ABSTRACT
Background and Aims
Hypertension is a leading cause of cardiovascular complications (CVCs), yet the contribution of socioeconomic and behavioral risk factors to CVC development in Zambia is poorly understood. This study aimed to assess the association between socioeconomic status (SES), lifestyle behaviors, and CVCs among hypertensive patients at the University Teaching Hospital (UTH) in Lusaka, Zambia.
Methods
A hospital‐based cross‐sectional study was conducted using secondary data from 384 medical records of hypertensive adults aged ≥ 18 years at UTH. Data on sociodemographic characteristics, SES, behavioral factors (salt intake, smoking, alcohol use, physical activity), and presence of CVCs (e.g., heart failure, left ventricular hypertrophy) were extracted. Multivariable logistic regression analysis was performed, and due to evidence of quasi‐complete separation, Firth's penalized logistic regression was applied to improve estimate stability.
Results
Increasing age was significantly associated with higher odds of CVCs (aOR = 1.097, p < 0.001). Male sex was not significantly associated with CVCs after adjustment (aOR = 1.76, p = 0.24). Low SES was associated with higher odds of CVCs (aOR = 4.09, p = 0.05), although this was borderline significant. Smoking (aOR = 3.09, p = 0.04) and alcohol intake (aOR = 1.13, p = 0.002) were significantly associated with increased odds, while physical activity was strongly protective (aOR = 0.08, p < 0.001).
Conclusion
Socioeconomic and behavioral factors were associated with the manifestation of CVCs in hypertensive patients. Older age was associated with increased odds of CVCs. Smoking and alcohol use were significant behavioral risk factors, while low SES showed a borderline association. Higher education and physical activity were protective. Public health interventions must integrate targeted lifestyle modification and address socioeconomic disparities to mitigate cardiovascular risk.
Keywords: behavioral risk factors, cardiovascular complications, cross‐sectional study, hypertension, socioeconomic status, Zambia
1. Background
Hypertension remains a leading global cause of morbidity and mortality [1, 2], with a significant burden in sub‐Saharan Africa, including Zambia [3]. Hypertension remains a major public health concern in Zambia, with about 1.3 million adults affected [4]. Hypertension is a primary risk factor for a spectrum of cardiovascular complications (CVCs) such as heart failure (HF), left ventricular hypertrophy (LVH), coronary artery disease, and arrhythmias [5, 6]. In Zambia, the prevalence of hypertension among adults is high and rising, contributing substantially to the national burden of non‐communicable diseases [7].
Beyond biological mechanisms, the development and progression of hypertensive complications are profoundly influenced by social and behavioral determinants of health [4]. Socioeconomic status (SES), encompassing factors like education, income, and occupation, is a well‐established predictor of cardiovascular outcomes [8]. Lower SES is often linked to limited healthcare access, poor health literacy, and increased exposure to stress, all of which can exacerbate hypertension control and accelerate end‐organ damage [8, 9].
Concurrently, modifiable behavioral risk factors including excessive dietary salt intake, tobacco use, harmful alcohol consumption, and physical inactivity are directly implicated in the pathogenesis and poor control of hypertension [10]. These behaviors are not randomly distributed; they are frequently patterned by socioeconomic circumstances, creating a cycle where low SES promotes unhealthy behaviors, which in turn increase the risk of adverse cardiovascular outcomes [11].
While the individual roles of hypertension, SES, and behavior are recognized, there is a paucity of context‐specific evidence in Zambia elucidating how these factors interact to influence the manifestation of CVCs among diagnosed hypertensive patients [12]. Understanding these associations is crucial for developing effective, holistic, and equity‐focused interventions. This study, therefore, sought to determine the association between socioeconomic and behavioral risk factors and the manifestation of CVCs among hypertensive adults attending the University Teaching Hospital in Lusaka, Zambia.
What is already known on this topic:
Hypertension is a major risk factor for CVCs like HF and stroke [13].
Low SES is generally associated with poorer health outcomes and a higher burden of non‐communicable diseases [14].
Unhealthy behaviors such as high salt intake and smoking contribute to poor blood pressure control and worse cardiovascular outcomes [11, 15, 16].
