COVID-19流行監視合併環境監測之成本效益分析

Abstract

研究背景與目的:2020年面對新型冠狀病毒肺炎(COVID-19)全球大流行，在藥物及疫苗發展有限的情況下，針對有症狀者採取隔離及病毒檢測措施為預防疾病傳播的重要策略之一。當社區疫情爆發時，對大規模的採檢，醫療資源、採檢人力負擔相當大。環境汙水監測可有效早期偵測潛在社區個案且已於許多國家實施，本研究希望借鏡過往傳染病防疫經驗，如小兒麻痺、傷寒疾病透過環境汙水監測了解社區人群的疾病風險，而有關汙水監測的成本與效果卻鮮少獲得關注，亦是我們想探討的問題。 研究方法: 本研究利用「易感－感染－復原」(SIR) 傳染病模型追蹤社區COVID-19傳播狀況，為Covid-19易感族群開發出馬可夫疾病進展決策模型，用於描述每日感染者在臨床狀態上的進展。分別探討原始及D614G株(2020年)流行與Omicron株(2022年)流行情境下評估症狀篩檢監視策略及污水監測合併症狀篩檢監視的兩種策略，了解這些策略對於感染病例數、住院病例數、重症病例數、死亡數的風險。本篇報告了兩種監測策略在上述四種結果的增加成本效果比(ICER)。 研究結果: 1、 在原始及D614G株流行下，經30天疫情模擬下發現污水監測合併症狀篩檢監視策略較症狀篩檢監視策略能多預防59.4%的疾病感染、59.6%的住院、58.7%的重症以及58.3%的死亡發生。對200萬族群進行監測，污水監測加症狀篩檢監視策略可較症狀篩檢監視策略減少198,312,014元的支出成本，ICRE值而言，每避免一個感染病例可減少省792,494元，每避免一個住院病例可節省1,028,011元，每避免一個重症7,341,468元，每避免一個死亡可節省28,758,444元。 2、 在Omicron株流行下，經30天疫情模擬下發現污水監測合併症狀篩檢監視策略較症狀篩檢監視策略能多預防43.6%的疾病感染、43.6%住院、43.5%的重症以及43.4%的死亡。對200萬族群進行監測，污水監測加症狀篩檢監視策略可較症狀篩檢監視策略減少1,807,827,841 (元/新台幣)的支出成本,ICRE值而言，每避免一個感染病例可減少省823,511元，每避免一個住院病例可節省1,990,835元，每避免一個重症病例可節省19,267,362元，每避免一個死亡可節省16,410,267元。 結論: 新冠肺炎防治使用RT-PCR雖然是確立診斷的可靠工具，但大流行期面對傳播速度快的病毒株，造成社區大規模的感染，有症狀採檢策略須大量人力、資源、時間。所以我們透過Covid-19傳播動態和感染病例臨床演變的信息證明了使用汙水監測加上症狀篩檢策略，可以有效減少感染病例、重症病例及死亡病例的發生，進而降低醫療支出，成為具成本效益的優勢策略。對於長期疾病追蹤，已有許多研究證實利用汙水定點監測，可以提前在疾病爆發前偵測到病毒，做為早期社區防疫介入的指標工具。

Background: Although the conventional epidemiological surveillance with symptom-based testing has been widely used it still a manpower-intensive task. In the face of rapidly emerging infectious disease such as SARS-CoV-2 variant, one of the surveillance approaches for preventing the spread of COVID-19, wastewater-based variants surveillance, has been proposed for the efficient surveillance of SARS-CoV-2 variant in the world. However, evaluating cost and effectiveness regarding the wastewater surveillance in community have been barely addressed. Study Aims: The aim of this study is to conduct a cost-effectiveness analysis where in the comparison between the conventional epidemiological surveillance with symptom-based testing alone and the counterpart of the add-on wastewater surveillance strategy was made for various kinds of SARS-CoV-2 variants. Materials and Methods A Markov decision tree depicting the evolution of COVID-19 cases through the spectrum of clinical states was developed for the subjects infected with COVID-19 from the community setting. The spreading COVID-19 in community by index case was captured by using a SIR model. The strategies of symptom-based testing and an aid-on wastewater monitoring with quarantine in preventing COVID-19 transmission, hospitalization, severe COVID-19 cases, and deaths were assessed.The incremental cost-effectiveness ratios (ICER) regarding the four outcomes for each strategy were reported. Results Under the wild type/D614G period, compared with the traditional epidemic surveillance with symptom-based testing, the add-on strategy of wastewater monitoring led to the reduction of infected cases, admission case, severe case, and death by 59.4%, 59.6%, 58.7%, and 58.3%, respectively, saving healthcare expenditure up to a total of NTD 198,312,014. Compared with the current strategy of symptom-based testing, wastewater monitoring saved NTD 792,494, NTD 1,028,011, NTD 7,341,468, and NTD 28,758,444 per case, per sever case, and per death averted, respectively.Under the Omicron period, compared with symptom-based testing, the strategy of the add-on wastewater monitoring resulted in the reduction of infected cases,admission case, severe case, and death by 43.6%, 43.6%, 43.5%, and 43.4%,respectively, saving healthcare expenditure, amounting to a total of NTD 198,312,014. Compared with the current strategy of symptom-based testing,wastewater monitoring saved NTD 823,511, NTD 1,990,853 NTD 19,267,362, and NTD 16,410,267 for per case, per sever case, and per death averted, respectively. Conclusion Evaluating the cost-effectiveness of symptom-based testing surveillance alone and an add-on wastewater surveillance by using the information on the dynamic of COVID-19 transmission and clinical evolution of infected cases. The add-on wastewater surveillance was a cost-saving strategy against the traditional epidemical surveillance with symptom-based testing strategy.