Melike Yildirim1, Bradley N Gaynes2, Pinar Keskinocak3, Brian W Pence4, Julie Swann5. 1. School of Industrial and Systems Engineering and Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia, USA. 2. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 3. School of Industrial and Systems Engineering and Center for Health and Humanitarian Systems, Georgia Institute of Technology, Atlanta, Georgia, USA; Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA. 4. Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 5. Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA. Electronic address: jlswann@ncsu.edu.
Abstract
BACKGROUND: Major depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression. METHODS: We propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved the system of linear equations and tested the parameter and transition probabilities empirically. RESULTS: We showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males. LIMITATIONS: This model is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct. CONCLUSION: We constructed a preliminary model for the natural history of major depression. We determined the lifetime prevalences are underestimated and the average incidence rates may be underestimated for males. We conclude that recall bias needs to be accounted for in modeling or burden estimates, where the recall bias should increase with age.
BACKGROUND: Major depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression. METHODS: We propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved the system of linear equations and tested the parameter and transition probabilities empirically. RESULTS: We showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males. LIMITATIONS: This model is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct. CONCLUSION: We constructed a preliminary model for the natural history of major depression. We determined the lifetime prevalences are underestimated and the average incidence rates may be underestimated for males. We conclude that recall bias needs to be accounted for in modeling or burden estimates, where the recall bias should increase with age.
Authors: Andrea Carta; Maria Del Zompo; Anna Meloni; Francesco Mola; Pasquale Paribello; Federica Pinna; Marco Pinna; Claudia Pisanu; Mirko Manchia; Alessio Squassina; Bernardo Carpiniello; Claudio Conversano Journal: Clin Drug Investig Date: 2022-08-05 Impact factor: 3.580