This article is freely available to all

See article by Patel et al

In this issue of JCP, Patel et al1 examine comorbidity and health related quality of life (HRQOL) among those with social anxiety disorder (SAD) using a nationally representative survey. The authors used survey-weighted regression analyses to determine significant factors associated with quality of life among those with SAD, finding multiple significant associated comorbid psychiatric disorders. The authors also found specific feared situations in SAD, which were negatively associated with HRQOL. The authors highlight the importance of considering comorbid (or multimorbid) conditions in the treatment of SAD and hope that their work may inform future clinical care and psychiatric epidemiologic research. The authors’ work opens a platform for a much broader comparison of human suffering attributable to psychiatric disorders, a platform which with further research may allow better comparison to disorders outside of psychiatric illness. This can be accomplished through the use of quality-adjusted life-years (QALYs), a metric which can help psychiatrists and other clinicians better understand the impact of our work across specialties while placing a patient-centered value on our clinical care.

QALYs are the most commonly used measure of health-adjusted life-years and are often used in assessing the cost-effectiveness of health care interventions.2 One QALY represents a year lived in a state of perfect health. One’s HRQOL, as noted in Patel et al’s article,1 takes into account both self reported physical and mental disability through survey data; this patient reported disability can readily be translated to QALYs through measure specific conversions to determine years of quality-adjusted lifespan lost to illness or gained through definitive treatment in a given patient.3 Converting diseases to quality-adjusted lifespan effects in this way is useful because it allows the characterization of disability and burden of disease across a variety of conditions, both psychiatric and medical, as reported previously elsewhere.4,5 This characterization gives physicians 3 incredible abilities: (1) the ability to benchmark the suffering associated with a condition, (2) the ability to compare degrees of human suffering across different conditions, and (3) the ability to quantify how our care improves patients’ lives.

In psychiatry, where experiences are difficult to quantify yet impairments are often profound, the QALY is a particularly important tool. The QALY allows psychiatric clinicians to highlight that the opioid use disorder we treat reduces our patients’ quality of life over 2 times as much as their diabetes (−0.16 vs −0.06 QALYs per year).4,6 It shows that generalized anxiety disorders (GADs) and major depressive disorder (−0.10 QALYs per year and −0.09 QALYs per year), while not quite as severe as opioid use disorder, are associated with over twice as much suffering in our patients than their obesity (−0.04 QALYs per year).7,8 Psychiatric illnesses, which are often chronic, are associated with an incredible amount of suffering. Yet psychiatrists provide the equally incredible abilities to mitigate this suffering through clinically validated pharmacologic and nonpharmacologic interventions; further work is needed to illustrate that scaled to the incredible benefits we can provide to our patients, psychiatric services receive comparatively little government funding.

Inquiry into the effect of anxiety disorders on quality of life is not new.9 There is sufficient literature available to allow meta-analysis of HRQOL in anxiety disorders,10 including individual components of quality-of life scores. Of these anxiety disorders, SAD may be associated with relatively more severe long-term disability than other types of anxiety disorders.11 More recent research, such as Patel et al’s work in this issue,1 has added nuance to our understanding of HRQOL in those with anxiety disorders, highlighting the role of concomitant contributors such as psychiatric comorbidities to quality of life in anxiety disorders, with an eye toward treatment of these concomitant conditions.12,13 Other work14 highlighting treatment as a differentiator of quality of life in those meeting criteria for psychiatric disorders could be easily applied to those with GAD, even within the same dataset that Patel et al used. A uniting feature of this past work is a focus on just 1 (benchmarking and characterization) of the 3 main abilities QALYs can give clinicians.

While recent work on HRQOL in psychiatric disorders has delineated specific predictors of quality of life, a return to the roots of the concept may yield particularly impactful future research across the fields. Specifically, comparing degrees of human suffering across different conditions, net of standardized sociodemographic factors may be useful in highlighting the benefit of psychiatry to the health care system and society more broadly. Treating oftentimes smoldering and stigmatized mental illnesses is how psychiatrists provide value to society, value largely captured by patients, caregivers, and other societal stakeholders rather than health systems as reimbursements for psychiatrists lag, and psychiatry is seen as a “money loser” by health care leaders.15,16 The ability to quantify how much our care improves patients’ lives gives psychiatry a concrete raison d’etre, an ability to attract the additional attention and funding we clinicians need. In a society that places substantial value on reducing human suffering, psychiatry’s patient centered effects, quantified in the QALYs we help give back to our patients, may be the best way we have to articulate our value.17

QALYs have several notable limitations that we would be remiss without mentioning. In particular, as an averaged measure, they cannot capture the substantial variation in the lives of those with persistent and chronic disabilities and do not capture the full spectrum of human suffering.18 This claim made by detractors is no small part of why QALYs have recently been proposed to be banned from use in determining US federal payment for health care, and the National Institutes of Health has funded development of next-generation measures of HRQOL19; other countries, most notably the United Kingdom, have continued to embrace QALYs and consequently have emerged as leaders in psychiatry for cost-effectiveness research as their health care systems face budgetary shortfalls.20,21 QALYs cannot capture all the value we provide, especially beyond our patients and to society at large. Finally, as a patient-centered metric, QALYs do not take into account systemic benefits to treatment, including increased employment and engagement with society, reduced burden on the health care system, and reduced reliance on downstream services, all of which are even more difficult to quantify than subjective HRQOL.

