, anxiety, and cancers. The primary exposure was HIV serostatus, designating HIV-negative as the reference category (aOR > 1.0 indicates higher odds among PLHIV). Models adjusted for age group, marital status, residence (urban/rural), education level, and county clusters. Wide confidence intervals and non-estimable parameters arising from complete separation were evaluated in the context of subgroup sparsity in rare conditions.

3. Results

3.1. Sociodemographic Characteristics by HIV Status

Table 1 presents demographics stratified by sex and HIV status. Notable sociodemographic differences exist between PLHIV and the general population. Men and women living with HIV were older than the general population, which was typically younger (p < 0.001 for men and women). PLHIV had lower levels of formal education than their HIV-negative counterparts. A significant proportion of men (71.5%) and women (65.4%) living with HIV had received only primary education, compared with 35.2% and 36.7%, respectively, in the general population. Higher educational attainment was notably less common among PLHIV.
Table 1. Socio-demographics of adults with and without HIV in Kenya, 2022. Values represent unweighted sample frequencies, n, and unweighted column percentages, (%). Total analytical samples for individuals with valid HIV serology were N = 10,068 for men and N = 13,689 for women. Certain sub-modules (such as occupation: total men n = 6603, total women n = 9583) reflect restricted questionnaire modules administered only to eligible respondents and do not sum to the overall analytical sample. ART status was assessed exclusively among respondents living with HIV (n = 130 men, n = 332 women).
Economic disparities were also evident. PLHIV were overrepresented in the lower wealth quintiles, with only 3.1% of men and 6.6% of women living with HIV classified in the wealthiest category, compared with 18.4% and 20.4% in the general population, respectively (p < 0.001 for men and women).
A higher proportion of PLHIV were engaged in manual or service-related jobs, and fewer men and women living with HIV held non-manual labor occupations. Rural residency was also more prevalent among PLHIV (71.5% of men and 65.4% of women) than in the general population.
Geographically, a disproportionate number of PLHIV resided in the former Nyanza region, which accounted for nearly 45% of both males and females living with HIV, despite comprising only about 14% of the general population.

3.2. Non-Communicable Disease Prevalence and Risk Factors

PLHIV had higher reported rates of tobacco use, particularly among men (23.98% vs. 13.17%, p = 0.0171). Alcohol use was also higher in PLHIV across both sexes, although the differences did not reach statistical significance.
Notable and significant disparities emerged across mental health outcomes. Depression prevalence was significantly higher among women living with HIV than HIV-negative women (9.8% vs. 3.0%, p < 0.001), remaining strongly associated in multivariable analysis (adjusted odds ratio [aOR] = 2.70, p = 0.001). Among men, depression prevalence was also elevated (7.6% vs. 2.6%); this disparity was statistically significant in unadjusted analysis (OR = 3.10, p = 0.005), but significance eased marginally following adjustment (aOR = 2.21, p = 0.069). The odds of anxiety were significantly higher among individuals living with HIV across both sexes at the 5% level: men living with HIV had 3.21 times the odds of anxiety (aOR = 3.21, p = 0.047), and women living with HIV had 2.74 times the odds (aOR = 2.74, p = 0.017) relative to their HIV-negative peers.
Prevalence of non-communicable diseases (NCDs) also varied by HIV status. Among men, hypertension prevalence was higher in HIV-positive (8.7%) than HIV-negative (4.2%) individuals, a significant difference in bivariate analysis (OR = 2.17, p = 0.039), though this association was no longer significant after multivariable adjustment. Diabetes prevalence was low and similar (0.9% vs. 1.2%, OR = 0.74, p = 0.769). Among women, hypertension (10.5% vs. 9.6%, OR = 1.11, p = 0.619) and diabetes (1.2% vs. 0.9%, OR = 1.33, p = 0.648) did not differ significantly. Cervical cancer was significantly more prevalent among HIV-positive women (1.3% vs. 0.2%, OR = 6.74, p = 0.001) but attenuated to borderline statistical significance following multivariable adjustment, whereas breast cancer prevalence remained low and comparable (0.5% vs. 0.2%, OR = 3.10, p = 0.140).
Among surveyed adults diagnosed with at least one non-communicable disease or chronic condition (hypertension, diabetes, cancer, depression, or anxiety), 18.4% exhibited multimorbidity (defined as living with two or more co-existing conditions). The prevalence of multimorbidity among those with a chronic condition was higher among men (20.8%) than women (17.2%). See Table 2.
Table 2. Survey-weighted prevalence percentages for NCD conditions by HIV status and sex. Percentages are survey-weighted. Multimorbidity (≥2 co-existing conditions) was calculated among individuals diagnosed with at least one chronic condition (hypertension, diabetes, cancer, depressive symptoms, or anxiety symptoms).
Table 3 presents adjusted odds ratios from multivariable models controlling sociodemographic covariates (age group, residence, education, marital status, and county). After adjustment, the association with hypertension attenuated and was no longer statistically significant in men (aOR = 1.44, p = 0.390), while it remained non-significant among women (aOR = 0.81, p = 0.354). Similarly, no significant differences in diabetes odds by HIV status were observed among men (aOR = 0.49, p = 0.483) or women (aOR = 0.99, p = 0.983). Finally, women living with HIV exhibited elevated odds of cervical cancer (aOR = 4.21, p = 0.053) and higher odds of breast cancer diagnosis (aOR = 3.45, p = 0.137), though neither was statistically significant.
Table 3. Odds of non-communicable diseases among people living with HIV (PLHIV) compared with HIV-negative individuals.

