Multilevel Characteristics of Cumulative Symptom Burden in Young Survivors of Childhood Cancer.

Importance: Symptom burden and its characteristics among survivors of pediatric cancers aged 8 to 18 years remain understudied. Objective: To examine the prevalence of symptom burden among young childhood cancer survivors and identify associations with sociodemographic, clinical, and psychological resilience skills, and health-related quality of life (HRQOL). Design, Setting, and Participants: A cross-sectional analysis using data collected from November 1, 2017, to January 31, 2019, in a survivorship clinic at a US-based comprehensive cancer center was conducted. Participants included 302 dyads of children aged 8 to 18 years who survived at least 5 years beyond diagnosis and their primary caregivers. Data analysis was performed from March 13, 2023, to February 29, 2024. Exposures: Diagnosis, caregiver-reported family conflict, self-reported caregiver anxiety, neighborhood-level social vulnerability, and survivor-reported meaning and purpose. Main Outcomes and Measures: Novel symptom-level burden, integrating the attributes of severity and daily activity interference using the pediatric version of the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events, global cumulative symptom burden, and HRQOL using the EuroQol-5D. Multinomial logistic regression identified characteristics associated with symptom burden; linear regression assessed symptom burden and HRQOL associations. Results: Among 302 survivors (mean [SD] age, 14.2 [2.9] years, mean [SD] time since diagnosis, 10.9 [2.9] years; 153 [50.7%] male), 186 (62.0%) had low, 77 (25.7%) moderate, and 37 (12.3%) high global cumulative symptom burden. Greater caregiver anxiety was associated with moderate (risk ratio [RR], 1.56; 95% CI, 1.09-2.24) global symptom burden. Greater neighborhood deprivation was associated with moderate global symptom burden (RR, 4.86; 95% CI, 1.29-18.26). Survivors with greater meaning/purpose were less likely to have moderate (RR, 0.42; 95% CI, 0.29-0.61) and high (RR, 0.27; 95% CI, 0.16-0.46) global symptom burden. The burden of individual symptoms displayed similar patterns. Low (Cohen d, -0.60; 95% CI, -0.87 to -0.32) and moderate/high (d, -0.98; 95% CI, -1.53 to -0.43) general pain, moderate/high numbness (d, -0.99; 95% CI, -1.69 to -0.29), and moderate/high worry (d, -0.55; 95% CI, -0.99 to -0.11) were associated with lower HRQOL. Conclusions and Relevance: In this cross-sectional study of young childhood cancer survivors, symptom burden was prevalent. Caregiver anxiety and disparity-related neighborhood factors were associated with greater symptom burden, whereas meaning and purpose was a protective factor. Greater specific symptom burden contributed to poorer HRQOL. The findings suggest that interventions targeting resilience and neighborhood adversity may alleviate symptom burden and improve HRQOL.

Health insurance among survivors of childhood cancer following affordable care act implementation.

BACKGROUND: The Affordable Care Act (ACA) increased private non-employer health insurance options, expanded Medicaid eligibility, and provided pre-existing health conditions protections. We evaluated insurance coverage among long-term adult survivors of childhood cancer pre/post-ACA implementation. METHODS: Using the multicenter Childhood Cancer Survivor Study, we included participants from two cross-sectional surveys: pre-ACA (2007-2009; survivors: N = 7,505; siblings: N = 2,175) and post-ACA (2017-2019; survivors: N = 4,030; siblings: N = 987). A subset completed both surveys (1,840 survivors; 646 siblings). Multivariable regression models compared post-ACA insurance coverage and type (private/public/uninsured) between survivors and siblings and identified associated demographic and clinical factors. Multinomial models compared gaining and losing insurance vs staying the same among survivors and siblings who participated in both surveys. RESULTS: The proportion with insurance was higher post-ACA (survivors pre-ACA 89.1% to post-ACA 92.0% [+2.9%]; siblings pre-ACA 90.9% to post-ACA 95.3% [+4.4%]). Post-ACA insurance coverage was greater among those age 18-25 (survivors: 15.8% vs < 2.3% ages 26+; siblings +17.8% vs < 4.2% ages 26+). Survivors were more likely to have public insurance than siblings post-ACA (18.4% vs 6.9%; odds ratios [OR]=1.7, 95%CI 1.1-2.6). Survivors with severe chronic conditions (OR = 4.7, 95%CI 3.0-7.3) and those living in Medicaid expansion states (OR = 2.4, 95%CI 1.7-3.4) had increased odds of public insurance coverage post-ACA. Among the subset completing both surveys, low/mid income survivors (<$60,000) experienced both insurance losses and gains in reference to highest household income survivors (≥$100,000), relative to odds of keeping the same insurance status. CONCLUSIONS: Post-ACA, more childhood cancer survivors and siblings had health insurance, although disparities remain in coverage.

