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      10-year risk for cardiovascular diseases using WHO prediction chart: findings from the civil servants in South-western Nigeria

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          Abstract

          Background

          Globally, cardiovascular diseases (CVDs) have continued to ravage the human existence through the premature deaths of its workforce. Despite this burden, many studies in Nigeria have focused on determining the prevalence of risk factors which alone are insufficient to assess the risk of future cardiovascular events. Therefore, we determined the pattern and predictors of 10-year risk for CVDs in South-western Nigeria.

          Methods

          We conducted a cross-sectional study among workers at the local government areas (LGAs) of Oyo State. Using a multi-stage sampling technique, we recruited 260 respondents from the LGA secretariats. A pre-tested, interviewer-administered questionnaire was administered to obtain information on the socio-demographics and behavioural attributes. Lipid analysis, anthropometric, blood pressure, fasting blood glucose measurements were done using standard protocols. The respondents’ CVD risk was assessed using WHO prediction chart. Data were analyzed using IBM SPSS version 25; bivariate analysis was done using Chi-square and binary logistic regression was used to identify the predictors of 10-year risk for CVDs at 5% level of significance.

          Results

          The mean age of respondents was 46.0  + 6.7 years. The proportion of respondents with good knowledge of risk factors was 57.7%. The prevalence of CVD risk factors were as follows: systolic hypertension (29.6%), visceral obesity (35.8%), diabetes mellitus (18.8%), smoking (5.8%), elevated total cholesterol (55.4%) and physical inactivity (84.6%). The proportion of respondents with low, moderate and high risk of developing CVDs within 10 years was 76.9, 8.5 and 14.6% respectively. Respondents with age ≥ 40 years (aOR = 2.6, 95% CI = 1.3–8.5), management cadre (aOR = 3.8, 95% CI = 1.6–9.6), obesity (aOR = 4.8, 95% CI = 1.2–120), abnormal waist circumference (aOR = 2.8, 95% CI = 1.3–5.2) and physical inactivity (aOR = 2.4, 95% CI = 1.2–4.7) were associated with the higher likelihood of developing CVDs.

          Conclusion

          About one-sixth of the respondents had high risk of developing CVDs within the next 10 years and it is likely that it will reduce the productivity of the State. Lifestyle modification and early detection of risk factors through regular screening programmes for those with high CVD risk is therefore recommended.

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          Most cited references26

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            State of disparities in cardiovascular health in the United States.

            Reducing health disparities remains a major public health challenge in the United States. Having timely access to current data on disparities is important for policy and program development. Accordingly, we assessed the current magnitude of disparities in cardiovascular disease (CVD) and its risk factors in the United States. Using national surveys, we determined CVD and risk factor prevalence and indexes of morbidity, mortality, and overall quality of life in adults > or =18 years of age by race/ethnicity, sex, education level, socioeconomic status, and geographic location. Disparities were common in all risk factors examined. In men, the highest prevalence of obesity (29.2%) was found in Mexican Americans who had completed a high school education. Black women with or without a high school education had a high prevalence of obesity (47.3%). Hypertension prevalence was high among blacks (39.8%) regardless of sex or educational status. Hypercholesterolemia was high among white and Mexican American men and white women in both groups of educational status. Ischemic heart disease and stroke were inversely related to education, income, and poverty status. Hospitalization was greater in men for total heart disease and acute myocardial infarction but greater in women for congestive heart failure and stroke. Among Medicare enrollees, congestive heart failure hospitalization was higher in blacks, Hispanics, and American Indians/Alaska Natives than among whites, and stroke hospitalization was highest in blacks. Hospitalizations for congestive heart failure and stroke were highest in the southeastern United States. Life expectancy remains higher in women than men and higher in whites than blacks by approximately 5 years. CVD mortality at all ages tended to be highest in blacks. Disparities in CVD and related risk factors remain pervasive. The data presented here can be invaluable for policy development and in the planning, implementation, and evaluation of interventions designed to eliminate health disparities.
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              Hypertension in Sub-Saharan Africa: Cross-Sectional Surveys in Four Rural and Urban Communities

