Rural health inequalities in perceived HIV vulnerability and nutritional status: a cross-comparison of rural communities in West and Southern Africa

Abstract

Persistent rural health inequities across Sub-Saharan Africa, particularly regarding HIV burden and nutritional deficiencies, remain a major public health concern, yet evidence indicates that rural disadvantage is neither uniform nor neutral. Anchored in the social determinants of health framework, this study compared household income, healthcare access, and housing quality/sanitation between rural populations in West and Southern Africa; examined cross-national differences in perceived HIV burden and nutritional deficiency; and assessed how these determinants relate to both outcomes. We analysed primary cross-sectional survey data from stratified samples in Ntabankulu villages, South Africa (n = 288) and Iwo villages, Nigeria (n = 205). Cross-country differences were tested using independent-samples t-tests, and associations were estimated using country-stratified multivariable OLS regressions mutually adjusting for income, healthcare access, and housing quality/sanitation. Results show clear contrasts: South Africa reports higher perceived income and better housing/sanitation, whereas Nigeria reports better perceived healthcare access; however, South Africa records higher perceived HIV burden and nutritional deficiency. In adjusted models, income (β = − 0.384) and healthcare access (β = − 0.196) were associated with lower perceived HIV burden in South Africa, while housing quality/sanitation (β = − 0.338) was associated with lower perceived HIV burden in Nigeria. For nutritional deficiency, income was the strongest predictor in South Africa (β = − 0.383), whereas healthcare access (β = − 0.344) and income (β = − 0.246) were significant in Nigeria. These findings suggest that rural health inequalities are structurally driven and context-dependent, stressing the need for country-specific (rather than uniform) policy responses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-026-27010-2.

Introduction

The burdens of HIV infection and nutritional deficiencies remain public health challenges in sub-Saharan Africa, where social determinants affect health performance [51, 64, 69]. Despite decades of efforts to improve the situation, rural populations in countries such as Nigeria and South Africa continue to experience disproportionate rates of HIV prevalence and undernutrition, worsened by socio-economic inequalities, limited healthcare access, and sociocultural factors [5, 52, 60]. For example, in Nigeria, HIV prevalence among adults remains at 1.3%, with regional disparities pushing figures above 2% in underserved northern states, while 37% of children under five suffer from stunting [64]. South Africa faces an even more severe HIV burden, with an adult prevalence of 19.6% and a rural environment in KwaZulu-Natal exceeding 27%, alongside a rural child stunting rate of approximately 27% [66]. Both Nigeria and South Africa have adopted national strategies to reduce HIV prevalence and address nutritional deficiencies. Nigeria’s National HIV/AIDS Strategic Framework (2019–2021) and National Multisectoral Plan of Action for Nutrition (2021–2025) aim to expand decentralised services and community-based interventions [24, 43]. Similarly, South Africa’s National Strategic Plan on HIV, TB, and STIs (2017–2022) and Food and Nutrition Security Plan (2018–2023) prioritise universal access to ART and nutritional support [18, 63]. However, persistent rural inequalities and systemic barriers continue to limit the full impact of these efforts.

The challenges of HIV prevalence and nutritional deficiencies in rural Nigeria and South Africa necessitate a shift beyond biomedical interventions toward a structural analysis, making the Social Determinants of Health (SDH) framework important [53, 54]. The SDH framework emphasises how factors such as poverty, education, gender inequality, food security, and access to healthcare fundamentally consistent to health performance [62, 69]. Interventions can be designed to target root causes rather than symptoms alone, such as HIV infection and malnutrition within these upstream social and economic factors. Evidence from global best practices shows that strategies grounded in SDH, such as improving rural livelihoods, enhancing women's education, and strengthening social protection systems, have led to more sustainable health gains [28, 61]. SDH enables a more integrated, equity-driven public health response, essential for disrupting the cyclical relationship between HIV vulnerability and undernutrition in marginalised rural populations [70, 71].

Guided by the Social Determinants of Health (SDH) framework [38], this study focuses on three upstream determinants that capture material, service, and environmental dimensions of rural disadvantage: household income, access to healthcare, and housing quality/sanitation. Within SDH theory, these determinants shape health through interconnected pathways. Income structures material living conditions and affects food security, the ability to absorb health shocks, and the capacity to afford transport and other costs associated with care-seeking. Healthcare access associates opportunities for prevention, early diagnosis, treatment continuity, and access to nutrition-related support services. Housing quality and sanitation reflect environmental risk exposure—poor sanitation and inadequate housing conditions can increase susceptibility to infectious morbidity and constrain nutritional well-being, and they also cluster with broader deprivation. Accordingly, we hypothesise that better performance on each determinant is associated with lower perceived HIV burden and lower nutritional deficiency [14], while recognising that the cross-sectional design supports assessment of associations consistent with these hypothesised pathways rather than definitive causal inference.

This study is motivated by three main factors. First, the problem-driven motivations are due to the persistently high rates of HIV and malnutrition, mainly due to inequalities within and between rural Nigeria and South Africa. Second, evidence-driven motivation stems from the limited availability of cross-country comparative studies and the growing recognition of social determinants as crucial factors influencing health outcomes. Third, policy- and equity-driven motivations emphasise ongoing policy fragmentation, the push toward integrated strategies aligned with the Sustainable Development Goals, and the urgent need for equity-focused approaches grounded in the Social Determinants of Health framework. The motivations are discussed below.

The persistently high rates of HIV infection and malnutrition in rural Nigeria and South Africa show the level of inequalities in these countries. Despite national and international health initiatives, rural populations continue to experience a disproportionate burden of disease, with Nigeria reporting an HIV prevalence of approximately 1.3% overall but with some rural regions exceeding 2%, and 37% of children under five suffering from stunting [64, 66]. In South Africa, the situation is even more severe, with an overall HIV prevalence of 19.6% among adults, and rural provinces such as KwaZulu-Natal facing rates exceeding 27%, alongside a rural child stunting rate of around 27% [64, 66]. These alarming statistics point to structural inequities in healthcare access, education, nutrition security, and poverty alleviation efforts, which are particularly acute in rural areas. Moreover, the intra-country disparities, where rural communities fare markedly worse than urban centres, stress the need to understand local social determinants more. Unless these persistent inequalities are addressed, traditional biomedical approaches or isolated nutrition interventions are unlikely to yield sustainable improvements [35], underscoring the need for a comprehensive comparative analysis, such as the one pursued in this study.

Despite extensive scholarship on HIV prevalence and nutritional deficiencies, most evidence remains single-country and outcome-specific [41, 46, 50], with recent contributions often taking the form of review studies rather than primary comparative analyses [15, 48, 56]. As a result, cross-national rural comparisons, including contrasts between major Sub-Saharan African economies such as Nigeria and South Africa, which differ in socio-political histories and health-system structures, remain scarce, limiting what can be generalised about effective interventions across diverse rural settings. At the same time, contemporary research increasingly emphasises that health outcomes are shaped not only by clinical care or individual behaviour, but also by social determinants such as income inequality, education, food security, and social protection [53, 54, 69]. Yet three empirical gaps persist for rural contexts: (1) HIV-related vulnerability and nutritional deficiency are frequently analysed in separate research streams despite shared structural associated; (2) cross-national comparative research focused explicitly on rural populations is limited, constraining understanding of whether SDH effects generalise across settings; and (3) housing quality and sanitation, though central to nutrition research, are less consistently integrated into comparative HIV-focused analyses alongside income and healthcare access. This study addresses these gaps by applying a single SDH-informed, determinants-oriented design with harmonised measures across two rural contexts, enabling direct comparisons of both determinant distributions and their associations with perceived HIV burden and nutritional deficiency across West and Southern Africa.

