Predictors of Blood Lipid and Renal Function Screening among Adults in a Peri-Urban Community in Ghana: A Combined Logistic and Dominance Analysis Approach
Bibliographic record
Abstract
Objective: Despite the critical role of screening in reducing the burden of non-communicable diseases (NCDs), its uptake remains low, particularly in peri-urban settings. This study aimed to identify and rank predictors of screening behaviors for blood lipid/cholesterol and renal function in a peri-urban community in Ghana. Methods: Secondary cross-sectional data from 136 adults aged 18–60, collected in January 2023, were analyzed. Associations and relative importance were examined using bootstrapped logistic regression and dominance analysis models. Results: Multivariate logistic regression analysis identified age (35-60 years) (aOR:7.6, 95% CI: 1.2–50.6) and employment status (aOR:4.4, 95% CI: 1.1–17.6) as significant predictors of renal screening. For blood lipid screening, significant predictors included body mass index (BMI) screening (aOR:3.6, 95% CI: 1.4–9.1) and diploma-level education (aOR:5.4, 95% CI: 1.3–21.8). Dominance analysis, which assesses the relative importance of predictors, revealed that age, blood glucose screening, and employment were the most important predictors for renal screening. In contrast, BMI and a history of raised blood pressure were the leading predictors for blood lipid screening. Conclusions: These findings highlight the need for targeted health promotion strategies that integrate comprehensive screening packages within broader health services, addressing the specific needs of various educational and occupational groups. Enhanced public health interventions could improve screening rates and contribute to better management of NCDs in peri-urban 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".