The Effect of COVID-19 Pandemic on Adolescent and Young Adult Cancer Patients and Survivors

Study Overview
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Brief Summary
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The study investigates how the COVID-19 pandemic has impacted the psychological, financial, physical, and social well-being of adolescent and young adult (AYA) cancer patients and survivors. AYA cancer survivors have inferior long-term survival compared to the general population, and the negative impact of the global COVID-19 pandemic may be even higher in this vulnerable group. The information gained from this study may provide an opportunity to determine the self-reported COVID-19 specific psychological distress in AYA cancer survivors, and may lead to the development of a targeted intervention to improve physical and psychosocial health for AYA cancer patients and survivors.

Detailed Description
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PRIMARY OBJECTIVE: I. To determine the self-reported coronavirus disease 2019 (COVID-19) specific psychological distress in adolescent and young adult (AYA) cancer survivors diagnosed between the ages of 15 to 39 and are currently between the ages of 18 to 39. SECONDARY OBJECTIVES: I. To determine the COVID-19 specific health care utilization, health behavior, financial and social disruptions, and health-related quality of life (HRQoL). II. To determine associations between patient demographic and treatment-related variables with COVID-19 specific psychological distress, healthcare utilization, health behavior, financial and social disruptions, and HRQoL. III. To determine associations between resilience factors (i.e., social support, perceived benefits under times of stress, and the ability to manage stress) with self-reported COVID-19 specific psychological distress, healthcare utilization, health behavior, financial and social disruptions, and HRQoL. IV. To determine the changes in COVID-19 specific psychosocial distress, healthcare utilization, health behavior, financial, and social disruptions. OUTLINE: Patients and survivors complete a survey online over 20-30 minutes at baseline about COVID-19 specific psychological distress, health care utilization, health behavior, social and financial disruptions, HRQoL, their social support, perceived benefits under times of stress, and the ability to manage stress. Patients and survivors may be contacted again at 6 months and 1 year for COVID-19 research.

Official Title
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Impacts of Coronavirus Disease 2019 (COVID-19) Pandemic on Adolescent and Young Adult (AYA) Cancer Patients and Survivors

Conditions
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COVID-19 Infection, Hematopoietic and Lymphoid Cell Neoplasm, Malignant Solid Neoplasm

Intervention / Treatment
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* Other: Quality-of-Life Assessment
* Other: Survey Administration


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: PATIENT COHORT INCLUSION: Initial cancer diagnosis between the ages of 15 to 39 Received any cancer treatment at MD Anderson Cancer Center with data available in the MD Anderson Cancer Center Tumor Registry For questionnaire provision: confirmed alive at time of contact Exclusion Criteria: PATIENT COHORT EXCLUSION: Inability to complete questionnaires in English Seen at MD Anderson for a second opinion or non-treatment related visit

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 39 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Observational (survey)<br>Patients and survivors complete a survey online over 20-30 minutes at baseline about COVID-19 specific psychological distress, health care utilization, health behavior, social and financial disruptions, HRQoL, their social support, perceived benefits under times of stress, and the ability to manage stress. Patients and survivors may be contacted again at 6 months and 1 year for COVID-19 research. | Other: Quality-of-Life Assessment<br>* Ancillary studies<br>* Other names: Quality of Life Assessment;Other: Survey Administration<br>* Complete survey<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Coronavirus disease 2019 (COVID-19) specific psychological stress | Assessed per responses to the 12 questions pertaining to COVID-19 specific psychological stress within the adolescent and young adults (AYA) Cancer Survivor COVID-19 Survey section titled, COVID-19 Related Distress (Emotional and Physical Reactions) and Health Behaviors.'' This survey includes both a 5-level Likert scale for the respondent's current level of concern (Not at all, A little, Neutral, A lot, Very Much), plus an ordinal 3-level scale for the respondent to rate the perceived level of change compared to before COVID-19 (Less, Same, More). Responses to individual questions will be summarized at each time point as means (for the Likert scale) and percentages (for discrete levels of change), together with 95% confidence intervals. For each question, will also summarize the percentages of patients in each group checking one of the 3 levels (Less, Same, More) indicating whether they perceived a change in that question since before COVID-19. | At baseline, 6 months, and 12 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Survey responses | Will be summarized by group and time point. Associations between endpoints and demographic, treatment-related and resilience variables, as well as differences among groups will be assessed by t-test, analysis of variance or Chi-square test. Non-parametric tests (Wilcoxon rank sum, Kruskal-Wallis, Fisher's exact) will be employed when appropriate. Regression models (e.g., linear, logistic etc.) will also be employed. Change from baseline to subsequent time points in Likert scores will be modeled by mixed-effect models, while blocking on patient to control for repeated measures. Models will include baseline demographic, treatment-related, and resilience factor variables as covariates. | At baseline, 6 months, and 12 months | 
| Patient reported outcomes | Will be summarized by group and time point. Associations between endpoints and demographic, treatment-related and resilience variables, as well as differences among groups will be assessed by t-test, analysis of variance or Chi-square test. Non-parametric tests (Wilcoxon rank sum, Kruskal-Wallis, Fisher's exact) will be employed when appropriate. Regression models (e.g., linear, logistic etc.) will also be employed. Change from baseline to subsequent time points in Likert scores will be modeled by mixed-effect models, while blocking on patient to control for repeated measures. Models will include baseline demographic, treatment-related, and resilience factor variables as covariates. | At baseline, 6 months, and 12 months | 
| Changes of survey responses | Will be summarized by group and time point. Associations between endpoints and demographic, treatment-related and resilience variables, as well as differences among groups will be assessed by t-test, analysis of variance or Chi-square test. Non-parametric tests (Wilcoxon rank sum, Kruskal-Wallis, Fisher's exact) will be employed when appropriate. Regression models (e.g., linear, logistic etc.) will also be employed. Change from baseline to subsequent time points in Likert scores will be modeled by mixed-effect models, while blocking on patient to control for repeated measures. Models will include baseline demographic, treatment-related, and resilience factor variables as covariates. | At baseline, 6 months, and 12 months | 
| Changes in discrete responses | Will be summarized by group and time point. Associations between endpoints and demographic, treatment-related and resilience variables, as well as differences among groups will be assessed by t-test, analysis of variance or Chi-square test. Non-parametric tests (Wilcoxon rank sum, Kruskal-Wallis, Fisher's exact) will be employed when appropriate. Regression models (e.g., linear, logistic etc.) will also be employed. Change from baseline to subsequent time points in Likert scores will be modeled by mixed-effect models, while blocking on patient to control for repeated measures. Models will include baseline demographic, treatment-related, and resilience factor variables as covariates. | At baseline, 6 months, and 12 months | 
| Incidence of survey question non-response | Will be separately modeled by logistic regression with relation to group and time point as well as demographic and cancer characteristics in order to assess factors associated with non-response and to assess associated bias. Other statistical approaches might be used as appropriate. | At baseline, 6 months, and 12 months |