Decision-Analytic Modeling Studies in Prevention and Treatment of Iodine Deficiency and Thyroid Disorders: A Systematic Overview

Decision-Analytic Modeling Studies in Prevention and Treatment of Iodine Deficiency and Thyroid Disorders: A Systematic Overview: Thyroid, Ahead of Print.

Abstract

Background: Prevention and treatment of iodine deficiency-related diseases remain an important public health challenge. Iodine deficiency can have severe health consequences, such as cretinism, goiter, or other thyroid disorders, and it has economic implications. Our aim was to give an overview of studies applying decision-analytic modeling to evaluate the effectiveness and/or cost-effectiveness of iodine deficiency-related prevention strategies or treatments related to thyroid disorders.

Methods: We performed a systematic literature search in PubMed/MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica Database), Tuft's Cost-Effectiveness Analysis Registry, and National Health System Economic Evaluation Database (NHS EED) to identify studies published between 1985 and 2018 comparing different prevention or treatment strategies for iodine deficiency and thyroid disorders by applying a mathematical decision-analytic model. Studies were required to evaluate patient-relevant health outcomes (e.g., remaining life years, quality-adjusted life years [QALYs]).

Results: Overall, we found 3950 studies. After removal of duplicates, abstract/title, and full-text screening, 17 studies were included. Eleven studies evaluated screening programs (mainly newborns and pregnant women), five studies focused on treatment approaches (Graves' disease, toxic thyroid adenoma), and one study was about primary prevention (consequences of iodine supplementation on offspring). Most of the studies were conducted within the U.S. health care context (n = 7). Seven studies were based on a Markov state-transition model, nine studies on a decision tree model, and in one study, an initial decision tree and a long-term Markov state-transition model were combined. The analytic time horizon ranged from 1 year to lifetime. QALYs were evaluated as health outcome measure in 15 of the included studies. In all studies, a cost-effectiveness analysis was performed. None of the models reported a formal model validation. In most cases, the authors of the modeling studies concluded that screening is potentially cost-effective or even cost-saving. The recommendations for treatment approaches were rather heterogeneous and depending on the specific research question, population, and setting.

Conclusions: Overall, we predominantly identified decision-analytic modeling studies evaluating specific screening programs or treatment approaches; however, there was no model evaluating primary prevention programs on a population basis. Conclusions deriving from these studies, for example, that prevention is cost-saving, need to be carefully interpreted as they rely on many assumptions.