Trends in Hypertension Diagnosis and Self-Reported Cases: A Retrospective Analysis of National Health Interview Survey (NHIS) Database
Bibliographic record
Abstract
BACKGROUND: Hypertension is a major public health issue, contributing significantly to morbidity and mortality. Understanding trends in hypertension diagnosis and self-reported cases can help inform strategies for prevention and management. OBJECTIVE: The objective of this study is to evaluate the trends in hypertension diagnosis and self-reported cases in the United States (U.S.) through the use of National Health Interview Survey (NHIS) data (2019-2023). In particular, the study analyzes the changes in the prevalence rates across the major demographics (race, age, and gender), socioeconomic (social vulnerabilities, education, and income) and geographical factors through the use of statistical modelling. This study seeks to recognize the key determinants that shape such trends and evaluate their implication with regard to targeted interventions and public health policies. METHOD: Data from the NHIS (2019-2023) were examined, focusing on trends in hypertension prevalence based on demographic factors such as age, gender, race, nativity, and social determinants of health (e.g., social vulnerability, employment status, education level, and family income). RESULT: Hypertension prevalence among U.S. adults remained consistently high. Age-adjusted rates were 27.0% in 2019 and increased slightly to 27.5% in 2023. Males showed higher hypertension rates (28.3% in 2023) compared to females (26.7%). Among age groups, the highest rates were observed in older adults: 54.3% for those aged 65-74 and 62.7% for individuals 75 years and older in 2023. Racial disparities persisted, with Black adults having the highest hypertension prevalence at 34.8% in 2023, while Asians had the lowest at 22.3%. Hypertension rates also varied with socioeconomic factors: individuals with lower income (28.4% for those below 100% Federal Poverty Level (FPL)) and lower educational attainment (40.5% for those without a high school diploma) had higher prevalence rates. Social vulnerability and employment status also influenced hypertension trends, with higher rates in individuals with high social vulnerability or non-employment. CONCLUSION: Hypertension remains a persistent health issue, particularly among vulnerable populations. Targeted interventions are needed to address these disparities and reduce the burden of hypertension in the U.S.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".