Bibliographic record
Abstract
Background Although cardiovascular disease (CVD) is of growing importance in low- and middle-income countries (LMICs), there are conflicting views regarding CVD as a major public health problem for the urban poor, including those living in slums. We examined multivariable risk prediction in a slum population and assessed the number of cardiovascular related deaths within 10 years of application of the tool. Methods We analysed data from a cross sectional survey conducted in the Nairobi Urban Health Demographic Surveillance population (residents of two slum communities) between May 2008 and April 2009. We used the World Health Organisation/International Society of Hypertension (WHO/ISH) cardiovascular risk prediction tool to examine 10-year risk of major CVD events in a slum population. 3063 men and women aged over 40 years with complete data for variables needed for the WHO/ISH risk prediction tool were eligible for inclusion in our analysis. CVD deaths in the cohort, reported up until June 2018 in regular demographic data collection rounds, with the cause identified through verbal autopsy are also presented. Non-fatal CVD events were not captured. Results The majority of study members (2895, 94.5%) were predicted to have ‘low’ risk (<10%) of a cardiovascular event over the next 10 years and just 51 (1.7%) to have ‘high’ CVD risk (≥20%). 91 CVD deaths were reported for the cohort up until June 2018. Of individuals classified as low risk, 74 (2.6%) were identified as having died of CVD. Nine (7.7%) of individuals classified at 10–20% risk and eight (15.9%) classified at >20% were identified as dying of CVD. Discussion To the best of our knowledge this is the first study to apply a multivariable risk prediction tool to a population in a slum or informal settlement. This is a low risk population profile in comparison to results from application of multivariable risk prediction tools in other LMIC populations. This indicates that CVD may be lesser issue in slums than in other areas of LMICs cities. We found evidence that the WHO/ISH tool distinguished groups at relatively lower or higher risk of CVD events. While the absolute risk in this population is over-estimated by the tool, this may be due to limitations in our study such as lack of data on non-fatal CVD events. Our findings have implications for health service planning in similar settings.
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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 | 0.046 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.250 | 0.143 |
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".