This study's motivation is driven by policy and equity considerations, highlighting the need to address persistent policy fragmentation and align national responses with integrated global strategies. In both Nigeria and South Africa, despite the presence of national HIV and nutrition policies, efforts often remain poorly coordinated across sectors and inconsistently implemented at the rural level [10]. This fragmentation undermines the effectiveness of interventions, particularly for marginalised populations. In response, there is increasing international momentum, exemplified by the Sustainable Development Goals (SDGs), to promote simultaneously multisectoral, integrated approaches that address health, nutrition, education, and poverty (United Nations, 2015). Achieving such integration requires a deliberate shift toward equity-driven frameworks that tackle structural inequalities rather than focusing narrowly on biomedical processes [33]. The Social Determinants of Health (SDH) framework provides a critical foundation for this shift, emphasising the importance of addressing upstream social, economic, and political factors to ensure that health interventions are sustainable and inclusive. Although HIV prevalence is commonly determined through laboratory confirmation, the present study utilises self-reported symptoms associated with HIV infection, which this study refers to as HIV burden or HIV prevalence. The indicator relies on perception, awareness, fear, and symptom reporting. Therefore, the findings reflect the burden of HIV-related symptoms rather than confirmed HIV prevalence.

Nigeria and South Africa were selected as comparative cases because they provide an analytically informative contrast between West and Southern Africa rural health contexts. The two countries differ markedly in HIV epidemiological burden and in the structure and capacity of health and social systems, yet both face persistent rural inequalities in living conditions and nutrition. This “contrastive” design allows assessment of whether similar SDH domains (income, healthcare access, housing/sanitation) relate to HIV-related vulnerability and nutritional deficiency in comparable ways across different regional contexts, or whether associations are context-dependent. Empirically, this study draws on rural communities in Ntabankulu (South Africa) and Iwo (Nigeria)—settings selected for rural accessibility and their relevance as typical rural environments characterised by documented socioeconomic and health vulnerabilities—while interpreting findings as analytically generalisable to similar rural settings rather than nationally representative estimates.

From the foregoing, this study aims to conduct a comparative analysis of how key social determinants of HIV transmission and nutritional deficiencies differ across rural communities in Nigeria and South Africa. The specific objectives are as follows:

• i.Describe and compare the distribution of key social determinants of health, household income, access to health care, and housing quality with sanitation, among rural populations in Nigeria and South Africa.
• ii.Examine cross-national differences in HIV prevalence and nutritional deficiencies within rural Nigeria and South Africa.
• iii.Assess associations between selected social determinants of health (income, health care access, and housing quality/sanitation) and HIV prevalence in rural environments of Nigeria and South Africa.
• iv.Assess associations between selected social determinants of health and nutritional deficiencies among rural populations in the two countries.

The remainder of the paper is organised as follows. "Theoretical perspectives and empirical evidence" section reviews the conceptual framework of social determinants of health and previous empirical studies. "Research method" section describes the study design, sampling strategy and analytical methods. "Presentation of results" section presents the empirical findings, and "Conclusion and policy recommendations" section discusses their implications for policy and practice before concluding with recommendations for future research.

Theoretical perspectives and empirical evidence

Conceptual foundations: social determinants of health

The study measures the distal and intermediate determinants directly as shown in Fig. 1, while proximal mechanisms are conceptually inferred from SDH theory and used to guide interpretation. To make the study’s use of the SDH framework explicit, we conceptualise the selected determinants as operating across a simplified hierarchy of influence. At the distal level, household income captures material resources and socioeconomic position, which condition food purchasing power, transport affordability, resilience to health shocks, and the ability to secure adequate living conditions. At the intermediate level, healthcare access and housing quality/sanitation reflect two pathways through which material conditions are translated into health risk: a service pathway, through prevention, testing, treatment continuity, and nutrition-related care; and an environmental pathway, through exposure to infectious morbidity, hygiene conditions, and the household context for nutritional well-being. These intermediate conditions are associated with more proximal processes, such as food insecurity, delayed care-seeking, reduced prevention uptake, heightened infectious exposure, and perceived vulnerability, which in turn may reflect perceived HIV burden and nutritional deficiency. Because our data are cross-sectional and perception-based, we use this hierarchy as a conceptual ordering to guide variable selection and interpretation rather than as a formally identified causal mediation model.

Fig. 1.

Conceptual framework. Source: Authors’ Computation

Health outcomes are impacted by biological processes and individual behaviours, as well as by the social, economic, and environmental conditions in which people are born, live, and work. The social determinants of health (SDH) framework emphasises these upstream conditions as fundamental, linked to population-level health inequalities, particularly in low- and middle-income countries where structural constraints are pronounced [42]. In rural contexts, limited infrastructure, constrained economic opportunities, and uneven service provision intensify the association of social determinants on patterns of disease burden and nutritional vulnerability. The SDH framework conceptualises health disparities as the cumulative outcome of structural and material conditions rather than the result of isolated individual choices [30]. These conditions affect exposure to health risks, access to protective resources, and the ability to respond to illness throughout the life course. From this perspective, income, access to healthcare, and housing quality, including sanitation, are widely recognised as core determinants that reflect socioeconomic and policy environments operating at the household, community, and national levels [11, 13, 57]. Income represents a fundamental dimension of social stratification and material well-being. In rural areas of Sub-Saharan Africa, household income strongly conditions food security, diet quality, and resilience to health shocks [47]. Economic constraints may limit the ability to seek timely health care, sustain long-term treatment, or invest in adequate living conditions, thereby affecting vulnerabilities to both HIV infection and nutritional deficiencies. As a structural determinant, income improves health indirectly through interrelated mechanisms of material deprivation and social exclusion [3].

Perceived HIV vulnerability can be theoretically anchored in health‐behaviour models that treat risk perception as a key motivational construct: in the Health Belief Model, vulnerability corresponds to perceived susceptibility (part of perceived threat) and links preventive action alongside perceived benefits/barriers, cues to action, and self-efficacy, while Protection Motivation Theory places perceived vulnerability within threat appraisal (vulnerability and severity) that combines with coping appraisal (response efficacy, self-efficacy, and response costs) to generate motivation for protective behavior (e.g., testing, condom use, linkage to care) [7]. However, HIV-specific evidence cautions that perceived vulnerability does not always map neatly onto precautionary behaviour (and can reflect prior behaviour or cognitive biases), so interpretation, especially in cross-sectional analyses, should acknowledge possible bidirectionality [26]. Nutritional status can be framed using the UNICEF Conceptual Framework, which explains nutrition outcomes through immediate determinants (diet and care/illness), associated by underlying determinants (household food, caregiving practices, and access to services) and broader enabling determinants (resources, norms, and governance) [65]. In HIV-affected, nutrition is also impacted by the bidirectional HIV–malnutrition ‘vicious cycle,’ where HIV can worsen nutritional status through reduced intake, malabsorption and increased energy demands, while malnutrition further weakens immunity and can accelerate morbidity; for example, WHO guidance notes increased energy needs of about 10% in asymptomatic adults and 20–30% in more advanced disease [19].

