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A Practical Approach for Calculating Reliable Cost Estimates from Observational Data: Application to Cost Analyses in Maternal and Child Health

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Abstract

Background

Comparative effectiveness research (CER) and cost-effectiveness analysis are valuable tools for informing health policy and clinical care decisions. Despite the increased availability of rich observational databases with economic measures, few researchers have the skills needed to conduct valid and reliable cost analyses for CER.

Objective

The objectives of this paper are to (i) describe a practical approach for calculating cost estimates from hospital charges in discharge data using publicly available hospital cost reports, and (ii) assess the impact of using different methods for cost estimation in maternal and child health (MCH) studies by conducting economic analyses on gestational diabetes (GDM) and pre-pregnancy overweight/obesity.

Methods

In Florida, we have constructed a clinically enhanced, longitudinal, encounter-level MCH database covering over 2.3 million infants (and their mothers) born alive from 1998 to 2009. Using this as a template, we describe a detailed methodology to use publicly available data to calculate hospital-wide and department-specific cost-to-charge ratios (CCRs), link them to the master database, and convert reported hospital charges to refined cost estimates. We then conduct an economic analysis as a case study on women by GDM and pre-pregnancy body mass index (BMI) status to compare the impact of using different methods on cost estimation.

Results

Over 60 % of inpatient charges for birth hospitalizations came from the nursery/labor/delivery units, which have very different cost-to-charge markups (CCR = 0.70) than the commonly substituted hospital average (CCR = 0.29). Using estimated mean, per-person maternal hospitalization costs for women with GDM as an example, unadjusted charges ($US14,696) grossly overestimated actual cost, compared with hospital-wide ($US3,498) and department-level ($US4,986) CCR adjustments. However, the refined cost estimation method, although more accurate, did not alter our conclusions that infant/maternal hospitalization costs were significantly higher for women with GDM than without, and for overweight/obese women than for those in a normal BMI range.

Conclusions

Cost estimates, particularly among MCH-related services, vary considerably depending on the adjustment method. Our refined approach will be valuable to researchers interested in incorporating more valid estimates of cost into databases with linked hospital discharge files.

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References

  1. Pinkerton SD, Johnson-Masotti AP, Derse A, Layde PM. Ethical issues in cost-effectiveness analysis. Eval Progr Plan. 2002;25:71–83.

    Article  Google Scholar 

  2. Initial National Priorities for Comparative Effectiveness Research. 2009.

  3. Gluck ME. Research insights: incorporating costs into comparative effectiveness research. Washington, DC: Academy Health. 2009. http://www.academyhealth.org/files/publications/ResearchInsightsCER. Accessed 7 Jan 2013.

  4. Garber AM, Sox HC. The role of costs in comparative effectiveness research. Health Aff (Millwood). 2010;29(10):1805–11. doi:10.1377/hlthaff.2010.0647.

    Article  Google Scholar 

  5. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ. 1996;312(7040):1215–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet. 2005;365(9453):82–93. doi:10.1016/S0140-6736(04)17670-8.

    Article  PubMed  Google Scholar 

  7. Sanson-Fisher RW, Bonevski B, Green LW, D’Este C. Limitations of the randomized controlled trial in evaluating population-based health interventions. Am J Prev Med. 2007;33(2):155–61. doi:10.1016/j.amepre.2007.04.007.

    Article  PubMed  Google Scholar 

  8. Data Innovations. Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality; Rockville, MD; 2012. http://www.hcup-us.ahrq.gov/datainnovations/grants.jsp. Accessed 13 Nov 2012.

  9. Pine M, Sonneborn M, Schindler J, Stanek M, Maeda JL, Hanlon C. Harnessing the power of enhanced data for healthcare quality improvement: lessons from a Minnesota Hospital Association Pilot Project. J Healthc Manag. 2012;57(6):406–18. discussion 19–20.

    PubMed  Google Scholar 

  10. Reinhardt UE. The pricing of U.S. hospital services: chaos behind a veil of secrecy. Health Aff (Millwood). 2006;25(1):57–69. doi:10.1377/hlthaff.25.1.57.

    Article  Google Scholar 

  11. Centers for Medicare and Medicaid Services. DRG summary for Medicare inpatient prospective payment hospitals, FY2011. 2013. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index.html. Accessed 10 May 2013.

