Epidemiology of hypertensive heart disease in Poland: findings from the Global Burden of Disease Study 2016
Bibliographic record
Abstract
INTRODUCTION: Hypertension may cause target organ damage leading to hypertensive heart disease (HHD). The burden caused by HHD in Poland has not been studied systematically. The purpose of this study was to describe the burden of HHD in Poland in terms of prevalence, mortality, disability-adjusted life years lost (DALY) and key risk factors. MATERIAL AND METHODS: Data were obtained from the Global Burden of Diseases, Injuries and Risk Factors (GBD) Study database. The GBD uses a wide range of data sources and complex statistical methods to estimate disease burden for all countries by age, sex, and year. HHD was defined by ICD-9 codes 402-402.91 and ICD-10 codes I11-I11.9. From the GBD 2016 estimates, we extracted data for Poland between 1990 and 2016. RESULTS: Hypertensive heart disease is the fourth most important cause of cardio- and cerebrovascular death, after ischemic heart disease, stroke and cardiomyopathy. In 2016, there were about 180 000 people diagnosed with HHD in Poland and close to 5000 HHD-related deaths. HHD prevalence increased from 0.29% in 1990 to 0.47% in 2016 and was higher in women, while mortality increased from 11.2 to 12.7 per 100 000, largely due to population aging. Age-standardized death and DALY rates declined between 1990 and 2016 and were lower than in Central Europe but higher than in Western Europe. CONCLUSIONS: Our data suggest a need for national initiatives to improve the diagnosis and treatment of hypertension, slow the progression of HHD, and reduce the related risks and premature deaths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".