Literature DB >> 12118816

A structural equation modeling approach to examining the predictive power of determinants of individuals' health expenditures.

Jin-Yuan Chern1, Thomas T H Wan, James W Begun.   

Abstract

Understanding the determinants of health expenditures is essential for a fair and effective utilization profiling, particularly in the setting of capitation rates in risk-adjustment models. The objective of the study was to examine the relative importance of determinants in predicting future health expenditures, using structural equation modeling. Based on Andersen's behavioral system model, individual determinants along with prior utilization and measures of health status from 1994 are evaluated in a longitudinal design for theirpredictive powerfor health expenditures in 1995. A total of 4,255 policy-holders enrolled in three health plans at Trigon BlueCross/BlueShield of Virginia who responded to a mail survey were included for analysis. Person-level annual charges for health services utilization were used as the dependent variable. Five health scales were excerpted from Health Survey SF-36 to represent an individual's health status. Excluding prior utilization in 1994, health status (gamma = -0.19, p < 0.001) and having diabetes (gamma = 0.08, p < 0.001) are two statistically significant predictors of health expenditures in 1995. Including prior utilization, both health status (gamma = -0.15, p < 0.001) and prior utilization (gamma = 0.15, p < 0.001) are the most important predictors, followed by having diabetes (gamma = 0.08, p < 0.001). Health status is a powerful predictor offuture health expenditures, even when prior utilization is controlled.

Entities:  

Mesh:

Year:  2002        PMID: 12118816     DOI: 10.1023/a:1015868720789

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  37 in total

1.  Discretionary hospital use and diagnostic risk adjustment of Medicare HMO capitation rates.

Authors:  F W Porell; L Gruenberg
Journal:  Inquiry       Date:  2000       Impact factor: 1.730

2.  Associations between health risk appraisal scores and employee medical claims costs in a manufacturing company.

Authors:  L T Yen; D W Edington; P Witting
Journal:  Am J Health Promot       Date:  1991 Sep-Oct

3.  Demographic risk factors derived from HMO data.

Authors:  S T Hayes
Journal:  Adv Health Econ Health Serv Res       Date:  1991

4.  Ethnic differences in the demand for physician and hospital utilization among older adults in major American cities: conspicuous evidence of considerable inequalities.

Authors:  F D Wolinsky; B E Aguirre; L J Fann; V M Keith; C L Arnold; J C Niederhauer; K Dietrich
Journal:  Milbank Q       Date:  1989       Impact factor: 4.911

5.  Medical outcomes study short form 36: testing and cross-validating a second-order factorial structure for health system employees.

Authors:  P J Reed
Journal:  Health Serv Res       Date:  1998-12       Impact factor: 3.402

6.  Measuring the need for medical care in an ethnically diverse population.

Authors:  D H Osmond; K Vranizan; D Schillinger; A L Stewart; A B Bindman
Journal:  Health Serv Res       Date:  1996-12       Impact factor: 3.402

7.  Including a measure of health status in Medicare's health maintenance organization capitation formula: reliability issues.

Authors:  R Lichtenstein; J W Thomas
Journal:  Med Care       Date:  1987-02       Impact factor: 2.983

8.  Changes in physician utilization over time among older adults.

Authors:  T E Stump; R J Johnson; F D Wolinsky
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  1995-01       Impact factor: 4.077

9.  Revisiting the behavioral model and access to medical care: does it matter?

Authors:  R M Andersen
Journal:  J Health Soc Behav       Date:  1995-03

10.  Adjusting capitation using chronic disease risk factors: a preliminary study.

Authors:  J Howland; J Stokes; S C Crane; A J Belanger
Journal:  Health Care Financ Rev       Date:  1987
View more
  5 in total

1.  On the application of structural equation modeling for the construction of a health index.

Authors:  Ferra Yanuar; Kamarulzaman Ibrahim; Abdul Aziz Jemain
Journal:  Environ Health Prev Med       Date:  2010-04-06       Impact factor: 3.674

2.  Disparities in health care utilization by smoking status in Canada.

Authors:  Sunday Azagba; Mesbah Fathy Sharaf; Christina Xiao Liu
Journal:  Int J Public Health       Date:  2013-02-24       Impact factor: 3.380

3.  Predictors of primary health care pharmaceutical expenditure by districts in Uganda and implications for budget setting and allocation.

Authors:  Paschal N Mujasi; Jaume Puig-Junoy
Journal:  BMC Health Serv Res       Date:  2015-08-20       Impact factor: 2.655

4.  A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems.

Authors:  Christin Juhnke; Susanne Bethge; Axel C Mühlbacher
Journal:  Int J Integr Care       Date:  2016-10-26       Impact factor: 5.120

5.  Factors Influencing Variations in Hospitalization for Diabetes with Hypoglycemia.

Authors:  Waleed Kattan; Thomas T H Wan
Journal:  J Clin Med       Date:  2018-10-18       Impact factor: 4.241

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.