Health-related quality of life as a predictor of pediatric healthcare costs: a two-year prospective cohort analysis

Michael Seid, James W Varni, Darron Segall, Paul S Kurtin, Michael Seid, James W Varni, Darron Segall, Paul S Kurtin

Abstract

Background: The objective of this study was to test the primary hypothesis that parent proxy-report of pediatric health-related quality of life (HRQL) would prospectively predict pediatric healthcare costs over a two-year period. The exploratory hypothesis tested anticipated that a relatively small group of children would account for a disproportionately large percent of healthcare costs.

Methods: 317 children (157 girls) ages 2 to 18 years, members of a managed care health plan with prospective payment participated in a two-year prospective longitudinal study. At Time 1, parents reported child HRQL using the Pediatric Quality of Life Inventory (PedsQL 4.0) Generic Core Scales, and chronic health condition status. Costs, based on health plan utilization claims and encounters, were derived for 6, 12, and 24 months.

Results: In multiple linear regression equations, Time 1 parent proxy-reported HRQL prospectively accounted for significant variance in healthcare costs at 6, 12, and 24 months. Adjusted regression models that included both HRQL scores and chronic health condition status accounted for 10.1%, 14.4%, and 21.2% of the variance in healthcare costs at 6, 12, and 24 months. Parent proxy-reported HRQL and chronic health condition status together defined a 'high risk' group, constituting 8.7% of the sample and accounting for 37.4%, 59.2%, and 62% of healthcare costs at 6, 12, and 24 months. The high risk group's per member per month healthcare costs were, on average, 12 times that of other enrollees' at 24 months.

Conclusions: While these findings should be further tested in a larger sample, our data suggest that parent proxy-reported HRQL can be used to prospectively predict healthcare costs. When combined with chronic health condition status, parent proxy-reported HRQL can identify an at risk group of children as candidates for proactive care coordination.