Access to health care constitutes another central social determinant, particularly in rural regions characterised by geographic isolation, limited health infrastructure, and shortages of skilled personnel. Access includes the availability and affordability of services, as well as physical accessibility and functional quality [68]. In the context of HIV and nutrition, disparities in health care access limit opportunities for prevention, early diagnosis, treatment initiation, and nutritional support. Cross-national differences in rural health system capacity contribute to observed variations in population-level health outcomes across countries [36]. Housing quality and sanitation reflect the environmental dimension of the social determinants of health. Adequate housing and improved sanitation reduce exposure to infectious agents and support conditions necessary for effective nutrient absorption and overall health [45]. In many rural areas, poor housing materials, overcrowding, and inadequate sanitation infrastructure persist as manifestations of long-standing structural inequalities. These environmental conditions contribute to increased susceptibility to illness and worsen nutritional deficiencies, particularly among economically disadvantaged populations [40]. In this study, the social determinants of health framework is employed to contextualise observed population-level differences in HIV prevalence and nutritional outcomes across rural Nigeria and South Africa. Consistent with comparative public health research, the framework is used to situate empirical patterns within a structural context rather than to specify or test individual-level causal pathways [53, 54].

Review of empirical literature and development of hypotheses

Empirical studies across Sub-Saharan Africa show that rural populations face persistent structural disadvantages in income, access to healthcare, housing quality, and sanitation, although the severity and patterning of these disadvantages differ by country. In Nigeria, rural poverty, constrained household resources, and limited sanitation infrastructure remain prominent, with many rural communities characterised by unimproved sanitation and poorer housing conditions [2, 23]. In South Africa, despite higher national income levels, spatially patterned inequality continues to shape rural living conditions, with historically marginalised rural areas reporting poorer access to services, housing quality, and sanitation [1]. These contextual differences motivate a comparative focus on rural Nigeria and rural South Africa to examine whether similar social determinants operate in comparable ways across distinct rural environments.

Cross-national evidence also points to substantial differences in HIV burden and nutritional outcomes between the two countries, reinforcing the value of a harmonised comparison. South Africa remains among the highest-burden settings globally, with population-based evidence documenting substantial HIV prevalence across both urban and rural localities, including rural formal and informal settlements [25, 27]. Nigeria’s national HIV prevalence is considerably lower by comparison [49]. Nutritional deficiencies likewise persist across rural Sub-Saharan Africa, but the magnitude and gradients differ: Nigerian evidence shows strong socioeconomic patterning in child undernutrition, with rural children disproportionately affected [6], while South African studies indicate that undernutrition (including stunting) remains concentrated among disadvantaged and rural groups despite broader improvements over time [58, 59]. Together, these outcome differences suggest that comparing rural settings across Nigeria and South Africa can illuminate how rural social and material conditions translate into HIV and nutritional vulnerabilities under different national.

Social determinants and HIV burden (H1–H3)

The relationship between household income and HIV burden has produced mixed empirical findings across Sub-Saharan Africa, often reflecting differences in epidemic phase, gender dynamics, and local context. Earlier evidence suggested higher prevalence among wealthier groups in the early epidemic, whereas more recent work increasingly documents heightened vulnerability among poorer populations—particularly in generalised epidemics and rural environments—implying a protective role of economic resources in contemporary rural settings [55]. In rural South Africa, longitudinal analyses link socioeconomic deprivation to elevated HIV risk and vulnerability, supporting an expected negative association between income and HIV burden [12, 17]. Nigerian studies also report associations between socioeconomic status and HIV prevalence, although the direction and magnitude vary depending on the indicators used [22, 44]. Taken together, this literature supports testing the hypothesis that higher household income is associated with lower HIV burden in rural settings (H1), while recognising that the strength of this association may vary across Nigeria and South Africa.

Access to healthcare is more consistently implicated in HIV-related outcomes in rural contexts, where distance to facilities, financial barriers, and weaker infrastructure can constrain testing, treatment initiation, and continuity of care. Evidence across Sub-Saharan Africa links these barriers to poorer HIV outcomes and reduced service uptake [21]. Within South Africa, spatial analyses indicate that rural service environments are associated with differential HIV outcomes even in a comparatively well-resourced health system [34]. In Nigeria, persistent gaps in rural health service coverage and utilisation have been documented with implications for diagnosis and treatment cascades [4]. This body of evidence motivates the expectation that better healthcare access is associated with lower HIV burden among rural populations (H2), and that cross-national differences in rural service environments may condition the size of this association.

Compared with income and healthcare access, housing quality and sanitation are less frequently incorporated into HIV prevalence analyses, yet emerging evidence suggests they matter as markers of structural vulnerability and clustered deprivation. Research in urban and peri-urban contexts links informal housing, overcrowding, and poor living conditions to higher HIV prevalence, plausibly reflecting broader material disadvantage [67]. Work at the intersection of HIV and water, sanitation, and hygiene further highlights how inadequate household environments co-occur with health vulnerabilities, including in households affected by HIV [72]. Although rural comparative evidence is limited, these findings motivate examining whether better housing quality and sanitation are negatively associated with HIV burden in rural Nigeria and South Africa (H3).

Social determinants and nutritional deficiency (H4–H6)

In contrast, the roles of income, healthcare access, and household environments in shaping nutritional outcomes are well established across Sub-Saharan Africa. Multi-country evidence shows that poverty and limited access to services are strongly associated with child undernutrition and other nutritional deficiencies, and that these relationships are often intensified in rural contexts where structural constraints are more pronounced [5, 8]. Nigerian studies consistently link socioeconomic inequality and inadequate sanitation to poorer nutritional outcomes [20, 31], reinforcing the expectation that higher household income and improved household environments reduce nutritional deficiencies (H4, H6). South African evidence similarly indicates that nutritional deficits remain patterned by socioeconomic and household-level conditions, even with comparatively stronger system capacity [9], motivating the expectation that improved access to healthcare is negatively associated with nutritional deficiency (H5), and that the magnitude of these associations may vary by national rural context.

Gaps and hypotheses

Three gaps in the empirical literature motivate the present study. First, HIV burden and nutritional deficiencies are often analysed in separate empirical traditions despite overlapping social and material conditions documented across rural settings. Second, explicitly rural, cross-national comparisons using harmonised indicators remain limited, constraining inference about whether similar determinants operate in a similar way across different rural national contexts. Third, housing quality and sanitation, central in nutrition research, remain less consistently integrated into HIV analyses alongside income and healthcare access [67, 72]. Building on the reviewed literature and a Social Determinants of Health perspective, the study proposes the following hypotheses for rural populations:

• H₁: Household income is negatively associated with HIV burden among rural populations.