4. Discussion

This study compared NCDs and related conditions among PLHIV with those without HIV, using nationally representative data. Compared with participants without HIV, both men and women living with HIV had significantly higher odds of anxiety, while significantly higher odds of depression were observed among women only. Hypertension in men and cervical cancer were significantly associated with HIV status before adjustment, but not after adjustment. No significant associations were observed for diabetes in either sex or self-reported breast cancer among women.
Our findings align with previous studies demonstrating an association between HIV and a higher prevalence of chronic physical and mental health conditions. PLHIV experience higher rates of psychological stress due to stigma, trauma, and depression related to their diagnosis or social circumstances [27]. Tobacco and alcohol may serve as maladaptive coping mechanisms for psychosocial distress [28,29]. Furthermore, PLHIV may be more vulnerable to poverty, marginalization, and social exclusion, all of which are determinants of increased substance use [30]. Substance use screening and counseling are often poorly integrated into HIV care programs in Kenya, allowing problematic use to persist undetected and untreated.
We found that depression was more prevalent among PLHIV than among individuals without HIV, with a larger magnitude of association for women compared to men. Globally, the prevalence of depression is consistently higher among women compared to men, a pattern also observed in our study [31]. Receiving an HIV-positive diagnosis can trigger emotional responses such as fear, shock, shame, and hopelessness, which can develop into depression, particularly when combined with internalized stigma or social rejection [32]. Additionally, experiencing HIV-related stigma and discrimination can lead to social withdrawal, loss of support systems, and chronic stress, all contributors to depression [27]. The chronic nature of HIV infection, which involves living with a lifelong, incurable condition, brings ongoing concerns about physical health, medication adherence, transmission risk, future relationships, and employment; these factors can heighten vulnerability to depressive symptoms [33]. Biologically, HIV can affect the central nervous system, and neuroinflammation, immune activation, and HIV-related neurocognitive impairment are linked to an increased risk of depression [34]. Certain ART regimens may also contribute to mood disturbances as they are associated with neuropsychiatric side effects [35,36]. Although depression screening for PLHIV has been initiated in Kenya, implementation remains inconsistent. Furthermore, individuals identified with moderate to severe symptoms often do not receive comprehensive assessments or referrals to specialized mental health services for appropriate treatment. This gap underscores the need to strengthen integration of mental health within HIV care through systematic screening, timely referral pathways, and task-shared interventions such as brief psychotherapies delivered by trained HIV care providers.
The link between HIV and hypertension is increasingly recognized, with growing evidence that PLHIV are at heightened risk of elevated blood pressure. One reason for this link is that HIV infection is associated with persistent low-grade inflammation and immune activation. This chronic inflammatory state contributes to endothelial dysfunction, arterial stiffness, and atherosclerosis, which are key pathways in the development of hypertension [37,38,39]. Additionally, certain ART regimens are linked to metabolic side effects that increase the risk of hypertension [14]. Moreover, as indicated earlier, PLHIV often face a higher prevalence of behavioral risk factors, such as smoking, alcohol use, poor diet, and physical inactivity; these behaviors independently contribute to the risk of hypertension. Notably, although crude analysis showed higher odds of hypertension among men living with HIV (OR = 2.17, p = 0.039), the association was attenuated and became non-significant after controlling for age and other sociodemographic characteristics (aOR = 1.44, p = 0.390). Higher odds of hypertension observed among men living with HIV, but not women, are consistent with findings from other studies [40]. This sex-specific pattern may be attributed to physiological differences, such as the protective effects of estrogen in premenopausal women, behavioral factors, such as lower rates of alcohol and tobacco use among women, and differences in healthcare utilization, with women generally more likely to engage with health services and having more opportunities for treatment [41].
Women living with HIV face a substantially higher risk of cervical cancer, with meta-analyses estimating a six-fold increase compared to HIV-negative women, broadly aligning with the four- to seven-fold elevated odds observed in our study (crude OR = 6.74; adjusted aOR = 4.21). This elevated vulnerability is largely attributed to HIV-associated immunosuppression, which impairs clearance of oncogenic human papillomavirus (HPV) and promotes persistence of high-risk HPV types [42] thereby increasing the likelihood of progression from cervical precancer to cancer. The burden is particularly pronounced in sub-Saharan Africa, where an estimated 20–64% of cervical cancer cases occur among women with HIV [43]. A Kenyan study found that, among women living with HIV who had high-risk HPV, abnormal cervical cytology was more frequent in those with multiple HPV infections than in those with a single infection [44]. These findings underscore the critical importance of scaling up HPV vaccination, implementing cervical cancer screening according to practice guidelines [45], and early initiation of antiretroviral therapy (ART) among women living with HIV.

