The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention Tier 1 genomic conditions is unknown. This study estimated the cost-effectiveness of simultaneous genomic screening for Lynch syndrome, hereditary breast and ovarian cancer syndrome, and familial hypercholesterolemia.
Abstract
Background:
The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown.
Objective:
To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH).
Design:
Decision analytic Markov model.
Data Sources:
Published literature.
Target Population:
Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults.
Time Horizon:
Lifetime.
Perspective:
U.S. health care payer.
Intervention:
Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands.
Outcome Measures:
Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs.
Results of Base-Case Analysis:
Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900).
Results of Sensitivity Analysis:
Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters.
Limitations:
Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments.
Conclusion:
Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions.
Primary Funding Source:
National Human Genome Research Institute.
References
- 1.
Khoury MJ ,McCabe LL ,McCabe ER . Population screening in the age of genomic medicine. N Engl J Med. 2003;348:50-58. [PMID: 12510043] doi:10.1056/NEJMra013182 CrossrefMedlineGoogle Scholar - 2.
Dotson WD ,Douglas MP ,Kolor K ,et al . Prioritizing genomic applications for action by level of evidence: a horizon-scanning method. Clin Pharmacol Ther. 2014;95:394-402. [PMID: 24398597] doi:10.1038/clpt.2013.226 CrossrefMedlineGoogle Scholar - 3.
Buchanan AH ,Lester Kirchner H ,Schwartz MLB ,et al . Clinical outcomes of a genomic screening program for actionable genetic conditions. Genet Med. 2020;22:1874-1882. [PMID: 32601386] doi:10.1038/s41436-020-0876-4 CrossrefMedlineGoogle Scholar - 4. Centers for Disease Control and Prevention. Tier 1 Genomics Applications and Their Importance to Public Health. 2014. Accessed at www.cdc.gov/genomics/implementation/toolkit/tier1.htm on 27 December 2019. Google Scholar
- 5. Neumann PJ, Sanders GD, Russell LB, et al. Cost-Effectiveness in Health and Medicine. Oxford Univ Pr; 2016. Google Scholar
- 6.
Guzauskas GF ,Garbett S ,Zhou Z ,et al . Cost-effectiveness of population-wide genomic screening for hereditary breast and ovarian cancer in the United States. JAMA Netw Open. 2020;3:e2022874. [PMID: 33119106] doi:10.1001/jamanetworkopen.2020.22874 CrossrefMedlineGoogle Scholar - 7.
Guzauskas GF ,Jiang S ,Garbett S ,et al . Cost-effectiveness of population-wide genomic screening for Lynch syndrome in the United States. Genet Med. 2022;24:1017-1026. [PMID: 35227606] doi:10.1016/j.gim.2022.01.017 CrossrefMedlineGoogle Scholar - 8.
Spencer SJ ,Jones LK ,Guzauskas GF ,et al . Cost-effectiveness of population-wide genomic screening for familial hypercholesterolemia in the United States. J Clin Lipidol. 2022;16:667-675. [PMID: 35961838] doi:10.1016/j.jacl.2022.07.014 CrossrefMedlineGoogle Scholar - 9.
Zhang L ,Bao Y ,Riaz M ,et al . Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis. Genet Med. 2019;21:1958-1968. [PMID: 30773532] doi:10.1038/s41436-019-0457-6 CrossrefMedlineGoogle Scholar - 10.
Daly MB ,Pal T ,Berry MP ,et al ;CGC . Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021;19:77-102. [PMID: 33406487] doi:10.6004/jnccn.2021.0001 CrossrefMedlineGoogle Scholar - 11.
Grundy SM ,Stone NJ ,Bailey AL ,et al . 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139:e1082-e1143. [PMID: 30586774] doi:10.1161/CIR.0000000000000625 CrossrefMedlineGoogle Scholar - 12.
Weiss JM ,Gupta S ,Burke CA ,et al . NCCN Guidelines® Insights: Genetic/Familial High-Risk Assessment: Colorectal, Version 1.2021. J Natl Compr Canc Netw. 2021;19:1122-1132. [PMID: 34666312] doi:10.1164/jnccn.2021.0048 CrossrefMedlineGoogle Scholar - 13.
