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District-Level Inequalities in Hypertension among Adults in Indonesia: A Cross-Sectional Analysis by Sex and Age Group

Author

Listed:
  • Puput Oktamianti

    (Health Administration and Policy Department, Faculty of Public Health, Universitas Indonesia, Depok 16424, Indonesia)

  • Dian Kusuma

    (Department of Health Services Research and Management, School of Health & Psychological Sciences, City University of London, London EC1V 0HB, UK)

  • Vilda Amir

    (Center for Health Administration and Policy Studies, Faculty of Public Health, Universitas Indonesia, Depok 16424, Indonesia)

  • Dwi Hapsari Tjandrarini

    (Research Center for Public Health and Nutrition, National Research and Innovation Agency, Bogor 16915, Indonesia)

  • Astridya Paramita

    (Research Center for Public Health and Nutrition, National Research and Innovation Agency, Bogor 16915, Indonesia)

Abstract

Background: An estimated 1.28 billion adults 30–79 years old had hypertension globally in 2021, of which two-thirds lived in low- and middle-income countries (LMICs). Previous studies on geographic and socioeconomic inequalities in hypertension among adults have limitations: (a) most studies used individual-level data, while evidence from locality-level data is also crucial for policymaking; (b) studies from LMICs are limited. Thus, our study examines geographic and socioeconomic inequalities in hypertension among adults across districts in Indonesia. Methods: We combined geospatial and quantitative analyses to assess the inequalities in hypertension across 514 districts in Indonesia. Hypertension data were obtained from the Indonesian Basic Health Survey (Riskesdas) 2018. Socioeconomic data were obtained from the World Bank. Six dependent variables included hypertension prevalence among all adults (18+ years), male adults, female adults, young adults (18–24 years), adults (25–59 years), and older adults (60+ years). Results: We also found significant geographic and socioeconomic inequalities in hypertension among adults across 514 districts. All hypertension indicators were higher in the most developed region than in the least developed region. Districts in the Java region had up to 50% higher prevalence of hypertension among all adults, males, females, young adults, adults, and older adults. Notably, districts in the Kalimantan region had the highest prevalence of hypertension, even compared to those in Java. Moreover, income level was positively associated with hypertension; the wealthiest districts had higher hypertension than the poorest districts by up to 30%, but only among males and older adults were statistically significant. Conclusions: There were significant inequalities in hypertension among adults across 514 districts in the country. Policies to reduce such inequalities may need to prioritize more affluent urban areas and rural areas with a higher burden.

Suggested Citation

  • Puput Oktamianti & Dian Kusuma & Vilda Amir & Dwi Hapsari Tjandrarini & Astridya Paramita, 2022. "District-Level Inequalities in Hypertension among Adults in Indonesia: A Cross-Sectional Analysis by Sex and Age Group," IJERPH, MDPI, vol. 19(20), pages 1-18, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13268-:d:942460
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    References listed on IDEAS

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    1. Morenoff, Jeffrey D. & House, James S. & Hansen, Ben B. & Williams, David R. & Kaplan, George A. & Hunte, Haslyn E., 2007. "Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context," Social Science & Medicine, Elsevier, vol. 65(9), pages 1853-1866, November.
    2. Dwi Hapsari & Olwin Nainggolan & Dian Kusuma, 2020. "Hotspots and Regional Variation in Smoking Prevalence Among 514 Districts in Indonesia: Analysis of Basic Health Research 2018," Global Journal of Health Science, Canadian Center of Science and Education, vol. 12(10), pages 1-32, September.
    3. Alaa Ashraf AlQurashi & Dian Kusuma & Hala AlJishi & Ali AlFaiz & Abdulaziz AlSaad, 2021. "Density of Fast Food Outlets around Educational Facilities in Riyadh, Saudi Arabia: Geospatial Analysis," IJERPH, MDPI, vol. 18(12), pages 1-10, June.
    4. Sri Handayani & Enny Rachmani & Kriswiharsi Kun Saptorini & Yusthin Merianti Manglapy & Nurjanah & Abdillah Ahsan & Dian Kusuma, 2021. "Is Youth Smoking Related to the Density and Proximity of Outdoor Tobacco Advertising Near Schools? Evidence from Indonesia," IJERPH, MDPI, vol. 18(5), pages 1-8, March.
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