Associations between education and ideal cardiovascular health metrics across 36 low- and middle-income countries

Yi Zhang; Guangyu Tong; Ning Ma; Shaoru Chen; Yuhao Kong; Lhuri Dwianti Rahmartani; Justice Moses K Aheto; Andrew Marvin Kanyike; Pengyang Fan; Md Ashfikur Rahman; Abdallah Mkopi; Rockli Kim; Peter Karoli; John Lapah Niyi; Melkamu Aderajew Zemene; Lin Zhang; Feng Cheng; Chunling Lu; S V Subramanian; Pascal Geldsetzer; Yue Qiu; Zhihui Li

doi:10.1186/s12916-025-04032-y

Associations between education and ideal cardiovascular health metrics across 36 low- and middle-income countries

BMC Med. 2025 Apr 7;23(1):204. doi: 10.1186/s12916-025-04032-y.

Authors

Yi Zhang¹, Guangyu Tong^{2

3}, Ning Ma¹, Shaoru Chen¹, Yuhao Kong¹, Lhuri Dwianti Rahmartani⁴, Justice Moses K Aheto^{5

6}, Andrew Marvin Kanyike^{7

8}, Pengyang Fan¹, Md Ashfikur Rahman⁹, Abdallah Mkopi¹⁰, Rockli Kim^{11

12}, Peter Karoli¹³, John Lapah Niyi¹⁴, Melkamu Aderajew Zemene¹⁵, Lin Zhang^{16

17}, Feng Cheng^{1

18}, Chunling Lu^{19

20}, S V Subramanian^{21

22}, Pascal Geldsetzer^{23

24}, Yue Qiu²⁵, Zhihui Li²⁶

Affiliations

¹ Vanke School of Public Health, Tsinghua University, Beijing, China.
² Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
³ Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
⁴ Department of Epidemiology, Faculty of Public Health, Universitas Indonesia, Depok, Jawa Barat, Indonesia.
⁵ Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.
⁶ WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
⁷ Faculty of Health Sciences, Busitema University, Mbale, Uganda.
⁸ Department of Internal Medicine, Mengo Hospital, Kampala, Uganda.
⁹ Development Studies Discipline, Khulna University, Khulna, 9208, Bangladesh.
¹⁰ Ifakara Health Institute, Dar Es Salaam, Tanzania.
¹¹ Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, Republic of Korea.
¹² Division of Health Policy and Management, College of Health Science, Korea University, Seoul, Republic of Korea.
¹³ National Institute for Medical Research, Dar Es Salaam, Tanzania.
¹⁴ Ghana Health Service, Gushegu Municipal Health Directorate, Gushegu, Ghana.
¹⁵ Department of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
¹⁶ The School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
¹⁷ Suzhou Industrial Park Monash Research Institute of Science and Technology, Monash University, Melbourne, Australia.
¹⁸ Institute for Healthy China, Tsinghua University, Beijing, China.
¹⁹ Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
²⁰ Division of Global Health Equity, Brigham & Women's Hospital, Boston, MA, USA.
²¹ Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, USA.
²² Harvard Center for Population & Development Studies, Cambridge, MA, USA.
²³ Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
²⁴ Chan Zuckerberg Biohub, San Francisco, CA, USA.
²⁵ Institute for Hospital Management, Tsinghua University, Beijing, China. qiuyue8965@tsinghua.edu.cn.
²⁶ Vanke School of Public Health, Tsinghua University, Beijing, China. zhihuili@tsinghua.edu.cn.

Abstract

Background: The relationship between education and cardiovascular health (CVH) metrics in low- and middle-income countries (LMICs) remains unclear. This study explores the associations between education and ideal cardiovascular health score (CVHS), as well as seven CVH metrics.

Methods: This cross-sectional study extracted data from the STEPwise approach to surveillance surveys in 36 LMICs between 2010 and 2020. We assessed CVHS using the sum score in seven metrics defined by American Heart Association: (1) ≥ 150 min/week of moderate, or 75 min/week of vigorous activity, or an equivalent combination; (2) BMI < 25 kg/m² for non-Asians (< 23 kg/m² for Asians); (3) fruit and vegetable intake ≥ 4.5 servings per day; (4) nonsmoking; (5) blood pressure < 120/80 mmHg (untreated); (6) total cholesterol < 200 mg/dL (untreated); and (7) fasting blood glucose < 100 mg/dL (untreated). The ideal CVHS score ranged from 5 to 7. We disaggregated prevalence of ideal CVHS and seven metrics by education, and constructed Poisson regression models to adjust for other socioeconomic factors.

Results: Among 81,327 adult participants, the overall ideal CVHS prevalence for the studied countries was highest among individuals with primary education (52.9%, 95% CI: 51.0-54.9), surpassing those of other education levels - 48.0% (95% CI: 44.6-51.3, P = 0.003) for those with no education and 39.1% (95% CI: 36.5-41.8, P < 0.001) for those with tertiary education. Five (ideal physical activity, BMI, blood pressure, total cholesterol, and blood glucose) in seven CVH metrics peaked among participants with primary or secondary education. For instance, the prevalence of ideal blood pressure among individuals with primary education was 34.4% (95% CI: 32.7-36.1), higher than the prevalence in other education levels, ranging from 28.6% to 32.3%. These patterns were concentrated in low-income countries and lower-middle-income countries, while in upper-middle-income countries, the prevalence of ideal CVHS increased with higher education levels, ranging from 15.4% for individuals with no education to 33.1% for those with tertiary education.

Conclusions: In LMICs, the association between education and ideal CVHS, along with several CVH metrics, exhibited un inverted U-shape, which may be closely related to the different stages of epidemiologic transition.

Keywords: Education; Ideal cardiovascular health metrics; Ideal cardiovascular health score; Inverted U-shape; Low- and middle-income countries.

MeSH terms

Adult
Blood Pressure
Cardiovascular Diseases* / epidemiology
Cross-Sectional Studies
Developing Countries* / statistics & numerical data
Educational Status*
Female
Humans
Male
Middle Aged
Young Adult