Illuminating an Invisible Epidemic: A Systemic Review of the Clinical and Economic Benefits of Early Diagnosis and Treatment in Inflammatory Disease and Related Syndromes

Abstract

Healthcare expenditures in the United States are growing at an alarming level with the Centers for Medicare and Medicaid Services (CMS) projecting that they will reach $5.7 trillion per year by 2026. Inflammatory diseases and related syndromes are growing in prevalence among Western societies. This growing population that affects close to 60 million people in the U.S. places a significant burden on the healthcare system. Characterized by relatively slow development, these diseases and syndromes prove challenging to diagnose, leading to delayed treatment against the backdrop of inevitable disability progression. Patients require healthcare attention but are initially hidden from clinician's view by the seemingly generalized, non-specific symptoms. It is imperative to identify and manage these underlying conditions to slow disease progression and reduce the likelihood that costly comorbidities will develop. Enhanced diagnostic criteria coupled with additional technological innovation to identify inflammatory conditions earlier is necessary and in the best interest of all healthcare stakeholders. The current total cost to the U.S. healthcare system is at least $90B dollars annually. Through unique analysis of financial cost drivers, this review identifies opportunities to improve clinical outcomes and help control these disease-related costs by 20% or more.

Keywords: Crohn’s disease; diagnostics; direct costs; disease-modifying therapy; early diagnosis; fibromyalgia syndrome; inflammation; irritable bowel syndrome; multiple sclerosis; ulcerative colitis.

Figure 1

Equation for disease specific cost-savings estimate. Variable definitions by factor. Factor 1: b = all direct costs without condition specific treatment; i = all direct costs with a range from condition specific average treatment cost (i₁) to cost-efficient treatment (i₂). Factor 2: ΔP = P_H − P_L; net cost of condition progression (P_H = all direct costs when treated high severity, P_L = all direct costs when treated low severity); t = time of condition progression. Factor 3: E = high-cost event probability without condition specific treatment; e = high-cost event probability with condition specific average treatment; c = average high-cost event cost. Factor 4: a = treatment adherence savings. The resulting condition specific cost savings are per affected patient, per year.