Sociodemographic correlates of urine culture test utilization in Calgary, Alberta
Bibliographic record
Abstract
BACKGROUND: Many clinical practice guidelines encourage diagnosis and empiric treatment of lower urinary tract infection without laboratory investigation; however, urine culture testing remains one of the largest volume tests in the clinical microbiology laboratory. In this study, we sought to determine if there were specific patient groups to which increased testing was directed. To do so, we combined laboratory data on testing rates with Census Canada sociodemographic data. METHODS: Urine culture testing data was obtained from the Calgary Laboratory Services information system for 2011. We examined all census dissemination areas within the city of Calgary and, for each area, testing rates were determined for age and gender cohorts. We then compared these testing rates to sociodemographic factors obtained from Census Canada and used Poisson regression and generalized estimating equations to test associations between testing rates and sociodemographic variables. RESULTS: Per capita urine culture testing is increasing in Calgary. For 2011, 100,901 individuals (9.2% of all people) received urine cultures and were included in this analysis. The majority of cultures were received from the community (67.9%). Substantial differences in rate of testing were observed across the city. Most notably, urine culture testing was drastically lower in areas of high (≥ $100000) household income (RR = 0.07, p < 0.0001) and higher employment rate (RR = 0.36, p < 0.0001). Aboriginal - First Nations status (RR = 0.29, p = 0.0008) and Chinese visible minority (RR = 0.67, p = 0.0005) were also associated with decreased testing. Recent immigration and visible minority status of South Asian, Filipino or Black were not significant predictors of urine culture testing. Females were more likely to be tested than males (RR = 2.58, p < 0.0001) and individuals aged 15-39 were the most likely to be tested (RR = 1.69, p < 0.0001). CONCLUSIONS: Considerable differences exist in urine culture testing across Calgary and these are associated with a number of sociodemographic factors. In particular, areas of lower socioeconomic standing had significantly increased rates of testing. These observations highlight specific groups that should be targeted to improve healthcare delivery and, in turn, enhance laboratory utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".