Abul-Husn NS ,Manickam K ,Jones LK ,et al . Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science. 2016;354. [PMID: 28008010] doi:10.1126/science.aaf7000 CrossrefMedlineGoogle Scholar - 14.
Manickam K ,Buchanan AH ,Schwartz MLB ,et al . Exome sequencing-based screening for BRCA1/2 expected pathogenic variants among adult biobank participants. JAMA Netw Open. 2018;1:e182140. [PMID: 30646163] doi:10.1001/jamanetworkopen.2018.2140 CrossrefMedlineGoogle Scholar - 15.
Husereau D ,Drummond M ,Petrou S ,et al ;ISPOR Health Economic Evaluation Publication Guidelines-CHEERS Good Reporting Practices Task Force . Consolidated Health Economic Evaluation Reporting Standards (CHEERS)—explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. Value Health. 2013;16:231-250. [PMID: 23538175] doi:10.1016/j.jval.2013.02.002 CrossrefMedlineGoogle Scholar - 16. National Cancer Institute. NCI Dictionary of Genetics Terms. Accessed at www.cancer.gov/publications/dictionaries/genetics-dictionary/def/proband on 23 August 2022. Google Scholar
- 17. University of Michigan Institute for Social Research. PSID Data. Accessed at https://simba.isr.umich.edu/data/data.aspx on 12 November 2019. Google Scholar
- 18.
Elrick A ,Ashida S ,Ivanovich J ,et al . Psychosocial and clinical factors associated with family communication of cancer genetic test results among women diagnosed with breast cancer at a young age. J Genet Couns. 2017;26:173-181. [PMID: 27422778] doi:10.1007/s10897-016-9995-0 CrossrefMedlineGoogle Scholar - 19.
Roberts MC ,Dotson WD ,DeVore CS ,et al . Delivery of cascade screening for hereditary conditions: a scoping review of the literature. Health Aff (Millwood). 2018;37:801-808. [PMID: 29733730] doi:10.1377/hlthaff.2017.1630 CrossrefMedlineGoogle Scholar - 20.
Dinh T ,Ladabaum U ,Alperin P ,et al . Health benefits and cost-effectiveness of a hybrid screening strategy for colorectal cancer. Clin Gastroenterol Hepatol. 2013;11:1158-1166. [PMID: 23542330] doi:10.1016/j.cgh.2013.03.013 CrossrefMedlineGoogle Scholar - 21.
Grzymski JJ ,Elhanan G ,Morales Rosado JA ,et al . Population genetic screening efficiently identifies carriers of autosomal dominant diseases. Nat Med. 2020;26:1235-1239. [PMID: 32719484] doi:10.1038/s41591-020-0982-5 CrossrefMedlineGoogle Scholar - 22.
Manchanda R ,Patel S ,Gordeev VS ,et al . Cost-effectiveness of population-based BRCA1, BRCA2, RAD51C, RAD51D, BRIP1, PALB2 mutation testing in unselected general population women. J Natl Cancer Inst. 2018;110:714-725. [PMID: 29361001] doi:10.1093/jnci/djx265 CrossrefMedlineGoogle Scholar - 23.
Kuchenbaecker KB ,Hopper JL ,Barnes DR ,et al ;BRCA1 and BRCA2 Cohort Consortium . Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA. 2017;317:2402-2416. [PMID: 28632866] doi:10.1001/jama.2017.7112 CrossrefMedlineGoogle Scholar - 24. National Cancer Institute. Cancer Stat Facts: Female Breast Cancer. Accessed at https://seer.cancer.gov/statfacts/html/breast.html on 19 September 2019. Google Scholar
- 25. National Cancer Institute. Cancer Stat Facts: Ovarian Cancer. Accessed at https://seer.cancer.gov/statfacts/html/ovary.html on 19 September 2019. Google Scholar
- 26.
Bonadona V ,Bonaïti B ,Olschwang S ,et al ;French Cancer Genetics Network . Cancer risks associated with germline mutations in MLH1, MSH2, and MSH6 genes in Lynch syndrome. JAMA. 2011;305:2304-2310. [PMID: 21642682] doi:10.1001/jama.2011.743 CrossrefMedlineGoogle Scholar - 27.
Dominguez-Valentin M ,Sampson JR ,Seppälä TT ,et al . Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. Genet Med. 2020;22:15-25. [ [PMID: 31337882] doi:10.1038/s41436-019-0596-9 CrossrefMedlineGoogle Scholar - 28.