  12. Song X, Friedman B. Calculate cost adjustment factors by APR-DRG and CCS using selected states with detailed charges. In: HCUP Methods Series Report #2008-04. Online October 8, 2008. U.S. Agency for Healthcare Research and Quality; 2008. http://www.hcup-us.ahrq.gov/reports/methods.jsp.

  13. Krieger EV, Landzberg MJ, Economy KE, Webb GD, Opotowsky AR. Comparison of risk of hypertensive complications of pregnancy among women with versus without coarctation of the aorta. Am J Cardiol. 2011;107(10):1529–34. doi:10.1016/j.amjcard.2011.01.033.

    Article  PubMed  Google Scholar 

  14. Patrick SW, Schumacher RE, Benneyworth BD, Krans EE, McAllister JM, Davis MM. Neonatal abstinence syndrome and associated health care expenditures: United States, 2000–2009. JAMA. 2012;307(18):1934–40. doi:10.1001/jama.2012.3951.

    Article  CAS  PubMed  Google Scholar 

  15. DiMaio H, Edwards RK, Euliano TY, Treloar RW, Cruz AC. Vaginal birth after cesarean delivery: an historic cohort cost analysis. Am J Obstet Gynecol. 2002;186(5):890–2 pii:S0002937802056429.

    Article  PubMed  Google Scholar 

  16. Elixhauser A, Wier LM. Complicating conditions of pregnancy and childbirth, 2008: statistical brief #113. 2006. [bookaccession].

  17. Nikkel LE, Fox EJ, Black KP, Davis C, Andersen L, Hollenbeak CS. Impact of comorbidities on hospitalization costs following hip fracture. J Bone Jt Surg Am. 2012;94(1):9–17. doi:10.2106/JBJS.J.01077.

    Article  Google Scholar 

  18. Rogowski J. Measuring the cost of neonatal and perinatal care. Pediatrics. 1999;103(1 Suppl E):329–35.

    CAS  PubMed  Google Scholar 

  19. Taira DA, Seto TB, Siegrist R, Cosgrove R, Berezin R, Cohen DJ. Comparison of analytic approaches for the economic evaluation of new technologies alongside multicenter clinical trials. Am Heart J. 2003;145(3):452–8. doi:10.1067/mhj.2003.3.

    Article  PubMed  Google Scholar 

  20. Podulka J, Stranges E, Steiner C. Hospitalizations related to childbirth, 2008. In: Agency for Healthcare Research and Quality: Healthcare Cost Utilization Project. 2011. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb110.jsp.

  21. Zupancic JA. The economics of elective cesarean section. Clin Perinatol. 2008;35(3):591-9, xii. doi:10.1016/j.clp.2008.07.001.

    Google Scholar 

  22. Basatemur E, Gardiner J, Williams C, Melhuish E, Barnes J, Sutcliffe A. Maternal prepregnancy BMI and child cognition: a longitudinal cohort study. Pediatrics. 2013;131(1):56–63. doi:10.1542/peds.2012-0788.

    Article  PubMed  Google Scholar 

  23. Huda SS, Brodie LE, Sattar N. Obesity in pregnancy: prevalence and metabolic consequences. Semin Fetal Neonatal Med. 2010;15(2):70–6. doi:10.1016/j.siny.2009.09.006.

    Article  PubMed  Google Scholar 

  24. Kim SY, England L, Sappenfield W, Wilson HG, Bish CL, Salihu HM, et al. Racial/ethnic differences in the percentage of gestational diabetes mellitus cases attributable to overweight and obesity, Florida, 2004–2007. Prev Chronic Dis. 2012;9:E88

    PubMed Central  PubMed  Google Scholar 

  25. Kristensen J, Vestergaard M, Wisborg K, Kesmodel U, Secher NJ. Pre-pregnancy weight and the risk of stillbirth and neonatal death. BJOG. 2005;112(4):403–8. doi:10.1111/j.1471-0528.2005.00437.x.

    Article  PubMed  Google Scholar 

  26. Torloni MR, Betran AP, Horta BL, Nakamura MU, Atallah AN, Moron AF, et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev. 2009;10(2):194–203. doi:10.1111/j.1467-789X.2008.00541.x.

    Article  CAS  PubMed  Google Scholar 

  27. Whiteman VE, Aliyu MH, August EM, McIntosh C, Duan J, Alio AP, et al. Changes in prepregnancy body mass index between pregnancies and risk of gestational and type 2 diabetes. Arch Gynecol Obstet. 2011;284(1):235–40. doi:10.1007/s00404-011-1917-7.

