Our study revealed a pro-rich inequality in hypertension in Kenya, disfavouring poor individuals. The inequality is explained by body mass index, socioeconomic (wealth index, occupation, and education), sociodemographic (gender, age, and marital status) factors, regions and individual health behaviours (current history of alcohol use and smoking). The prevalence of hypertension reported in the current study is similar to previous studies conducted in Kenya [12, 15, 46-48]. Similarly, the high prevalence of hypertension among men and older adults compared to women and younger adults have also been established [12, 15, 46-48].
Similar to other studies in LMICs [8, 29, 49, 50], our findings indicate the presence of inequalities in hypertension disfavouring the poor population. The magnitude of the inequalities in our study (C: –0.08) is lower compared to that of Iran (C: –0.15) [29, 50] and among rural residents in Bangladesh (C: –0.20) [29, 50] despite an almost similar hypertension prevalence reflecting the varying levels of inequalities. However, our findings differ from the pro-poor inequality in high blood pressure reported in a study among women of reproductive age in sub-Saharan Africa [13]. The study reported cumulative inequality for sub-Saharan Africa and did not compute country-specific inequalities which could explain the difference. Nevertheless, our study shows the size of inequality to be lower among women than men (C: –0.05 vs –0.09) and the hazardous effect of hypertension concentrated among the underprivileged populations who are poor.
Body mass index was the largest independent contributor to the inequality in hypertension explaining about half of the inequality. Our study shows that almost one in three individuals were overweight or obese and had some of the highest prevalence of hypertension. Obesity and overweight are known risk factors for hypertension [28, 34-36] and independently contributed to 23.5% and 9.5% of the observed inequality. About 40% of the obese participants in our study belonged to the poorest wealth quintile. Obesity/overweight increases an individual risk of hypertension especially among individual belonging to the poorest group [12, 15, 35]. The high burden of overweight and obesity in Kenya could be attributed to the rapid urbanisation, economic development, and the related unhealthy behaviours such as consumption of energy dense processed food and sedentary lifestyles [51]. As an ecological study in Kenya has showed, there is a positive association between the rise of NCDs including hypertension in Kenya and the increase in per capita gross domestic product, urbanisation, physical inactivity and consumption of high dense processed foods such as cooking oil and wheat [52].
Surprisingly, undernutrition independently contributed 14.1% of the observed inequality, which could reflect the high prevalence of hypertension (16.7%) among the 16% of our study population who were undernourished. High prevalence of hypertension has previously been reported among undernourished individuals [53]. We hypothesise that the life course approach showing the relationship between childhood/adulthood malnutrition and hypertension could help explain this finding. Malnutrition limits renal development resulting in kidney malfunction in adulthood and eventually hypertension [54, 55]. However, we studied adult individuals some of whom were already malnourished and could not ascertain the timing of the occurrence of malnutrition and any causal linkage to their hypertension. This calls for further evaluation to establish the causal relationship between adult undernutrition and hypertension with a focus on the poor populations.
Occupation, education, and wealth index are the socioeconomic factors contributing to slightly more than a quarter of the observed inequalities in hypertension. Similarly, these factors also explained inequality in hypertension in Iran [29]. In this study, individuals in formal paid employment were more concentrated among the poor and had a relatively high prevalence of hypertension compared to the unemployed and self-employed individuals. In Kenya, studies have shown that casual workers and individuals on formal employment have increased odds of hypertension [12] which could be attributed to sedentary lifestyle. Our findings also revealed that individuals with low education level (incomplete or complete primary educations) were more likely to be hypertensive and poor. Low education level is associated with the risk of developing hypertension [15, 27, 28]. However, among older adults in Kenya, education was found not be associated with hypertension [16]. Despite this, educated individuals have a better awareness of hypertension and its preventive strategies compared to the uneducated [10, 11]. Wealth index explained one-tenth of the observed inequality in hypertension with almost equal individual contribution from both the richest and poorer wealth quintiles. Our findings show a high prevalence of hypertension among individual in the poorest wealth quintile, which is inconsistent with previous studies [12, 13]. For example, one of the studies in the urban slum Nairobi Kenya found that individuals in the richest wealth quintile had the highest prevalence of hypertension and were at an increased odd of being hypertensive [12]. This could be due to the differences in study population. However, we hypothesize the poorest individuals faces several financial barriers, which hinder access to health services for control and treatment of hypertension [56] due to huge out of pocket expenditure [6, 57]. In addition, despite the hypertension screening and early diagnosis being key in averting hypertension, it is likely that individuals in the richest wealth quintile are screened more for high blood pressure than the poorest individuals. Among urban poor populations in Kenya, Olack B, Wabwire-Mangen F, Smeeth L, Montgomery JM, Kiwanuka N and Breiman RF [12] argued that increased wealth may contribute to unhealthy behaviours such as consumption of energy dense processed food and sedentary lifestyles [12].