Snowsill T ,Coelho H ,Huxley N ,et al . Molecular testing for Lynch syndrome in people with colorectal cancer: systematic reviews and economic evaluation. Health Technol Assess. 2017;21:1-238. [PMID: 28895526] doi:10.3310/hta21510 CrossrefMedlineGoogle Scholar - 29.
Järvinen HJ ,Aarnio M ,Mustonen H ,et al . Controlled 15-year trial on screening for colorectal cancer in families with hereditary nonpolyposis colorectal cancer. Gastroenterology. 2000;118:829-834. [PMID: 10784581] doi:10.1016/s0016-5085(00)70168-5 CrossrefMedlineGoogle Scholar - 30. National Cancer Institute. Cancer of the Colon and Rectum (Invasive). SEER Cancer Statistics Review 1975–2015. Accessed at https://seer.cancer.gov/archive/csr/1975_2015/results_merged/sect_06_colon_rectum.pdf on 13 December 2020. Google Scholar
- 31.
Khera AV ,Won HH ,Peloso GM ,et al . Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol. 2016;67:2578-2589. [PMID: 27050191] doi:10.1016/j.jacc.2016.03.520 CrossrefMedlineGoogle Scholar - 32.
D'Agostino RB ,Vasan RS ,Pencina MJ ,et al . General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743-753. [PMID: 18212285] doi:10.1161/CIRCULATIONAHA.107.699579 CrossrefMedlineGoogle Scholar - 33.
Domchek SM ,Friebel TM ,Singer CF ,et al . Association of risk-reducing surgery in BRCA1 or BRCA2 mutation carriers with cancer risk and mortality. JAMA. 2010;304:967-975. [PMID: 20810374] doi:10.1001/jama.2010.1237 CrossrefMedlineGoogle Scholar - 34.
Chai X ,Friebel TM ,Singer CF ,et al . Use of risk-reducing surgeries in a prospective cohort of 1,499 BRCA1 and BRCA2 mutation carriers. Breast Cancer Res Treat. 2014;148:397-406. [PMID: 25311111] doi:10.1007/s10549-014-3134-0 CrossrefMedlineGoogle Scholar - 35.
Stupart DA ,Goldberg PA ,Algar U ,et al . Cancer risk in a cohort of subjects carrying a single mismatch repair gene mutation. Fam Cancer. 2009;8:519-523. [PMID: 19688281] doi:10.1007/s10689-009-9281-5 CrossrefMedlineGoogle Scholar - 36.
Palomaki GE ,McClain MR ,Melillo S ,et al . EGAPP supplementary evidence review: DNA testing strategies aimed at reducing morbidity and mortality from Lynch syndrome. Genet Med. 2009;11:42-65. [PMID: 19125127] doi:10.1097/GIM.0b013e31818fa2db CrossrefMedlineGoogle Scholar - 37.
Snowsill T ,Huxley N ,Hoyle M ,et al . A model-based assessment of the cost-utility of strategies to identify Lynch syndrome in early-onset colorectal cancer patients. BMC Cancer. 2015;15:313. [PMID: 25910169] doi:10.1186/s12885-015-1254-5 CrossrefMedlineGoogle Scholar - 38. Color Genomics. Color Extended. Accessed at https://home.color.com/purchase/ordering-physician?sku=combo%203 on 1 March 2020. Google Scholar
- 39. Invitae Corporation. Billing. Accessed at www.invitae.com/en/providers/billing on 7 January 2023. Google Scholar
- 40.
Perrier L ,Heinz D ,Baffert S ,et al . Cost of genome analysis: the Sanger sequencing method. Value Health. 2015;18:A353. doi:10.1016/j.jval.2015.09.654 CrossrefGoogle Scholar - 41.
Roberson ML ,Padi-Adjirackor NA ,Hooker G ,et al . Evaluating costs associated with genetic counseling among commercially insured US patients with cancer from 2013 to 2019. JAMA Health Forum. 2022;3:e222260. [PMID: 35983580] doi:10.1001/jamahealthforum.2022.2260 CrossrefMedlineGoogle Scholar - 42.