• H₂: Access to healthcare is negatively associated with HIV burden among rural populations.

• H₃: Housing quality and sanitation are negatively associated with HIV burden among rural populations.

• H₄: Household income is negatively associated with nutritional deficiency among rural populations.

• H₅: Access to healthcare is negatively associated with nutritional deficiency among rural populations.

• H₆: Housing quality and sanitation are negatively associated with nutritional deficiency among rural populations.

Consistent with the comparative design, these relationships are further assessed cross-nationally to determine whether their magnitude and statistical significance differ between rural South Africa and rural Nigeria.

Research method

Study design and setting

This study employed a quantitative cross-sectional survey design in two rural environments: Ntabankulu villages (South Africa) and Iwo villages (Nigeria). Data were collected through primary fieldwork using a structured questionnaire administered manually with face-to-face support from trained research assistants.

Definition and selection of rural communities

Rural communities were defined as village-based settlements characterised by limited infrastructure and access to services relative to urban centres. The selection of Ntabankulu and Iwo was purposive at the community level, guided by feasibility, rural accessibility, and their relevance as typical rural environments with documented socioeconomic and health vulnerabilities. Accordingly, findings are interpreted as analytically generalisable to similar rural environments rather than statistically representative of national rural populations.

Sampling technique

A stratified random sampling technique was adopted. The study population in each country was stratified by district; the intended sample size was allocated proportionally across strata, and simple random sampling (lottery-based) was used to select respondents within each stratum. Inclusion criteria: (i) resident of the selected rural community, (ii) met the study’s eligibility threshold (e.g., adult household member able to provide informed consent), and (iii) completed the questionnaire with sufficient item completion for scale construction. Exclusion criteria: refusal or inability to provide informed consent and questionnaires with substantial missing responses on key constructs. The achieved sample comprised 483 respondents: South Africa (n = 288) and Nigeria (n = 205).

Measures and variable construction

The questionnaire included demographic items (gender, education, occupation) and Likert-scale items for the study constructs. Independent variables (SDH domains): Household income: 4 five-point Likert items. Healthcare access: 4 five-point Likert items. Housing quality/sanitation: 6 five-point Likert items. Dependent variables: Perceived HIV burden (HIV vulnerability): 4 five-point Likert items. In this study, “HIV burden” reflects perceived HIV-related symptoms, perceived exposure risk, community awareness, and perceived vulnerability, not laboratory-confirmed prevalence. Nutritional deficiency: 4 five-point Likert items. Controls: gender (binary), education (five categories), and occupation (seven categories) were included as covariates in adjusted models (coding described in the revised Methods). Scale scoring and direction: Composite scores were constructed by aggregating item responses (e.g., mean of items per construct), where higher values reflect higher levels of the underlying construct (e.g., higher perceived HIV burden/higher nutritional deficiency, and higher perceived income/healthcare/housing quality as applicable).

Instrument validation and common-method checks

Given the questionnaire's self-report nature, we conducted a pilot study (n = 50) to assess reliability and validity and to test for common method variance using collinearity diagnostics and a single-factor approach. Reliability coefficients exceeded the 0.70 benchmark, indicating acceptable internal consistency.

Statistical analysis and model specification

Analyses proceeded in three stages: (1) descriptive statistics and independent-samples t-tests to assess cross-country differences in mean perception scores; (2) country-disaggregated estimation of associations between SDH indicators and each outcome using multivariable linear regression; and (3) comparative interpretation of effect sizes across countries consistent with the study’s model specification.

The models of the study are presented below:

The mediation models are:

where:

.n = nutritional deficiency

.himd = HIV burden

.inc = income

.hc-healthcare

.hqs = housing quality and sanitation

 = proportion of the variation in nutritional deficiency that variations in HIV burden do not explain

. =Proportion of the variation in nutritional deficiency that is explained by (himd, inc, hc and hqs0

 = proportion of the variation in HIV burden that variations in nutritional deficiency not explain

. =Proportion of the variation in nutritional HIV burden that is explained by social determinants (himd, inc, hc and hqs0

.edisturbance error term.

The study employed a quantitative research method, collecting and analysing data on the research problem to compare the social determinants influencing HIV prevalence and nutritional deficiencies in rural Nigeria and South Africa, thereby providing an objective generalisation of the study’s findings. The design was a cross-sectional survey of respondents from rural settings in Nigeria and South Africa. The data were triangulated to enhance the research quality by reducing data bias. Specifically, the study collected data from 483 respondents, comprising 288 South Africans and 205 Nigerians.

Data collection method and research instrument

The study utilised a structured questionnaire designed by the authors to collect the research data (See Appendix A1 in the supplementary file). The questionnaire was distributed manually to the respondents in the respective districts. The research assistants had face-to-face contact with the sampled respondents. The study utilised a structured questionnaire to collect primary data. The questionnaire consisted of three demographic items and 22 Likert items related to the research variables (dependent and independent). The selection of Ntabankulu Villages (South Africa) and Iwo Villages (Nigeria) was purposive at the community level, guided by feasibility, rural accessibility, and their relevance as typical rural environments characterised by documented socioeconomic and health vulnerabilities. While this approach strengthens depth and comparability, it also means that the findings reflect these specific rural environments rather than all rural areas nationally. As such, the results should be interpreted as analytically generalisable to similar rural environments rather than statistically generalisable to entire national populations.

Tests for common method variance

Given that the questionnaire is a self-report instrument, the study conducted a pilot test with 50 respondents and used the pilot results to assess common method bias, validity, and reliability. The test for common method bias used collinearity diagnostics and the Hausman single-factor methods. The results of the common collinearity diagnostic test indicate that all tolerance limits are less than 1, while all variance inflation factors (VIFs) are less than 0.5, indicating that the instrument is free from common method bias (See Table 1).

Table 1.

Respondents’ perception and by demographic characteristics

Respondents’ perception versus Demographic variables
South Africa					Nigeria
	HIV		Nutritional Deficiency			HIV		Nutritional Deficiency
Variable	F	p value	F	p value	Variable	F	p value	F	p value
Gender	6.164	0.014	18.93	0.000	Gender	0.010	0.822	1.00	0.319
Education	3.512	0.008	4.39	0.002	Education	0.125	0.923	0.539	0.71
Occupation	0.544	0.774	1.291	0.261	Occupation	1.518	0.796	0.645	0.694

Source: Authors Computation

Clarification of HIV measure and construct meaning

In this study, the term 'HIV burden' does not refer to laboratory-confirmed prevalence. Rather, it reflects respondents’ perceived burden of HIV-related symptoms, perceived exposure risk, awareness of infection within the community, and perceived vulnerability; this approach aligns with perception-based measurement traditions in public health, where perceived disease burden has been used as a proxy indicator in areas where biomedical confirmation is limited, particularly in rural and resource-constrained environments [39]. It captures community-level awareness, lived experience, and perceived susceptibility, which are recognised as important dimensions influencing health behaviour, health-seeking patterns, and stigma dynamics in HIV research.

