Age, Sex and Body Weight Drive Blood Pressure Risk in Rural Ghana, Twin Statistical Methods Confirm

In the dry savannah belt of northern Ghana, where the Kassena-Nankana districts stretch along the border with Burkina Faso, a quiet epidemic is unfolding. More than four in ten adults in their middle years are now living with hypertension, many of them unaware of it. A new analysis of nearly two thousand adults aged 40 to 60, drawn from the Africa Wits-INDEPTH Partnership for Genomic Studies (AWI-Gen), has mapped the factors that push blood pressure upward in this rural population, and in doing so has settled a methodological question that statisticians and epidemiologists have long debated: does it matter whether you analyse health data with classical frequentist tools or with modern Bayesian machinery? The answer, according to the study published in BMC Public Health, is a resounding no, provided the underlying data and study design are handled with care.

The research team, led by Richmond Balinia Adda of the University of Technology and Applied Sciences in Navrongo and the Navrongo Health Research Centre, classified participants into four ordered blood pressure categories following the 2017 American College of Cardiology and American Heart Association thresholds: normal, elevated, stage 1 hypertension and stage 2 hypertension. The resulting picture was sobering. Only 46.1 percent of the adults fell into the normal category, 12.4 percent had elevated blood pressure, 19.6 percent met the criteria for stage 1 hypertension and 21.9 percent had stage 2 hypertension. Combined, more than 41 percent of this middle-aged rural population carried blood pressure levels that meaningfully raise the long-term risk of stroke, heart disease and kidney failure, conditions that are increasingly straining health systems across sub-Saharan Africa.

What makes the study distinctive is not just what it found but how it found it. Rather than defaulting to a single statistical tradition, the researchers deliberately ran two parallel analyses of the same data. The first used an ordered probit model, a classical frequentist technique designed for outcomes that fall into a natural ranking, such as the four severity grades of blood pressure. The second used a Bayesian version of the same model, estimated with Hamiltonian Monte Carlo, a sophisticated sampling algorithm that explores the probability landscape of the model using four independent chains and weakly informative priors. The Bayesian approach does not simply reject or fail to reject a null hypothesis; it produces a full posterior distribution for every effect, allowing researchers to state directly how plausible each value of an effect is given the data.

Before a single coefficient was estimated, the team invested heavily in study design. They constructed a directed acyclic graph, a formal diagram of the causal relationships among variables, to determine the minimal set of confounders that needed to be adjusted for. This graph-informed approach yielded a compact adjustment set: age, sex, marital status, occupation, smoking, alcohol use, pesticide exposure, body mass index and moderate-to-vigorous physical activity. The reasoning matters because adjusting for the wrong variables, or failing to adjust for the right ones, can distort estimates in ways that no amount of statistical sophistication can repair. The analysis was carried out on a complete-case sample of 1,979 of the original 2,010 participants, and the proportional-odds assumption, the premise that each predictor shifts all severity thresholds equally, was formally tested using the Brant test.

The substantive findings were consistent and, in places, surprising. Older age was strongly associated with higher blood pressure category, with a probit coefficient of 0.032 per year of age in both frameworks, and confidence and credible intervals that overlapped almost perfectly, from 0.023 to 0.041. Male sex carried a coefficient of 0.279, and each additional unit of body mass index added 0.054 to the latent blood pressure score, again with virtually identical estimates under both frameworks. These three factors, age, sex and adiposity, emerged as the robust drivers of hypertension risk in this population, echoing patterns seen across the African continent and reinforcing the message that as rural populations age and body weights rise, the cardiovascular burden will climb with them.

One finding, however, broke the tidy pattern. The Brant test revealed that the association with sex was not constant across the severity thresholds of the outcome. With a chi-squared statistic of 10.78 on two degrees of freedom and a p-value of 0.005, the test indicated that the male excess in blood pressure risk differs in magnitude depending on whether one is crossing the line from normal to elevated, or from stage 1 to stage 2 hypertension. In practical terms, being male matters more at some points on the severity spectrum than at others, a nuance that a single summary coefficient would have concealed. All other covariates passed the proportional-odds test, meaning their effects could be legitimately summarised by one number each.