Multilevel Social Determinants of Patient-Reported Outcomes in Young Survivors of Childhood Cancer.

In this study, the social determinants of patient-reported outcomes (PROs) in young survivors of childhood cancer aged <18 years are researched. This cross-sectional study investigated social determinants associated with poor PROs among young childhood cancer survivors. We included 293 dyads of survivors receiving treatment at St. Jude Children's Research Hospital who were <18 years of age during follow-up from 2017 to 2018 and their primary caregivers. Social determinants included family factors (caregiver-reported PROs, family dynamics) and county-level deprivation (socioeconomic status, physical environment via the County Health Rankings & Roadmaps). PROMIS measures assessed survivors' and caregivers' PROs. General linear regression tested associations of social determinants with survivors' PROs. We found that caregivers' higher anxiety was significantly associated with survivors' poorer depression, stress, fatigue, sleep issues, and reduced positive affect (p < 0.05); caregivers' sleep disturbances were significantly associated with lower mobility in survivors (p < 0.05). Family conflicts were associated with survivors' sleep problems (p < 0.05). Residing in socioeconomically deprived areas was significantly associated with survivors' poorer sleep quality (p < 0.05), while higher physical environment deprivation was associated with survivors' higher psychological stress and fatigue and lower positive affect and mobility (p < 0.05). Parental, family, and neighborhood factors are critical influences on young survivors' quality of life and well-being and represent new intervention targets.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer.

Introduction: Cancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects. Methods: This prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes. Analysis: Machine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics. Conclusion: The individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Financial hardship and neighborhood socioeconomic disadvantage in long-term childhood cancer survivors.

BACKGROUND: Long-term survivors of childhood cancer face elevated risk for financial hardship. We evaluate whether childhood cancer survivors live in areas of greater deprivation and the association with self-reported financial hardships. METHODS: We performed a cross-sectional analysis of data from the Childhood Cancer Survivor Study between 1970 and 1999 and self-reported financial information from 2017 to 2019. We measured neighborhood deprivation with the Area Deprivation Index (ADI) based on current zip code. Financial hardship was measured with validated surveys that captured behavioral, material and financial sacrifice, and psychological hardship. Bivariate analyses described neighborhood differences between survivors and siblings. Generalized linear models estimated effect sizes between ADI and financial hardship adjusting for clinical factors and personal socioeconomic status. RESULTS: Analysis was restricted to 3475 long-term childhood cancer survivors and 923 sibling controls. Median ages at time of evaluation was 39 years (interquartile range [IQR] = 33-46 years and 47 years (IQR = 39-59 years), respectively. Survivors resided in areas with greater deprivation (ADI ≥ 50: 38.7% survivors vs 31.8% siblings; P < .001). One quintile increases in deprivation were associated with small increases in behavioral (second quintile, P = .017) and psychological financial hardship (second quintile, P = .009; third quintile, P = .014). Lower psychological financial hardship was associated with individual factors including greater household income (≥$60 000 income, P < .001) and being single (P = .048). CONCLUSIONS: Childhood cancer survivors were more likely to live in areas with socioeconomic deprivation. Neighborhood-level disadvantage and personal socioeconomic circumstances should be evaluated when trying to assist childhood cancer survivors with financial hardships.

Health-Related quality of life and DNA Methylation-Based aging biomarkers among survivors of childhood cancer.