It is undoubtedly reductionist to simplify human suffering to numbers; these numbers, however, can help us make better decisions for our patients. QALYs, while flawed, are the least bad widely used measure we have to quantify both the human suffering we seek to alleviate and how the value we provide to our patients compares to our colleagues in adjacent medical and surgical fields. This measure is a valuable tool to justify why the work we do each day providing psychiatric care is so needed, particularly as clinical budgets are squeezed in the pursuit of ever-greater efficiency in health care. In a field often defined by heterogeneity and limited biomarkers of disease, this quantification helps us define our value to patients and society. Future work is needed to better quantify the impact psychiatrists have on the communities we serve.

Article Information

Published Online: May 27, 2024. https://doi.org/10.4088/JCP.23com15379
© 2024 Physicians Postgraduate Press, Inc.
J Clin Psychiatry 2024;85(2):23com15379
Submitted: April 9, 2024; accepted April 22, 2024.
To Cite: Havlik JL, Rhee TG. How to value a life without limits: quantifying suffering with quality-adjusted life years in social anxiety disorder. J Clin Psychiatry. 2024;85(2):23com15379.
Author Affiliations: Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut (Havlik, Rhee); Department of Public Health Sciences, University of Connecticut School of Medicine, Farmington, Connecticut (Rhee).
Corresponding Author: Taeho Greg Rhee, PhD, Department of Public Health Sciences, University of Connecticut School of Medicine, 195 Farmington Ave, Farmington, CT 06030 ([email protected]).
Relevant Financial Relationships: The authors report no disclosures or conflicts of interest for this article.
Funding/Support: None reported.

  1. Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
  2. Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
  3. Department of Public Health Sciences, University of Connecticut School of Medicine, Farmington, Connecticut
  4. Corresponding Author: Taeho Greg Rhee, PhD, Department of Public Health Sciences, University of Connecticut School of Medicine, 195 Farmington Ave, Farmington, CT 06030 ([email protected]).
  1. Patel TA, Schubert FT, Cougle JR. Comorbidity and quality of life in DSM-5 social anxiety disorder among a nationally representative sample. J Clin Psychiatry. 2024;85(2):23m15217.
  2. Freath LL, Curry AS, Cork DMW, et al. QALYs and ambulatory status: societal preferences for healthcare decision making. J Med Econ. 2022;25(1):888–893. PubMed
  3. Spencer A, Rivero-Arias O, Wong R, et al. The QALY at 50: one story many voices. Soc Sci Med. 2022;296:114653. PubMed CrossRef
  4. Olfson M, Wall M, Liu SM, et al. Insomnia and impaired quality of life in the United States. J Clin Psychiatry. 2018;79(5):17m12020. PubMed CrossRef
  5. Rhee TG, Gillissie ES, Nierenberg AA, et al. Association of current and remitted bipolar disorders with health-related quality of life: findings from a nationally representative sample in the US. J Affect Disord. 2023;321:33–40. PubMed
  6. Rhee TG, Rosenheck RA. Association of current and past opioid use disorders with health-related quality of life and employment among US adults. Drug Alcohol Depend. 2019;199:122–128. PubMed CrossRef
  7. Penner-Goeke K, Henriksen CA, Chateau D, et al. Reductions in quality of life associated with common mental disorders: results from a nationally representative sample. J Clin Psychiatry. 2015;76(11):1506–1512. PubMed CrossRef
  8. Rhee TG, Steffens DC. Major depressive disorder and impaired health-related quality of life among US older adults. Int J Geriatr Psychiatry. 2020;35(10):1189–1197. PubMed CrossRef
  9. Mendlowicz MV, Stein MB. Quality of life in individuals with anxiety disorders. Am J Psychiatry. 2000;157(5):669–682. PubMed CrossRef
  10. Olatunji BO, Cisler JM, Tolin DF. Quality of life in the anxiety disorders: a meta-analytic review. Clin Psychol Rev. 2007;27(5):572–581. PubMed CrossRef
  11. Hendriks SM, Spijker J, Licht CMM, et al. Long-term disability in anxiety disorders. BMC Psychiatry. 2016;16:248. PubMed CrossRef
  12. Hendriks SM, Spijker J, Licht CMM, et al. Disability in anxiety disorders. J Affect Disord. 2014;166:227–233. PubMed CrossRef
  13. Penninx BW, Pine DS, Holmes EA, et al. Anxiety disorders. Lancet. 2021;397(10277):914–927. PubMed CrossRef
  14. Havlik JL, Rhee TG, Rosenheck RA. Characterization of quality of life among individuals with current treated, untreated, and past alcohol use disorder. Am J Drug Alcohol Abuse. 2023;49(6):787–798. PubMed CrossRef
  15. Mark TL, Parish W, Zarkin GA, et al. Comparison of Medicaid reimbursements for psychiatrists and primary care physicians. Psychiatr Serv. 2020;71(9):947–950. PubMed CrossRef
  16. Rubinow DR. Out of sight, out of mind: mental illness behind bars. Am J Psychiatry. 2014;171(10):1041–1044. PubMed CrossRef
  17. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness — the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371(9):796–797. PubMed CrossRef
  18. Sawhney TG, Dobes A, O’Charoen S. QALYs: the math doesn’t work. J Health Econ Outcomes Res. 2023;10(2):10–13.
  19. Hanmer J, Dewitt B, Yu L, et al. Cross-sectional validation of the PROMIS-Preference scoring system. PLoS One. 2018;13(7):e0201093. PubMed CrossRef
  20. Tong N. House Republicans vote to ban QALY in federal programs. Accessed March 18, 2024. https://www.fiercehealthcare.com/payers/house-republicans-vote-ban-pricing-metricsfederal-programs
  21. Catarino A, Harper S, Malcolm R, et al. Economic evaluation of 27,540 patients with mood and anxiety disorders and the importance of waiting time and clinical effectiveness in mental healthcare. Nat Ment Health. 2023;1(9):667–678.