Two inverse associations raised eyebrows. Adults who were married showed a lower blood pressure category than unmarried adults, with a probit coefficient of -0.129, and those reporting pesticide exposure also showed lower blood pressure, with a coefficient of -0.122. Current smoking showed a negative but statistically non-significant association, with a coefficient of -0.119 and a p-value of 0.106. The authors are careful to frame these results as hypothesis-generating rather than established protective effects. A cross-sectional design captures a single moment in time, and it cannot untangle whether healthier, leaner individuals are more likely to remain married and to work in agriculture, or whether unmeasured factors such as weight loss from illness or occupational physical exertion lie behind the apparent protection. The pesticide finding in particular invites follow-up, since occupational exposure patterns in farming communities are entangled with diet, activity and socioeconomic position.

The methodological verdict may prove to be the study’s most influential contribution. When the frequentist and Bayesian estimates were placed side by side, the coefficients agreed to within 0.001 to 0.002 across every predictor in the model, a difference so small it has no practical meaning. Model fit was likewise indistinguishable once the two frameworks’ fit statistics were placed on a commensurable deviance scale: the frequentist Akaike Information Criterion came to 4,936.5, while the Bayesian leave-one-out cross-validation measure, expressed as minus two times the expected log pointwise predictive density, came to 4,937.3, a difference of just 0.8. In other words, once the adjustment set was chosen thoughtfully and the data were cleaned rigorously, the choice between frequentist and Bayesian inference made essentially no difference to what the data were saying about blood pressure.

That conclusion carries a broader lesson for health research in low- and middle-income countries, where Bayesian methods are sometimes promoted as a cure-all for small samples, sparse data and unstable estimates. This study, with nearly two thousand participants and a well-behaved outcome, shows that the two paradigms converge when the fundamentals are right. The real determinants of trustworthy inference, the authors suggest, are a defensible causal structure, transparent handling of missing data, and checks on model assumptions such as the Brant test. Bayesian machinery still earns its keep in settings with genuine prior information, complex hierarchical structure or severe data limitations, but it is not a substitute for disciplined design.

For the people of the Kassena-Nankana districts, the immediate implications are more concrete. The study, conducted under the H3Africa AWI-Gen programme with funding from the US National Institutes of Health and the Wellcome Trust, confirms that hypertension is no longer a disease of urban affluence. Ageing, male sex and rising body mass index are pushing a large share of rural adults into dangerous territory, and the inverse associations with marriage and pesticide exposure flag social and occupational pathways that deserve targeted investigation. As health services in Ghana and across sub-Saharan Africa grapple with the transition from infectious to chronic disease, this dual-framework analysis offers both a clear picture of who is most at risk and a quiet reassurance that, with careful methods, the statistical tools for understanding that risk are already in hand.

Subject of Research: Statistical modelling of factors associated with blood pressure status among adults in rural Ghana

Article Title: Bayesian and probit comparative analysis of factors associated with blood pressure status among adults in the Kassena-Nankana Districts, Ghana: an AWI-Gen sub-study

Article References: Adda, R. B., Abonongo, I. A. L., Debpuur, C., Oduro, A. R., Agongo, G., & Nonterah, E. A. (2026). Bayesian and probit comparative analysis of factors associated with blood pressure status among adults in the Kassena-Nankana Districts, Ghana: an AWI-Gen sub-study. BMC Public Health. https://doi.org/10.1186/s12889-026-29836-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29836-2

Keywords: hypertension, blood pressure, Bayesian inference, ordered probit, rural Ghana, AWI-Gen, H3Africa, body mass index, Brant test, cross-sectional study, cardiovascular risk, biostatistics