BACKGROUND: Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related-quality-of-life (HRQOL). However, associations between DNA methylation (DNAm)-based aging biomarkers and HRQOL have not been evaluated. METHODS: DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw = 34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (eg, GrimAge) and others (eg, DNAmB2M beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, eg, EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (eg, ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided. RESULTS: Worse HRQOL was associated with greater EAA_GrimAge (PCS ß[95%CI]=-0.18[-0.251,-0.11] years, P = 1.85 × 10-5; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P = .003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P = .002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL. CONCLUSIONS: Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism underlying the association between poor HRQOL and increased risk of clinically assessed adverse health outcomes.

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.

INTRODUCTION: Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. AREAS COVERED: This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. EXPERT OPINION: Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.

Job lock among survivors of childhood cancer and their spouses post Affordable Care Act implementation: A Childhood Cancer Survivor Study brief report.

It is unknown how common job lock (i.e., staying at job to maintain health insurance) remains among childhood cancer survivors after Affordable Care Act (ACA) implementation in 2010. We examined prevalence of and factors associated with job lock using a cross-sectional survey from the Childhood Cancer Survivor Study (3503 survivors; 942 siblings). Survivor, spousal, and any survivor/spouse job lock were more frequently reported by survivors than siblings. Survivor job lock/any job lock was associated with older age, low income, severe chronic conditions, and debt/inability to pay debt. Job lock remains more common among survivors than siblings after ACA implementation.

Financial hardship among siblings of long-term survivors of childhood cancer: A Childhood Cancer Survivor Study report.

BACKGROUND: Siblings of children with cancer may experience adverse household economic consequences, but their financial outcomes in adulthood are unknown. METHODS: A total of 880 siblings (aged 18-64 years) of adult-aged childhood cancer survivors were surveyed to estimate the prevalence of financial hardship by three established domains (behavioral, material, and psychological). For individual financial hardship items matching the contemporaneous National Health Interview Survey or Behavioral Risk Factor Surveillance System, siblings were compared with the general population by calculating adjusted prevalence odds ratios (ORs) to sample-weighted responses. Multivariable logistic regression models examined associations between sibling characteristics and each hardship domain and between sibling hardship and survivors' cancer/treatment characteristics. RESULTS: Behavioral, material, and psychological hardship was reported by 24%, 35%, and 28%, respectively. Compared with national survey respondents, siblings were more likely to report worries about medical bills (OR, 1.14; 95% confidence interval [CI], 1.06-1.22), difficulty affording nutritious foods (OR, 1.79; 95% CI, 1.54-2.07), and forgoing needed medical care (OR, 1.38; 95% CI, 1.10-1.73), prescription medications (OR, 2.52; 95% CI, 1.99-3.20), and dental care (OR, 1.34; 95% CI, 1.15-1.57) because of cost. Sibling characteristics associated with reporting financial hardship in one or more domains included female sex, older age, chronic health conditions, lower income, not having health insurance, high out-of-pocket medical expenditures, and nonmedical/nonhome debt. No survivor cancer/treatment characteristics were associated with sibling financial hardship. CONCLUSIONS: Adult siblings of childhood cancer survivors were more likely to experience financial hardship compared with the general population. Childhood cancer may adversely affect entire households, with potentially lasting implications.

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.

OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care. METHODS: We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs. RESULTS: Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP. CONCLUSIONS: This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

Interventions with Social Integration Components Addressing Psychosocial Outcomes of Young- and Middle-Aged Adult Cancer Individuals: A Systematic Review.

BACKGROUND: The majority of adult cancer patients/survivors encounter social challenges (e.g., obtaining social support, maintaining social relationships, feelings of social isolation). This systematic review summarizes intervention studies addressing social integration or social connectedness issues among young- and middle-aged cancer patients/survivors. METHODS: We searched the PubMed, CINAHL, and Web of Science databases (January 2000-May 2021) to identify intervention studies that addressed social integration, social connectedness, social support, and social isolation for cancer patients/survivors in young- and middle-aged adulthood (18-64.9 years) through a randomized controlled trial (RCT). We categorized the interventions as technology-based, non-technology-based, and mixed-type (technology- and non-technology-based). RESULTS: A total of 28 studies were identified. These interventions demonstrated improved social outcomes (e.g., increased social support, decreased loneliness), increased awareness of available cancer-related resources, and better patient-reported outcomes among patients/survivors versus controls. Specifically, the use of internet-based discussion sessions was associated with improved social cohesion and social support. Receiving social support from peers through networking sites was associated with improved physical activity. Additionally, implementing mixed-type interventions led to better social support from peer survivors, less fear of social interactions, and improved social connectedness. CONCLUSIONS: Using existing technology- and/or non-technology-based platforms to facilitate social connectedness among cancer patients/survivors in young- or middle-aged adulthood can help them cope with stressful life circumstances and improve quality-of-life. Further interventions targeting social integration (e.g., social network interventions) are needed to improve the complex social integration challenges experienced by cancer patients and survivors.

