Diego Chambergo-Michilot1,2, Alexis Rebatta-Acuña3, Carolina J Delgado-Flores4, Carlos J Toro-Huamanchumo5,6. 1. Universidad Científica del Sur, Lima, Peru. 2. Red Latinoamericana de Cardiología, Lima, Peru. 3. Sociedad Científica de Estudiantes de Medicina de Ica, Universidad Nacional San Luis Gonzaga, Ica, Peru. 4. Universidad Nacional Jorge Basadre Grohmann, Tacna, Peru. 5. Universidad Católica Los Ángeles de Chimbote, Instituto de Investigación, Chimbote, Peru. 6. Clínica Avendaño, Unidad de Investigación Multidisciplinaria, Lima, Peru.
Abstract
BACKGROUND: Peru is a Latin American country with a significant burden of hypertension that presents worrying rates of disparities in socioeconomic determinants. However, there is a lack of studies exploring the association between those determinants, hypertension and prehypertension in Peruvian population. OBJECTIVE: We aimed to assess the association betwgeen socioeconomic determinants, hypertension and prehypertension using a nationally representative survey of Peruvians. METHODS: We performed a cross-sectional analysis of the Peruvian Demographic and Health Survey (2018), which is a two-staged regional-level representative survey. We used data from 33,336 people aged 15 and older. The dependent variable was blood pressure classification (normal, prehypertension and hypertension) following the Seventh Report of the Joint National Committee (JNC-7) on hypertension management. Independent variables were socioeconomic: age, sex, marital status, wealth index, health insurance, education, region and area of residence. Due to the nature of the dependent variable (more than two categories), we opted to use the multinomial regression model, adjusting the effect of the multistage sample using the svy command. We tested interactions with the adjusted Wald test. RESULTS: The prevalence of prehypertension and hypertension was 33.68% and 19.77%, respectively. Awareness was higher in urban than in rural areas (9.61% vs. 8.31%, p = 0.008). Factors associated with a higher prevalence ratio of both prehypertension and hypertension were age (ratios rose with each age group), male sex (prehypertension aRPR 5.15, 95%CI 4.63-5.73; hypertension aRPR 3.85, 95% CI 3.37-4.40) and abdominal obesity (prehypertension aRPR 2.11, 95%CI 1.92-2.31; hypertension aRPR 3.04, 95% CI 2.69-3.43). Factors with a lower prevalence ratio of both diseases were secondary education (prehypertension aRPR 0.76, 95%CI 0.60-0.95; hypertension aRPR 0.75, 95% CI 0.58-0.97), higher education (prehypertension aRPR 0.78, 95%CI 0.61-0.99; hypertension aRPR 0.62, 95% CI 0.46-0.82), being married/cohabiting (prehypertension aRPR 0.87, 95%CI 0.79-0.95; hypertension aRPR 0.77, 95% CI 0.68-0.87), richest wealth index (only prehypertension aRPR 0.76, 95%CI 0.63-0.92) and living in cities different to Lima (rest of the Coastline, Highlands and Jungle). Having health insurance (only hypertension aRPR 1.26, 95%CI 1.03-1.53) and current drinking (only prehypertension aRPR 1.15, 95%CI 1.01-1.32) became significant factors in rural areas. CONCLUSIONS: We evidenced socioeconomic disparities among people with hypertension and prehypertension. Better health policies on reducing the burden of risk factors are needed, besides, policy decision makers should focus on hypertension preventive strategies in Peru.
BACKGROUND: Peru is a Latin American country with a significant burden of hypertension that presents worrying rates of disparities in socioeconomic determinants. However, there is a lack of studies exploring the association between those determinants, hypertension and prehypertension in Peruvian population. OBJECTIVE: We aimed to assess the association betwgeen socioeconomic determinants, hypertension and prehypertension using a nationally representative survey of Peruvians. METHODS: We performed a cross-sectional analysis of the Peruvian Demographic and Health Survey (2018), which is a two-staged regional-level representative survey. We used data from 33,336 people aged 15 and older. The dependent variable was blood pressure classification (normal, prehypertension and hypertension) following the Seventh Report of the Joint National Committee (JNC-7) on hypertension management. Independent variables were socioeconomic: age, sex, marital status, wealth index, health insurance, education, region and area of residence. Due to the nature of the dependent variable (more than two categories), we opted to use the multinomial regression model, adjusting the effect of the multistage sample using the svy command. We tested interactions with the adjusted Wald test. RESULTS: The prevalence of prehypertension and hypertension was 33.68% and 19.77%, respectively. Awareness was higher in urban than in rural areas (9.61% vs. 8.31%, p = 0.008). Factors associated with a higher prevalence ratio of both prehypertension and hypertension were age (ratios rose with each age group), male sex (prehypertension aRPR 5.15, 95%CI 4.63-5.73; hypertension aRPR 3.85, 95% CI 3.37-4.40) and abdominal obesity (prehypertension aRPR 2.11, 95%CI 1.92-2.31; hypertension aRPR 3.04, 95% CI 2.69-3.43). Factors with a lower prevalence ratio of both diseases were secondary education (prehypertension aRPR 0.76, 95%CI 0.60-0.95; hypertension aRPR 0.75, 95% CI 0.58-0.97), higher education (prehypertension aRPR 0.78, 95%CI 0.61-0.99; hypertension aRPR 0.62, 95% CI 0.46-0.82), being married/cohabiting (prehypertension aRPR 0.87, 95%CI 0.79-0.95; hypertension aRPR 0.77, 95% CI 0.68-0.87), richest wealth index (only prehypertension aRPR 0.76, 95%CI 0.63-0.92) and living in cities different to Lima (rest of the Coastline, Highlands and Jungle). Having health insurance (only hypertension aRPR 1.26, 95%CI 1.03-1.53) and current drinking (only prehypertension aRPR 1.15, 95%CI 1.01-1.32) became significant factors in rural areas. CONCLUSIONS: We evidenced socioeconomic disparities among people with hypertension and prehypertension. Better health policies on reducing the burden of risk factors are needed, besides, policy decision makers should focus on hypertension preventive strategies in Peru.
Authors: Mario J Valladares-Garrido; Anderson N Soriano-Moreno; Paola K Rodrigo-Gallardo; Enrique Moncada-Mapelli; Josmel Pacheco-Mendoza; Carlos J Toro-Huamanchumo Journal: Diabetes Metab Syndr Date: 2020-02-04
Authors: A Bernabe-Ortiz; J F Sanchez; R M Carrillo-Larco; R H Gilman; J A Poterico; R Quispe; L Smeeth; J J Miranda Journal: J Hum Hypertens Date: 2016-02-11 Impact factor: 2.877