What this study adds:
Context‐specific evidence from a Zambian tertiary hospital quantifying the strong independent association between low SES and CVCs in a hypertensive population.
Identification of smoking and alcohol use as key behavioral risk factors for CVCs in this setting, even after adjustment for other factors.
Demonstration of the strong graded protective effect of physical activity, independent of socioeconomic factors, among hypertensive patients in Zambia.
2. Methods
2.1. Study Design and Setting
A hospital‐based, analytical cross‐sectional study was conducted using secondary data from medical records. The study was conducted in January 2025. The study was set at the University Teaching Hospital‐Adult Hospital (UTH‐AH), the largest tertiary referral center in Zambia, located in Lusaka. The reporting of this study follows the STROBE guidelines [17].
2.2. Study Population and Sampling
The target population was hypertensive patients aged 18 years and above who attended UTH. A sample size of 384 was calculated using the single population proportion formula (assuming a 50% proportion, 95% confidence level, and 5% margin of error) [18]. A simple random sampling method was used to select patient files from the hospital registry until the required sample size was attained.
2.3. Inclusion and Exclusion Criteria
Inclusion criteria were: (1) diagnosis of hypertension, (2) age ≥ 18 years, and (3) availability of records with documented echocardiography/ECG results and relevant sociodemographic/behavioral data. Records were excluded if they were incomplete (missing key study variables), belonged to patients under 18, or were duplicates.
2.4. Variables and Measurement
Dependent variable: Presence of CVCs (yes/no). This was ascertained from documented diagnoses of HF, LVH (by echocardiography), ischemic heart disease, or arrhythmias like atrial fibrillation in the patient file.
Independent variables:
Socioeconomic factors: Age, sex, residence (urban/rural), education level (none, primary, secondary, tertiary), occupation (manual/sedentary), and a composite SES (low/medium/high) based on occupation and income as recorded.
Behavioral factors: Salt intake (categorized as low, medium, high based on dietary history), smoking status (smoker/non‐smoker), alcohol intake (units consumed per week), and level of physical activity (not active, lightly, moderately, very active).
2.5. Data Collection and Management
A structured data extraction tool was used to collect information from the selected medical records. The tool was pre‐tested on 10 records for clarity and consistency. Data collection was conducted by the principal investigator. All extracted data were anonymized, assigned unique codes, and entered into a secure Microsoft Excel spreadsheet.
2.6. Data Analysis
Data were analyzed using Stata Statistical Software: Release 18 (StataCorp LLC, College Station, TX, USA). Descriptive statistics (frequencies, percentages, means, and standard deviations) were used to summarize the characteristics of the study population. Multivariable logistic regression analysis was performed, and due to evidence of quasi‐complete separation, Firth's penalized logistic regression was applied to improve estimate stability, adjusting for all other variables in the model. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) were calculated. A p‐value of less than 0.05 was considered statistically significant.
Due to observed complete or quasi‐complete separation in some exposure categories, the following measures were taken: (1) salt intake was excluded from the final model due to persistent quasi‐complete separation (almost all high‐salt consumers had complications), and (2) occupation was excluded due to high collinearity with the composite SES variable, and we acknowledge that maximum likelihood estimates from standard logistic regression may be unstable with exaggerated effect sizes. As a sensitivity analysis, we confirmed the direction and statistical significance of associations using Firth's penalized likelihood method, which provides more stable estimates with sparse data. The consistency of findings across methods supports the robustness of our conclusions regarding the presence and direction of associations, though precise effect magnitude estimation is limited by our data structure.
3. Results
3.1. Sociodemographic and Clinical Characteristics
A total of 384 patient records were analyzed. The mean age of participants was 44.2 years (SD ± 10.5), with the largest group aged 41–50 years (36.7%). There was a near‐equal distribution of males (50.8%) and females (49.2%). Most participants resided in urban areas (60.9%) and had at least secondary‐level education (66.9%). Over half (57.8%) were engaged in sedentary work. The prevalence of documented CVCs among the hypertensive patients was 33.7% (n = 130) (Table 1).
Table 1.
