Herd Immunity Effects in Cost-Effectiveness Analyses among Low- and Middle-Income Countries.

BACKGROUND: Herd immunity (HI) is a key benefit of vaccination programs, but the effects are not routinely included in cost-effectiveness analyses (CEAs). OBJECTIVE: This study investigated how the inclusion of HI in CEAs may influence the reported value of immunizations in low- and middle-income countries (LMICs) and illustrated the implications for COVID-19 immunization. METHODS: We reviewed immunization CEAs published from 2000 to 2018 focusing on LMICs using data from the Tufts Medical Center CEA Registries. We investigated the proportion of studies that included HI, the methods used, and the incremental cost-effectiveness ratios (ICERs) reported. When possible, we evaluated how ICERs would change with and without HI. RESULTS: Among the 243 immunization CEAs meeting inclusion criteria, 44 studies (18%) included HI. Of those studies, 11 (25%) used dynamic transmission models, whereas the remainder used static models. Sixteen studies allowed for ICER calculations with and without HI (n = 48 ratios). The inclusion of HI always resulted in more favorable ratios. In 20 cases (42%), adding HI decreased the ICERs enough to cross at least one or more common cost-effectiveness benchmarks for LMICs. Among pneumococcal vaccination studies, including HI in the analyses decreased seven of 24 ICERs enough to cross at least one cost-effectiveness benchmark. CONCLUSION: The full value of immunization may be underestimated without considering a scenario in which HI is achieved. Given the evidence in pneumococcal CEAs, COVID-19 vaccine value assessments should aim to show ICERs with and without HI to inform decision-making in LMICs.

Bridging the gap: aligning economic research with disease burden.

INTRODUCTION: Cost-effectiveness analysis (CEA) is critical for identifying high-value interventions that address significant unmet need. This study examines whether CEA study volume is proportionate to the burden associated with 21 major disease categories. METHODS: We searched the Tufts Medical Center CEA and Global Health CEA Registries for studies published between 2010 and 2019 that measured cost per quality-adjusted life-year or cost per disability-adjusted life-year (DALY). Stratified by geographical region and country income level, the relationship between literature volume and disease burden (as measured by 2019 Global Burden of Disease estimates of population DALYs) was analysed using ordinary least squares linear regression. Additionally, the number of CEAs per intervention deemed 'essential' for universal health coverage by the Disease Control Priorities Network was assessed to evaluate how many interventions are supported by cost-effectiveness evidence. RESULTS: The results located below the regression line but with relatively high burden suggested disease areas that were 'understudied' compared with expected study volume. Understudied disease areas varied by region. Higher-income and upper-middle-income country (HUMIC) CEA volume for non-communicable diseases (eg, mental/behavioural disorders) was 100-fold higher than that in low-income and lower-middle-income countries (LLMICs). LLMIC study volume remained concentrated in HIV/AIDS as well as other communicable and neglected tropical diseases. Across 60 essential interventions, only 33 had any supporting CEA evidence, and only 21 had a decision context involving a low-income or middle-income country. With the exception of one intervention, available CEA evidence revealed the 21 interventions to be cost-effective, with base-case findings less than three times the GDP per capita. CONCLUSION: Our analysis highlights disease areas that require significant policy attention. Research gaps for highly prevalent, lethal or disabling diseases, as well as essential interventions may be stifling potential efficiency gains. Large research disparities between HUMICs and LLMICs suggest funding opportunities for improving allocative efficiency in LLMIC health systems.

Measuring "Fearonomic Effects" in Valuing Therapies: An Application to COVID-19 in China.

OBJECTIVES: To develop a checklist that helps quantify the economic impact associated with fear of contagion and to illustrate how one might use the checklist by presenting a case study featuring China during the coronavirus disease 2019 (COVID-19) outbreak. METHODS: Based on "fearonomic effects," a qualitative framework that conceptualizes the direct and indirect economic effects caused by the fear of contagion, we created a checklist to facilitate empirical estimation. As a case study, we first identified relevant sectors affected by China's lockdown policies implemented just before the Lunar New Year (LNY) week. To quantify the immediate impact, we then estimated the projected spending levels in 2020 in the absence of COVID-19 and compared these projections with actual spending during the LNY week. Data sources used include Chinese and global websites. To characterize uncertainty, we reported upper and lower bound estimates and calculated midpoints for each range. RESULTS: The COVID-19 epidemic is estimated to cost China's economy $283 billion ($196-369 billion), that is, ¥2.0 trillion renminbi (¥1.4-¥2.6 trillion), during the LNY week. Reduced restaurant and movie theater business ($106 [$103-$109] billion, 37.5% [36.4%-38.5%]) and reduced public transportation utilization ($96 [$13-$179] billion dollars, 33.9% [4.6%-63.3%]) explain most of this loss, followed by travel restrictions and the resulting loss of hotel business and tourism ($80.36 billion, 28.4%). CONCLUSION: Our checklist can help quantify the immediate and near-term impact of COVID-19 on a country's economy. It can also help researchers and policy makers consider the broader economic and social consequences when valuing future vaccines and treatments.