Buchanan AH ,Datta SK ,Skinner CS ,et al . Randomized trial of telegenetics vs. in-person cancer genetic counseling: cost, patient satisfaction and attendance. J Genet Couns. 2015;24:961-970. [PMID: 25833335] doi:10.1007/s10897-015-9836-6 CrossrefMedlineGoogle Scholar - 43.
Sun L ,Brentnall A ,Patel S ,et al . A cost-effectiveness analysis of multigene testing for all patients with breast cancer. JAMA Oncol. 2019;5:1718-1730. [PMID: 31580391] doi:10.1001/jamaoncol.2019.3323 CrossrefMedlineGoogle Scholar - 44.
Dinh TA ,Rosner BI ,Atwood JC ,et al . Health benefits and cost-effectiveness of primary genetic screening for Lynch syndrome in the general population. Cancer Prev Res (Phila). 2011;4:9-22. [PMID: 21088223] doi:10.1158/1940-6207.CAPR-10-0262 CrossrefMedlineGoogle Scholar - 45. Zauber AG, Lansdorp-Vogelaar I, Wilschut J, et al. Cost-Effectiveness of DNA Stool Testing to Screen for Colorectal Cancer. Agency for Healthcare Research and Quality; 2007. [PMID: 25879132] Google Scholar
- 46.
O'Sullivan AK ,Rubin J ,Nyambose J ,et al . Cost estimation of cardiovascular disease events in the US. Pharmacoeconomics. 2011;29:693-704. [PMID: 21585226] doi:10.2165/11584620-000000000-00000 CrossrefMedlineGoogle Scholar - 47.
Pandya A ,Sy S ,Cho S ,et al . Cost-effectiveness of 10-year risk thresholds for initiation of statin therapy for primary prevention of cardiovascular disease. JAMA. 2015;314:142-150. [PMID: 26172894] doi:10.1001/jama.2015.6822 CrossrefMedlineGoogle Scholar - 48.
Djalalov S ,Rabeneck L ,Tomlinson G ,et al . A review and meta-analysis of colorectal cancer utilities. Med Decis Making. 2014;34:809-818. [PMID: 24903121] doi:10.1177/0272989X14536779 CrossrefMedlineGoogle Scholar - 49.
Havrilesky LJ ,Broadwater G ,Davis DM ,et al . Determination of quality of life-related utilities for health states relevant to ovarian cancer diagnosis and treatment. Gynecol Oncol. 2009;113:216-220 [PMID: 19217148] doi:10.1016/j.ygyno.2008.12.026 CrossrefMedlineGoogle Scholar - 50.
Peasgood T ,Ward SE ,Brazier J . Health-state utility values in breast cancer. Expert Rev Pharmacoecon Outcomes Res. 2010;10:553-566. [PMID: 20950071] doi:10.1586/erp.10.65 CrossrefMedlineGoogle Scholar - 51.
Li Y ,Arellano AR ,Bare LA ,et al . A multigene test could cost-effectively help extend life expectancy for women at risk of hereditary breast cancer. Value Health. 2017;20:547-555. [PMID: 28407996] doi:10.1016/j.jval.2017.01.006 CrossrefMedlineGoogle Scholar - 52.
Galper BZ ,Wang YC ,Einstein AJ . Strategies for primary prevention of coronary heart disease based on risk stratification by the ACC/AHA lipid guidelines, ATP III guidelines, coronary calcium scoring, and C-reactive protein, and a global treat-all strategy: a comparative-effectiveness modeling study. PLoS One. 2015;10:e0138092. [PMID: 26422204] doi:10.1371/journal.pone.0138092 CrossrefMedlineGoogle Scholar - 53.
Gandra SR ,Villa G ,Fonarow GC ,et al . Cost-effectiveness of LDL-C lowering with evolocumab in patients with high cardiovascular risk in the United States. Clin Cardiol. 2016;39:313-320 [PMID: 27092712] doi:10.1002/clc.22535 CrossrefMedlineGoogle Scholar - 54.
Lin L ,Teng M ,Zhao YJ ,et al . Long-term cost-effectiveness of statin treatment for primary prevention of cardiovascular disease in the elderly. Cardiovasc Drugs Ther. 2015;29:187-197. [PMID: 25860556] doi:10.1007/s10557-015-6584-7 CrossrefMedlineGoogle Scholar - 55.