Sociodemographic and behavioral characteristics of hypertensive patients at UTH (N = 384).
| Characteristic | No complication (n = 254) | Complication present (n = 130) | Total N (%) |
|---|---|---|---|
| Age, mean (SD) | 39.1 (6.8) | 47.7 (9.5) | 42.0 (8.8) |
| Sex, n (%) | |||
| Female | 134 (52.8) | 55 (42.3) | 189 (49.2) |
| Male | 120 (47.2) | 75 (57.7) | 197 (50.8) |
| Residence, n (%) | |||
| Rural | 62 (24.4) | 88 (67.7) | 150 (39.1) |
| Urban | 192 (75.6) | 42 (32.3) | 234 (60.9) |
| Education, n (%) | |||
| No formal | 2 (0.8) | 53 (40.8) | 55 (14.3) |
| Primary | 27 (10.6) | 47 (36.2) | 74 (19.3) |
| Secondary | 106 (41.7) | 23 (17.7) | 129 (33.6) |
| Tertiary | 119 (46.9) | 7 (5.4) | 126 (32.8) |
| Occupation, n (%) | |||
| Manual | 102 (40.2) | 120 (92.3) | 222 (57.8) |
| Sedentary | 152 (59.8) | 10 (7.7) | 162 (42.2) |
| SES, n (%) | |||
| Low | 30 (11.8) | 109 (83.8) | 139 (36.2) |
| Medium | 104 (40.9) | 15 (11.5) | 119 (31.0) |
| High | 120 (47.2) | 6 (4.6) | 126 (32.8) |
| Alcohol (units) mean (SD) | 2.20 (4.29) | 11.9 (6.06) | 5.45 (6.74) |
| Salt intake, n (%) | |||
| Low | 118 (46.5) | 7 (5.4) | 125 (32.6) |
| Medium | 122 (48.0) | 19 (14.6) | 141 (36.7) |
| High | 14 (5.5) | 104 (80.0) | 118 (30.7) |
| Smoking, n (%) | |||
| No | 174 (68.5) | 10 (7.7) | 184 (47.9) |
| Yes | 80 (31.5) | 120 (92.3) | 200 (52.1) |
| Physical activity, n (%) | |||
| Not active | 10 (3.9) | 69 (53.1) | 79 (20.6) |
| Lightly active | 61 (24.0) | 52 (40.0) | 113 (29.4) |
| Moderately active | 77 (30.3) | 6 (4.6) | 83 (21.6) |
| Very active | 106 (41.7) | 3 (2.3) | 109 (28.4) |
3.2. Behavioral Risk Factors
Regarding behavioral factors, 31.3% had a high salt intake, 52.1% were smokers, and 46.6% reported alcohol intake. A substantial proportion (50.1%) reported moderate to high levels of physical activity (Table 1).
3.3. Multivariable Analysis of Factors Associated With CVCs
The results of the multivariable Firth logistic regression analysis are presented in Table 2. After adjusting for all covariates, increasing age was significantly associated with higher odds of CVCs (aOR = 1.097 per year increase, p < 0.001), indicating that the risk of the outcome increased with advancing age. Male sex was associated with higher odds of CVCs (aOR = 1.76, p = 0.24), although this relationship was not statistically significant after adjustment. In contrast, higher educational attainment demonstrated a protective effect, with individuals having secondary education showing significantly lower odds of CVCs compared to those with primary education (aOR = 0.16, p < 0.001), while tertiary education was also associated with reduced odds, though not statistically significant (aOR = 0.29, p = 0.17). Similarly, individuals from lower SES groups had higher odds of CVCs compared to those from higher socioeconomic groups (aOR = 4.09, p = 0.05), although this association was borderline significant and exhibited some imprecision.
Table 2.
