The Prevalence, Prevention, and Treatment of Cardiovascular Diseases in Twelve African Countries (2014-2019): An Analysis of the World Health Organisation STEPwise Approach to Chronic Disease Risk Factor Surveillance

This study analyzed WHO STEPS data from twelve African countries between 2014 and 2019, revealing a 5% prevalence of cardiovascular diseases and critically low rates of prevention and treatment uptake, thereby highlighting an urgent need to improve diagnosis and expand care to reduce premature deaths.

The Gist

Imagine the human body as a bustling city. The heart is the central power plant, and the blood vessels are the roads delivering fuel (oxygen) to every neighborhood. Cardiovascular Disease (CVD) is like a traffic jam or a power outage in this city—it happens when the roads get clogged (blockages) or the power plant struggles, leading to heart attacks or strokes.

This paper is a massive "city inspection" report covering 12 African countries between 2014 and 2019. The researchers used data from the World Health Organization (WHO) to answer three big questions:

1. How many cities are currently having power outages? (Prevalence)
2. How many people are trying to fix the roads before they break? (Prevention)
3. How many people whose power plants are already failing are actually getting repairs? (Treatment)

Here is the breakdown of their findings, translated into everyday language:

1. The Current State of the City (Prevalence)

The researchers found that about 5% of the people surveyed had already experienced a "power outage" (a heart attack, angina, or stroke).

• Who is most at risk? Think of the city's older residents. The older you get, the more likely your roads are to wear out. Women were slightly more likely to report issues than men in this specific dataset.
• The Villains: The biggest culprits clogging the roads were high blood pressure (too much pressure on the pipes), smoking (like pouring grease into the engine), and eating too much salt (which makes the pipes stiff and brittle).
• The Surprise: The risk wasn't the same everywhere. Just like some cities have better infrastructure than others, countries like Uganda and Malawi had higher rates of "outages," while Sudan and Morocco had lower rates. This suggests that local rules, diets, and healthcare systems play a huge role.

2. The "Prevention" Gap (The Missed Maintenance)

Imagine a mechanic offering to fix your car's brakes before you crash. This is prevention.

• The study looked at people over 40 (who are at high risk) who hadn't crashed yet.
• The Bad News: Only 11% of these at-risk people were getting any kind of preventive maintenance (like taking aspirin, statins, or getting advice to quit smoking).
• The Gap: In wealthy countries, this number is often 50% or higher. In Africa, the "maintenance crew" is barely showing up.
• Who got help? People who lived in cities, were educated, or already had high blood pressure were more likely to get help. Men and people in rural villages were largely left out, often because they don't visit the "garage" (clinic) until it's too late.

3. The "Treatment" Gap (The Broken Power Plants)

Now, imagine the power plant has already failed. The city needs emergency repairs. This is treatment.

• Out of the people who already had heart disease, only 22% were actually receiving treatment (medication or counseling).
• The "High Blood Pressure" Lifeline: Here is a crucial finding: People with high blood pressure were 7 times more likely to get treatment than those without it.
  • Why? High blood pressure is like a loud alarm bell. When the alarm rings, doctors pay attention and give medicine. But if you have a silent heart problem (no high blood pressure), the alarm doesn't ring, and you get ignored.
• The Inequality: If you live in a city, you are more likely to get a repair crew. If you live in a rural village, you might be waiting for a truck that never comes.

The "Care Cascade" Analogy

The authors describe a "Care Cascade," which is like a funnel.

1. Top of the funnel: Everyone who could have a heart problem.
2. Middle: The few who actually get diagnosed (the funnel is very narrow here).
3. Bottom: The tiny sliver of people who actually get treated.

In these 12 African countries, the funnel is leaking badly. Most people fall out of the top before they ever reach the bottom where the treatment happens.

