Health Check: Analyzing Trends in West Michigan
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».