Literature DB >> 33106995

Use of patient-reported controls for secular trends to study disparities in cancer-related job loss.

Victoria S Blinder1, Carolyn E Eberle2, Christina Tran3, Ting Bao4, Manmeet Malik5, Gabriel Jung6, Caroline Hwang7, Lewis Kampel8,9, Sujata Patil10, Francesca M Gany3.   

Abstract

PURPOSE: Racial/ethnic minorities experience greater job loss than whites during periods of economic downturn and after a cancer diagnosis. Therefore, race/ethnicity-matched controls are needed to distinguish the impact of illness on job loss from secular trends
METHODS: Surveys were administered during and 4-month post-completion of breast cancer treatment. Patients were pre-diagnosis employed women aged 18-64, undergoing treatment for stage I-III breast cancers, who spoke English, Chinese, Korean, or Spanish. Each patient was asked to: (1) nominate peers who were surveyed in a corresponding timeframe (active controls), (2) report a friend's work status at baseline and follow-up (passive controls). Both types of controls were healthy, employed at baseline, and shared the nominating patient's race/ethnicity, language, and age. The primary outcome was number of evaluable patient-control pairs by type of control. A patient-control pair was evaluable if work status at follow-up was reported for both individuals.
RESULTS: Of the 180 patients, 25% had evaluable active controls (45 patient-control pairs); 84% had evaluable passive controls (151 patient-control pairs). Although patients with controls differed from those without controls under each strategy, there was no difference in the percentage of controls who were working at follow-up (96% of active controls; 91% of passive controls). However, only 65% of patients were working at follow-up.
CONCLUSIONS: The majority of patients had evaluable passive controls. There was no significant difference in outcome between controls ascertained through either method IMPLICATIONS FOR CANCER SURVIVORS: Passive controls are a low-cost, higher-yield option to control for secular trends in racially/ethnically diverse samples.
© 2020. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Disparities; Health economics; Health services research; Immigrant health; Measurement

Mesh:

Year:  2020        PMID: 33106995      PMCID: PMC8071836          DOI: 10.1007/s11764-020-00960-1

Source DB:  PubMed          Journal:  J Cancer Surviv        ISSN: 1932-2259            Impact factor:   4.062


  23 in total

1.  Indirect economic effects of long-term breast cancer survival.

Authors:  Thomas N Chirikos; Anita Russell-Jacobs; Alan B Cantor
Journal:  Cancer Pract       Date:  2002 Sep-Oct

2.  Working after a metastatic cancer diagnosis: Factors affecting employment in the metastatic setting from ECOG-ACRIN's Symptom Outcomes and Practice Patterns study.

Authors:  Amye J Tevaarwerk; Ju-Whei Lee; Abigail Terhaar; Mary E Sesto; Mary Lou Smith; Charles S Cleeland; Michael J Fisch
Journal:  Cancer       Date:  2015-12-21       Impact factor: 6.860

3.  Early predictors of not returning to work in low-income breast cancer survivors: a 5-year longitudinal study.

Authors:  Victoria Blinder; Sujata Patil; Carolyn Eberle; Jennifer Griggs; Rose C Maly
Journal:  Breast Cancer Res Treat       Date:  2013-07-25       Impact factor: 4.872

4.  Characteristics and correlates of fatigue after adjuvant chemotherapy for breast cancer.

Authors:  J A Broeckel; P B Jacobsen; J Horton; L Balducci; G H Lyman
Journal:  J Clin Oncol       Date:  1998-05       Impact factor: 44.544

5.  Women With Breast Cancer Who Work For Accommodating Employers More Likely To Retain Jobs After Treatment.

Authors:  Victoria Blinder; Carolyn Eberle; Sujata Patil; Francesca M Gany; Cathy J Bradley
Journal:  Health Aff (Millwood)       Date:  2017-02-01       Impact factor: 6.301

6.  Trends in the Family Income Distribution by Race/Ethnicity and Income Source, 1988-2009.

Authors:  Shannon M Monnat; Lawrence E Raffalovich; Hui-Shien Tsao
Journal:  Popul Rev       Date:  2012

7.  Financial status, employment, and insurance among older cancer survivors.

Authors:  Marie Norredam; Ellen Meara; Mary Beth Landrum; Haiden A Huskamp; Nancy L Keating
Journal:  J Gen Intern Med       Date:  2009-11       Impact factor: 5.128

8.  Cancer survivors and unemployment: a meta-analysis and meta-regression.

Authors:  Angela G E M de Boer; Taina Taskila; Anneli Ojajärvi; Frank J H van Dijk; Jos H A M Verbeek
Journal:  JAMA       Date:  2009-02-18       Impact factor: 56.272

9.  Recruitment of minority and underserved populations in the United States: the Centers for Population Health and Health Disparities experience.

Authors:  Electra D Paskett; Katherine W Reeves; John M McLaughlin; Mira L Katz; Ann Scheck McAlearney; Mack T Ruffin; Chanita Hughes Halbert; Cristina Merete; Faith Davis; Sarah Gehlert
Journal:  Contemp Clin Trials       Date:  2008-07-31       Impact factor: 2.226

10.  Medical costs and productivity losses of cancer survivors--United States, 2008-2011.

Authors:  Donatus U Ekwueme; K Robin Yabroff; Gery P Guy; Matthew P Banegas; Janet S de Moor; Chunyu Li; Xuesong Han; Zhiyuan Zheng; Anita Soni; Amy Davidoff; Ruth Rechis; Katherine S Virgo
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2014-06-13       Impact factor: 17.586

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  1 in total

1.  Vulnerabilities in workplace features for essential workers with breast cancer: Implications for the COVID-19 pandemic.

Authors:  Madelyn Klugman; Sujata Patil; Francesca Gany; Victoria Blinder
Journal:  Work       Date:  2022
  1 in total

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