Why is this happening? (The Root Causes)

The paper suggests several reasons for these leaks:

• The "Silent" Enemy: Many people don't know they have high blood pressure or heart issues because they feel fine. Without a check-up, they don't get the "alarm" that triggers treatment.
• The Rural Roadblock: Getting to a clinic in a remote village is hard, expensive, and time-consuming.
• The Gender Gap: Men often ignore health warnings until it's an emergency, while women are more likely to visit clinics for other reasons (like family health), giving them a chance to get heart care too.
• The Salt Trap: Traditional diets in some areas are very salty, which stiffens the pipes, but people aren't always taught how to fix this.

The Bottom Line

The study concludes that while heart disease isn't as common in Africa as in Europe or America yet, it is rising fast. The biggest problem isn't just that people are getting sick; it's that the healthcare system isn't catching them early enough or fixing them quickly enough.

The Solution?
We need to stop waiting for the "alarm bells" (like high blood pressure) to ring before we act. We need to:

• Send more "mechanics" (doctors and nurses) to rural villages.
• Teach men to visit the clinic before they crash.
• Make salt and smoking less attractive (and cheaper to avoid).
• Treat everyone at risk, not just those with high blood pressure.

If we don't fix these leaks in the funnel, the "power outages" (deaths from heart disease) will become a major crisis for the continent.

Technical Summary

1. Problem Statement

Cardiovascular diseases (CVDs) are a leading cause of global mortality, with over 75% of CVD deaths occurring in low- and middle-income countries (LMICs). Despite a steady increase in CVD prevalence in Africa, there is a significant lack of multi-country analyses assessing the full care continuum (diagnosis, prevention, and treatment) in the region. Existing research often focuses on high-income settings or single-country studies, leaving a gap in understanding the epidemiology, risk factor distribution, and treatment uptake across diverse African contexts. Furthermore, while the WHO STEPwise approach (STEPS) provides standardized data, these datasets remain underutilized for comprehensive CVD care cascade analysis in Africa.

2. Methodology

Study Design and Data Source:

• Type: Secondary analysis of cross-sectional survey data.
• Data Source: WHO STEPS surveys from 12 African countries: Algeria, Benin, Botswana, Eswatini, Ethiopia, Kenya, Malawi, Morocco, São Tomé and Príncipe, Sudan, Uganda, and Zambia.
• Timeframe: Data collected between 2014 and 2019.
• Sample Size: 60,294 individuals (aged 18–69 years) after excluding those with missing CVD history data.

Variables and Definitions:

• Outcome Variables:
  • CVD Prevalence: Self-reported history of heart attack, angina, or stroke.
  • Prevention Uptake: Among individuals aged ≥40 without prior CVD history, defined as receiving aspirin, statins, or counseling on risk factors.
  • Treatment Uptake: Among individuals with a history of CVD, defined as receiving aspirin, statins, counseling, or a combination thereof.
• Predictor Variables: Demographics (age, sex, education, residence, occupation), behavioral risk factors (smoking, alcohol, diet, physical activity), and biological risk factors (hypertension, diabetes, BMI, cholesterol).
• Missing Data Handling: 26% of the dataset had missing values. The authors employed simulation-based imputation (using binomial, multinomial, and Gaussian distributions) rather than standard Multiple Imputation by Chained Equations (MICE) to handle convergence issues and heterogeneity across countries. Sensitivity analyses confirmed the robustness of these imputations against complete-case analysis.

Statistical Analysis:

• Software: R (version 4.3.2).
• Models: Weighted quasibinomial logistic regression models were fitted to account for the complex survey design (clustering, stratification, and weights).
• Adjustments: Country fixed effects were included to control for inter-country heterogeneity.
• Selection: Backward variable selection (stepAIC) was used to optimize model fit, retaining clinically significant variables (age, sex, hypertension) regardless of selection criteria.