Ethical considerations

Ethical approval for this study was obtained from the Institutional Research Ethics Committee of a University in South Africa prior to data collection. All participants were fully informed about the purpose of the study, their rights to voluntary participation, and their ability to withdraw at any time without consequence. Written informed consent was obtained from all respondents. Participation was anonymous, and no identifying personal information was collected. Data were handled confidentially, securely stored, and used solely for academic purposes in accordance with established ethical research guidelines.

Presentation of results

Preliminary analysis

The data collected from the pilot study were further used to conduct exploratory and confirmatory factor analysis with a specific focus on principal component analysis. The results indicate that there are more than three factors in the test, suggesting that no single factor dominates. The implication is that the instrument is not subject to common method bias. The preliminary analysis results are given as follows:

Respondents’ perception and by demographic characteristics

Nigeria’s population is approximately 240 million. 42% of the population is below 15 years, and 54–56% of them are between 15 −64 years. The gender distribution is approximately even. On the other hand, South Africa’s population is estimated at over 63 million. 26–27% of the population is aged 14 years and below, while 67% is between 15 and 67 years. Thus, Nigeria’s population is about 3.81 times that of South Africa, but South Africa has a higher proportion of youths than Nigeria (See Table 1).

A comparison of respondents’ perceptions with their demographic characteristics based on the South African data indicates a significant association between perceptions of HIV and both gender and educational attainment, whereas no significant relationship was observed between perception and occupation. A similar pattern emerged with respect to nutritional deficiency, where gender and education demonstrated significant relationships, but occupation did not show any significant influence (see Table 1). In contrast, the Nigerian data revealed no significant relationships between respondents’ perceptions of HIV and the demographic variables considered—gender, education, and occupation. Likewise, no significant associations were observed between these demographic characteristics and perceptions of nutritional deficiency (see Table 1).

Collinearity diagnostics

The Variance Inflation Factor (VIF) and tolerance statistics in Table 2 indicate no evidence of multicollinearity in either the South African or Nigerian models. For South Africa, VIF values range from 1.044 to 1.244, with tolerance values between 0.804 and 0.968. For Nigeria, VIF values range from 1.06 to 1.507, with tolerance values between 0.684 and 0.925. These are well below the conventional multicollinearity concern thresholds (VIF ≥ 5 or 10; tolerance ≤ 0.10), implying that the explanatory variables are not excessively correlated, the regression coefficients are stable and reliable, and the estimated effects of each predictor can be interpreted with confidence.

Table 2.

Collinearity diagnostics

Variable	South Africa		Nigeria
	Tolerance	VIF	Tolerance	VIF
Constant
Income	0.804	1.244	0.685	1.438
Healthcare	0.866	1.154	0.684	1.507
Housing Quality/Sanitation	0.968	1.044	0.774	1.292
Nutritional Deficiency	0.946	1.06	0.925	1.06

Source: Authors Computation

Test for the validity and reliability of the questionnaire

Convergent and discriminant validity were assessed using established methodological procedures. For convergent validity, the factor loadings derived from the exploratory factor analysis served as the basis for evaluation. The results show that all Average Variance Extracted (AVE) values were at least 0.5, indicating that each construct explains more than half of the variance in its observed indicators and therefore meets the accepted criterion for convergent validity (see Table 3). To complement these results, discriminant (divergent) validity was examined using a correlation matrix in which the diagonal elements were replaced with the square roots of the corresponding AVEs. The results reveal that, in each case, the square root of the AVE exceeded the inter-construct correlations in the corresponding rows and columns, indicating adequate discriminant validity. The evidence from both convergent and discriminant validity assessments confirms that the measurement instrument is a valid tool (see Table 3).

Table 3.

Convergent validity and divergent validity tests

Convergent Validity
	. Income	. Healthcare	Housing Q.S	.Human IMD	. Nutritional D
South Africa	0.529	0.539	0.573	0.699	0.508
Nigeria	0.541	0.534	0.561	0.508	0.517

Divergent Validity
South Africa
	INC	HC	HQS	HIMD	ND
.INC	0.73
.HC	0.43	0.753
.HQS	0.149	0.138	0.83
.HIMD	−0.22	−0.034	0.013	0.78
. ND	−0.24	−0.044	0.04	0.63	0.71

Nigeria
	INC	HC	HQS	HIMD	ND
.INC	0.69
.HC	0.47	0.759
.HQS	0.141	0.142	0.49
.HIMD	−0.26	−0.031	0.017	0.79
. ND	−0.26	−0.047	0.04	0.59	0.67

Source: Authors Computation

Note: Bold diagonal entries represent the square root of the Average Variance Extracted (AVE) for each construct and are shown for discriminant validity assessment; they do not indicate statistical significance

Lastly, the reliability of the measurement instrument was assessed using composite reliability, with factor loadings serving as the input parameters. The results indicate that all reliability coefficients exceeded the recommended threshold of 0.7, demonstrating acceptable internal consistency. Consistent with the reliability benchmarks reported by Inegbedion [32] and Hair et al. [29], these results confirm that the instrument is highly reliable (see Table 4).

Table 4.

Composite reliability

Composite Reliability
	South Africa	Nigeria
. INC	0.724	0.738
.HC	0.849	0.831
.HQS	0.832	0.844
.HIMD	0.859	0.843
ND	0.813	0.809

Source: Authors Computation

Comparative analysis

Guided by the model specification outlined in "Research method" section, the analysis initially employed South African data to examine the significance of the parameters in the path linking social determinants to nutritional deficiency and to HIV burden. The same analytical procedures were subsequently applied to Nigerian data. Comparative analyses were then conducted to assess differences in the significance and strength of these associations across the two countries. Finally, respondents’ perceptions in South Africa were compared with those in Nigeria with respect to nutritional deficiency, HIV burden, and social determinants of health.

Table 5 presents the comparative analysis of respondents’ perceptions across key socio-economic and health dimensions. Regarding household income, the mean perception scores were 2.92 (58.4%) for South Africa and 2.03 (40.6%) for Nigeria, resulting in a mean difference of 0.89. A t-test indicated that this difference is statistically significant (t = 12.48, p < 0.01), suggesting that respondents perceive household income levels to be relatively higher in South Africa than in Nigeria. In terms of healthcare perception, the mean scores were 2.71 (54.2%) for South Africa and 3.01 (60.2%) for Nigeria, resulting in a mean difference of 0.30. The difference was statistically significant (t = 3.62, p < 0.01), indicating that respondents perceive healthcare in Nigeria as relatively better than in South Africa. For housing quality and sanitation, the mean perception scores were 3.76 (75.6%) for South Africa and 3.47 (69.4%) for Nigeria, with a mean difference of 0.29. The t-test confirmed that this difference is statistically significant (t = 3.82, p < 0.01), implying that respondents perceive housing quality and sanitation conditions to be more favourable in South Africa.

Table 5.

