Department of Chemistry, The State University of New York at Binghamton, Binghamton, NY, USA.
Department of Population Health Sciences, Division of Health System Innovation & Research, University of Utah, Salt Lake City, UT, USA.
J Public Health Policy. 2020 Jun;41(2):155-169. doi: 10.1057/s41271-020-00219-0.
Lyme disease (LD) is endemic in many regions of the Northeastern United States. Given the elusive nature of the disease, a systematic approach to identify efficient interventions would be useful for policymakers in addressing LD. We used Markov modeling to investigate the efficiency of interventions. These interventions range from awareness-based to behavioral-based strategies. Targeting animal reservoirs of LD using fungal spray or bait boxes did not prove to be an effective intervention. Results of awareness-based interventions, including distribution of signage, fliers, and presentations, implementable in different geographical scales, suggest that policymakers should focus on these interventions, as they are both cost-effective and have the highest impact on lowering LD risk. Populations may lose focus of LD warnings over time, thus quick succession of these interventions is vital. Our modeling results identify the awareness-based intervention as the most cost-effective strategy to lower the number of LD cases. These results can aid in the establishment of effective LD risk reduction policy at various scales of implementation.
莱姆病（LD）在美国东北部的许多地区流行。鉴于这种疾病难以捉摸的性质，为决策者制定一种系统的方法来识别有效的干预措施将是有用的。我们使用马尔可夫模型来研究干预措施的效率。这些干预措施从基于意识的策略到基于行为的策略不等。使用真菌喷雾或诱饵盒来靶向 LD 的动物宿主被证明不是一种有效的干预措施。基于意识的干预措施的结果，包括分发标志、传单和演示文稿，可在不同的地理尺度上实施，这表明决策者应该关注这些干预措施，因为它们既具有成本效益，又对降低 LD 风险的影响最大。随着时间的推移，人们可能会对 LD 警告失去关注，因此这些干预措施的快速连续实施至关重要。我们的建模结果确定基于意识的干预措施是降低 LD 病例数量的最具成本效益的策略。这些结果可以帮助在各种实施规模上制定有效的 LD 减少风险政策。