Financial hardship in families of children or adolescents with cancer: a systematic literature review.

Financial hardship in childhood cancer contributes to poor health outcomes and global disparities in survival, but the extent of the financial burden on families is not yet fully understood. We systematically reviewed financial hardship prevalence and individual components characterising financial hardship across six domains (medical, non-medical, and indirect costs, financial strategies, psychosocial responses, and behavioural responses) and compared characteristics across country income levels using an established theory of human needs. We included 123 studies with data spanning 47 countries. Extensive heterogeneity in study methodologies and measures resulted in incomparable prevalence estimates and limited analysis. Components characterising financial hardship spanned the six domains and showed variation across country income contexts, yet a synthesis of existing literature cannot establish whether these are true differences in characterisation or burden. Our findings emphasise a crucial need to implement a data-driven methodological framework with validated measures to inform effective policies and interventions to address financial hardship in childhood cancer.

Financial Hardship in Childhood Cancer Survivors Treated with Hematopoietic Cell Transplantation: A Report from the Childhood Cancer Survivor Study.

Long-term survivors of childhood cancer are at risk for financial hardship. However, it is not known if HCT leads to an incremental change in financial hardship for survivors who received it versus those who did not. We examined financial outcomes among adult survivors of childhood cancer who had undergone HCT. Using a cross-sectional survey in the Childhood Cancer Survivor Study population between 2017 and 2019, self-reported financial hardship was compared between survivors who received HCT, survivors treated without HCT ("non-HCT"), and siblings and categorized into 3 domains: material hardship/financial sacrifices, behavioral, and psychological hardship. The standardized score of each domain of financial hardship was calculated by adding the item responses and dividing by the standard deviation among siblings. Multivariable linear and logistic regression were used to evaluate associations between sociodemographic characteristics, cancer diagnosis, post-treatment complications, and financial hardship among survivors. The mean adjusted score for each hardship domain was not significantly different between HCT survivors (n = 133) and non-HCT survivors (n = 2711); mean differences were .18 (95% confidence interval [CI], -.05 to .41) for material hardship/financial sacrifices, .07 (95% CI, -.18 to .32) for behavioral hardship, and .19 (95% CI, -.04 to .42) for psychological hardship. Within specific items, a higher proportion of survivors treated with HCT reported greater financial hardship compared to non-HCT survivors. HCT survivors also had significantly higher mean domain scores compared to sibling controls (n = 1027) in all domains. Household income and chronic health conditions, but not HCT, were associated with financial hardship among all survivors. Adult survivors of childhood cancer treated with HCT do not report greater overall financial hardship compared to non-HCT survivors but do report greater overall financial hardship compared to sibling controls. Surveillance and intervention may be necessary for all survivors regardless of HCT status.

Health-related quality of life profiles in adolescents and young adults with chronic conditions.

PURPOSE: To assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions. METHODS: AYAs (N = 872) aged 14-20 years completed NIH's Patient-Reported Outcomes Measurement Information System® (PROMIS®) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores. The optimal number of profiles was determined by model fit statistics, likelihood ratio test, and entropy. Multinomial logistic regression models were used to examine how LPA's HRQOL profile membership was associated with patient demographic and chronic conditions. The model prediction accuracy on profile membership was evaluated using Huberty's I index with a threshold of 0.35 for good effect. RESULTS: A 4-profile LPA model was selected. A total of 161 (18.5%), 256 (29.4%), 364 (41.7%), and 91 (10.4%) AYAs were classified into Minimal, Mild, Moderate, and Severe HRQOL Impact profiles. AYAs in each profile had distinctive mean scores with over a half standard deviation (5-points in PROMIS T-scores) of difference between profiles across most HRQOL domains. AYAs who were female or had conditions such as mental health condition, hypertension, and self-reported chronic pain were more likely to be in the Severe HRQOL Impact profile. The Huberty's I index was 0.36. CONCLUSIONS: Approximately half of AYAs with a chronic condition experience moderate to severe HRQOL impact. The availability of risk prediction models for HRQOL impact will help to identify AYAs who are in greatest need of closer clinical care follow-up.

Applying latent profile analysis to identify adolescents and young adults with chronic conditions at risk for poor health-related quality of life.

The impact of chronic diseases on health-related quality of life (HRQOL) in adolescents and young adults (AYAs) is understudied. Latent profile analysis (LPA) can identify profiles of AYAs based on their HRQOL scores reflecting physical, mental, and social well-being. This paper will (1) demonstrate how to use LPA to identify profiles of AYAs based on their scores on multiple HRQOL indicators; (2) explore associations of demographic and clinical factors with LPA-identified HRQOL profiles of AYAs; and (3) provide guidance on the selection of adult or pediatric versions of Patient-Reported Outcomes Measurement Information System® (PROMIS®) in AYAs. A total of 872 AYAs with chronic conditions completed the adult and pediatric versions of PROMIS measures of anger, anxiety, depression, fatigue, pain interference, social health, and physical function. The optimal number of LPA profiles was determined by model fit statistics and clinical interpretability. Multinomial regression models examined clinical and demographic factors associated with profile membership. As a result of the LPA, AYAs were categorized into 3 profiles: Minimal, Moderate, and Severe HRQOL Impact profiles. Comparing LPA results using either the pediatric or adult PROMIS T-scores found approximately 71% of patients were placed in the same HRQOL profiles. AYAs who were female, had hypertension, mental health conditions, chronic pain, and those on medication were more likely to be placed in the Severe HRQOL Impact Profile. Our findings may facilitate clinicians to screen AYAs who may have low HRQOL due to diseases or treatments with the identified risk factors without implementing the HRQOL assessment.

Response-shift effects in childhood cancer survivors: A prospective study.

BACKGROUND: Treatment-related late effects can worsen over time among cancer survivors. Such worsening health states may trigger changes in internal standards, values, or conceptualization of quality-of-life (QOL). This "response-shift" phenomenon can jeopardize the validity of QOL assessment, and misrepresent QOL comparisons over time. This study tested response-shift effects in reporting future-health concerns among childhood cancer survivors who experienced progression in chronic health conditions (CHCs). METHODS: 2310 adult survivors of childhood cancer from St. Jude Lifetime Cohort Study completed a survey and clinical assessment at two or more timepoints. Based on 190 individual CHCs graded for adverse-event severity, global CHC burden was classified as "progression" or "non-progression". QOL was assessed using the SF-36TM eight domains and physical- and mental-component summary scores (PCS, MCS). A single global item measured concerns about future health. Random-effects models comparing survivors with and without progressive global CHC burden (progressors vs. non-progressors) evaluated response-shift effects (recalibration, reprioritization, reconceptualization) in reporting future-health concerns. RESULTS: Compared with non-progressors, progressors were more likely to de-emphasize (or downplay) overall physical and mental health in evaluating future-health concerns (p-values<0.05), indicating recalibration response-shift, and more likely to de-emphasize physical health earlier rather than later in follow-up (p-value<0.05), indicating reprioritization response-shift. There was evidence for a reconceptualization response-shift with progressor classification associated with worse-than-expected future-health concerns and physical health, and better-than-expected pain and role-emotional functioning (p-values<0.05). CONCLUSION: We identified three types of response-shift phenomena in reporting concerns about future health among childhood cancer survivors. Survivorship care or research should consider response-shift effects when interpreting changes in QOL over time.

Determinants of health-related quality-of-life in adult survivors of childhood cancer: integrating personal and societal values through a health utility approach.

Background: Childhood cancer survivors are at elevated risk for poor health-related quality-of-life (HRQOL). Identification of potentially modifiable risk factors associated with HRQOL is needed to inform survivorship care. Methods: Participants included 4294 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study who completed a survey and clinical assessment at entry into the survivorship cohort (baseline) and follow-up (median interval: 4.3 years) between 2007 and 2019. The SF-6D compared utility-based HRQOL of survivors to an independent sample from the U.S. Medical Expenditures Panel Survey. Chronic health conditions (CHCs) were graded using modified Common Terminology Criteria for Adverse Events. General linear models examined cross-sectional and temporal associations of HRQOL with CHC burden (total and by organ-system), adjusting for potential risk factors. Findings: Survivors reported poorer HRQOL compared to the general population (effect size [d] = -0.343). In cross-sectional analyses at baseline, significant non-demographic risk factors included higher total CHC burden (driven by more severe cardiovascular [d = -0.119, p = 0.002], endocrine [d = -0.112, p = 0.001], gastrointestinal [d = -0.226, p < 0.001], immunologic [d = -0.168, p = 0.035], neurologic [d = -0.388, p < 0.001], pulmonary [d = -0.132, p = 0.003] CHCs), public (d = -0.503, p < 0.001) or no health insurance (d = -0.123, p = 0.007), current smoking (d = -0.270, p < 0.001), being physically inactive (d = -0.129, p < 0.001), ever using illicit drugs (d = -0.235, p < 0.001), and worse diet quality (d = -0.004, p = 0.016). In temporal analyses, poorer utility-based HRQOL at follow-up was associated with risk factors at baseline, including higher total CHC burden (driven by cardiovascular [d = -0.152, p = 0.002], endocrine [d = -0.092, p = 0.047], musculoskeletal [d = -0.160, p = 0.016], neurologic [d = -0.318, p < 0.001] CHCs), public (d = -0.415, p < 0.001) or no health insurance (d = -0.161, p = 0.007), current smoking (d = -0.218, p = 0.001), and ever using illicit drugs (d = -0.217, p < 0.001). Interpretation: Adult survivors report worse utility-based HRQOL than the general population, and potentially modifiable risk factors were associated with HRQOL. Interventions to prevent the early onset of CHCs, promote healthy lifestyle, and ensure access to health insurance in the early survivorship stage may provide opportunities to improve HRQOL. Funding: The research reported in this manuscript was supported by the U.S. National Cancer Institute under award numbers U01CA195547 (Hudson/Ness), R01CA238368 (Huang/Baker), R01CA258193 (Huang/Yasui), R01CA270157 (Bhakta/Yasui), and T32CA225590 (Krull). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Distinct DNA methylation signatures associated with blood lipids as exposures or outcomes among survivors of childhood cancer: a report from the St. Jude lifetime cohort.

BACKGROUND: DNA methylation (DNAm) plays an important role in lipid metabolism, however, no epigenome-wide association study (EWAS) of lipid levels has been conducted among childhood cancer survivors. Here, we performed EWAS analysis with longitudinally collected blood lipid data from survivors in the St. Jude lifetime cohort study. METHODS: Among 2052 childhood cancer survivors of European ancestry (EA) and 370 survivors of African ancestry (AA), four types of blood lipids, including high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG), were measured during follow-up beyond 5-years from childhood cancer diagnosis. For the exposure EWAS (i.e., lipids measured before blood draw for DNAm), the DNAm level was an outcome variable and each of the blood lipid level was an exposure variable; vice versa for the outcome EWAS (i.e., lipids measured after blood draw for DNAm). RESULTS: Among EA survivors, we identified 43 lipid-associated CpGs in the HDL (n = 7), TC (n = 3), and TG (n = 33) exposure EWAS, and 106 lipid-associated CpGs in the HDL (n = 5), LDL (n = 3), TC (n = 4), and TG (n = 94) outcome EWAS. Among AA survivors, we identified 15 lipid-associated CpGs in TG exposure (n = 6), HDL (n = 1), LDL (n = 1), TG (n = 5) and TC (n = 2) outcome EWAS with epigenome-wide significance (P < 9 × 10-8). There were no overlapping lipids-associated CpGs between exposure and outcome EWAS among EA and AA survivors, suggesting that the DNAm changes of different CpGs could be the cause or consequence of blood lipid levels. In the meta-EWAS, 12 additional CpGs reached epigenome-wide significance. Notably, 32 out of 74 lipid-associated CpGs showed substantial heterogeneity (Phet < 0.1 or I2 > 70%) between EA and AA survivors, highlighting differences in DNAm markers of blood lipids between populations with diverse genetic ancestry. Ten lipid-associated CpGs were cis-expression quantitative trait methylation with their DNAm levels associated with the expression of corresponding genes, out of which seven were negatively associated. CONCLUSIONS: We identified distinct signatures of DNAm for blood lipids as exposures or outcomes and between EA and AA survivors, revealing additional genes involved in lipid metabolism and potential novel targets for controlling blood lipids in childhood cancer survivors.

