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Assessing the Costs of Neuropsychiatric Disease in the Systemic Lupus International Collaborating Clinics Cohort Using Multistate Modeling

Clarke, Ann E. ; Hanly, John G. ; Urowitz, Murray B. ; St. Pierre, Yvan ; Gordon, Caroline ; Bae, Sang Cheol ; Romero-Diaz, Juanita ; Sanchez-Guerrero, Jorge ; Bernatsky, Sasha and Wallace, Daniel J. , et al. (2023) In Arthritis Care and Research 75(9). p.1859-1870
Abstract

Objective: To estimate direct and indirect costs associated with neuropsychiatric (NP) events in the Systemic Lupus International Collaborating Clinics inception cohort. Methods: NP events were documented annually using American College of Rheumatology definitions for NP events and attributed to systemic lupus erythematosus (SLE) or non-SLE causes. Patients were stratified into 1 of 3 NP states (no, resolved, or new/ongoing NP event). Change in NP status was characterized by interstate transition rates using multistate modeling. Annual direct costs and indirect costs were based on health care use and impaired productivity over the preceding year. Annual costs associated with NP states and NP events were calculated by averaging all... (More)

Objective: To estimate direct and indirect costs associated with neuropsychiatric (NP) events in the Systemic Lupus International Collaborating Clinics inception cohort. Methods: NP events were documented annually using American College of Rheumatology definitions for NP events and attributed to systemic lupus erythematosus (SLE) or non-SLE causes. Patients were stratified into 1 of 3 NP states (no, resolved, or new/ongoing NP event). Change in NP status was characterized by interstate transition rates using multistate modeling. Annual direct costs and indirect costs were based on health care use and impaired productivity over the preceding year. Annual costs associated with NP states and NP events were calculated by averaging all observations in each state and adjusted through random-effects regressions. Five- and 10-year costs for NP states were predicted by multiplying adjusted annual costs per state by expected state duration, forecasted using multistate modeling. Results: A total of 1,697 patients (49% White race/ethnicity) were followed for a mean of 9.6 years. NP events (n = 1,971) occurred in 956 patients, 32% attributed to SLE. For SLE and non-SLE NP events, predicted annual, 5-, and 10-year direct costs and indirect costs were higher in new/ongoing versus no events. Direct costs were 1.5-fold higher and indirect costs 1.3-fold higher in new/ongoing versus no events. Indirect costs exceeded direct costs 3.0 to 5.2 fold. Among frequent SLE NP events, new/ongoing seizure disorder and cerebrovascular disease accounted for the largest increases in annual direct costs. For non-SLE NP events, new/ongoing polyneuropathy accounted for the largest increase in annual direct costs, and new/ongoing headache and mood disorder for the largest increases in indirect costs. Conclusion: Patients with new/ongoing SLE or non-SLE NP events incurred higher direct and indirect costs.

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@article{6f873a72-d6db-471d-ab5f-48c956e80744,
  abstract     = {{<p>Objective: To estimate direct and indirect costs associated with neuropsychiatric (NP) events in the Systemic Lupus International Collaborating Clinics inception cohort. Methods: NP events were documented annually using American College of Rheumatology definitions for NP events and attributed to systemic lupus erythematosus (SLE) or non-SLE causes. Patients were stratified into 1 of 3 NP states (no, resolved, or new/ongoing NP event). Change in NP status was characterized by interstate transition rates using multistate modeling. Annual direct costs and indirect costs were based on health care use and impaired productivity over the preceding year. Annual costs associated with NP states and NP events were calculated by averaging all observations in each state and adjusted through random-effects regressions. Five- and 10-year costs for NP states were predicted by multiplying adjusted annual costs per state by expected state duration, forecasted using multistate modeling. Results: A total of 1,697 patients (49% White race/ethnicity) were followed for a mean of 9.6 years. NP events (n = 1,971) occurred in 956 patients, 32% attributed to SLE. For SLE and non-SLE NP events, predicted annual, 5-, and 10-year direct costs and indirect costs were higher in new/ongoing versus no events. Direct costs were 1.5-fold higher and indirect costs 1.3-fold higher in new/ongoing versus no events. Indirect costs exceeded direct costs 3.0 to 5.2 fold. Among frequent SLE NP events, new/ongoing seizure disorder and cerebrovascular disease accounted for the largest increases in annual direct costs. For non-SLE NP events, new/ongoing polyneuropathy accounted for the largest increase in annual direct costs, and new/ongoing headache and mood disorder for the largest increases in indirect costs. Conclusion: Patients with new/ongoing SLE or non-SLE NP events incurred higher direct and indirect costs.</p>}},
  author       = {{Clarke, Ann E. and Hanly, John G. and Urowitz, Murray B. and St. Pierre, Yvan and Gordon, Caroline and Bae, Sang Cheol and Romero-Diaz, Juanita and Sanchez-Guerrero, Jorge and Bernatsky, Sasha and Wallace, Daniel J. and Isenberg, David A. and Rahman, Anisur and Merrill, Joan T. and Fortin, Paul R. and Gladman, Dafna D. and Bruce, Ian N. and Petri, Michelle and Ginzler, Ellen M. and Dooley, Mary Anne and Ramsey-Goldman, Rosalind and Manzi, Susan and Jönsen, Andreas and Alarcón, Graciela S. and Van Vollenhoven, Ronald F. and Aranow, Cynthia and Mackay, Meggan and Ruiz-Irastorza, Guillermo and Lim, S. Sam and Inanc, Murat and Kalunian, Kenneth C. and Jacobsen, Soren and Peschken, Christine A. and Kamen, Diane L. and Askanase, Anca and Farewell, Vernon}},
  issn         = {{2151-464X}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{1859--1870}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Arthritis Care and Research}},
  title        = {{Assessing the Costs of Neuropsychiatric Disease in the Systemic Lupus International Collaborating Clinics Cohort Using Multistate Modeling}},
  url          = {{http://dx.doi.org/10.1002/acr.25090}},
  doi          = {{10.1002/acr.25090}},
  volume       = {{75}},
  year         = {{2023}},
}