Skip to main content
SpringerLink
Log in

Physician and informal care use explained by the Pediatric Quality of Life Inventory (PedsQL) in children with suspected genetic disorders

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

To examine associations between Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales with formal health care resource utilization (HCRU) and informal caregiver burden.

Methods

We studied a pediatric cohort of 837 patients (median age: 8.4 years) with suspected genetic disorders enrolled January 2019 through July 2021 in the NYCKidSeq program for diagnostic sequencing. Using linked ~ nine-month longitudinal survey and physician claims data collected through May 2022, we modeled the association between baseline PedsQL scores and post-baseline HCRU (median follow-up: 21.1 months) and informal care. We also assessed the longitudinal change in PedsQL scores with physician services using linear mixed-effects models.

Results

Lower PedsQL total and physical health scores were independently associated with increases in 18-month physician services, encounters, and weekly informal care. Comparing low vs. median total scores, increases were 10.6 services (95% CI: 1.0-24.6), 3.3 encounters (95% CI: 0.5–6.8), and $668 (95% CI: $350–965), respectively. For the psychosocial domain, higher scores were associated with decreased informal care. Based on adjusted linear mixed-effects modeling, every additional ten physician services was associated with diminished improvement in longitudinal PedsQL total score trajectories by 1.1 point (95% confidence interval: 0.6–1.6) on average. Similar trends were observed in the physical and psychosocial domains.

Conclusion

PedsQL scores were independently associated with higher utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores became less favorable with increased physician services. Adding PedsQL survey instruments to conventional measures for improved risk stratification should be evaluated in further research.

Plain English summary

The Pediatric Quality of Life Inventory (PedsQL) is widely used to measure health-related quality of life in pediatric patients; however, few studies have examined whether the PedsQL is indicative of longitudinal outcomes of morbidity and health care needs. This study captures associations between PedsQL scores with utilization of physician and informal care in children with suspected genetic disorders. We demonstrate that lower PedsQL total and physical health scores are independently associated with greater utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores become less favorable with increased physician services. Results can inform future applications of PedsQL instruments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Connolly, M. A., & Johnson, J. A. (1999). Measuring quality of life in paediatric patients. Pharmacoeconomics, 16(6), 605–625.

    Article  CAS  PubMed  Google Scholar 

  2. Gold, M., Franks, P., & Erickson, P. (1996). Assessing the health of the nation: The predictive validity of a preference-based measure and self-rated health. Medical Care, 34(2), 163–177.

  3. Snyder, C. F., Aaronson, N. K., Choucair, A. K., Elliott, T. E., Greenhalgh, J., Halyard, M. Y., Hess, R., Miller, D. M., Reeve, B. B., & Santana, M. (2012). Implementing patient-reported outcomes assessment in clinical practice: A review of the options and considerations. Quality of Life Research, 21(8), 1305–1314.

    Article  PubMed  Google Scholar 

  4. Kaplan, R. M., & Hays, R. D. (2022). Health-related quality of life measurement in public health. Annual Review of Public Health, 43, 355–373.

  5. Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Medical Care, 39(8), 800–812.

    Article  CAS  PubMed  Google Scholar 

  6. Varni, J. W., Limbers, C. A., Neighbors, K., Schulz, K., Lieu, J. E., Heffer, R. W., Tuzinkiewicz, K., Mangione-Smith, R., Zimmerman, J. J., & Alonso, E. M. (2011). The PedsQL Infant scales: Feasibility, internal consistency reliability, and validity in healthy and ill infants. Quality of Life Research, 20(1), 45–55.

    Article  PubMed  Google Scholar 

  7. Varni, J. W., Burwinkle, T. M., Seid, M., & Skarr, D. (2003). The PedsQL 4.0 as a pediatric population health measure: Feasibility, reliability, and validity. Ambulatory Pediatrics, 3(6), 329–341.

