Contribution of modifiable risk factors for hypertension and type-2 diabetes in Peruvian resource-limited settings

Antonio Bernabé-Ortiz, Rodrigo M Carrillo-Larco, Robert H Gilman, William Checkley, Liam Smeeth, J Jaime Miranda, CRONICAS Cohort Study Group, Juan P Casas, George Davey Smith, Shah Ebrahim, Héctor H García, Robert H Gilman, Luis Huicho, Germán Málaga, Víctor M Montori, Gregory B Diette, Luis Huicho, Fabiola León-Velarde, María Rivera, Robert A Wise, Héctor H García, Katherine Sacksteder, Antonio Bernabé-Ortiz, Rodrigo M Carrillo-Larco, Robert H Gilman, William Checkley, Liam Smeeth, J Jaime Miranda, CRONICAS Cohort Study Group, Juan P Casas, George Davey Smith, Shah Ebrahim, Héctor H García, Robert H Gilman, Luis Huicho, Germán Málaga, Víctor M Montori, Gregory B Diette, Luis Huicho, Fabiola León-Velarde, María Rivera, Robert A Wise, Héctor H García, Katherine Sacksteder

Abstract

Background: It is important to understand the local burden of non-communicable diseases including within-country heterogeneity. The aim of this study was to characterise hypertension and type-2 diabetes profiles across different Peruvian geographical settings emphasising the assessment of modifiable risk factors.

Methods: Analysis of the CRONICAS Cohort Study baseline assessment was conducted. Cardiometabolic outcomes were blood pressure categories (hypertension, prehypertension, normal) and glucose metabolism disorder status (diabetes, prediabetes, normal). Exposures were study setting and six modifiable factors (smoking, alcohol drinking, leisure time and transport-related physical activity levels, TV watching, fruit/vegetables intake and obesity). Poisson regression models were used to report prevalence ratios (PR). Population attributable risks (PAR) were also estimated.

Results: Data from 3238 participants, 48.3% male, mean age 45.3 years, were analysed. Age-standardised (WHO population) prevalence of prehypertension and hypertension was 24% and 16%, whereas for prediabetes and type-2 diabetes it was 18% and 6%, respectively. Outcomes varied according to study setting (p<0.001). In multivariable model, hypertension was higher among daily smokers (PR 1.76), heavy alcohol drinkers (PR 1.61) and the obese (PR 2.06); whereas only obesity (PR 2.26) increased the prevalence of diabetes. PAR showed that obesity was an important determinant for hypertension (15.7%) and type-2 diabetes (23.9%).

Conclusions: There is an evident heterogeneity in the prevalence of and risk factors for hypertension and diabetes within Peru. Prehypertension and prediabetes are highly prevalent across settings. Our results emphasise the need of understanding the epidemiology of cardiometabolic conditions to appropriately implement interventions to tackle the burden of non-communicable diseases.

Keywords: DIABETES; Epidemiology of chronic non communicable diseases; HYPERTENSION.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Figures

Figure 1
Figure 1
Forest plot of the association between outcomes of interest and number of modifiable risk factors. Participants aware of hypertension or diabetes diagnosis were excluded from the analysis accordingly. *Prevalence ratios (PR) were adjusted by sex, age, education level and socioeconomic status. Study site was included in the model as cluster with robust SEs.