Multivariable logistic regression for predictors of cardiovascular complications.
| Variable | Category (reference) | Adjusted odds ratio (aOR) | 95% CI for aOR | p‐value |
|---|---|---|---|---|
| Age | Per year increase | 1.097 | 1.041–1.155 | < 0.001 |
| Sex | Female | 1 | ||
| Male | 1.757 | 0.692–4.459 | 0.24 | |
| Residence | Rural | 1 | ||
| Urban | 0.773 | 0.309–1.933 | 0.58 | |
| Education | Primary | 1 | ||
| Secondary | 0.161 | 0.063–0.312 | < 0.001 | |
| Tertiary | 0.293 | 0.0508–1.694 | 0.17 | |
| SES | High | 1 | ||
| Low | 4.09 | 0.975–17.15 | 0.05 | |
| Alcohol intake | Per unit increase | 1.13 | 1.046–1.22 | 0.002 |
| Smoking | No | 1 | ||
| Yes | 3.09 | 1.081–8.86 | 0.04 | |
| Physical activity | Not active | 1 | ||
| Active | 0.081 | 0.031–0.213 | < 0.001 |
With respect to behavioral factors, smoking was significantly associated with increased odds of CVCs (aOR = 3.09, p = 0.04), and higher alcohol intake was also positively associated with increased odds (aOR = 1.13 per unit increase, p = 0.002), highlighting their role as important risk factors. Conversely, being physically active was strongly protective, with active individuals having substantially lower odds of CVCs compared to those who were inactive (aOR = 0.08, p < 0.001). Residence was not significantly associated with the outcome (aOR = 0.77, p = 0.58). Overall, the findings suggest that both socio‐demographic and behavioral factors play an important role in shaping the risk of CVCs, with modifiable lifestyle factors such as smoking, alcohol consumption, and physical activity showing particularly strong associations.
4. Discussion
This study demonstrates a substantial burden of CVC among hypertensive patients at the University Teaching Hospital, with risk strongly influenced by both socio‐demographic and behavioral factors. In the adjusted analysis, increasing age remained a significant predictor of CVCs (aOR = 1.097, p < 0.001), consistent with global evidence linking cumulative exposure to hypertension with progressive cardiovascular damage [19, 20, 21]. Although male sex was associated with higher odds of CVCs (aOR = 1.76), this relationship was not statistically significant after adjustment (p = 0.24), suggesting that previously observed sex differences may be partly explained by confounding factors such as age and behavioral risk profiles [22].
SES emerged as an important determinant, with individuals from lower SES groups exhibiting higher odds of CVCs (aOR = 4.09, p = 0.05), although the wide CI indicates some imprecision. This finding highlights the critical role of social and economic conditions in shaping cardiovascular risk in the Zambian context. Patients with lower SES may face structural barriers to accessing care, including limited ability to afford medications, reduced access to follow‐up services, and challenges in adhering to recommended lifestyle modifications, thereby increasing the likelihood of poor blood pressure control and subsequent complications [9, 23]. Similarly, higher educational attainment, particularly at the secondary level, was strongly protective (aOR = 0.16, p < 0.001), underscoring the importance of health literacy in enabling effective self‐management of chronic conditions [24, 25].
Behavioral risk factors showed strong and consistent associations with CVCs. Smoking was significantly associated with increased odds of complications (aOR = 3.09, p = 0.04), reinforcing its well‐established role in accelerating cardiovascular damage among hypertensive individuals [10, 26]. Alcohol consumption was also independently associated with increased risk (aOR = 1.13 per unit increase, p = 0.002), supporting evidence of its dose‐dependent contribution to hypertension and cardiovascular disease [27, 28]. In contrast, physical activity demonstrated a markedly protective effect, with active individuals having substantially reduced odds of CVCs (aOR = 0.08, p < 0.001). This strong association highlights physical activity as a key, low‐cost intervention for reducing cardiovascular risk and aligns with global findings on the benefits of exercise in mitigating hypertension‐related complications [29, 30, 31, 32].
Residence was not significantly associated with CVCs (aOR = 0.77, p = 0.58), suggesting that, within this study population, geographic location may be less influential than individual‐level socioeconomic and behavioral factors [33].
Despite these important findings, some estimates particularly for SES were accompanied by relatively wide CIs, indicating limited precision. This is likely attributable to sample size constraints and variability within subgroups. However, the consistency in the direction of associations, their biological plausibility, and alignment with existing literature support the robustness of the overall findings. Importantly, the use of Firth's penalized logistic regression and model refinement in the current analysis addressed the instability observed in earlier models, resulting in more reliable and interpretable estimates, as recommended in small‐sample epidemiological modeling [34].