    Article  PubMed  Google Scholar 

  8. Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). Parent proxy-report of their children’s health-related quality of life: An analysis of 13,878 parents’ reliability and validity across age subgroups using the PedsQL 4.0 generic core scales. Health and Quality of Life Outcomes, 5, 2.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Aspesberro, F., Fesinmeyer, M. D., Zhou, C., Zimmerman, J. J., & Mangione-Smith, R. (2016). Construct validity and responsiveness of the Pediatric Quality of Life Inventory 4.0 generic core scales and Infant scales in the PICU. Pediatric Critical Care Medicine : A Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 17(6), e272–279.

    Article  PubMed  Google Scholar 

  10. Desai, A. D., Zhou, C., Stanford, S., Haaland, W., Varni, J. W., & Mangione-Smith, R. M. (2014). Validity and responsiveness of the pediatric quality of life inventory (PedsQL) 4.0 generic core scales in the pediatric inpatient setting. JAMA Pediatr, 168(12), 1114–1121.

    Article  PubMed  Google Scholar 

  11. Meert, K. L., Reeder, R., Maddux, A. B., Banks, R., Berg, R. A., Zuppa, A., Newth, C. J., Wessel, D., Pollack, M. M., Hall, M. W., Quasney, M., Sapru, A., Carcillo, J. A., McQuillen, P. S., Mourani, P. M., Chima, R. S., Holubkov, R., Sorenson, S., Varni, J. W., McGalliard, J., Haaland, W., Whitlock, K. B., Dean, J. M., & Zimmerman, J. J. (2020). Trajectories and risk factors for altered physical and psychosocial health-related quality of life after pediatric community-acquired septic shock. Pediatric Critical Care Medicine: A Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 21(10), 869–878. 

  12. Ikeda, E., Hinckson, E., & Krageloh, C. (2014). Assessment of quality of life in children and youth with autism spectrum disorder: A critical review. Quality of Life Research, 23(4), 1069–1085.

    Article  PubMed  Google Scholar 

  13. Fitzpatrick, S. E., Schmitt, L. M., Adams, R., Pedapati, E. V., Wink, L. K., Shaffer, R. C., Sage, J., Weber, J. D., Dominick, K. C., & Erickson, C. A. (2020). Pediatric Quality of Life Inventory (PedsQL) in fragile X syndrome. Journal of Autism and Developmental Disorders, 50(3), 1056–1063.

  14. Coffman, M. C., Shaffer, R. C., Schmitt, L. M., Dominick, K. C., Pedapati, E., Wang, A., Berry-Kravis, E., Tartaglia, N., & Erickson, C. A. (2020). Examination of correlates to health-related quality of life in individuals with fragile X syndrome. Brain Sci, 10(4).

  15. Bolbocean, C., Andujar, F. N., McCormack, M., Suter, B., & Holder, J. L. Jr. (2022). Health-related quality of life in pediatric patients with syndromic autism and their caregivers. Journal of Autism and Developmental Disorders, 52(3), 1334–1345.

  16. Chiu, A. T. G., Wong, S. S. N., Wong, N. W. T., Wong, W. H. S., Tso, W. W. Y., & Fung, C. W. (2022). Quality of life and symptom burden in children with neurodegenerative diseases: Using PedsQL and SProND, a new symptom-based scale. Orphanet Journal of Rare Diseases, 17(1), 334.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Limbers, C. A., Heffer, R. W., & Varni, J. W. (2009). Health-related quality of life and cognitive functioning from the perspective of parents of school-aged children with Asperger’s syndrome utilizing the PedsQL. Journal of Autism and Developmental Disorders, 39(11), 1529–1541.

    Article  PubMed  Google Scholar 

  18. Smith, H. S., Leo, M., Goddard, K., Muessig, K., Angelo, F., Knight, S., Outram, S., Kelly, N. R., & Rini, C. (2024). Measuring health-related quality of life in children with suspected genetic conditions: Validation of the PedsQL proxy-report versions. Quality of Life Research.

  19. Amendola, L. M., Berg, J. S., Horowitz, C. R., Angelo, F., Bensen, J. T., Biesecker, B. B., Biesecker, L. G., Cooper, G. M., East, K., Filipski, K., Fullerton, S. M., Gelb, B. D., Goddard, K. A. B., Hailu, B., Hart, R., Hassmiller-Lich, K., Joseph, G., Kenny, E. E., Koenig, B. A., Knight, S., Kwok, P. Y., Lewis, K. L., McGuire, A. L., Norton, M. E., Ou, J., Parsons, D. W., Powell, B. C., Risch, N., Robinson, M., Rini, C., Scollon, S., Slavotinek, A. M., Veenstra, D. L., Wasserstein, M. P., Wilfond, B. S., Hindorff, L. A., consortium, Plon, C., S. E., & Jarvik, G. P. (2018). The Clinical Sequencing Evidence-Generating Research Consortium: Integrating genomic sequencing in diverse and medically underserved populations. Am J Hum Genet, 103(3), 319–327.

  20. Odgis, J. A., Gallagher, K. M., Suckiel, S. A., Donohue, K. E., Ramos, M. A., Kelly, N. R., Bertier, G., Blackburn, C., Brown, K., Fielding, L., Lopez, J., Aguiniga, K. L., Maria, E., Rodriguez, J. E., Sebastin, M., Teitelman, N., Watnick, D., Yelton, N. M., Abhyankar, A., Abul-Husn, N. S., Baum, A., Bauman, L. J., Beal, J. C., Bloom, T., Cunningham-Rundles, C., Diaz, G. A., Dolan, S., Ferket, B. S., Jobanputra, V., Kovatch, P., McDonald, T. V., McGoldrick, P. E., Rhodes, R., Rinke, M. L., Robinson, M., Rubinstein, A., Shulman, L. H., Stolte, C., Wolf, S. M., Yozawitz, E., Zinberg, R. E., Greally, J. M., Gelb, B. D., Horowitz, C. R., Wasserstein, M. P., & Kenny, E. E. (2021). The NYCKidSeq project: Study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children. Trials, 22(1), 56.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sebastin, M., Odgis, J. A., Suckiel, S. A., Bonini, K. E., Di Biase, M., Brown, K., Marathe, P., Kelly, N. R., Ramos, M. A., Rodriguez, J. E., Aguiniga, K. L., Lopez, J., Maria, E., Rodriguez, M. A., Yelton, N. M., Cunningham-Rundles, C., Gallagher, K., McDonald, T. V., McGoldrick, P. E., Robinson, M., Rubinstein, A., Shulman, L. H., Wolf, S. M., Yozawitz, E., Zinberg, R. E., Abul-Husn, N. S., Bauman, L. J., Diaz, G. A., Ferket, B. S., Greally, J. M., Jobanputra, V., Gelb, B. D., Horowitz, C. R., Kenny, E. E., & Wasserstein, M. P. (2023). The TeleKidSeq pilot study: Incorporating telehealth into clinical care of children from diverse backgrounds undergoing whole genome sequencing. Pilot Feasibility Stud, 9(1), 47.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hebert, P. L., Sisk, J. E., Wang, J. J., Tuzzio, L., Casabianca, J. M., Chassin, M. R., Horowitz, C., & McLaughlin, M. A. (2008). Cost-effectiveness of nurse-led disease management for heart failure in an ethnically diverse urban community. Annals of Internal Medicine, 149(8), 540–548.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hoefman, R., Van Exel, N., & Brouwer, W. (2013). iMTA valuation of informal care questionnaire. iVICQ. iMTA.

  24. AAMC-Clinical Practice Solutions Center®. Irving, TX: Vizient, Inc. (2023). https://www.vizientinc.com. Accessed April 5, 2024.

  25. Liptak, G. S., Shone, L. P., Auinger, P., Dick, A. W., Ryan, S. A., & Szilagyi, P. G. (2006). Short-term persistence of high health care costs in a nationally representative sample of children. Pediatrics, 118(4), e1001–1009.

    Article  PubMed  Google Scholar 

  26. Oldfield, B. J., Pasha, S., Mun, S., Sedghi, T., Zhu, W., DeCew, A., Flaherty-Hewitt, M., & Olson, D. P. (2021). Construction of a pediatrics risk score to predict high health care costs among a community health center cohort. Popul Health Manag, 24(3), 345–352.

  27. Wijeysundera, H. C., Wang, X., Tomlinson, G., Ko, D. T., & Krahn, M. D. (2012). Techniques for estimating health care costs with censored data: An overview for the health services researcher. Clinicoecon Outcomes Res, 4, 145–155.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Williams, R. (2012). Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal, 12(2), 308–331.

  29. Fleishman, J. A., Cohen, J. W., Manning, W. G., & Kosinski, M. (2006). Using the SF-12 health status measure to improve predictions of medical expenditures. Medical Care, 44(5 Suppl), I54–63.

    PubMed  Google Scholar 

  30. Dorr, D. A., Jones, S. S., Burns, L., Donnelly, S. M., Brunker, C. P., Wilcox, A., & Clayton, P. D. (2006). Use of health-related, quality-of-life metrics to predict mortality and hospitalizations in community-dwelling seniors. Journal of the American Geriatrics Society, 54(4), 667–673.

    Article  PubMed  Google Scholar 

  31. Wherry, L. R., Burns, M. E., & Leininger, L. J. (2014). Using self-reported health measures to predict high-need cases among Medicaid-eligible adults. Health Services Research, 49(Suppl 2(Suppl 2), 2147–2172.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Frakking, T. T., Teoh, H. J., Shelton, D., Moloney, S., Ward, D., Annetts, K., David, M., Levitt, D., Chang, A. B., Carty, C., Barber, M., Carter, H. E., Mickan, S., Weir, K. A., & Waugh, J. (2022). Effect of care coordination using an allied health liaison officer for chronic noncomplex medical conditions in children: A multicenter randomized clinical trial. JAMA Pediatr, 176(3), 244–252.

  33. Council on Children with, D., & Medical Home Implementation Project Advisory. (2014). Patient- and family-centered care coordination: A framework for integrating care for children and youth across multiple systems. Pediatrics, 133(5), e1451–1460.

    Article  Google Scholar 

  34. Mokkink, L., Terwee, C., & de Vet, H. (2021). Key concepts in clinical epidemiology: Responsiveness, the longitudinal aspect of validity. Journal of Clinical Epidemiology, 140, 159–162.

    Article  PubMed  Google Scholar 

  35. Upton, P., Eiser, C., Cheung, I., Hutchings, H. A., Jenney, M., Maddocks, A., Russell, I. T., & Williams, J. G. (2005). Measurement properties of the UK-English version of the Pediatric Quality of Life Inventory 4.0 (PedsQL) generic core scales. Health and Quality of Life Outcomes, 3, 22.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Varni, J. W., Sherman, S. A., Burwinkle, T. M., Dickinson, P. E., & Dixon, P. (2004). The PedsQL Family Impact Module: Preliminary reliability and validity. Health and Quality of Life Outcomes, 2, 55.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lipstein, E. A., Brinkman, W. B., Fiks, A. G., Hendrix, K. S., Kryworuchko, J., Miller, V. A., Prosser, L. A., Ungar, W. J., & Fox, D. (2015). An emerging field of research: Challenges in pediatric decision making. Medical Decision Making, 35(3), 403–408.

    Article  PubMed  Google Scholar 

  38. Zarit, S. H., Todd, P. A., & Zarit, J. M. (1986). Subjective burden of husbands and wives as caregivers: A longitudinal study. The Gerontologist, 26(3), 260–266.

    Article  CAS  PubMed  Google Scholar 

  39. Dang, S., Badiye, A., & Kelkar, G. (2008). The dementia caregiver–a primary care approach. Southern Medical Journal, 101(12), 1246–1251.

    Article  PubMed  Google Scholar 

  40. Sanders, G. D., Neumann, P. J., Basu, A., Brock, D. W., Feeny, D., Krahn, M., Kuntz, K. M., Meltzer, D. O., Owens, D. K., Prosser, L. A., Salomon, J. A., Sculpher, M. J., Trikalinos, T. A., Russell, L. B., Siegel, J. E., & Ganiats, T. G. (2016). Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second panel on cost-effectiveness in health and medicine. Journal of the American Medical Association, 316(10), 1093–1103.

  41. Lambe, T., Frew, E., Ives, N. J., Woolley, R. L., Cummins, C., Brettell, E. A., Barsoum, E. N., Webb, N. J. A., & Team, P. T. (2018). Mapping the Paediatric Quality of Life Inventory (PedsQL) Generic Core Scales onto the Child Health Utility Index-9 dimension (CHU-9D) score for economic evaluation in children. Pharmacoeconomics, 36(4), 451–465.

    Article  PubMed  Google Scholar 

  42. Khan, K. A., Petrou, S., Rivero-Arias, O., Walters, S. J., & Boyle, S. E. (2014). Mapping EQ-5D utility scores from the PedsQL generic core scales. Pharmacoeconomics, 32(7), 693–706.

    Article  PubMed  Google Scholar 

  43. Matiz, L. A., Robbins-Milne, L., & Rausch, J. A. (2019). EMR adaptations to support the identification and risk stratification of children with special health care needs in the medical home. Maternal and Child Health Journal, 23(7), 919–924.

  44. Severin, F., Borry, P., Cornel, M. C., Daniels, N., Fellmann, F., Victoria Hodgson, S., Howard, H. C., John, J., Kaariainen, H., Kayserili, H., Kent, A., Koerber, F., Kristoffersson, U., Kroese, M., Lewis, C., Marckmann, G., Meyer, P., Pfeufer, A., Schmidtke, J., Skirton, H., Tranebjaerg, L., Rogowski, W. H., EuroGentest, & Consortium, E. P. P. (2015). Points to consider for prioritizing clinical genetic testing services: A European consensus process oriented at accountability for reasonableness. European Journal of Human Genetics, 23(6), 729–735.

    Article  PubMed  Google Scholar 

  45. Doble, B., Schofield, D. J., Roscioli, T., & Mattick, J. S. (2017). Prioritising the application of genomic medicine. NPJ Genom Med, 2, 35.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cohen, E., Berry, J. G., Camacho, X., Anderson, G., Wodchis, W., & Guttmann, A. (2012). Patterns and costs of health care use of children with medical complexity. Pediatrics, 130(6), e1463–1470.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Miller, T. R., Elliott, T. R., McMaughan, D. M., Patnaik, A., Naiser, E., Dyer, J. A., Fournier, C. J., Hawes, C., & Phillips, C. D. (2013). Personal care services provided to children with special health care needs (CSHCN) and their subsequent use of physician services. Disabil Health J, 6(4), 317–324.

    Article  PubMed  Google Scholar 

  48. Sobotka, S. A., Hall, D. E., Thurm, C., Gay, J., & Berry, J. G. (2022). Home health care utilization in children with Medicaid. Pediatrics, 149(2).

  49. Simpser, E., & Hudak, M. L. & Section on Home Care, C. O. C. H. F. (2017). Financing of pediatric home health care. Pediatrics, 139(3).

  50. Wolfe, J., Orellana, L., Cook, E. F., Ullrich, C., Kang, T., Geyer, J. R., Feudtner, C., Weeks, J. C., & Dussel, V. (2014). Improving the care of children with advanced cancer by using an electronic patient-reported feedback intervention: Results from the PediQUEST randomized controlled trial. Journal of Clinical Oncology, 32(11), 1119–1126.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bevans, K. B., Moon, J., Carle, A. C., Mara, C. A., Lai, J. S., DiMarco, L., Muller, N., & Woods, D. (2014). Patient reported outcomes as indicators of pediatric health care quality. Academic Pediatric, 14(5 Suppl), S90–96.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank all of the children, parents, and families who participated in this study. The authors also thank the New York Genome Center, Rady Children’s Institute for Genomic Medicine, and Sema4 laboratory; the members of the Mount Sinai Hospital Genomics Stakeholder Board; referring physicians in the Mount Sinai and Montefiore Health Systems. In CSER, we aim to improve the use of genetic information in medicine and reduce barriers to genetic services among underserved groups. Our research seeks to better understand connections between genes, other drivers of health and disease, and health outcomes. We have worked with study participants and community partners to help make our research more inclusive. We still have much more work to do to ensure that our findings are applied in fair and just ways. We also acknowledge the need for more diversity among our own researchers. As we publish the results of CSER, we commit to carrying efforts forward to make sure people of all backgrounds benefit from genomic research and medicine.

Funding

This research was supported by the National Human Genome Research Institute (NHGRI) and the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health under Award Number U01HG009610. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funder had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1077, New York, NY, 10029-6574, USA

    Asem Berkalieva & Bart S. Ferket

  2. Division of Pediatric Genetic Medicine, Department of Pediatrics, Children’s Hospital at Montefiore/Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Nicole R. Kelly & Melissa P. Wasserstein

  3. Center for Advanced Analytics, Vizient, Chicago, IL, USA

    Ashley Fisher & Samuel F. Hohmann

  4. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny & Bruce D. Gelb

  5. Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Carol R. Horowitz

  6. Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Carol R. Horowitz

  7. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Noura S. Abul-Husn & Eimear E. Kenny

  8. 23andMe Inc, Sunnyvale, CA, USA

    Noura S. Abul-Husn

  9. Division of Genomics, Department of Genetics, Children’s Hospital at Montefiore/Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

    John M. Greally

  10. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce D. Gelb

  11. Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce D. Gelb

  12. Division for Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Noura S. Abul-Husn & Eimear E. Kenny

Authors
  1. Asem Berkalieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicole R. Kelly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashley Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel F. Hohmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noura S. Abul-Husn
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John M. Greally
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carol R. Horowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Melissa P. Wasserstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eimear E. Kenny
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bruce D. Gelb
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Bart S. Ferket
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AB: Conceptualization, Formal analysis, Investigation, Methodology, Writing-original draft, Writing-review and editing. NRK: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing-original draft, Writing-review and editing. AF: Conceptualization, Data curation, Investigation, Writing-review and editing. SFH: Conceptualization, Data curation, Investigation, Writing-review and editing. NSA-H: Writing-review and editing. JMG: Writing-review and editing. CRH: Funding acquisition, Writing-review and editing. MPW: Funding acquisition, Writing-review and editing. EEK: Funding acquisition, Supervision, Writing-review and editing. BDG: Funding acquisition, Supervision, Writing-review and editing. BSF: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing-original draft, Writing-review and editing.

Corresponding author

Correspondence to Bart S. Ferket.

Ethics declarations

Ethical approval

The NYCKidSeq and TeleKidseq studies were approved by the Icahn School of Medicine at Mount Sinai and the Albert Einstein College of Medicine Institutional Review Boards.

Consent to participate

Written informed consent was obtained from pediatric participants who were capable of providing (≥ 18 years of age and cognitively able) and all parent or legal guardian participants.

Competing interests

Dr. Abul-Husn is an employee and equity holder of 23andMe; serves as a scientific advisory board member for Allelica; received personal fees from Genentech, Allelica, and 23andMe; received research funding from Akcea; and was previously employed by Regeneron Pharmaceuticals. Dr. Wasserstein has received speaker honoraria and consulting fees from Sanofi, and has received research funding from Abeona, Alexion, BioMarin, Orchard, PassageBio, Sio, Takeda, Travere, and Ultragenyx. Dr. Kenny has received speaker honoraria from Illumina, 23&Me, Allelica, and Regeneron Pharmaceuticals, received research funding from Allelica, and serves as a scientific advisory board member for Encompass Biosciences, Overtone, and Galateo Bio. All other authors declare they have no conflicts of interest to report.

Clinical Trial registry name, registration number, and data sharing statement

The NYCKidSeq program (Trial Registration ClinicalTrials.gov Identifier: NCT03738098). The information contained in this article was based in part on data accessed as part of AAMC–Vizient Clinical Practice Solutions Center® subscription services. Claims data are proprietary and therefore cannot be shared. The NYCKidSeq program datasets and statistical codes used during the current study are available from the corresponding authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berkalieva, A., Kelly, N.R., Fisher, A. et al. Physician and informal care use explained by the Pediatric Quality of Life Inventory (PedsQL) in children with suspected genetic disorders. Qual Life Res (2024). https://doi.org/10.1007/s11136-024-03677-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11136-024-03677-1

Keywords

Search

Navigation