Population-based study of the association between food insecurity and preventable hospitalization among persons with diabetes using linked survey and administrative data
Bibliographic record
Abstract
BACKGROUND: Studies have found food insecurity to be more prevalent among persons with diabetes mellitus. Other research using areal-based measures of socioeconomic status have pointed to a social gradient in diabetes hospitalizations, but without accounting for individuals' health status. Linking person-level data from health surveys to population-based hospital records enables profiling of the role of food insecurity with hospital morbidity, focusing on the high-risk diabetic population. OBJECTIVE: This national study aims to assess the association between income-related household food insecurity and potentially avoidable hospital admissions among community-dwelling persons living with diagnosed diabetes. METHODS: We use three cycles of the Canadian Community Health Survey (2007, 2008, and 2011) linked to multiple years of hospital records from the Discharge Abstract Database (2005/06 to 2012/13), covering 12 of Canada's 13 provinces and territories. We apply multiple logistic regression for testing the association of household food insecurity with the risk of hospitalization for diabetes and common comorbid ambulatory care sensitive conditions among persons aged 12 and over living with diabetes. ANALYSIS: Data linkage allowed us to analyze inpatient hospital records among 10,260 survey respondents with diabetes; 590 respondents had been hospitalized at least once for diabetes or a common comorbid chronic physical or mental illness. The regression results indicated that the odds of experiencing a preventable hospital admission were significantly higher among persons with diabetes who were food insecure compared to their counterparts who were food secure (OR=1.66 [95%CI=1.24-2.23]), after controlling for age, sex and other characteristics. CONCLUSION: We found food insecurity to significantly increase the odds of hospital admission for ambulatory care sensitive conditions among Canadians living with diabetes. These results reinforce the need to consider food insecurity in public health and clinical strategies to reduce the hospital burden of diabetes and other nutrition-related chronic diseases, from primary prevention to post-discharge care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".