A Systematic Review of Economic Evaluations of COVID-19 Interventions: Considerations of Non-Health Impacts and Distributional Issues.

OBJECTIVES: This study aims to conduct a systematic review of economic evaluations of COVID-19 interventions and to examine whether and how these studies incorporate non-health impacts and distributional concerns. METHODS: We searched the National Institutes of Health's COVID-19 Portfolio as of May 20, 2021, and supplemented our search with additional sources. We included original articles, including preprints, evaluating both the health and economic effects of a COVID-19-related intervention. Using a pre-specified data collection form, 2 reviewers independently screened, reviewed, and extracted information about the study characteristics, intervention types, and incremental cost-effectiveness ratios (ICERs). We used an Impact Inventory to catalog the types of non-health impacts considered. RESULTS: We included 70 articles, almost half of which were preprints. Most articles (56%) included at least one non-health impact, but fewer (21%) incorporated non-economic consequences. Few articles (17%) examined subgroups of interest. After excluding negative ICERs, the median ICER for the entire sample (n = 243 ratios) was $67,000/quality-adjusted life-year (QALY) (interquartile range [IQR] $9000-$893,000/QALY). Interventions including a pharmaceutical component yielded a median ICER of $93,000/QALY (IQR $4000-$7,809,000/QALY), whereas interventions including a non-pharmaceutical component were slightly more cost-effective overall with a median ICER of $81,000/QALY (IQR $12,000-$1,034,000/QALY). Interventions reported to be highly cost-effective were treatment, public information campaigns, quarantining identified contacts/cases, canceling public events, and social distancing. CONCLUSIONS: Our review highlights the lack of consideration of non-health and distributional impacts among COVID-19-related economic evaluations. Accounting for non-health impacts and distributional effects is essential for comprehensive assessment of interventions' value and imperative for generating cost-effectiveness evidence for both current and future pandemics.

Do Cost-Effectiveness Analyses Account for Drug Genericization? A Literature Review and Assessment of Implications.

OBJECTIVES: We investigated how health technology assessment (HTA) organizations around the world have handled drug genericization (an allowance for future generic drug entry and subsequent drug price declines) in their guidelines for cost-effectiveness analyses (CEAs). We also analyzed a large sample of published CEAs to examine prevailing practices in the field. METHODS: We reviewed 43 HTA guidelines to determine whether and how they addressed drug genericization in their CEAs. We also selected a sample of 270 US-based CEAs from the Tufts Medical Center's CEA Registry, restricting the sample to studies on pharmaceuticals published from 1991 to 2019 and to analyses taking a lifetime time horizon. We determined whether each CEA examined genericization (and if so, whether in base case or sensitivity analyses), and how inclusion of genericization influenced the estimated incremental cost-effectiveness ratios. RESULTS: Fourteen (33%) of the 43 HTA guidelines mention genericization for CEAs and 4 (9%) recommend that base case analyses include assumptions about future drug price changes due to genericization. Most published CEAs (95%) do not include assumptions about future generic prices for intervention drugs. Only 2% include such assumptions about comparator drugs. Most studies (72%) conduct sensitivity analyses on drug prices unrelated to genericization. CONCLUSIONS: The omission of assumptions about genericization means that CEAs may misrepresent the long run opportunity costs for drugs. The field needs clearer guidance for when CEAs should account for genericization, and for the inclusion of other price dynamics that might influence a drug's cost-effectiveness.

Metabolic Disease and The Risk of Post-COVID Conditions: A Retrospective Cohort Study.

Objective: To examine the influence of having a baseline metabolic disorder (diabetes, hypertension, and/or obesity) on the risk of developing new clinical sequelae potentially related to SARS-CoV-2 in a large sample of commercially insured adults in the US. Design setting and participants: Deidentified data were collected from the IBM/Watson MarketScan Commercial Claims and Encounters (CCAE) Databases and Medicare Supplemental and Coordination of Benefits (MDCR) Databases from 2019 to 2021. A total of 839,344 adults aged 18 and above with continuous enrollment in the health plan were included in the analyses. Participants were grouped into four categories based on their COVID-19 diagnosis and whether they had at least one of the three common metabolic disorders at baseline (diabetes, obesity, or hypertension). Measures and methods: ICD-10-CM codes were used to determine new symptoms and conditions after the acute phase of SARS-CoV-2 infection, defined as ending 21 days after initial diagnosis date, or index period for those who did not have a COVID-19 diagnosis. Propensity score matching (PSM) was used to create comparable reference groups. Cox proportional hazard models were conducted to estimate hazard ratios and 95% confidence intervals. Results: Among the 772,377 individuals included in the analyses, 36,742 (4.8%) without and 20,912 (2.7%) with a baseline metabolic disorder were diagnosed with COVID-19. On average, COVID-19 patients with baseline metabolic disorders had more 2.4 more baseline comorbidities compared to those without baseline metabolic disorders. Compared to adults with no baseline metabolic condition, the risks of developing new clinical sequelae were highest among COVID-19 patients with a baseline metabolic condition (HRs ranging from 1.51 to 3.33), followed by those who had a baseline metabolic condition but with no COVID-19 infection (HRs ranging from 1.33 to 2.35), and those who had COVID-19 but no baseline metabolic condition (HRs ranging from 1.34 to 2.85). Conclusions: In a large national cohort of commercially insured adults, COVID-19 patients with a baseline metabolic condition had the highest risk of developing new clinical sequelae post-acute infection phase, followed by those who had baseline metabolic condition but no COVID-19 infection and those who had COVID-19 but no baseline metabolic disorder.