3. Key Contributions

• First Multi-Country Analysis: This is the first study to analyze the CVD care cascade (prevalence, prevention, and treatment) across 12 diverse African nations using harmonized WHO STEPS data.
• Care Cascade Mapping: The study explicitly maps the drop-off rates from the eligible population to diagnosis, and from diagnosis to treatment, highlighting systemic gaps.
• Methodological Rigor: The use of simulation-based imputation for missing data and the application of survey-weighted quasibinomial models with country fixed effects provides a robust statistical framework for analyzing heterogeneous multi-country survey data.
• Contextual Insights: The study identifies specific demographic and clinical predictors (e.g., hypertension status, urban/rural divide, gender) that drive disparities in CVD management across the continent.

4. Key Results

Prevalence:

• Overall Prevalence: 5% (2,895/60,294) of the population reported a history of CVD.
• Risk Factors: Prevalence increased significantly with age. Key associations included:
  • Hypertension: Individuals with hypertension were nearly 3 times more likely to have CVD (aOR: 2.68).
  • Gender: Females had higher prevalence than males (aOR: 0.65 for males vs. females).
  • Lifestyle: High salt intake (aOR: 1.23) and smoking were significant predictors.
• Geographic Variation: Prevalence ranged from 1% in Sudan to 9% in Uganda.

Prevention Uptake (High-Risk, CVD-Naïve Population):

• Target Group: 23,630 individuals aged ≥40 without prior CVD.
• Uptake Rate: Only 11% received any form of prevention (medication or counseling).
• Disparities:
  • Hypertension: The strongest predictor; hypertensive individuals were 17 times more likely to receive prevention (aOR: 16.65).
  • Demographics: Females, urban residents, and those with tertiary education had higher uptake. Men were significantly less likely to receive prevention.

Treatment Uptake (CVD-Positive Population):

• Target Group: 2,895 individuals with a history of CVD.
• Uptake Rate: Only 22% received treatment and counseling.
• Treatment Breakdown:
  • 34% received aspirin only.
  • 11% received statins only.
  • 24% received both.
  • 32% received counseling.
• Disparities:
  • Hypertension: Hypertensive patients were 7 times more likely to receive treatment (aOR: 7.19).
  • Geography: Treatment uptake varied widely, from 42% in Sudan to 5% in Kenya.
  • Age: Older adults (50+) had significantly higher uptake than younger adults.

Care Cascade:
The study reveals a "leaky" cascade: fewer than 1 in 10 individuals are diagnosed, and less than half of those diagnosed receive treatment. The gap is most pronounced in rural areas and among men.

5. Significance and Implications

• Public Health Crisis: The low uptake of prevention (11%) and treatment (22%) indicates a critical failure in the management of non-communicable diseases (NCDs) in Africa, threatening the achievement of UN Sustainable Development Goal 3.4 (reducing premature NCD mortality by one-third by 2030).
• Hypertension as a Gateway: The strong correlation between hypertension status and treatment/prevention uptake suggests that hypertension management is the primary entry point for CVD care. However, this leaves non-hypertensive high-risk groups (e.g., diabetics, smokers) underserved.
• Equity Gaps: Significant disparities exist based on gender (men are underserved), geography (rural populations have poor access), and socioeconomic status (proxied by education and urbanization).
• Policy Recommendations:
  • Task-Shifting: Integrate CVD screening and management into primary care and delegate tasks to community health workers.
  • Targeted Interventions: Develop gender-specific strategies to engage men and rural-focused mobile health interventions.
  • Dietary Policy: Implement multi-sectoral policies to reduce salt intake, a major modifiable risk factor identified in the study.
  • Data Improvement: Future WHO STEPS surveys should include screening data and wealth indices to better capture the full care cascade and socioeconomic determinants.

In conclusion, while CVD prevalence in Africa is currently lower than in high-income regions, the region faces a severe "treatment gap." Without concerted efforts to improve diagnosis, expand access to essential medications (aspirin, statins), and address social determinants, the burden of CVD in Africa is poised to rise dramatically.