              Background Cardiovascular disease (CVD) is the leading cause of adult mortality in low-income countries but data on the prevalence of cardiovascular risk factors such as hypertension are scarce, especially in sub-Saharan Africa (SSA). This study aims to assess the prevalence of hypertension and determinants of blood pressure in four SSA populations in rural Nigeria and Kenya, and urban Namibia and Tanzania. Methods and Findings We performed four cross-sectional household surveys in Kwara State, Nigeria; Nandi district, Kenya; Dar es Salaam, Tanzania and Greater Windhoek, Namibia, between 2009–2011. Representative population-based samples were drawn in Nigeria and Namibia. The Kenya and Tanzania study populations consisted of specific target groups. Within a final sample size of 5,500 households, 9,857 non-pregnant adults were eligible for analysis on hypertension. Of those, 7,568 respondents ≥18 years were included. The primary outcome measure was the prevalence of hypertension in each of the populations under study. The age-standardized prevalence of hypertension was 19.3% (95%CI:17.3–21.3) in rural Nigeria, 21.4% (19.8–23.0) in rural Kenya, 23.7% (21.3–26.2) in urban Tanzania, and 38.0% (35.9–40.1) in urban Namibia. In individuals with hypertension, the proportion of grade 2 (≥160/100 mmHg) or grade 3 hypertension (≥180/110 mmHg) ranged from 29.2% (Namibia) to 43.3% (Nigeria). Control of hypertension ranged from 2.6% in Kenya to 17.8% in Namibia. Obesity prevalence (BMI ≥30) ranged from 6.1% (Nigeria) to 17.4% (Tanzania) and together with age and gender, BMI independently predicted blood pressure level in all study populations. Diabetes prevalence ranged from 2.1% (Namibia) to 3.7% (Tanzania). Conclusion Hypertension was the most frequently observed risk factor for CVD in both urban and rural communities in SSA and will contribute to the growing burden of CVD in SSA. Low levels of control of hypertension are alarming. Strengthening of health care systems in SSA to contain the emerging epidemic of CVD is urgently needed.
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                Author and article information

                Contributors
                tundebabson23@gmail.com
                oolarewaju7@gmail.com
                leyeadeomi@yahoo.com
                joel.akande@bowenuniversity.edu.ng
                abashorun@afenet.net
                chukwumau@gmail.com
                bjobam2004@yahoo.co.uk
                Journal
                BMC Cardiovasc Disord
                BMC Cardiovasc Disord
                BMC Cardiovascular Disorders
                BioMed Central (London )
                1471-2261
                31 March 2020
                31 March 2020
                2020
                : 20
                : 154
                Affiliations
                [1 ]GRID grid.411270.1, ISNI 0000 0000 9777 3851, Department of Community Medicine, , Ladoke Akintola University of Technology, ; Ogbomoso, Oyo State Nigeria
                [2 ]Nigeria Field Epidemiology and Laboratory Training Program, Abuja, Nigeria
                [3 ]Oyo State Primary Healthcare Board, Agodi, Ibadan, Oyo State Nigeria
                [4 ]GRID grid.412422.3, ISNI 0000 0001 2045 3216, Department of Community Medicine, Osun State University, ; Osogbo, Osun State Nigeria
                [5 ]GRID grid.10824.3f, ISNI 0000 0001 2183 9444, Department of Community Medicine, Obafemi Awolowo University, ; Ile-Ife, Osun State Nigeria
                [6 ]GRID grid.459398.a, Department of Chemical Pathology, BOWEN University Teaching Hospital, ; Ogbomoso, Oyo State Nigeria
                [7 ]GRID grid.474986.0, African Field Epidemiology Network, ; Abuja, Nigeria
                [8 ]Department of Community Medicine, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State Nigeria
                [9 ]GRID grid.412361.3, ISNI 0000 0000 8750 1780, Department of Community Medicine, Ekiti State University, ; Ado-Ekiti, Ekiti State Nigeria
                Article
                1438
                10.1186/s12872-020-01438-9
                7110661
                32234017
                0576e809-1aea-48eb-a895-f9f69066b0cb
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 February 2020
                : 16 March 2020
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Cardiovascular Medicine
                cardiovascular diseases,risk factors,prevalence,prediction chart,civil servants

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