References

    1. Newhouse JP, Sloss EM, W.G. Manning, Keeler EB. Risk adjustment for a children's capitation rate. Health Care Financing Review. 1993;15:39–54.
    1. Iezzoni LI. Risk Adjustment for Measuring Health Care Outcomes. 3rd. Chicago, Health Administration Press; 2003.
    1. Parkerson G. R., Jr., Harrell F. E., Jr., Hammond WE, Wang XQ. Characteristics of adult primary care patients as predictors of future health services charges. Med Care. 2001;39:1170–1181. doi: 10.1097/00005650-200111000-00004.
    1. Miilunpalo S, Vuori I, Oja P, Pasanen M, Urponen H. Self-rated health status as a health measure: the predictive value of self-reported health status on the use of physician services and on mortality in the working-age population. Journal of Clinical Epidemiology. 1997;50:517–528. doi: 10.1016/S0895-4356(97)00045-0.
    1. Brody KK, Johnson RE, Douglas Ried L. Evaluation of a self-report screening instrument to predict frailty outcomes in aging populations. Gerontologist. 1997;37:182–191.
    1. Kaplan SH. Patient reports of health status as predictors of physiologic health measures in chronic disease. J Chronic Dis. 1987;40:27S–40S.
    1. Dancey J, Zee B, Osoba D, Whitehead M, Lu F, Kaizer L, Latreille J, Pater JL. Quality of life scores: an independent prognostic variable in a general population of cancer patients receiving chemotherapy. The National Cancer Institute of Canada Clinical Trials Group. Qual Life Res. 1997;6:151–158. doi: 10.1023/A:1026442201191.
    1. Covinsky KE, Justice AC, Rosenthal GE, Palmer RM, Landefeld CS. Measuring prognosis and case mix in hospitalized elders. The importance of functional status. J Gen Intern Med. 1997;12:203–208. doi: 10.1046/j.1525-1497.1997.012004203.x.
    1. Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients. Jama. 1998;279:1187–1193. doi: 10.1001/jama.279.15.1187.
    1. Mayer-Oakes SA, Oye RK, Leake B. Predictors of mortality in older patients following medical intensive care: the importance of functional status. J Am Geriatr Soc. 1991;39:862–868.
    1. Covinsky KE, Kahana E, Chin MH, Palmer RM, Fortinsky RH, Landefeld CS. Depressive symptoms and 3-year mortality in older hospitalized medical patients. Ann Intern Med. 1999;130:563–569.
    1. Fowles JB, Weiner JP, Knutson D, Fowler E, Tucker AM, Ireland M. Taking health status into account when setting capitation rates: a comparison of risk-adjustment methods. JAMA. 1996;276:1316–1321. doi: 10.1001/jama.276.16.1316.
    1. 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.
    1. Fowler EJ, Anderson GF. Capitation adjustment for pediatric populations. Pediatrics. 1996;98:10–17.
    1. Muldoon JH, Neff JM, Gay JC. Profiling the health service needs of populations using diagnosis-based classification systems. J Ambulatory Care Manage. 1997;20:1–18.
    1. Forrest CB, Simpson L, Clancy C. Child health services research. Challenges and opportunities. JAMA. 1997;277:1787–1793. doi: 10.1001/jama.277.22.1787.
    1. Homer CJ, Kleinman LC, Goldman DA. Improving the quality of care for children in health systems. Health Serv Res. 1998;33:1091–1109.
    1. McGlynn EA, Halfon N, Leibowitz A. Assessing the quality of care for children. Prospects under health reform. Arch Pediatr Adolesc Med. 1995;149:359–368.
    1. Varni JW, Seid M, Kurtin PS. PedsQL 4.0: Reliability and Validity of the Pediatric Quality of Life Inventory Version 4.0 Generic Core Scales in Healthy and Patient Populations. Med Care. 2001;39:800–812. doi: 10.1097/00005650-200108000-00006.
    1. Varni JW, Seid M, Rode CA. The PedsQL: Measurement model for the pediatric quality of life inventory. Med Care. 1999;37:126–139. doi: 10.1097/00005650-199902000-00003.
    1. Eiser C, Morse R. A review of measures of quality of life for children with chronic illness. Arch Dis Child. 2001;84:205–211. doi: 10.1136/adc.84.3.205.
    1. Eiser C, Morse R. Quality-of-life measures in chronic diseases of childhood. Health Technol Assess. 2001;5:1–157.
    1. Landgraf JM, Abetz L, J. E. Ware Jr. Child Health Questionnaire (CHQ): A user's manual. 1. Vol. 1. Boston, The Health Institute, New England Medical Center; 1996. p. 39.
    1. Starfield B, Bergner M, Ensminger M, Riley A, Ryan S, Green B, McGauhey P, Skinner A, Kim S. Adolescent health status measurement: Development of the child health and illness profile. Pediatrics. 1993;91:430–435.
    1. Starfield B, Riley AW, BF Green, Ensminger ME, Ryan SA, Kelleher K, et al. The adolescent CHIP: A population-based measure of health. Medical Care. 1995;33:553–566.
    1. Starfield B. Development of an adolescent health status measure. AHCPR Publication #PB96-182563. Washington, D.C., Agency for Health Care Policy and Research; 1996.
    1. Stein RE, Jessop DJ. Functional Status II (R): A measure of child health status. Med Care. 1990;28:1041–1055.
    1. Varni JW, Seid M, Smith Knight T, Burwinkle T, Brown J, Szer IS. The PedsQL in pediatric rheumatology: reliability, validity, and responsiveness of the Pediatric Quality of Life Inventory Generic Core Scales and Rheumatology Module. Arthritis Rheum. 2002;46:714–725. doi: 10.1002/art.10095.
    1. Varni JW, Burwinkle TM, Katz ER, Meeske K, Dickinson P. The PedsQL in pediatric cancer: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, Multidimensional Fatigue Scale, and Cancer Module. Cancer. 2002;94:2090–2106. doi: 10.1002/cncr.10428.
    1. Varni JW, Seid M, Knight TS, Uzark K, Szer IS. The PedsQL 4.0 Generic Core Scales: sensitivity, responsiveness, and impact on clinical decision-making. J Behav Med. 2002;25:175–193. doi: 10.1023/A:1014836921812.
    1. Seid M, Varni JW, Olson-Bermudez L, Zivkovic M, Davodi Far M, Nelson M, Kurtin PS. Parent's Perceptions of Primary Care measure (P3C): Measuring parents' experiences of pediatric primary care quality. Pediatrics. 2001;108:264–270. doi: 10.1542/peds.108.2.264.
    1. Seid M, Sobo EJ, Gelhard LR, Varni JW. Parents' reports of barriers to care for children with special health care needs: Development and validation of the Barriers to Care Questionnaire. Ambul Pediatr. 2004;4:323–331. doi: 10.1367/A03-198R.1.
    1. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL™ 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3:329–341. doi: 10.1367/1539-4409(2003)003<0329:TPAAPP>;2.
    1. Forrest CB, Starfield B, Riley AW, Kang M. The impact of asthma on the health status of adolescents. Pediatrics. 1997;99:e1. doi: 10.1542/peds.99.2.e1.
    1. Varni JW, Setoguchi Y. Screening for behavioral and emotional problems in children and adolescents with congenital or acquired limb deficiencies. Am J Dis Child. 1992;146:103–107.
    1. Janicke DM, Finney JW, Riley AW. Children's health care use: A prospective investigation of factors related to care-seeking. Med Care. 2001;39:990–1001. doi: 10.1097/00005650-200109000-00009.
    1. World Health Organization . Constitution of the World Health Organization basic document. Geneva, Switzerland, World Health Organization; 1948.
    1. Fairclough DL, Gelber R. Statistical issues and consideration in quality of life and pharmacoeconomics in clinical trials. In: B Spilker, editor. Quality of life and pharmacoeconomics in clinical trials. New York, Raven Press; 1996. pp. 427–435.
    1. Stein REK, Jessop DJ. A noncategorical approach to chronic childhood illness. Public Health Reports. 1982;97:354–362.
    1. Stein REK, Bauman LJ, Westbrook LE, et al. Framework for identifying children who have chronic conditions: The case for a new definition. Journal of Pediatrics. 1993;122:342–347.
    1. Newacheck PW, Taylor WR. Childhood chronic illness: prevalence, severity, and impact. Am J Public Health. 1992;82:364–371.
    1. Newacheck PW,, Strickland B, Shonkoff JP, Perrin JM, McPherson M, McManus M, Lauver C, Fox H, Arango P. An epidemiologic profile of children with special health care needs. Pediatrics. 1998;102:117–123. doi: 10.1542/peds.102.1.117.
    1. Greineder DK, Loane KC, Parks P. Reduction in resource utilization by an asthma outreach program. Arch Pediatr Adolesc Med. 1995;149:415–420.
    1. Home-taught pediatric asthma program improves outcomes, cuts hospital, physician visits. Health Care Cost Reengineering Rep. 1997;2:40–43.
    1. Bastiaansen D, Koot HM, Bongers IL, Varni JW, Verhulst FC. Measuring quality of life in children referred for psychiatric problems: Psychometric properties of the PedsQL(tm) 4.0 Generic Core Scales. Quality of Life Research. 2004;13:489–495. doi: 10.1023/B:QURE.0000018483.01526.ab.
    1. Zupancic JA, Richardson DK, Lee K, McCormick MC. Economics of prematurity in the era of managed care. Clin Perinatol. 2000;27:483–497.

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi