Literature DB >> 35880605

Using health insurance claims data to assess long-term disease progression in a prostate cancer cohort.

Saira Khan1,2, Sanah Vohra3, Laura Farnan4, Shekinah N C Elmore4,5, Khadijah Toumbou5, Madhav K C6,7, Elizabeth T H Fontham7, Edward S Peters7,8, James L Mohler9, Jeannette T Bensen3,4.   

Abstract

BACKGROUND: Long-term population-based cohort studies of men diagnosed with prostate cancer are limited. However, adverse outcomes can occur many years after treatment. Herein, we aim to assess the utility of using claims data to identify prostate cancer progression 10-15 years after diagnosis.
METHODS: The study population was derived from the North Carolina-Louisiana Prostate Cancer Project (PCaP). PCaP-North Carolina (NC) included 1031 men diagnosed with prostate cancer from 2004 to 2009. An initial follow-up with a survey and manual medical record abstraction occurred from 2008 to 2011 (Follow-up 1). Herein, we extended this follow-up with linkage to healthcare claims data from North Carolina (2011-2017) and a second, supplementary 10-year follow-up survey (2018-2020) (Follow-up 2). Vital statistics data also were utilized. Long-term oncological progression was determined using these data sources in combination with expert clinical input.
RESULTS: Among the 1031 baseline PCaP-NC participants, 652 were linked to medical claims. Forty-two percent of the men had insurance coverage for the entire 72 months of follow-up. In addition, 275 baseline participants completed the supplementary 10-year follow-up survey. Using all sources of follow-up data, we identified a progression event in 259 of 1031 (25%) men with more than 10 years of follow-up data after diagnosis.
CONCLUSIONS: Understanding long-term clinical outcomes is essential for improving the lives of prostate cancer survivors. However, access and utility of long-term clinical outcomes with claims alone remain a challenge due to individualized agreements required with each insurer for data access, lack of detailed clinical information, and gaps in insurance coverage. We were able to utilize claims data to determine long-term progression due to several unique advantages that included the availability of detailed baseline clinical characteristics and treatments, detailed manually abstracted clinical data at 5 years of follow-up, vital statistics data, and a supplementary 10-year follow-up survey.
© 2022 Wiley Periodicals LLC.

Entities:  

Keywords:  PCaP-NC; insurance claims; long-term follow-up; progression; prostate cancer

Mesh:

Year:  2022        PMID: 35880605      PMCID: PMC9492636          DOI: 10.1002/pros.24418

Source DB:  PubMed          Journal:  Prostate        ISSN: 0270-4137            Impact factor:   4.012


  30 in total

1.  Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database.

Authors:  Changhee Lee; Alexander Light; Ahmed Alaa; David Thurtle; Mihaela van der Schaar; Vincent J Gnanapragasam
Journal:  Lancet Digit Health       Date:  2021-02-03

2.  The Association of Diabetes and Obesity With Prostate Cancer Progression: HCaP-NC.

Authors:  Saira Khan; Jianwen Cai; Matthew E Nielsen; Melissa A Troester; James L Mohler; Elizabeth T H Fontham; Laura H Hendrix; Laura Farnan; Andrew F Olshan; Jeannette T Bensen
Journal:  Prostate       Date:  2017-03-06       Impact factor: 4.104

3.  Randomized recruitment in case-control studies.

Authors:  C R Weinberg; D P Sandler
Journal:  Am J Epidemiol       Date:  1991-08-15       Impact factor: 4.897

4.  Electronic Health Record (EHR) Abstraction.

Authors:  Amal A Alzu'bi; Valerie J M Watzlaf; Patty Sheridan
Journal:  Perspect Health Inf Manag       Date:  2021-03-15

5.  Contemporary Incidence and Outcomes of Prostate Cancer Lymph Node Metastases.

Authors:  Adrien N Bernstein; Jonathan E Shoag; Ron Golan; Joshua A Halpern; Edward M Schaeffer; Wei-Chun Hsu; Paul L Nguyen; Art Sedrakyan; Ronald C Chen; Scott E Eggener; Jim C Hu
Journal:  J Urol       Date:  2017-12-26       Impact factor: 7.450

6.  Cancer statistics, 2016.

Authors:  Rebecca L Siegel; Kimberly D Miller; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2016-01-07       Impact factor: 508.702

7.  Nationally representative trends and geographic variation in treatment of localized prostate cancer: the Urologic Diseases in America project.

Authors:  K C Cary; S Punnen; A Y Odisho; M S Litwin; C S Saigal; M R Cooperberg
Journal:  Prostate Cancer Prostatic Dis       Date:  2015-02-10       Impact factor: 5.554

8.  Prognosis of prostate cancer and bone metastasis pattern of patients: a SEER-based study and a local hospital based study from China.

Authors:  Dongyu Liu; Yue Kuai; Ruohui Zhu; Chenhe Zhou; Yiqing Tao; Weidong Han; Qixin Chen
Journal:  Sci Rep       Date:  2020-06-04       Impact factor: 4.379

9.  Treatment patterns of prostate cancer with bone metastasis in Beijing: A real-world study using data from an administrative claims database.

Authors:  Yinchu Cheng; Lin Zhuo; Yuting Pan; Shengfeng Wang; Jihong Zong; Wentao Sun; Shuangqing Gao; Jian Lu; Siyan Zhan
Journal:  Pharmacoepidemiol Drug Saf       Date:  2019-08-08       Impact factor: 2.890

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