The effect of disease co-occurrence measurement on multimorbidity networks
Bibliographic record
Abstract
Background: Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic diseases. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. Objectives: The research objectives were to (1) compare structural differences among chronic disease networks constructed from different co-occurrence measures, and (2) demonstrate how co-occurrences among three or more chronic diseases can be analyzed using network techniques. Methods: A retrospective cohort study was conducted using four years of Manitoba administrative health data (2015/16 – 2018/19, 1.5 million individuals). Association rule mining was used to identify disease triads. Separate disease networks were constructed using seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Influential diseases were identified using degree centrality and community detection was used to identify disease clusters. Community structure similarity was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (<1%), moderate (1% to <7%), and high (≥7%). Results: Relative risk and lift highlighted co-occurrences between pairs of low prevalent diseases. Kulczynski emphasized relationships between high and low prevalent diseases. Joint prevalence focused on highly prevalent conditions. Phi, Jaccard, and cosine emphasized associations with moderately prevalent conditions. Co-occurrence measurement differences significantly affected how disease clusters were defined, including the number of clusters identified. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest degree nodes. Conclusion: Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering research objectives and the prevalence relationships of greatest interest. Researchers should be cautious in their interpretation of findings from network analysis and should conduct sensitivity analyses using different co-occurrence measures. Many chronic diseases co-occur in groups of three or more and these higher-order associations can be visualized and analyzed using hypergraphs.
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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 | 0.024 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".