References

    1. Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol 2014;2:634–47. 10.1016/S2213-8587(14)70102-0
    1. Murray CJ, Vos T, Lozano R, et al. . Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197–223. 10.1016/S0140-6736(12)61689-4
    1. Kontis V, Mathers CD, Rehm J, et al. . Contribution of six risk factors to achieving the 25×25 non-communicable disease mortality reduction target: a modelling study. Lancet 2014;384:427–37. 10.1016/S0140-6736(14)60616-4
    1. Bloom DE, Cafiero ET, Jane-Llopis E, et al. . The global economic burden of noncommunicable diseases. Geneva, Switzerland: World Economic Forum, 2011.
    1. Lall D, Prabhakaran D. Organization of primary health care for diabetes and hypertension in high, low and middle income countries. Expert Rev Cardiovasc Ther 2014;12:987–95. 10.1586/14779072.2014.928591
    1. Ebrahim S, Pearce N, Smeeth L, et al. . Tackling non-communicable diseases in low- and middle-income countries: is the evidence from high-income countries all we need?. PLoS Med 2013;10:e1001377 10.1371/journal.pmed.1001377
    1. Miranda JJ, Herrera VM, Chirinos JA, et al. . Major cardiovascular risk factors in Latin America: a comparison with the United States. The Latin American Consortium of Studies in Obesity (LASO). PloS ONE 2013;8:e54056 10.1371/journal.pone.0054056
    1. Miranda JJ, Bernabe-Ortiz A, Smeeth L, et al. . Addressing geographical variation in the progression of non-communicable diseases in Peru: the CRONICAS cohort study protocol. BMJ Open 2012;2:e000610.
    1. Chobanian AV, Bakris GL, Black HR, et al. . Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003;42:1206–52. 10.1161/01.HYP.0000107251.49515.c2
    1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014;37(Suppl 1):S81–90. 10.2337/dc14-S081
    1. Hallal PC, Gomez LF, Parra DC, et al. . Lessons learned after 10 years of IPAQ use in Brazil and Colombia. J Phys Act Health 2010;7(Suppl 2):S259–64.
    1. World Health Organization and Food and Agriculture of the United Nations. Fruit and vegetables for health. Report of a Joint FAO/WHO workshop Geneva, Switzerland, 2005.
    1. World Health Organization. Reducing risks, promoting healthy life. Geneva, Switzerland, 2002.
    1. Alberti KG, Eckel RH, Grundy SM, et al. . Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640–5. 10.1161/CIRCULATIONAHA.109.192644
    1. Ahmad OB, Boschi-Pinto C, Lopez AD, et al. . Age standardization of rates: a new WHO standard. Geneva, Switzerland: WHO, 2001.
    1. Coutinho LM, Scazufca M, Menezes PR. Methods for estimating prevalence ratios in cross-sectional studies. Rev Saude Publica 2008;42:992–8. 10.1590/S0034-89102008000600003
    1. Newson RB. Attributable and unattributable risks and fractions and other scenario comparisons. Stata J 2013;13:672–98.
    1. Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993;49:865–72. 10.2307/2532206
    1. Perel P, Miranda JJ, Ortiz Z, et al. . Relation between the global burden of disease and randomized clinical trials conducted in Latin America published in the five leading medical journals. PloS ONE 2008;3:e1696 10.1371/journal.pone.0001696
    1. Goldstein J, Jacoby E, del Aguila R, et al. . Poverty is a predictor of non-communicable disease among adults in Peruvian cities. Prev Med 2005;41:800–6. 10.1016/j.ypmed.2005.06.001
    1. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012;70:3–21. 10.1111/j.1753-4887.2011.00456.x
    1. Yusuf S, Ounpuu S. Tackling the growing global burden of atherosclerotic cardiovascular diseases. Eur J Cardiovasc Prev Rehabil 2003;10:236–9. 10.1097/00149831-200308000-00003
    1. Pan American Health Organization. Health in Americas—2012 edition. Washington DC, US: PAHO, 2012.
    1. Allender S, Wickramasinghe K, Goldacre M, et al. . Quantifying urbanization as a risk factor for noncommunicable disease. J Urban Health 2011;88:906–18. 10.1007/s11524-011-9586-1
    1. Burroughs Pena MS, Bernabe-Ortiz A, Carrillo-Larco RM, et al. . Migration, urbanisation and mortality: 5-year longitudinal analysis of the PERU MIGRANT study. J Epidemiol Community Health 2015;69:715–18. 10.1136/jech-2015-205657
    1. Ogunmola OJ, Olaifa AO, Oladapo OO, et al. . Prevalence of cardiovascular risk factors among adults without obvious cardiovascular disease in a rural community in Ekiti State, Southwest Nigeria. BMC Cardiovasc Disord 2013;13:89 10.1186/1471-2261-13-89
    1. Mulenga D, Siziya S, Rudatsikira E, et al. . District specific correlates for hypertension in Kaoma and Kasama rural districts of Zambia. Rural Remote Health 2013;13:2345.
    1. Do HT, Geleijnse JM, Le MB, et al. . National prevalence and associated risk factors of hypertension and prehypertension among Vietnamese adults. Am J Hypertens 2015;28:89–97. 10.1093/ajh/hpu092
    1. Vaidya A, Krettek A. Physical activity level and its sociodemographic correlates in a peri-urban Nepalese population: a cross-sectional study from the Jhaukhel-Duwakot health demographic surveillance site. Int J Behav Nutr Phys Act 2014;11:39 10.1186/1479-5868-11-39
    1. Sun J, Buys NJ, Hills AP. Dietary pattern and its association with the prevalence of obesity, hypertension and other cardiovascular risk factors among Chinese older adults. Int J Environ Res Public Health 2014;11:3956–71. 10.3390/ijerph110403956
    1. Costanian C, Bennett K, Hwalla N, et al. . Prevalence, correlates and management of type 2 diabetes mellitus in Lebanon: findings from a national population-based study. Diabetes Res Clin Pract 2014;105:408–15. 10.1016/j.diabres.2014.06.005
    1. Sibai AM, Costanian C, Tohme R, et al. . Physical activity in adults with and without diabetes: from the ‘high-risk’ approach to the ‘population-based’ approach of prevention. BMC Public Health 2013;13:1002 10.1186/1471-2458-13-1002
    1. Maher D, Waswa L, Baisley K, et al. . Distribution of hyperglycaemia and related cardiovascular disease risk factors in low-income countries: a cross-sectional population-based survey in rural Uganda. Int J Epidemiol 2011;40:160–71. 10.1093/ije/dyq156
    1. Wijesuriya M, Gulliford M, Charlton J, et al. . High prevalence of cardio-metabolic risk factors in a young urban Sri-Lankan population. PloS ONE 2012;7:e31309 10.1371/journal.pone.0031309
    1. Selvarajah S, Haniff J, Kaur G, et al. . Clustering of cardiovascular risk factors in a middle-income country: a call for urgency. Eur J Prev Cardiol 2013;20:368–75. 10.1177/2047487312437327
    1. Benichou J. A review of adjusted estimators of attributable risk. Stat Methods Med Res 2001;10:195–216. 10.1191/096228001680195157
    1. Gefeller O. Comparison of adjusted attributable risk estimators. Stat Med 1992;11:2083–91. 10.1002/sim.4780111606

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel