Patient gender differences in the prediction of medical expenditures

Journal of Women's Health

Volumn 19, Issue 10, 2010, Pages 1925-1932

  Bertakis, Klea D ^a   Azari, Rahman ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; ALCOHOL ABUSE; ALCOHOLISM; ARTICLE; BODY MASS; CONTROLLED STUDY; DATA COLLECTION METHOD; DEMOGRAPHY; EDUCATIONAL STATUS; EMERGENCY WARD; FEMALE; GENDER; HEALTH BEHAVIOR; HEALTH CARE COST; HEALTH CARE UTILIZATION; HEALTH STATUS; HOSPITAL BILLING; HOSPITAL CHARGE; HOSPITALIZATION; HUMAN; INCOME; INTENSIVE CARE UNIT; INTERVIEW; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MEDICAL RECORD REVIEW; MENTAL HEALTH; OBESITY; OUTCOME ASSESSMENT; PREDICTION AND FORECASTING; PREVENTIVE HEALTH SERVICE; PRIMARY MEDICAL CARE; PRIORITY JOURNAL; RISK ASSESSMENT; SELF REPORT; SEX DIFFERENCE; SHORT FORM 36; SMOKING; STATISTICAL MODEL;

ADULT; BODY MASS INDEX; FEMALE; HEALTH EXPENDITURES; HEALTH STATUS INDICATORS; HUMANS; LINEAR MODELS; MALE; PRIMARY HEALTH CARE; PSYCHOMETRICS; REGRESSION ANALYSIS; RISK ASSESSMENT; SEX FACTORS; SOCIOECONOMIC FACTORS;

EID: 77957990835     PISSN: 15409996     EISSN: None     Source Type: Journal    
DOI: 10.1089/jwh.2009.1448     Document Type: Article

References (26)

1. Cucciare MA, O'Donohue W. Predicting future healthcare costs: How well does risk adjustment work? J Health Organ Manage 2006;20:150-162.
2. Centers for Disease Control and Prevention and The Merck Company Foundation. The state of aging and health in America, 2007. Whitehouse Station, NJ: The Merck Company Foundation, 2007.
3. Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in the utilization of health care services. J Fam Pract 2000;49:147-152.
4. Cohen SB, Ezzati-Rice T, Yu W. The utility of extended longitudinal profiles in predicting future health care expenditures. Med Care 2006;44(Suppl 5):I45-53.
5. Newhouse JP, Manning WG, Keeler EB, Sloss EM. Adjusting capitation rates using objective health measures and prior utilization. Health Care Finance Rev 1989;10:41-54.
6. Ash AS, Zhao Y, Ellis RP, Schlein Kramer M. Finding future high-cost cases: Comparing prior cost versus diagnosisbased methods. Health Serv Res 2001;36:194-206.
7. Pope G, Kautter J, Ellis R, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Finance Rev 2004;4:119-141.
8. Hornbrook MC, Goodman MJ. Assessing relative health plan risk with the RAND-36 health survey. Inquiry 1995;32:56-74.
9. Hornbrook MC, Goodman MJ. Chronic disease, functional health status, and demographics: A multi-dimensional approach to risk adjustment. Health Serv Res 1996;31:283-307.
10. Pietz K, Ashton CM, McDonnell M, Wray NP. Predicting healthcare costs in a population of Veterans Affairs beneficiaries using diagnosis-based risk adjustment and self-reported health status. Med Care 2004;42:1027-1035.
11. Fleishman JA, Cohen JW, Manning WG, Kosinski M. Using the SF-12 health status measure to improve predictions of medical expenditures. Med Care 2006;44(Suppl 5):I54-63.
12. Bertakis KD, Azari R. The influence of obesity, alcohol abuse, and smoking on utilization of health care services. Fam Med 2006;38:427-434.
13. Fleishman JA, Lawrence WF. Demographic variation in SF- 12 scores: True differences or differential item functioning? Med Care 2003;41(Suppl 7):III75-III86.
14. Burdine JN, Felix MRL, Abel AL, Wiltraut CJ, Musselman YJ. The SF-12 as a population health measure: An exploratory examination of potential for application. Health Serv Res 2000;35:885-904.
15. Street RL, Gordon HS, Ward MM, Krupat E, Kravitz RL. Patient participation in medical conditions: Why some patients are more involved than others. Med Care 2005;43: 960-969.
16. Stewart AL, Hays RD, Ware JE Jr. The MOS Short-Form General Health Survey. Reliability and validity in a patient population. Med Care 1988;26:724-735.
17. Stewart AL, Greenfield S, Hay RD. Functional status and well-being of patients with chronic disease conditions. Results from the Medical Outcomes Study. JAMA 1989;262: 907-913. (Pubitemid 19208947)
18. McHorney CA, Ware JE, Raczek AE. The MOS 36-item Short-Form Health Survey (SF-36): II. Psychrometric and clinical tests validity in measuring physical and mental health constructs. Med Care 1993;31:247-263.
19. Ware JE, Kosinki M, Keller SD. SF-36 Physical and Mental Health Summary Scales: A user's manual. Boston: The Health Institute, 1994.
20. National Heart, Lung, and Blood Institute. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Bethesda, MD: National Institutes of Health, 1998.
21. U.S. Department of Health and Human Services. The health consequences of smoking: A report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004.
22. Selzer ML. The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. Am J Psychiatry 1971;127:1653-1658.
23. Pokorny AD, Miller BA, Kaplan HB. The brief MAST: A shortened version of the Michigan Alcoholism Screening Test. Am J Psychiatry 1972;129:342-345.
24. Cherpitel CJ. Brief screening instruments of alcoholism. Alcohol Res Health 1997;21:348-351.
25. Buntin MB, Zaslavsky MA. Too much ado about two-part models and transformations? Comparing methods of modeling Medicare expenditures. J Health Econ 2004;23:525-542.
26. Verbrugge LM, Wingard DL. Sex differentials in health and mortality. Women Health 1987;12:103-145.