Socioeconomic inequalities in hypertension in Kenya: A decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors

Abstract

Background: One in four Kenyans aged 18–69 years have raised blood pressure. Despite this high prevalence of hypertension and known association between socioeconomic status and hypertension, there is limited understanding of factors explaining inequalities in raised blood pressure in Kenya. Hence, we quantified the socioeconomic inequality in hypertension in Kenya and decomposed the determinants contributing to such inequality.Methods: We used data from the 2015 Kenya STEPwise survey for non-communicable diseases risk factors. We included 4,422 respondents aged 18–69 years. We estimated the socioeconomic inequality using the concentration index (C) and decomposed the C using Wagstaff decomposition analysis.Results: The overall concentration index of hypertension in Kenya was −0.08 (95% CI: −0.14, −0.02; p = 0.005), showing socioeconomic inequalities in hypertension disfavouring the poor population. About half (47.1%) of the pro-rich inequalities in hypertension was explained by body mass index while 26.7% by socioeconomic factors (wealth index (10.4%), education (9.3%) and paid employment (7.0%)) and 17.6% by sociodemographic factors (female gender (10.5%), age (4.3%) and marital status (0.6%)). Regional differences explained 7.1% of the estimated inequality with the Central region alone explaining 6.0% of the observed inequality. Our model explained 99.7% of the estimated socioeconomic inequality in hypertension in Kenya with a small non-explained part of the inequality (−0.0002).Conclusion: The present study shows substantial socioeconomic inequalities in hypertension in Kenya, mainly explained by metabolic risk factors (body mass index), individual health behaviours, and socioeconomic factors. Kenya needs gender- and equity-focused interventions to curb the rising burden of hypertension and inequalities in hypertension.

Inequality in prevalence, awareness, treatment, and control of hypertension in Iran: the analysis of national households’ data

“…Our study confirmed the minor pro-rich inequality of hypertension prevalence in Iran [15]. Studies report the pro-rich inequality of hypertension prevalence in the low-and middleincome countries such as Kenya [27] and China [28]. Yet, the magnitude of wealth-related inequalities in hypertension tends to be higher in poorer than in richer countries.…”

“…However, they included body mass index that explained 47% of variation in the inequality of hypertension. Most of the pro-rich inequality in hypertension control in Iran was explained by living in the urban area, complementary insurance, wealth index, and education, which confirms the established associations between health state and wealth status and education [27,30,31]. The underlying argument that explains such association is that wealth or income is materialized in resources such as nutrition and housing that affects health and wellbeing [32].…”

“…In a study conducted in Kenya, about 10% of inequity in hypertension prevalence was explained by wealth index, 9% by education and 7% by paid employment. Sociodemographic factors explained 18% of inequality [27]. In total, our model explained 33% of variation in C of hypertension prevalence, in contrast, Gatimu et al explained 99.7% of inequality in hypertension.…”

No woman should be left behind: A decomposition analysis of socioeconomic inequalities in unsafe abortion among women presenting for abortion care services in Lusaka and Copperbelt provinces of Zambia

“…First, we did not collect an income variable which some studies have recommended as a socioeconomic ranking variable. However, some surveys have indicated that household assets can be used as a proxy indicator for inequalities and it is sensitive to socioeconomic distribution [25, 37]. Second, we collected data from just two out of 10 provinces and therefore, generation of results may be limited.…”

Decomposing socioeconomic inequality in blood pressure and blood glucose testing: evidence from four districts in Kerala, India

“…)), social caste group (Scheduled Caste [SC], Scheduled Tribe [ST], Other Backward Caste [OBC], Other), household wealth quintile (poorest, poorer, middle, richer, richest), family history of high blood pressure, high glucose level and heart diseases (no, yes). These variables were decided based on the variable availability in the study data set and well-established literature [20,29,31,32,39,40,41].…”