Measuring social determinants of health and their impact on service use and medical complexity
Bibliographic record
Abstract
IntroductionPopulation based data on the social determinants of health are not widely available, despite a wide body of evidence pointing to their importance. The Mantioba Population Research Data Repository offers a unique opportunity to leverage data from multiple government departments to assess the relationship between measurable social determinants and health. Objectives and ApproachUsing population based data from health, small area level census survey questions, social assisitance, education, social housing, child protective services and justice, linked at the individual level, we measured indicators of social complexity and mapped them in the province of Manitoba. Individuals with high level of social complexity were then compared with indicators of medical complexity and/or high use of medical services to determine the degree of overlap between these attributes of individuals. A matched group of individuals without any of the measured social complexities was developed and the number and reason for visits to primary care providers was compared. ResultsThe rate of individuals having three or more social complexities varied from a low of ~7% to a high of 35%, depending on the geographic location. High residential mobiity, involvement with the justice system and history of social assistance were the most frequent (>15%). Individuals with social complexities tended to be younger and live in poorer neighbourhoods than medically complex individuals or high users of health services. Socially complex persons had on average 5.5 primary care visits annually, compared to only ~3.5 for matched individuals with no social complexities. The overlap with high users of health services was slight (14.4%) and depended on the characteristics of the population. The overlap with medically complex patients ws higher (16.2%), particularly when medical complexity included mental health related diagnoses (20.4%). Conclusion/ImplicationsThe proportion of individuals with social complexities is large, and a substantial number have multiple risk factors. These individuals are for the most part a unique group, distinct from medically complex patients. Different strategies for care may be necessary to promote and sustain mental and physical health and wellbeing.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".