Strengths and Limitations

Our study uses data from a large, nationally representative dataset, enabling robust population-level insights. We conducted sex-specific analyses to highlight differences in disease burden between men and women. Importantly, we included NCDs and mental health outcomes, recognizing their interconnectedness. However, several limitations should be noted. As a secondary data analysis, the study is limited by the variables available in the dataset, which may not encompass all relevant confounders or exposures, restricting our ability to fully address bias or explore every aspect of our hypotheses. Additionally, NCD risk behaviors and conditions, except for hypertension, were not directly measured; they were instead self-reported diagnoses provided by health workers. Therefore, there may be underestimation, especially in regions with limited access to healthcare, and self-reporting may be affected by recall or reporting bias. Since this is a secondary analysis of cross-sectional data, the results are restricted to identifying associations rather than establishing causal relationships between HIV status and the assessed NCD health outcomes. Lastly, the relatively small HIV-positive subgroups and low numbers of participants with uncommon outcomes may have reduced the precision of some estimates.

5. Conclusions

In this analysis, women and men living with HIV showed distinct patterns of NCDs and behavioral risk factors compared with those without HIV. These findings suggest that routine HIV care visits may offer opportunities to assess blood pressure, tobacco and alcohol use, and mental health, supported by referral and follow-up pathways. For women living with HIV, strengthening access to cervical cancer screening and treatment may also be an important component of care.

Author Contributions

Conceptualization, L.O., P.M., S.S. and S.B.; methodology, L.O., P.M., M.V., S.S., E.A.B., Y.O. and S.B.; formal analysis, P.M.; writing—original draft preparation, L.O.; writing—review and editing, P.M., M.V., S.S., E.A.B., Y.O., C.P., M.P., F.O. and S.B.; supervision, S.B.; project administration, C.P. and F.O. All authors have read and agreed to the published version of the manuscript.

Funding

This study involved secondary analysis of publicly available data from the 2022 Kenya Demographic and Health Survey and received no direct funding. Protected research time for the analysis and manuscript preparation was supported by the National Institutes of Health (NIH) under grant number U01 CA261620/CA/NCI NIH HHS/United States.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the use of secondary data.

Data Availability Statement

The data analyzed in this study are available from the Demographic and Health Surveys (DHS) Program upon registration and approval of a data-access request at https://dhsprogram.com/data/available-datasets.cfm (26 June 2023). No new data were generated for this study.

Acknowledgments

The authors gratefully acknowledge the institutions and field teams responsible for in the implementation of the 2022 KDHS. We also thank the DHS Program for providing access to the dataset used in this analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CD4Cluster of differentiation 4
NCDNon-communicable disease
HIVHuman Immunodeficiency Virus
KDHSKenya Demographic and Health Survey
HPVHuman Papillomavirus
ARTAntiretroviral Treatment
LMICsLow- and middle-income countries
PLHIVPerson Living with HIV
SSASub-Saharan Africa

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