Trends in health service use among persons with Parkinson’s disease by rurality: A population-based repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The global burden of Parkinson's disease (PD) has more than doubled over the past three decades, and this trend is expected to continue. Despite generally poorer access to health care services in rural areas, little previous work has examined health system use in persons with PD by rurality. We examined trends in the prevalence of PD and health service use among persons with PD by rurality in Ontario, Canada. METHODS: We conducted a repeated, cross-sectional analysis of persons with prevalent PD aged 40+ years on April 1st of each year from 2000 to 2018 using health administrative databases and calculated the age-sex standardized prevalence of PD. Prevalence of PD was also stratified by rurality and sex. Negative binomial models were used to calculate rate ratios with 95% confidence intervals comparing rates of health service use in rural compared to urban residents in 2018. RESULTS: The age-sex standardized prevalence of PD in Ontario increased by 0.34% per year (p<0.0001) and was 459 per 100,000 in 2018 (n = 33,479), with a lower prevalence in rural compared to urban residents (401 vs. 467 per 100,000). Rates of hospitalizations and family physician visits declined over time in both men and women with PD in rural and urban areas, while rates of emergency department, neurologist, and other specialist visits increased. Adjusted rates of hospitalizations were similar between rural and urban residents (RR = 1.04, 95% CI [0.96, 1.12]), while rates of emergency department visits were higher among rural residents (RR = 1.35, 95% CI [1.27, 1.42]). Rural residents had lower rates of family physician (adjusted RR = 0.82, (95% CI [0.79, 0.84]) and neurologist visits (RR = 0.74, 95% CI [0.72, 0.77]). INTERPRETATION: Lower rates of outpatient health service use among persons residing in rural regions, contrasting with higher rates of emergency department visits suggest inequities in access. Efforts to improve access to primary and specialist care for persons with PD in rural regions are needed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".