Socioeconomic determinants of cardiovascular risk in underserved communities
Bibliographic record
Abstract
Approximately 27% of Kenya’s 50 million people live in urban areas. The majority (56%) live in slums or slum-like settings, with limited access to functional [public] health care in a setting where poverty and insecurity are rampant. In 2015/16, one in every three Kenyan lived below the international poverty line (US$1.90 per day in 2011 per person). The poor, who are disproportionately affected by cardiovascular diseases (CVD), lack livelihood opportunities due to unemployment and are likely to possess low levels of education or be ignorant about CVD risk factors. The goal of this study was to determine the link between socioeconomic factors and risk and mortality from CVD, with the aim of informing interventions for prevention and control of CVD in underserved populations in Kenya. Based on data from the 2015 STEPWise survey on non-communicable diseases risk factors (STEPs), we determined that three in four Kenyan adults possessed between four and six non-communicable diseases (NCD) risk factors from among insufficient physical activity, smoking/tobacco use, harmful alcohol consumption, overweight and obesity, hypertension, and diabetes, indicating an emerging NCD epidemic in the country. Age, sex, level of education and socioeconomic status were key determinants. In slums of Nairobi, the understanding of and perceptions towards NCD and CVD were influenced by literacy levels, while ignorance towards risk factors, stigma and poverty, and perceived high cost of health care negatively affected care-seeking and treatment-adherence for CVD. Mortality from CVD was inversely correlated with gainful unemployment and higher levels of education. Investing in educating the public on CVD and risk factors, while empowering their access to livelihood opportunities are socioeconomic interventions that can enhance primordial prevention and treatment-adherence for CVD in Kenya.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".