Overall, these findings emphasize that the burden of CVC among hypertensive patients is driven not only by clinical factors but also by modifiable behavioral risks and underlying socioeconomic inequalities. Interventions targeting smoking cessation, alcohol reduction, and increased physical activity, alongside strategies to address socioeconomic barriers, are likely to yield substantial benefits in reducing CVCs in this population, consistent with recent WHO hypertension control frameworks and global cardiovascular prevention strategies [35].
4.1. Limitations
This study has several limitations that should be considered when interpreting the findings. First, the cross‐sectional design precludes causal inference regarding the direction of observed associations, as exposure and outcome are measured simultaneously [36, 37]. Second, the use of secondary data from medical records may be subject to incompleteness and reporting bias, particularly for behavioral factors which were likely self‐reported, a common limitation in routine clinical data [38, 39].
Third, earlier analyses produced inflated estimates due to sparse data and quasi‐complete separation in some exposure categories. However, model refinement and the application of Firth's penalized logistic regression improved the stability and interpretability of the estimates in the final model [40].
Fourth, our measurement of SES, while pragmatic, was based on available record data and may not capture the full complexity of this multidimensional construct, which typically includes income, education, and occupation [41]. Fifth, we lacked data on important clinical confounders including hypertension duration, degree of blood pressure control, medication adherence patterns, and comorbid conditions such as diabetes mellitus, which are known to significantly influence cardiovascular outcomes [42, 43].
Sixth, the single‐center tertiary hospital setting may limit generalizability to primary care or community settings in Zambia, as hospital‐based populations often represent more severe cases [44].
5. Conclusion
This study provides compelling evidence that among hypertensive patients at Zambia's largest referral hospital, the manifestation of CVC is significantly and independently associated with lower SES and modifiable high‐risk behaviors, particularly smoking, alcohol use, and physical inactivity. Conversely, higher education, better socioeconomic standing, and physical activity are protective. Findings highlight the need for integrated lifestyle and socioeconomic interventions. Further, these findings argue for a paradigm shift in hypertension management in Zambia from a purely clinical model to a holistic one that integrates targeted lifestyle counseling, patient empowerment through education, and policy actions aimed at mitigating the underlying “social and economic determinants of health.”
Author Contributions
Gunet Mwalungali: conceptualization, methodology, software, formal analysis, supervision, writing – review and editing. Epiphany Chola: conceptualization, methodology, writing – original draft, formal analysis, software, visualization. Patience Hampango: writing – review and editing, methodology. Martin Kampamba: methodology, formal analysis, writing – review and editing. Larry Mweetwa: writing – review and editing, visualization, validation. Christine Mutati: methodology, visualization, writing – review and editing. Chanda Chisunka: formal analysis, visualization, writing – review and editing, methodology. Annie Silwimba: validation, visualization, writing – review and editing. Jimmy Hangomma: methodology, formal analysis, writing – review and editing.
Funding
The authors have nothing to report.
Ethics Statement
Ethical approval was obtained from the Cavendish University Zambia School of Medicine and Health Sciences Research Ethics Committee (Ref: CUZSoMHSREC/2024/001) and administrative clearance from the University Teaching Hospital (UTH), Lusaka. The study utilized secondary anonymised data from medical records; therefore, the requirement for informed consent was waived by the ethics committee. All methods were carried out in accordance with relevant guidelines and regulations.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Disclosure Statement
The authors confirm that no AI tools were used to generate the content, analysis, or interpretation in this manuscript. Grammarly was used solely for language refinement and formatting assistance. All substantive content, data analysis, and conclusions are the original work of the authors, who take full responsibility for the integrity and accuracy of the manuscript.
Transparency Statement
The lead author (Gunet Mwalungali) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
Acknowledgments
We thank the administration and records department of the University Teaching Hospital, Lusaka, for granting access to the medical records. We also acknowledge Cavendish University Zambia for the academic support.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. The datasets used and/or analyzed during the current study are not publicly available due to institutional and ethical restrictions but are available from the corresponding author on reasonable request and with permission from the University Teaching Hospital.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. The datasets used and/or analyzed during the current study are not publicly available due to institutional and ethical restrictions but are available from the corresponding author on reasonable request and with permission from the University Teaching Hospital.