Targeting Hypertension Screening in Low‐ and Middle‐Income Countries: A Cross‐Sectional Analysis of 1.2 Million Adults in 56 Countries

Author:

Kirschbaum Tabea K.1ORCID,Theilmann Michaela1ORCID,Sudharsanan Nikkil1ORCID,Manne‐Goehler Jennifer2ORCID,Lemp Julia M.1ORCID,De Neve Jan‐Walter1ORCID,Marcus Maja E.3ORCID,Ebert Cara4ORCID,Chen Simiao15ORCID,Aryal Krishna K.6ORCID,Bahendeka Silver K.7ORCID,Norov Bolormaa8ORCID,Damasceno Albertino9,Dorobantu Maria10,Farzadfar Farshad11ORCID,Fattahi Nima11ORCID,Gurung Mongal S.12ORCID,Guwatudde David13ORCID,Labadarios Demetre14ORCID,Lunet Nuno15,Rayzan Elham11ORCID,Saeedi Moghaddam Sahar16ORCID,Webster Jacqui17,Davies Justine I.181920ORCID,Atun Rifat21ORCID,Vollmer Sebastian3ORCID,Bärnighausen Till15,Jaacks Lindsay M.212223ORCID,Geldsetzer Pascal124ORCID

Affiliation:

1. Heidelberg Institute of Global Health Medical Faculty and University Hospital University of Heidelberg Germany

2. Division of Infectious Diseases Massachusetts General HospitalHarvard Medical School Boston MA

3. Department of Economics and Centre for Modern Indian Studies University of Goettingen Germany

4. RWI–Leibniz Institute for Economic Research Berlin Germany

5. Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

6. Monitoring Evaluation and Operational Research Project Abt Associates Kathmandu Nepal

7. Saint Francis Hospital, Nsambya Kampala Uganda

8. National Center for Public Health Ulaanbaatar Mongolia

9. Faculty of Medicine Eduardo Mondlane University Maputo Mozambique

10. Cardiology Department Emergency Hospital of Bucharest Romania

11. Non‐Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran

12. Health Research and Epidemiology Unit Policy and Planning Division Ministry of Health Thimphu Bhutan

13. Department of Epidemiology and Biostatistics School of Public Health Makerere University Kampala Uganda

14. Faculty of Medicine and Health Sciences Stellenbosch University Stellenbosch South Africa

15. Departamento de Ciências da Saúde Pública e Forenses e Educação Médica Faculdade de Medicina da Universidade do Porto Porto Portugal

16. Endocrinology and Metabolism Research Center Endocrinology and Metabolism Clinical Sciences Institute Tehran University of Medical Sciences Tehran Iran

17. The George Institute for Global HealthUniversity of New South Wales Sydney Australia

18. Institute of Applied Health Research University of Birmingham United Kingdom

19. Centre for Global Surgery Department of Global Health Stellenbosch University Cape Town South Africa

20. Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit Faculty of Health Sciences School of Public Health University of the Witwatersrand Johannesburg South Africa

21. Department of Global Health and Population Harvard T.H. Chan School of Public Health Boston MA

22. Public Health Foundation of India New Delhi India

23. Global Academy of Agriculture and Food Security The University of Edinburgh Midlothian United Kingdom

24. Division of Primary Care and Population Health Department of Medicine Stanford University Stanford CA

Abstract

Background As screening programs in low‐ and middle‐income countries (LMICs) often do not have the resources to screen the entire population, there is frequently a need to target such efforts to easily identifiable priority groups. This study aimed to determine (1) how hypertension prevalence in LMICs varies by age, sex, body mass index, and smoking status, and (2) the ability of different combinations of these variables to accurately predict hypertension. Methods and Results We analyzed individual‐level, nationally representative data from 1 170 629 participants in 56 LMICs, of whom 220 636 (18.8%) had hypertension. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or reporting to be taking blood pressure–lowering medication. The shape of the positive association of hypertension with age and body mass index varied across world regions. We used logistic regression and random forest models to compute the area under the receiver operating characteristic curve in each country for different combinations of age, body mass index, sex, and smoking status. The area under the receiver operating characteristic curve for the model with all 4 predictors ranged from 0.64 to 0.85 between countries, with a country‐level mean of 0.76 across LMICs globally. The mean absolute increase in the area under the receiver operating characteristic curve from the model including only age to the model including all 4 predictors was 0.05. Conclusions Adding body mass index, sex, and smoking status to age led to only a minor increase in the ability to distinguish between adults with and without hypertension compared with using age alone. Hypertension screening programs in LMICs could use age as the primary variable to target their efforts.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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