Global, regional, and national prevalence and risk factors for peripheral artery disease in 2015: an updated systematic review and analysis
Bibliographic record
Abstract
BACKGROUND: Peripheral artery disease is a major cardiovascular disease that affected 202 million people worldwide in 2010. In the past decade, new epidemiological data on peripheral artery disease have emerged, enabling us to provide updated estimates of the prevalence and risk factors for peripheral artery disease globally and regionally and, for the first time, nationally. METHODS: For this systematic review and analysis, we did a comprehensive literature search for studies reporting on the prevalence of peripheral artery disease in the general population that were published between Jan 1, 2011, and April 30, 2019, in PubMed, MEDLINE, Embase, the Global Health database, CINAHL, the Global Health Library, the Allied and Complementary Medicine Database, and ProQuest Dissertations and Theses Global. We also included the Global Peripheral Artery Disease Study of 2013 and the China Peripheral Artery Disease Study as sources. Peripheral artery disease had to be defined as an ankle-brachial index lower than or equal to 0·90. With a purpose-built data collection form, data on study characteristics, sample characteristics, prevalence, and risk factors were abstracted from all the included studies identified from the sources. Age-specific and sex-specific prevalence of peripheral artery disease was estimated in both high-income countries (HICs) and low-income and middle-income countries (LMICs). We also did random-effects meta-analyses to pool the odds ratios of 30 risk factors for peripheral artery disease in HICs and LMICs. UN population data were used to generate the number of people affected by the disease in 2015. Finally, we derived the regional and national numbers of people with peripheral artery disease on the basis of a risk factor-based model. FINDINGS: We included 118 articles for systematic review and analysis. The prevalence of peripheral artery disease increased consistently with age. At younger ages, prevalence was slightly higher in LMICs than HICs (4·32%, 95% CI 3·01-6·29, vs 3·54%, 1·17-10·24, at 40-44 years), but the increase with age was greater in HICs than LMICs, leading to a higher prevalence in HICs than LMICs at older ages (21·24%, 15·22-28·90, vs 12·04%, 8·67-16·60, at 80-84 years). In HICs, prevalence was slightly higher in women than in men up to age 75 years (eg, 7·81%, 3·97-14·77, vs 6·60%, 3·74-11·38, at 55-59 years), whereas in LMICs little difference was found between women and men (eg, 6·40%, 5·06-8·05, vs 6·37%, 4·74-8·49, at 55-59 years). Overall, the global prevalence of peripheral artery disease in people aged 25 years and older was 5·56%, 3·79-8·55, and the prevalence estimate was higher in HICs than that in LMICs (7·37%, 4·35-13·66, vs 5·09%, 3·64-7·24). Smoking, diabetes, hypertension, and hypercholesterolaemia were major risk factors for peripheral artery disease. Globally, a total of 236·62 million people aged 25 years and older were living with peripheral artery disease in 2015, among whom 72·91% were in LMICs. The Western Pacific Region had the most peripheral artery disease cases (74·08 million), whereas the Eastern Mediterranean Region had the least (14·67 million). More than two thirds of the global peripheral artery disease cases were concentrated in 15 individual countries in 2015. INTERPRETATION: Peripheral artery disease continues to become an increasingly serious public health problem, especially in LMICs. With the demographic trend towards ageing and projected rise in important risk factors, a larger burden of peripheral artery disease is to be expected in the foreseeable future. FUNDING: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".