alexa Abstract | Identifying Future High Cost Individuals within an Intermediate Cost Population

Quality in Primary Care
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access


Background: Improving health and controlling healthcare costs requires better tools for predicting future health needs across populations. We sought to identify factors associated with transitioning of enrollees in an indigent care program from an intermediate cost segment to a high cost segment of this population.

Methods: We analyzed data from 9,624 enrollees of the Virginia Coordinated Care program between 2010 and 2013. Each fiscal year included all enrollees who were classified in an intermediate cost segment in the preceding year and also enrolled in the program in the following year. Using information from the preceding year, we built logistic regression models to identify the individuals in the top 10% of expenditures in the following year. The effect of demographics, count of chronic conditions, presence of the prevalent chronic conditions, and utilization indicators were evaluated and compared. Models were compared via the Bayesian information criterion and c-statistic.

Results: The count of chronic conditions, diagnosis of congestive heart failure, and numbers of total hospital visits and prescriptions were significantly and independently associated with being in the future high cost segment. Overall, the model that included demographics and utilization indicators had a reasonable discrimination (c=0.67).

Conclusions: A simple model including demographics and health utilization indicators predicted high future costs. The count of chronic conditions and certain medical diagnoses added additional predictive value. With further validation, the approach could be used to identify high-risk individuals and target interventions that decrease utilization and improve health.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Juan Lu


Primary Care, Health Care Cost, Chronic Diseases, and Administrative Data Uses, Quality in Primary Care, Primary care medicines, Comprehensive primary care

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version