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Pathways to ovarian cancer diagnosis: a qualitative study

BACKGROUND: Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis. METHODS: We conducted semi-structured...

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Autores principales: Lawson-Michod, Katherine A., Watt, Melissa H., Grieshober, Laurie, Green, Sarah E., Karabegovic, Lea, Derzon, Samantha, Owens, Makelle, McCarty, Rachel D., Doherty, Jennifer A., Barnard, Mollie E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636716/
https://www.ncbi.nlm.nih.gov/pubmed/36333689
http://dx.doi.org/10.1186/s12905-022-02016-1
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author Lawson-Michod, Katherine A.
Watt, Melissa H.
Grieshober, Laurie
Green, Sarah E.
Karabegovic, Lea
Derzon, Samantha
Owens, Makelle
McCarty, Rachel D.
Doherty, Jennifer A.
Barnard, Mollie E.
author_facet Lawson-Michod, Katherine A.
Watt, Melissa H.
Grieshober, Laurie
Green, Sarah E.
Karabegovic, Lea
Derzon, Samantha
Owens, Makelle
McCarty, Rachel D.
Doherty, Jennifer A.
Barnard, Mollie E.
author_sort Lawson-Michod, Katherine A.
collection PubMed
description BACKGROUND: Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis. METHODS: We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews. RESULTS: The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis. CONCLUSION: Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12905-022-02016-1.
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spelling pubmed-96367162022-11-06 Pathways to ovarian cancer diagnosis: a qualitative study Lawson-Michod, Katherine A. Watt, Melissa H. Grieshober, Laurie Green, Sarah E. Karabegovic, Lea Derzon, Samantha Owens, Makelle McCarty, Rachel D. Doherty, Jennifer A. Barnard, Mollie E. BMC Womens Health Research BACKGROUND: Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis. METHODS: We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews. RESULTS: The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis. CONCLUSION: Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12905-022-02016-1. BioMed Central 2022-11-04 /pmc/articles/PMC9636716/ /pubmed/36333689 http://dx.doi.org/10.1186/s12905-022-02016-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lawson-Michod, Katherine A.
Watt, Melissa H.
Grieshober, Laurie
Green, Sarah E.
Karabegovic, Lea
Derzon, Samantha
Owens, Makelle
McCarty, Rachel D.
Doherty, Jennifer A.
Barnard, Mollie E.
Pathways to ovarian cancer diagnosis: a qualitative study
title Pathways to ovarian cancer diagnosis: a qualitative study
title_full Pathways to ovarian cancer diagnosis: a qualitative study
title_fullStr Pathways to ovarian cancer diagnosis: a qualitative study
title_full_unstemmed Pathways to ovarian cancer diagnosis: a qualitative study
title_short Pathways to ovarian cancer diagnosis: a qualitative study
title_sort pathways to ovarian cancer diagnosis: a qualitative study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636716/
https://www.ncbi.nlm.nih.gov/pubmed/36333689
http://dx.doi.org/10.1186/s12905-022-02016-1
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