Socio-demographic factors explained about 15% of the observed inequality. Specifically, gender had a substantial contribution to the inequality with men having a significantly higher prevalence of inequalities in hypertension than women. Previous studies have observed gender disparities in hypertension [26-28], which have been attributed to biological [58] and health behavioural factors [59]. In this study, a further gender-specific decomposition shows that the differences in body mass index, education, employment, and the region could explain the gender disparities in hypertension [Supplementary Table 2]. Age, especially 40–49 years also contributed to the observed inequality in hypertension. Adults aged 35 years and above have been shown to have increased the risk of hypertension in Kenya [12], which is supported by our finding that a third of these adults are hypertensive. Overall, older adults in Kenya have increased odds of multiple NCD risk factors [60] hence increased risk of hypertension. Moreover, the current study shows substantial inequality in hypertension for the older and poor population. These findings call for gender-focused approaches in prevention, treatment, and control of hypertension in Kenya.
Regional differences mainly attributed to Central region contributed to 7.1% of the observed inequalities in hypertension. The central region had the highest prevalence of hypertension. Its main inhabitants, the Kikuyu ethnic groups, has high prevalence hypertension [61] and cardiometabolic markers [60]. It is also one of the most unequal regions in Kenya [62] with a large population of older adults (7%) and high prevalence of alcohol use and smoking. At local level, the high prevalence of hypertension has been speculated to results from poor dietary practices such as high consumption of carbohydrates and sedentary lifestyles [63] and poor awareness on hypertension [64].
Behavioural risk factors contributing to the inequalities in hypertension were current history of smoking and alcohol use. Alcohol use and smoking are associated with increased risk of hypertension [27, 34, 65, 66]. The prevalence of smoking [13, 31, 32] and alcohol use [13, 33] was high among individuals in the poorest wealth quintile. These poor individuals are also likely to be hypertensive hence contribute to hypertension inequality in Kenya. To address the challenges of smoking and alcohol use, Kenya has through legislation banned advertisement of tobacco and its products, limited the number of cigarettes sold, criminalised the sale of illegal alcohol and increased taxes on alcohol and tobacco and its products.
Strengths and limitations
To our knowledge, this is the first study in Kenya quantifying and explaining the inequalities in hypertension. One of the strengths is the study uses data collected using the standardised WHO STEPwise approach from a large nationally representative sample making our finding generalisation to Kenya and easily comparable. The study variables included also explained the observed inequalities with minimal residual. However, the study had some limitations. First, the data used was cross-sectional and hence causal inference could not be made. Second, the blood pressure measurements were taken in one visit which could have resulted in an overestimation of the prevalence. Third, there was a potential for recall bias in self-reported variables such as physical activity, tobacco and alcohol use and fruits and vegetable consumption, which may have over- or underestimated their prevalence. Fourth, the study included some explanatory variables that are closely related such as wealth, education, and occupation in the decomposition analysis. Inclusion of all these variables may have resulted in the underestimate of the full effect of either of the variables and may have ignored the potential causal hierarchy among the variables, for example, education being a driver for wealth and occupation.