Association between Health and Wealth among Kenyan Adults with Hypertension

Abstract

BACKGROUND: This paper examines the relationship between hypertension and wealth in a national sample of Kenyan adults. 
 
 METHODS: Data from 27,552 individuals from the Demographic and Health Survey Program (DHS) for Kenya were analyzed. Wealth index, a cumulative measure of household standard of living, was the outcome. The final analysis was stratified by gender with covariates added in blocks (demographics, economic, and cultural) to investigate the independent association of hypertension with wealth index. 
 
 RESULTS: Approximately 7.6% of those with hypertension had a wealth index above the median. For women and men, hypertension was significantly associated with higher wealth index (women ß=0.26; CI=0.19; 0.34; men ß=0.36; CI=0.19; 0.53). After adjusting for age, rural location, children, employment, education, ethnicity, and religion, hypertension maintained statistical significance with wealth index for both women and men (women ß=0.06; CI=0.01; 0.11; men ß=0.20; CI=0.08; 0.31).
 
 CONCLUSIONS: As Kenya as a nation undergoes health care reform while also experiencing a high burden of hypertension, the results presented here provide preliminary evidence that may be used in support for decision makers for the wealth effects of health interventions. Additional work is needed to understand the longitudinal relationship between hypertension and wealth at the national level. 

Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

“…Nonetheless, the positive association between SES-NCD risk in LMICs seems to be common and has been attributed to the rapid urbanisation and epidemiological transition, and their effects on lifestyle factors 18,19,25 . This notion is also supported by the findings that NCDs such as obesity, diabetes, and hypertension are prevalent in affluent groups, especially in countries undergoing urbanisation and epidemiological transition including SA and Kenya [18][19][20]25 . Accordingly, we noted that the SES-NCD relationship was stronger in Kenya (a lower middle-income country in the early stages of transition) than in SA (an upper middle-income country that is farthest along the transition).…”

“…It is well established that the epidemiology of NCDs differs between high-income countries (HICs) and LMICs 16 – 19 . For instance, SES is inversely associated with NCDs such as obesity, type 2 diabetes, and hypertension in HICs 15 – 17 , however, this relationship tends to be positive in LMICs 18 – 20 . Although the mechanisms underlying these discrepancies are not fully understood, several risk factors including population growth, ageing, and urbanisation and epidemiological transition have been implicated in the rising prevalence of NCDs, particularly in LMICs 3 , 4 .…”

Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

“…Nonetheless, the positive association between SES-NCD risk in LMICs seems to be common and has been attributed to the rapid urbanisation and epidemiological transition, and their effects on lifestyle factors 13,14,22 . This notion is also supported by the ndings that NCDs such as obesity, diabetes, and hypertension are prevalent in a uent groups, especially in countries undergoing urbanisation and epidemiological transition including SA and Kenya [13][14][15]22 . Accordingly, we noted that the SES-NCD relationship was stronger in Kenya (a lower middle-income country in the early stages of transition) than in SA (an upper middle-income country that is farthest along the transition).…”

“…It is well established that the epidemiology of NCDs differs between high-income countries (HICs) and LMICs [11][12][13][14] . For instance, SES is inversely associated with NCDs such as obesity, type 2 diabetes, and hypertension in HICs [10][11][12] , however, this relationship tends to be positive in LMICs [13][14][15] . Although the mechanisms underlying these discrepancies are not fully understood, several risk factors including population growth, ageing, and urbanisation and epidemiological transition have been implicated in the rising prevalence of NCDs, particularly in LMICs 3,16 .…”

Subjective socioeconomic status: an alternative to objective socioeconomic status

“…Social determinants of health result in a gradient in health outcomes that has been studied extensively in numerous contexts. Socioeconomic status (SES) can serve two distinct purposes in epidemiological studies: first as a predictor of health outcomes and secondly as a confounder that must be controlled to elucidate the relationship between health outcomes and other key determinants [ 1 , 2 ]. Investigators traditionally capture SES using “objective” quantitative measures; most commonly, these include assets, income, education, and occupation.…”