Patterns of non-communicable disease and injury risk factors in Kenyan adult population: a cluster analysis
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases and unintentional injuries are emerging public health problems in sub-Saharan Africa. These threats have multiple risk factors with complex interactions. Though some studies have explored the magnitude and distribution of those risk factors in many populations in Kenya, an exploration of segmentation of population at a national level by risk profile, which is crucial for a differentiated approach, is currently lacking. The aim of this study was to examine patterns of non-communicable disease and injury risk through the identification of clusters and investigation of correlates of those clusters among Kenyan adult population. METHODS: We used data from the 2015 STEPs survey of non-communicable disease risk factors conducted among 4484 adults aged between 18 and 69 years in Kenya. A total of 12 risk factors for NCDs and 9 factors for injury were used as clustering variables. A K-medians Cluster Analysis was applied. We used matching as the measure of the similarity/dissimilarity among the clustering variables. While clusters were described using the risk factors, the predictors of the clustering were investigated using multinomial logistic regression. RESULTS: We have identified five clusters for NCDs and four clusters for injury based on the risk profile of the population. The NCD risk clusters were labelled as cluster hypertensives, harmful users, the hopefuls, the obese, and the fat lovers. The injury risk clusters were labelled as helmet users, jaywalkers, the defiant and the compliant. Among the possible predictors of clustering, age, gender, education and wealth index came out as strong predictors of the cluster variables. CONCLUSION: This cluster analysis has identified important clusters of adult Kenyan population for non-communicable disease and injury risk profiles. Risk reduction interventions could consider these clusters as potential target in the development and segmentation of a differentiated approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".