Predicting Cancer Mortality: Developing a New Cancer Care Variable Using Mixed Methods and the Quasi-Statistical Approach

Authors

  • Susan L. Zickmund,

    Corresponding author
    1. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA
    2. Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
    • Address correspondence to Susan L. Zickmund, Ph.D., Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 7180 Highland Drive (151C-H), Pittsburgh, PA 15206-1206; e-mail: susan.zickmund@va.gov.

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  • Suzanne Yang,

    1. Mental Illness Research, Education and Clinical Centers, VA Pittsburgh Healthcare System, Pittsburgh, PA
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  • Edward P. Mulvey,

    1. Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA
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  • James E. Bost,

    1. Children's Healthcare of Atlanta, Atlanta, GA
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  • Laura A. Shinkunas,

    1. Program in Bioethics and Humanities, University of Iowa Carver College of Medicine, Iowa City, IA
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  • Douglas R. LaBrecque

    1. Department of Internal Medicine, University of Iowa Healthcare, Iowa City, IA
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Abstract

Objective

To demonstrate the value of using a variable derived from qualitative analysis in subsequent quantitative analyses.

Data Sources/Study Setting

Mixed methods data were combined with 10-year mortality outcomes. Participants with cancer were recruited from services at a large teaching hospital, and mortality data were from the Social Security Death Index.

Study Design

An observational concurrent or convergent mixed methods design was used to collect demographics and structured ratings along with qualitative data from 909 cancer patients at baseline.

Data Collection/Extraction Methods

Coding rules for qualitative data were defined for open-ended responses from cancer participants speaking about their view of self, and a variable was numerically coded for each case. Mortality outcomes were matched to baseline data, including the view of self variable.

Principal Findings

Individuals with an improved view of self had a significantly lower mortality rate than those for whom it was worse or unchanged, even when adjusting for age, gender, and cancer stage.

Conclusions

Statistical analysis of qualitative data is feasible and can identify new predictors with health services' implications associated with cancer mortality. Future studies should consider the value of testing coded qualitative variables in relation with key health care outcomes.

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