Comparative analysis results

Comparison of Household Income
Country	South Africa	Nigeria
Mean	2.92	2.03
Mean Difference	0.8927
.t (sig.)	12.48	(0.000)
Comparison of Healthcare
Country	South Africa	Nigeria
Mean	2.71	3.01
Mean Difference	0.2973
.t (sig.)	3.62	(0.000)
Comparison of Housing Quality/Sanitation
Country	South Africa	Nigeria
Mean	3.76	3.47
Mean Difference	0.2899
.t (sig.)	3.82	(0.000)
Comparison of Nutritional Deficiencies
Country	South Africa	Nigeria
Mean	3.59	3.03
Mean Difference	0.5619
.t (sig.)	5.39	(0.000)
Comparison of HIVS
Country	South Africa	Nigeria
Mean	3.13	2.67
Mean Difference	0.460
.t (sig.)	3.27	(0.000)

Source: Authors Computation

Regarding nutritional deficiency, the mean perception scores were 3.59 (71.8%) for South Africa and 3.03 (60.6%) for Nigeria, producing a mean difference of 0.56. This difference was statistically significant (t = 3.82, p < 0.01), indicating that nutritional deficiency is perceived as being relatively higher in South Africa than in Nigeria. Finally, the comparison of perceptions of HIV burden revealed mean scores of 3.59 (71.8%) for South Africa and 2.67 (53.4%) for Nigeria, with a mean difference of 0.46. The difference was statistically significant (t = 3.27, p < 0.01), indicating a significantly higher perceived HIV burden in South Africa compared to Nigeria.

The regression results based on the disaggregated country data provide insight into the associations between the social determinants of health and nutritional deficiency. The findings show that household income and healthcare exhibit negative coefficients, whereas housing quality/sanitation displays a positive coefficient. The z-statistics and associated p-values indicate that the negative coefficients are statistically significant, while the positive coefficient is not. This suggests that household income and healthcare are significantly associated with reductions in nutritional deficiency, whereas housing quality/sanitation does not demonstrate a significant effect. Regarding the relationship between social determinants of health and HIV burden, the analysis reveals that household income has a negative coefficient, while healthcare and housing quality/sanitation have positive coefficients. The z-test results indicate that the negative coefficient and the positive coefficient of healthcare are statistically significant, whereas the positive coefficient of housing quality/sanitation is not. This implies that household income has a significant negative association with HIV burden, healthcare has a significant positive association with HIV, while housing quality/sanitation does not exhibit a statistically significant relationship with HIV (see Table 6).

Table 6.

Structural equation model of the social determinants of health

South Africa						Nigeria
	Coef.	z	p >|z|	Confidence Interval		Coef.	z	p >|z|	Confidence Interval
Structural
nd <-				Lower	Upper				Lower	Upper
inc	-0.224	-4.38	0.00	-1.27	0.82	-0.240	-4.50	0.00	-0.35	0.14
hc	-0.173	-3.51	0.00	-0.27	-0.08	-0.19	-3.91	0.00	-0.29	-0.09
hqs	0.049	1.03	0.31	-0.04	0.14	0.041	0.99	0.37	-0.04	0.12

South Africa						Nigeria
	Coef.	z	p >|z|	Confidence Interval		Coef.	z	p >|z|	Confidence Interval
Structural
hiv <-				Lower	Upper				Lower	Upper
inc	-0.38	-5.44	0.00	-0.52	-0.24	-0.35	-4.90	0.00	-0.49	-0.21
hc	1	1.52	0.14	-0.29	2.29	1.02	1.54	0.016	-0.28	2.32
hqs	0.06	0.94	0.35	-0.07	0.19	0.07	0.91	0.37	-0.08	0.22

Source: Authors Computation

Table 7 compares the perceptions of respondents in South Africa with those of respondents in Nigeria. The mean perception score for South African respondents was 3.11, whereas the corresponding mean for Nigerian respondents was 3.01, resulting in a mean difference of 0.10. A t-test assessing the significance of this difference produced a t-value of 2.17 with an associated p-value of 0.042, indicating statistical significance at the 95% confidence level. This suggests that perceptions of the social determinants associated with nutritional outcomes and HIV burden differ significantly between respondents in South Africa and Nigeria.

Table 7.

Test for difference in mean respondents’ perception

Test for Difference in Mean Respondents’ Perception
Country	South Africa	Nigeria
Mean	3.11	3.01
Mean Difference	0.09741
.t (sig.)	2.107	(0.042)

Source: Authors Computation

Table 8 presents the regression results. For South Africa, the regression results for social determinants and HIV burden indicate that the coefficients for household income and healthcare are negative, while the coefficient for housing quality/sanitation is positive. The corresponding t-statistics and p-values show that the negative coefficients for household income and healthcare are statistically significant, whereas the positive coefficient for housing quality/sanitation is not. This suggests that higher household income and improved access to healthcare are significantly associated with lower levels of HIV burden (HIV) infection in South Africa. In contrast, for Nigeria, the regression analysis reveals that household income and healthcare exhibit positive coefficients, while housing quality and sanitation have negative coefficients. However, the positive coefficients for household income and healthcare are statistically insignificant, whereas the negative coefficient for housing quality/sanitation is statistically significant. This implies that only housing quality/sanitation demonstrates a significant negative association with HIV burden in Nigeria. A sensitivity analysis was performed to assess confounding using both standardised and unstandardised regression coefficients. The results show that the differences between the standardised and unstandardised coefficients are minimal, as most of the differences are less than 15%, consistent with Macedo-Munoz & Cunza-Aranz [37] and Macedo-Munoz & Cunza-Aranz [37] and Bring [16] (See Table 8).

Table 8.

Regression results

Regression: Social Determinants and HIV burden
	South Africa					Nigeria
	Coefficient			Confidence Interval		Coefficient			Confidence Interval
	Std	Unstd	t (p value)	Lower	Upper	Std	Unstd	t (p value)	Lower	Upper
Income:	−0.384	−0.351	- 2.88 (0.000)	−0.65	−0.12	0.182	0.177	1.638 (0.11)	1.42	1.86
Healthcare:	−0.196	−0.188	−2.25 (0.025)	−0.67	−0.00	0.014	0.009	0.153 (0.88)	−0.17	0.193
Housing quality/Sanitation:	0.13	0.127	1.56 (0.119)	−0.033	0.29	−0.338	−0.322	- 2.134 (0.02)	−0.656	0.019
R – Squared	0.56					0.52
F (p-value)	5.55 (0.001)					3.87 (0.012)

Regression: Social Determinants and Nutritional Deficiency
	South Africa					Nigeria
	Coefficient			Confidence Interval		Coefficient			Confidence Interval
	Std	Unstd	t (p value)	Lower	Upper	Std	Unstd	t (p value)	Lower	Upper
Income:	−0.383	−0.379	- 3.95 (0.00)	−0.573	−0.193	−0.246	−0.238	- 2.32 (0.021)	−0.454	−0.038
Healthcare:	−0.161	−0.153	- 1.886 (0.06)	−0.328	0.006	−0.344	−0.329	−2.83 (0.00)	−0.582	−0.106
Housing quality/Sanitation:	0.104	−0.097	1.28 (0,202)	−0.055	0.263	−0.154	−0.146	- 1.50 (0.137)	−0.355	0.047
R – Squared	0.54					0.75
F (p-value)	5.38 (0.01)					5.48 (0.001)

Source: Authors Computation

Regarding nutritional deficiency, the South African results indicate that household income and healthcare have negative coefficients, whereas housing quality and sanitation have positive coefficients. The t-statistics and p-values indicate that the negative coefficient for household income is statistically significant, while the negative healthcare coefficient and the positive housing quality/sanitation coefficient are not. Thus, household income exhibits a significant negative association with nutritional deficiency in South Africa. In Nigeria, all three social determinants—household income, healthcare, and housing quality/sanitation—show negative coefficients with nutritional deficiency. The t-statistics demonstrate that the negative coefficients for household income and healthcare are statistically significant, while the negative coefficient for housing quality/sanitation is not. Accordingly, household income and healthcare are significantly negatively associated with nutritional deficiency in Nigeria.