Cyhaniuk A ,Coombes ME . Longitudinal adherence to colorectal cancer screening guidelines. Am J Manag Care. 2016;22:105-111. [PMID: 26885670] MedlineGoogle Scholar - 56.
Veenstra DL ,Guzauskas G ,Peterson J ,et al . Cost-effectiveness of population genomic screening [Letter]. Genet Med. 2019;21:2840-2841. [PMID: 31303645] doi:10.1038/s41436-019-0580-4 CrossrefMedlineGoogle Scholar - 57.
Burke W ,Parens E ,Chung WK ,et al . The challenge of genetic variants of uncertain clinical significance: a narrative review. Ann Intern Med. 2022;175:994-1000. [PMID: 35436152] doi:10.7326/M21-4109 LinkGoogle Scholar - 58.
Kelly MA ,Leader JB ,Wain KE ,et al . Leveraging population-based exome screening to impact clinical care: the evolution of variant assessment in the Geisinger MyCode research project. Am J Med Genet C Semin Med Genet. 2021;187:83-94. [PMID: 33576083] doi:10.1002/ajmg.c.31887 CrossrefMedlineGoogle Scholar - 59.
Murray MF ,Giovanni MA ,Doyle DL ,et al ;ACMG Board of Directors . DNA-based screening and population health: a points to consider statement for programs and sponsoring organizations from the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23:989-995. [PMID: 33727704] doi:10.1038/s41436-020-01082-w CrossrefMedlineGoogle Scholar
Author, Article, and Disclosure Information
Gregory F. Guzauskas,
The CHOICE Institute, Department of Pharmacy, University of Washington, Seattle, Washington (G.F.G., S.J.)
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee (S.G., J.S.S.)
Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee (Z.Z., J.A.G.)
Department of Genomic Health, Geisinger, Danville, Pennsylvania (M.S.W.)
Department of Genomic Health and Department of Population Health Sciences, Geisinger, Danville, Pennsylvania (J.H.)
Department of Population Health Sciences and Heart Institute, Geisinger, Danville, Pennsylvania (L.K.J.)
Institute for Public Health Genetics, University of Washington, Seattle, Washington (S.J.S.)
The CHOICE Institute, Department of Pharmacy, and Institute for Public Health Genetics, University of Washington, Seattle, Washington (D.L.V.)
Department of Biomedical Informatics and Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (J.F.P.).
Acknowledgment: The authors thank Hadley Stevens Smith, PhD, MPSA, for her valuable contributions to the cascade testing module.
Grant Support: By grant R01 HG009694 from the National Human Genome Research Institute.
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-0846.
Reproducible Research Statement:Study protocol, statistical code, and data set: The model structure and inputs are fully represented in the Supplement and in the referenced submodel publications. The modeling code is not available separately. Readers interested in recreating the model should contact Dr. Guzauskas (e-mail, greguz@uw.
Corresponding Author: Josh F. Peterson, MD, MPH, Director, Center for Precision Medicine, Professor of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1500, Nashville, TN 37203; e-mail, josh.
Author Contributions: Conception and design: G.F. Guzauskas, S. Garbett, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, S.J. Spencer, D.L. Veenstra, J.F. Peterson.
Analysis and interpretation of the data: G.F. Guzauskas, S. Garbett, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Drafting of the article: G.F. Guzauskas, S. Garbett, J.A. Graves, S.J. Spencer, J.F. Peterson.
Critical revision for important intellectual content: G.F. Guzauskas, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Final approval of the article: G.F. Guzauskas, S. Garbett, Z. Zhou, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Statistical expertise: G.F. Guzauskas, S. Garbett, J.S. Schildcrout, J.A. Graves, S.J. Spencer, D.L. Veenstra.
Obtaining of funding: J.A. Graves, M.S. Williams, J. Hao, D.L. Veenstra, J.F. Peterson.
Administrative, technical, or logistic support: J. Hao, D.L. Veenstra, J.F. Peterson.
Collection and assembly of data: G.F. Guzauskas, Z. Zhou, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
This article was published at Annals.org on 9 May 2023.
* Drs. Veenstra and Peterson contributed equally to this work.
Submit a Comment
Contributors must reveal any conflict of interest. Comments are moderated. Please see our information for authorsregarding comments on an Annals publication.
*All comments submitted after October 1, 2021 and selected for publication will be published online only.