Journal of the American Heart Association
October 16, 2026 · via Europe PMC
Global Prevalence of Adherence to Antihypertensives and Associated Moderating Factors Among Adults With Hypertension: A Meta-Analysis.
Ngolombe R, Chiona BC, Khangamwa Singano F, Sinyiza F, Chilinda ZB, Wang TJ
AI summary — for doctors, generated from the abstract only
Roughly 58% of adults with hypertension adhere to antihypertensives worldwide, leaving a large share with moderate or low adherence.
What was studied
A meta-analysis of 123 studies with 1 052 445 adults with hypertension (Scopus, PubMed, Web of Science, Embase, CINAHL), estimating pooled adherence prevalence with mixed-effects models and moderator analyses.
Key findings
- Pooled adherence prevalence was 58% (95% CI 52%-63%), with 39% high, 29% moderate and 35% low adherence
- By continent, adherence was 67% in Europe, 65% in South America, 60% in Africa, 56% in Asia and 45% in North America
- Adherence was similar across income groups (56%-61%)
- Only age and assessment tool were significantly associated with adherence in the multivariate model
Clinical relevance
Adherence is a major barrier to blood pressure control worldwide, and measurement methods strongly affect estimates.
Who should care
Limitations
Heterogeneity was substantial, and adherence was defined and measured inconsistently across studies, including self-report. The authors call for consensus definitions and cutoffs.
Adherence is suboptimal everywhere, and standardizing measurement is needed.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
BACKGROUND
Hypertension is a significant public health issue affecting over a billion adults worldwide with adherence to antihypertensives being crucial for achieving sustained blood pressure control and reducing cardiovascular morbidity and mortality. To extend previous evidence, we conducted a meta-analysis on global prevalence of adherence to antihypertensives among adults.
METHODS
A systematic search was conducted in Scopus, PubMed, Web of Science, Embase, and CINAHL. Pooled prevalence of antihypertensive medication adherence was estimated using generalized linear mixed-effects model in R software version 4.4.3. Cochran's Q, τ, andstatistics examined statistical heterogeneity with moderator analysis exploring sources of heterogeneity. Sensitivity analysis was analyzed using exact fixed-effects model.
RESULTS
A total of 123 studies with 1 052 445 adults with hypertension were included. Pooled global prevalence of adherence to antihypertensives was estimated at 58% (95% CI, 52%-63%), with 39% for high adherence, 29% for moderate adherence, and 35% for low adherence. Results for sensitivity analysis were 57.91% (95% CI, 57.81%-58.01%) with δ̂ being 24.3%. Prevalence across continents was 67% for Europe, 65% for South America, 60% for Africa, 56% for Asia, and 45% for North America. Adherence to antihypertensives was similar across low-income countries (61%), high-income countries (58), upper-middle-income countries (57%), and lower-middle-income countries (56%). In multivariate model, age and assessment tool only were significantly associated with medication adherence.
CONCLUSIONS
Globally, a large proportion of adults with hypertension exhibit moderate to low adherence to antihypertensive medications. Future studies should prioritize establishing consensus-based operational definitions and cutoff values to enhance adherence.
DOI
10.1161/JAHA.125.047824
PMID
42885806
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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