    Article  PubMed  Google Scholar 

  28. Salemi JL, Tanner JP, Bailey M, Mbah AK, Salihu HM. Creation and evaluation of a multi-layered maternal and child health database for comparative effectiveness research. J registry manag. 2013;40(1):14-28.

    Google Scholar 

  29. Centers for Medicare and Medicaid Services. Cost reports: general information. 2013. http://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/CostReports/index.html. Accessed 9 Jan 2013.

  30. The National Bureau of Economic Research. HCRIS cost center coded worksheets. http://www.nber.org/hcris/CSTCODES.pdf. Accessed 10 Nov 2012.

  31. Sun Y, Friedman B. Tools for more accurate inpatient cost estimates with HCUP databases, 2009. In: HCUP Methods Series Report # 2011-04. Online October 17, 2011. U.S. Agency for Healthcare Research and Quality. 2011. http://www.hcup-us.ahrq.gov/reports/methods.jsp.

  32. Friedman B, De La Mare J, Andrews R, McKenzie DH. Practical options for estimating cost of hospital inpatient stays. J Health Care Financ. 2002;29(1):1–13.

    Google Scholar 

  33. United States Department of Labor: Bureau of Labor Statistics. Consumer price index: all urban consumers (CPI-U). 2012. ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt. Accessed 16 Nov 2012.

  34. National Heart, Lung and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report (no. 98-4083) Washington (DC). 1998.

  35. Leslie S, Thiebaud P. Using propensity scores to adjust for treatment selection bias. SAS Global Forum. 2007.

  36. Shah BR, Laupacis A, Hux JE, Austin PC. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol. 2005;58(6):550–9. doi:10.1016/j.jclinepi.2004.10.016.

    Article  PubMed  Google Scholar 

  37. Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the panel on cost-effectiveness in health and medicine. JAMA. 1996;276(15):1253–8.

    Article  CAS  PubMed  Google Scholar 

  38. Muennig P. Cost-effectiveness analysis in health: a practical approach. 2nd ed. San Francisco: Jossey-Bass; 2008.

    Google Scholar 

  39. Kane NM, Magnus SA. The Medicare cost report and the limits of hospital accountability: improving financial accounting data. J Health Polit Policy Law. 2001;26(1):81–105.

    Article  CAS  PubMed  Google Scholar 

  40. Johnsson J. Conflicting hospital performance data: who’s right? Hospitals. 1991;65(2):30, 2, 4.

  41. Lutz S. Sorting out the differences in hospitals’ profit figures. Mod Healthc. 1993;23(43):60, 62.

    Google Scholar 

  42. Gold M, Siegel J, Russell L, Weinstein M, editors. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996.

    Google Scholar 

  43. Rogowski J, Harrison ES. Treatment costs for very low birthweight infants. Santa Monica, CA: RAND; MR-451-AHCPR. 1995.

  44. Carey K. Cost allocation patterns between hospital inpatient and outpatient departments. Health Serv Res. 1994;29(3):275–92.

    CAS  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge the following organizations and individuals for contributing to this project: the Agency for Healthcare Research and Quality (AHRQ) for promoting the enhancement of statewide, hospital-based, encounter-level databases (Grant Number R01HS019997); Social and Scientific Systems, Inc. for their coordination and direction of enhanced state data grants; and the staff of the Florida Department of Health and the Agency for Health Care Administration for providing access to the hospital discharge and vital statistics data used in this project.

Disclosures

This project was supported by grant number R01HS019997 from the AHRQ. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ, or the University of South Florida. The authors do not have any relevant financial interest in this manuscript.

Author Contributions

The introduction was written by JLS, MMC, and KC. The methodology for the project was written by JLS, MMC, and HMS. The results section, tables, and figures were prepared by JLS and MFM. The discussion section was written by JLS and HMS with support from the other authors. All authors contributed to the conceptualization and planning of the work that led to this manuscript.

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Correspondence to Jason L. Salemi.

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Salemi, J.L., Comins, M.M., Chandler, K. et al. A Practical Approach for Calculating Reliable Cost Estimates from Observational Data: Application to Cost Analyses in Maternal and Child Health. Appl Health Econ Health Policy 11, 343–357 (2013). https://doi.org/10.1007/s40258-013-0040-2

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