Skip to main content
SpringerLink
Log in

The Role of Chronic Disease, Obesity, and Improved Treatment and Detection in Accounting for the Rise in Healthcare Spending Between 1987 and 2011

  • Original Research Article
  • Published:
Applied Health Economics and Health Policy Aims and scope Submit manuscript

Abstract

Background

To curb rising healthcare expenditures in the USA, the factors underlying this growth must be well understood.

Objective

We aim to explore how chronic disease prevalence, obesity, and improved disease detection and treatment rates contributed to the growth in health spending in the USA between 1987 and 2011.

Methods

We use spending decomposition equations to estimate the portion of spending growth attributable to prevalence increases, rising treatment costs, and population growth, respectively. We use two-part models to estimate the portion of prevalence-related spending that is potentially due to obesity. We examine changing diagnosis and treatment rates to assess how much of the growth in spending might be desirable.

Results

We find that the share of total healthcare spending associated with the treatment of chronic disease has risen dramatically from 1987–2011. In particular, we estimate that 77.6 % of healthcare spending growth is attributable to patients with four or more chronic conditions. We find that rising obesity levels may explain between 11.4 and 23.5 % of the increase in healthcare expenditure for several specific chronic conditions. Diagnosis and treatment rates for chronic disease are improving.

Conclusions

Individuals with multiple chronic conditions are disproportionately responsible for rising healthcare expenditure. Much of spending growth associated with rising rates of chronic disease can be linked to rising obesity rates. Though much of the growth in spending is generally considered undesirable, disease detection and treatment rates are also rising, suggesting that at least some of the recent growth in healthcare expenditure may be beneficial.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. A total of 259 medical conditions were identified using the Clinical Classifications System (CCS) groups developed by the AHRQ. The 259 CCS groups were then aggregated to 60 broader condition categories to avoid problems with small sample sizes in individual CCS groups. The three-digit CCS codes used are available upon request.

  2. These three equations sum to 1, accounting for all the growth in spending between 1987 and 2011. To see this, label each of the six elements as a letter. For instance, A = 1987 population, B = 2011 population, C = 1987 prevalence, D = 2011 prevalence, E = 1987 cost, and F = 2011 cost. After substitution for each term, the letters can be rearranged to obtain [BDF − (ACE)]/[BDF − (ACE)] = 1.

  3. In all three guideline years used, 1988, 1993, and 2004, history of CHD is a factor in determining this risk, but CHD was not ascertained in NHANES III, so instead, a proxy variable for history of myocardial infarction (MI) was used. LDL goal cutpoints in 1988 and 1993 were thus determined by whether or not an individual had an MI, and number of risk factors. In 2004, the LDL goal cutpoints were defined on the basis of CHD (MI) or CHD risk equivalents (diabetes, history of stroke, or Framingham risk score >20 %) and number of risk factors.

References

  1. Centers for Medicare and Medicaid Services. NHE Fact Sheet. 2014. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet.html. Accessed 6 Mar 2015.

  2. Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis. 2014;11:E62.

    PubMed Central  PubMed  Google Scholar 

  3. Centers for Disease Control and Prevention National Center for Health Statistics. NCHS obesity data. 2014. http://www.cdc.gov/nchs/data/factsheets/factsheet_obesity.htm. Accessed 6 Mar 2015.

  4. Thorpe KE, Florence CS, Howard DH, Joski P. The rising prevalence of treated disease: effects on private health insurance spending. Health Aff. (Millwood). 2005;Suppl Web Exclusives:W5-317–W315-325.

  5. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of obesity on rising medical spending. Health Aff (Millwood). 2004;Suppl Web Exclusives:W4-480–486.

  6. Thorpe KE, Howard DH. The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity. Health Aff. (Millwood). 2006;25(5):w378–388.

  7. Agency for Health Care Research and Quality. Medical Expenditure Panel Survey background. 2009. http://meps.ahrq.gov/mepsweb/about_meps/survey_back.jsp. Accessed 23 Apr 2014.

  8. Centers for Disease Control and Prevention. About the National Health and Nutrition Examination Survey. 2014. http://www.cdc.gov/nchs/nhanes/about_nhanes.htm. Accessed 23 Apr 2014.

  9. Agency for Healthcare Research and Quality. HCUP CCS. Healthcare Cost and Utilization Project (HCUP). 2014. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed 6 Mar 2015.

  10. Zuvekas SH, Cohen JW. A guide to comparing health care expenditures in the 1996 MEPS to the 1987 NMES. Inquiry. Spring. 2002;39(1):76–86.

    Google Scholar 

  11. Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation? Comparing methods of modeling medicare expenditures. J Health Econ. 2004;23(3):525–542.

  12. Flegal KM, Ezzati TM, Harris MI, et al. Prevalence of diabetes in Mexican Americans, Cubans, and Puerto Ricans from the Hispanic Health and Nutrition Examination Survey, 1982–1984. Diabetes Care. 1991;14(7):628–38.

    Article  CAS  PubMed  Google Scholar 

  13. Goodman DS, Hulley SB, Clark LT, et al. Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and treatment of high blood cholesterol in adults. Arch Intern Med. 1988;148(1):36–69.

    Article  Google Scholar 

  14. Grundy SM, Bilheimer D, Chait A, et al. Summary of the second report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel ii). JAMA. 1993;269(23):3015–23.

    Article  Google Scholar 

  15. Grundy SM, Cleeman JI, Bairey Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines. J Am Coll Cardiol. 2004;44(3):720–732.

  16. World Health Organization. Obesity and overweight. 2014. http://www.who.int/mediacentre/factsheets/fs311/en/. Accessed 6 Mar 2015.

Download references

Acknowledgments

We thank the Robert Wood Johnson Foundation for their generous support of this work, and anonymous reviewers for helpful comments. All errors are the responsibility of the authors.

Conflicts of interest

The authors report no conflicts of interest.

Author contributions

KT developed the conceptual and methodological approach for this analysis. LA drafted the manuscript and provided conceptual input. PJ performed the statistical analyses. KT is the guarantor for the overall content.

Author information

Authors and Affiliations

  1. Emory University, Atlanta, GA, USA

    Kenneth E. Thorpe, Lindsay Allen & Peter Joski

Authors
  1. Kenneth E. Thorpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lindsay Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Joski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lindsay Allen.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 120 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thorpe, K.E., Allen, L. & Joski, P. The Role of Chronic Disease, Obesity, and Improved Treatment and Detection in Accounting for the Rise in Healthcare Spending Between 1987 and 2011. Appl Health Econ Health Policy 13, 381–387 (2015). https://doi.org/10.1007/s40258-015-0164-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40258-015-0164-7

Keywords

Search

Navigation