Discussion of findings

Guided by the social determinants of health framework, this study examined how distal material conditions and intermediate living and service conditions differ across rural Nigeria and South Africa, and how these differences relate to HIV burden and nutritional deficiency. The first objective was to describe and compare the distribution of key determinants, namely household income, access to healthcare, and housing quality/sanitation, across the two countries. The findings show that the distribution of these determinants is not uniform across settings: Nigeria performs better in access to healthcare, whereas South Africa records higher household income and better housing quality and sanitation. This pattern suggests that rural disadvantage is structured differently across the two countries rather than being uniformly experienced, consistent with the arguments of Teta and Wågsæther [1], Ezeudu [23], and Abubakar [2], who emphasise that contextual realities shape distinct forms of vulnerability.

The second objective assessed cross-national differences in HIV burden and nutritional deficiency in rural Nigeria and South Africa. The results indicate that South Africa has both a higher HIV burden and a higher nutritional deficiency than Nigeria. The higher HIV burden observed in South Africa is consistent with the findings of Gibbs et al. [27], Fundisi et al. [25], and Onovo et al. [49]. Similarly, the higher nutritional deficiency observed in South Africa reinforces the importance of explicit cross-national comparison in rural settings, since both the scale and structural factors linked to nutritional deprivation may vary across contexts rather than follow a single rural pattern [58, 59]. Taken together, these findings suggest that country differences in outcomes should be interpreted in relation to the configuration of underlying social determinants rather than assumed to arise from a common rural experience.

The third objective examined the associations between selected social determinants of health and HIV burden in the rural settings of both countries. In line with the study’s conceptual ordering, household income is interpreted here as a more distal material determinant, while healthcare access and housing quality/sanitation represent intermediate service and environmental conditions through which vulnerability may be shaped. The findings show that, in South Africa, household income and healthcare access are significantly and negatively associated with HIV burden, pointing to a material-service pathway in which economic resources and access to care may reduce vulnerability. In Nigeria, by contrast, housing quality/sanitation shows a significant negative association with HIV burden, suggesting that environmental and household living conditions may constitute the more salient pathway linking deprivation to HIV-related risk. This interpretation is consistent with Pellowski et al. [55], who observed that although earlier phases of the epidemic were often associated with wealthier populations, more recent dynamics increasingly locate vulnerability among poorer groups, particularly in generalised epidemics and rural environments. The South African findings also align with Bärnighausen et al. [12] and Cluver et al. [17], who show that socioeconomic deprivation in rural South Africa heightens HIV risk and, by implication, that economic resources may play a protective role in contexts of entrenched poverty. In addition, the findings support Ebosie [22] and Ndukwu [44], who demonstrate that socioeconomic status predicts HIV prevalence, although the direction and strength of the association vary across indicators. In the present study, that specificity is especially evident in Nigeria, where housing quality/sanitation emerges as the key predictor of HIV burden rather than income or healthcare access alone.

The fourth objective assessed the associations between selected social determinants of health and nutritional deficiencies among rural populations in both countries. This objective was addressed through hypotheses 9, 10 and 11. The results indicate that household income has a significant negative relationship with nutritional deficiency in South Africa, whereas household income and healthcare are significantly negatively associated with nutritional deficiency in Nigeria. These findings suggest that inadequate household income, which constrains access to quality food, is a key driver of nutritional deficiency in South Africa, while limited healthcare access appears to play a more critical role in Nigeria. These results support Adeyeye et al. [5] and Anteneh et al. [8], who emphasise that poor sanitation environments strongly predict child undernutrition and broader nutritional deficiencies across Sub-Saharan Africa, as well as Tlo et al. [31] and Dwomoh et al. [20], who document the link between socioeconomic inequality, inadequate sanitation, and adverse nutritional outcomes in Nigeria. The findings are also consistent with those of Arokiasamy et al. [9], who note that nutritional deficits in South Africa persist despite the health system's capacity, reflecting underlying socioeconomic and household-level conditions. However, this study demonstrates that, in South Africa, household income remains the only significant predictor of nutritional deficiency.

Although South Africa records relatively higher household income and better housing/sanitation conditions than Nigeria, it simultaneously demonstrates a higher perceived HIV burden and nutritional deficiency. This apparent paradox must be interpreted within the historical and structural context of the HIV epidemic in South Africa. The country has experienced one of the world’s longest and most generalised HIV epidemics, associated by earlier transmission, historical population mobility, labour migration, and delayed early policy response. As a result, HIV has become deeply entrenched in many rural communities, making prevalence and perceived burden persist even where socioeconomic indicators appear comparatively stronger. The implication of the gender and educational disparity of the South African data on HIV and nutritional deficiency indicates that the perception of HIV is gender and education-biased. Similarly, respondents’ perception of HIV and nutritional deficiency is gender and education-biased. The gender disparity is suggestive that there is significant gender inequality and harmful social norms associated with access to information about HIV statuses and nutritional deficiency in South Africa. Educational inequality could mean that educational attainment enhances the processing and comprehension of information about the variables. The results of the data from Nigeria indicated that there is no disparity in terms of information processing and comprehension of HIV and nutritional deficiency among people across different genders and various educational and occupational categories.

In addition, South Africa’s higher average household income and infrastructure development mask substantial internal inequality. The rural South African environment continues to experience structural deprivation, spatial marginalisation, and persistent poverty, despite the country’s comparatively stronger national economy. Thus, higher mean income does not automatically translate into improved health, particularly in historically disadvantaged rural communities where vulnerability remains embedded in social structure, service inequities, and intergenerational disadvantage. These findings support the argument that income alone is insufficient to predict health advantage. Rather, social vulnerability, including social exclusion, health system strain, structural inequities, stigma, and long-term epidemic, remains crucial in explaining variations in HIV burden and nutritional challenges. Accordingly, the results support a structural interpretation: rural health inequalities in South Africa are linked not simply to material resources, but also to the interaction of historical epidemics, internal socioeconomic inequalities, and persistent vulnerabilities.

The reliance on self-reported perceptions rather than biological testing has important validity implications that warrant acknowledgement. Perception-based indicators may be associated with stigma, fear, denial, misinformation, and social desirability bias, and individuals may under- or overestimate HIV burden relative to actual prevalence. At the same time, perceived HIV burden remains theoretically meaningful within the Social Determinants of Health framework, as it reflects community-level awareness, psychosocial climate, and perceived vulnerability, all of which affect risk behaviour, care-seeking, and nutritional vulnerability. Thus, while the measure does not capture the biomedical prevalence, it offers insights into lived experiences and perceived structural risk environments in rural environments. The study focused on rural communities in South Africa and Nigeria; therefore, the findings of this study should be interpreted in light of the specific rural environment examined. Although the results provide meaningful comparative insights into rural health inequalities and structural determinants in South Africa and Nigeria, they cannot be generalised to all rural populations in both countries with full statistical certainty. Instead, the study offers analytical generalisation, contributing theoretically grounded evidence on how key social determinants may operate in comparable rural African environments. Future research should incorporate nationally representative rural samples across multiple regions in each country to enhance wider generalisability.

