Health Check: Analyzing Trends in West Michigan
Bibliographic record
Abstract
INTRODUCTION: This study provides a framework for assessing key data elements and identifying trends in three areas: knowledge foundations, health care trends, and health related economic analysis; which can be used to address the challenges of cost-effectiveness of health-services and healthcare availability in West Michigan (Kent, Ottawa, Muskegon, and Allegan – KOMA). METHODS: Data sets were collected from several educational institutes, and governmental and non-profit organizations as well as a survey of area hospitals in KOMA. Statistical Analysis: Linear time-based analysis was used to identify knowledge foundation and healthcare trends. Occupational projections were calculated by extracting a KOMA population based component from overall state projections. Log-log regression analysis was used determine healthcare usage and cost drivers. RESULTS: Knowledge Foundations: Distinct increase in patent activity in Grand Rapids since 2005. Patent activity is growing at a faster rate than peer communities in Oregon and Ohio. Enough graduates are being produced to fulfill projected occupational requirements in 2018 with the exception of nurses. Health Care Trends: The KOMA and Michigan populations are ageing with a corresponding decline in the 18-34 year old cohort. Increasing trends were identified in select risk factors and disease incidence. Overall health status in KOMA is better than Detroit and Michigan, but worse than the nation. Economic Analysis: Biggest drivers of fair or poor health are smoking, obesity and binge-drinking. Range of medical facilities/services in Cuyahoga, OH is 3-4 times larger than that offered in Kent, MI but Cuyahoga’s population is only twice the size of Kent. Overall hospital confidence in health sector economic viability is high (87%). Emergency room visits, patient care costs and uncompensated charges are increasing. CONCLUSION:West Michigan faces significant challenges in the areas of obesity, and binge drinking which lead to diabetes, stroke and heart disease. Coupled with an ageing population and declining low-risk youth (18-34) cohort, demand for medical services is expected to continue to increase. Conversely, the disturbing trend of rising uncompensated charges reflects the increasing number of persons without medical insurance coverage in challenging economic times. Finally, increasing innovation (patents) in West Michigan may help support growth and investment in the health sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".