Dataset Information

Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: analysis of the Basic Health Research 2018.

ABSTRACT:

Background

Cardiovascular diseases (CVDs) accounted for over 17 million deaths and 353 million disability-adjusted life years lost in 2016. The risk factors are also high and increasing with high blood pressure, smoking, and high body mass index contributed to up to 212 million disability-adjusted life years in 2016. To help reduce the burden, it is crucial to understand the geographic and socioeconomic disparities in CVD risk factors.

Methods

Employing both geospatial and quantitative analyses, we analyzed the disparities in the prevalence of smoking, physical inactivity, obesity, hypertension, and diabetes in Indonesia. CVD data was from Riskesdas 2018, and socioeconomic data was from the World Bank.

Results

Our findings show a very high prevalence of CVD risk factors with the prevalence of smoking, physical activity, obesity, hypertension ranged from 28 to 33%. Results also show the geographic disparity in CVD risk factors in all five Indonesian regions. Moreover, results show socioeconomic disparity with the prevalence of obesity, hypertension, and diabetes are higher among urban and the richest and most educated districts while that physical inactivity and smoking is higher among rural and the least educated districts.

Conclusion

The CVD burden is high and increasing in particularly among urban areas and districts with higher income and education levels. While the government needs to continue tackling the persistent burden from maternal mortality and infectious diseases, they need to put more effort into the prevention and control of CVDs and their risk factors.

SUBMITTER: Adisasmito W

PROVIDER: S-EPMC7318418 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: analysis of the Basic Health Research 2018.

Adisasmito Wiku W Amir Vilda V Atin Anila A Megraini Amila A Kusuma Dian D

BMC public health 20200626 1

<h4>Background</h4>Cardiovascular diseases (CVDs) accounted for over 17 million deaths and 353 million disability-adjusted life years lost in 2016. The risk factors are also high and increasing with high blood pressure, smoking, and high body mass index contributed to up to 212 million disability-adjusted life years in 2016. To help reduce the burden, it is crucial to understand the geographic and socioeconomic disparities in CVD risk factors.<h4>Methods</h4>Employing both geospatial and quantit ...[more]

PMID: 32586296

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Project description:Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

Project description:ImportanceAfter decades of decline, the US cardiovascular disease mortality rate flattened after 2010, and racial and ethnic differences in cardiovascular disease mortality persisted.ObjectiveTo examine 20-year trends in cardiovascular risk factors in the US population by race and ethnicity and by socioeconomic status.Design, setting, and participantsA total of 50 571 participants aged 20 years or older from the 1999-2018 National Health and Nutrition Examination Surveys, a series of cross-sectional surveys in nationally representative samples of the US population, were included.ExposuresCalendar year, race and ethnicity, education, and family income.Main outcomes and measuresAge- and sex-adjusted means or proportions of cardiovascular risk factors and estimated 10-year risk of atherosclerotic cardiovascular disease were calculated for each of 10 two-year cycles.ResultsThe mean age of participants ranged from 49.0 to 51.8 years and the proportion of women from 48.2% to 51.3% in the surveys. From 1999-2000 to 2017-2018, age- and sex-adjusted mean body mass index increased from 28.0 (95% CI, 27.5-28.5) to 29.8 (95% CI, 29.2-30.4); mean hemoglobin A1c increased from 5.4% (95% CI, 5.3%-5.5%) to 5.7% (95% CI, 5.6%-5.7%) (both P < .001 for linear trends). Mean serum total cholesterol decreased from 203.3 mg/dL (95% CI, 200.9-205.8 mg/dL) to 188.5 mg/dL (95% CI, 185.2-191.9 mg/dL); prevalence of smoking decreased from 24.8% (95% CI, 21.8%-27.7%) to 18.1% (95% CI, 15.4%-20.8%) (both P < .001 for linear trends). Mean systolic blood pressure decreased from 123.5 mm Hg (95% CI, 122.2-124.8 mm Hg) in 1999-2000 to 120.5 mm Hg (95% CI, 119.6-121.3 mm Hg) in 2009-2010, then increased to 122.8 mm Hg (95% CI, 121.7-123.8 mm Hg) in 2017-2018 (P < .001 for nonlinear trend). Age- and sex-adjusted 10-year atherosclerotic cardiovascular disease risk decreased from 7.6% (95% CI, 6.9%-8.2%) in 1999-2000 to 6.5% (95% CI, 6.1%-6.8%) in 2011-2012, then did not significantly change. Age- and sex-adjusted body mass index, systolic blood pressure, and hemoglobin A1c were consistently higher, while total cholesterol was lower in non-Hispanic Black participants compared with non-Hispanic White participants (all P < .001 for group differences). Individuals with college or higher education or high family income had consistently lower levels of cardiovascular risk factors. The mean age- and sex-adjusted 10-year risk of atherosclerotic cardiovascular disease was significantly higher in non-Hispanic Black participants compared with non-Hispanic White participants (difference, 1.4% [95% CI, 1.0%-1.7%] in 1999-2008 and 2.0% [95% CI, 1.7%-2.4%] in 2009-2018]). This difference was attenuated (-0.3% [95% CI, -0.6% to 0.1%] in 1999-2008 and 0.7% [95% CI, 0.3%-1.0%] in 2009-2018) after further adjusting for education, income, home ownership, employment, health insurance, and access to health care.Conclusions and relevanceIn this serial cross-sectional survey study that estimated US trends in cardiovascular risk factors from 1999 through 2018, differences in cardiovascular risk factors persisted between Black and White participants; the difference may have been moderated by social determinants of health.

Dataset Information

Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: analysis of the Basic Health Research 2018.

Background

Methods

Results

Conclusion

Publications

Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: analysis of the Basic Health Research 2018.

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