Although the findings are consistent with the Social Determinants of Health framework, the study’s cross-sectional design supports interpretation in terms of associations rather than causal effects. Associations may be linked by unmeasured confounding, including behavioural risk factors, mobility and migration patterns, local health-service quality, and community-level norms that were not directly measured in the questionnaire. In addition, reverse causality is possible: poorer health and nutrition can reduce productivity and earnings and may shape perceptions of healthcare access. Finally, the outcome “HIV burden” is perception-based rather than biomarker-confirmed and may be reflect by stigma, social desirability, awareness, fear, and misreporting; accordingly, results should be interpreted as reflecting perceived HIV burden rather than clinical prevalence. While pilot testing and common-method checks were conducted, residual measurement bias cannot be ruled out.

The different “dominant” determinants across countries likely reflect context-specific configurations of disadvantage. In South Africa, both income and healthcare access were negatively associated with perceived HIV burden (β = − 0.384 and β = − 0.196, respectively), suggesting that economic resilience and perceived ability to obtain services may reduce perceived vulnerability in a setting where HIV is historically entrenched and widely recognised. In such contexts, higher income can buffer exposure to risk environments and reduce reliance on coping strategies linked to vulnerability (e.g., transactional relationships), while better access to care can strengthen perceived control through prevention, testing, and sustained engagement with services. By contrast, in Nigeria, housing quality/sanitation showed the strongest negative association with perceived HIV burden (β = − 0.338), implying that environmental deprivation and inadequate living conditions may function as a marker of concentrated structural vulnerability in rural areas. Importantly, these interpretations are offered as plausible pathways consistent with SDH theory; the study does not claim causal direction.

Policy implications

The policy implications of the findings are best understood in relation to the country-specific determinants identified in the analysis. In South Africa, household income and healthcare access show the strongest negative associations with perceived HIV burden, while household income is also the strongest correlate of lower nutritional deficiency. This suggests that policy attention in rural South Africa should prioritise interventions that strengthen household economic resilience while improving practical access to care. In concrete terms, this could include better targeting of social protection and livelihood support in vulnerable rural households, combined with stronger community-based outreach for HIV screening, referral, and nutrition-related support. In Nigeria, the findings point to a somewhat different policy profile. Housing quality/sanitation is the strongest factor associated with lower perceived HIV burden, while both household income and healthcare access are associated with lower nutritional deficiency. This suggests that rural policy responses in Nigeria may be more effective when basic environmental conditions are addressed alongside service access and household welfare. In practice, this means greater emphasis on rural sanitation infrastructure, household environmental improvements, and strengthened access to primary healthcare, especially when these can be linked to nutrition support and other community-level services.

These findings suggest that policy responses should not rely on uniform rural health strategies across countries. Rather, interventions should be aligned with the dominant pattern of disadvantage observed in each setting: a more income- and service-oriented response in South Africa, and a more environment-, service-, and income-oriented response in Nigeria. This also indicates that intersectoral collaboration is most useful when tied to specific leverage points identified in the results, rather than framed only in broad general terms.

Limitations and future research

The findings should be interpreted in light of the study’s limitations. The analysis is based on cross-sectional and perception-based data, so the results should be interpreted as associations rather than causal effects. In addition, the HIV burden measure reflects perceived burden rather than biomarker-confirmed prevalence, and the findings are analytically generalisable to comparable rural settings rather than statistically representative of all rural populations in both countries. Future research could therefore strengthen this line of inquiry by using longitudinal or mixed methods designs, incorporating biomarker or clinical data where feasible, and extending comparative analysis to a wider range of rural settings and social determinants.

Conclusion and policy recommendations

This study was motivated by the persistent inequities in rural health across Sub-Saharan Africa, particularly the disproportionate burden of HIV and nutritional deficiencies, and by the recognition that rural disadvantage is neither uniform nor neutral. Framed within the social determinants of health perspective, the study compared household income, healthcare access, and housing quality with sanitation between rural populations in West and Southern Africa; examined cross-national differences in HIV prevalence and nutritional deficiencies; and assessed the extent to which these determinants linked to both outcomes. Using data obtained from sampled rural communities, the study applied comparative statistical techniques, including t-tests and regression modelling, complemented by disaggregated country-level analyses to uncover specific dynamics influencing rural health.

The findings reveal clear and meaningful cross-national contrasts. South Africa demonstrates relatively higher household income and superior housing quality with sanitation, whereas Nigeria records more favourable perceptions of access to healthcare. Despite these advantages, South Africa exhibits a higher perceived HIV burden and nutritional deficiency. The regression analyses further show that household income and healthcare are significantly associated with lower HIV vulnerability in South Africa, while housing quality and sanitation play a more decisive role in Nigeria. Similarly, household income is the strongest predictor of nutritional deficiency in South Africa, whereas both household income and healthcare are significant predictors in Nigeria. These findings demonstrate that structural determinants of health operate differently across and that rural disadvantage manifests in diverse ways. Therefore, uniform, region-wide policy prescriptions are unlikely to be effective; instead, responsive, and evidence-driven interventions are required.

Policy responses should closely align with the country-specific determinants identified. Strengthening economic resilience among rural households remains critical, particularly in South Africa, where enhanced income security can simultaneously reduce both HIV vulnerability and nutritional deficiencies. Improving the accessibility, quality, and effectiveness of healthcare services is particularly important in Nigeria, where access to healthcare is closely tied to nutritional and health outcomes. Equally, sustained investment in rural housing infrastructure and sanitation is essential, most urgently in Nigeria, where inadequate environmental and housing conditions heighten HIV vulnerability. Beyond sector-specific initiatives, coordinated multisectoral strategies that integrate social protection, public health, and rural development are imperative to address structural disadvantage holistically and sustainably.

Authors’ contributions

Conceptualisation – M.P.; Methodology – M.P. & H.I.; Software – M.P. & H.I.; Validation – M.P.; Formal analysis – M.P. & H.I.; Investigation – M.P. & H.I.; Resources – M.P.; Data curation – M.P. & H.I.; Writing – Original Draft- M.P.; Writing – Review & Editing – M.P. & H.I.; Visualisation – M.P.; Supervision – M.P. & H.I.; Project administration – M.P.; Funding acquisition – M.P.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments. Ethical approval for this study was obtained from the Walter Sisulu University Health Sciences Research Ethics Committee (NHREC Reg. No. REC120209020), with approval number WSU HERC 020/2025. Informed consent was obtained from all participants prior to their inclusion in the study.

Consent for publication

Written informed consent for publication of the findings of this study was obtained from all participants included in the study. All authors have reviewed the final manuscript and consent to its submission and publication.

Competing interests

The authors declare no competing interests.