Association of Modifiable Health Conditions and Social Determinants of Health With Late Mortality in Survivors of Childhood Cancer.

Importance: Associations between modifiable chronic health conditions (CHCs), social determinants of health, and late mortality (defined as death occurring ≥5 years after diagnosis) in childhood cancer survivors are unknown. Objective: To explore associations between modifiable CHCs and late mortality within the context of social determinants of health. Design, Setting, and Participants: This longitudinal cohort study used data from 9440 individuals who were eligible to participate in the St Jude Lifetime Cohort (SJLIFE), a retrospective cohort study with prospective clinical follow-up that was initiated in 2007 to characterize outcomes among childhood cancer survivors. Eligible individuals had survived 5 or more years after childhood cancer diagnosis, were diagnosed between 1962 and 2012, and received treatment at St Jude Children's Research Hospital were included in mortality estimates. A total of 3407 adult SJLIFE participants (aged ≥18 years) who completed an on-campus assessment were included in risk factor analyses. Vital status, date of death, and cause of death were obtained by linkage with the National Death Index (coverage from inception to December 31, 2016). Deaths occurring before inception of the National Death Index were obtained from the St Jude Children's Research Hospital Cancer Registry. Data were analyzed from June to December 2022. Exposures: Data on treatment exposures and causes of death were abstracted for individuals who were eligible to participate in the SJLIFE study. Information on modifiable CHCs (dyslipidemia, hypertension, diabetes, underweight or obesity, bone mineral deficiency, hypogonadism, hypothyroidism, and adrenal insufficiency, all graded by the modified Common Terminology Criteria for Adverse Events), healthy lifestyle index (smoking status, alcohol consumption, body mass index [calculated as weight in kilograms divided by height in meters squared], and physical activity), area deprivation index (ADI; which measures neighborhood-level socioeconomic disadvantage), and frailty (low lean muscle mass, exhaustion, low energy expenditure, slowness, and weakness) was obtained for participants. Main Outcomes and Measures: National Death Index causes of death were used to estimate late mortality using standardized mortality ratios (SMRs) and 95% CIs, which were calculated based on US mortality rates. For the risk factor analyses (among participants who completed on-campus assessment), multivariable piecewise exponential regression analysis was used to estimate rate ratios (RRs) and 95% CIs for all-cause and cause-specific late mortality. Results: Among 9440 childhood cancer survivors who were eligible to participate in the SJLIFE study, the median (range) age at assessment was 27.5 (5.3-71.9) years, and the median (range) duration of follow-up was 18.8 (5.0-58.0) years; 55.2% were male and 75.3% were non-Hispanic White. Survivors experienced increases in all-cause mortality (SMR, 7.6; 95% CI, 7.2-8.1) and health-related late mortality (SMR, 7.6; 95% CI, 7.0-8.2). Among 3407 adult SJLIFE participants who completed an on-campus assessment, the median (range) age at assessment was 35.4 (17.9-69.8) years, and the median (range) duration of follow-up was 27.3 (7.3-54.7) years; 52.5% were male and 81.7% were non-Hispanic White. Models adjusted for attained age, sex, race and ethnicity, age at diagnosis, treatment exposures, household income, employment status, and insurance status revealed that having 1 modifiable CHC of grade 2 or higher (RR, 2.2; 95% CI, 1.2-4.0; P = .01), 2 modifiable CHCs of grade 2 or higher (RR, 2.6; 95% CI, 1.4-4.9; P = .003), or 3 modifiable CHCs of grade 2 or higher (RR, 3.6; 95% CI, 1.8-7.1, P < .001); living in a US Census block with an ADI in the 51st to 80th percentile (RR, 5.5; 95% CI, 1.3-23.5; P = .02), an ADI in the 81st to 100th percentile (RR, 8.7; 95% CI, 2.0-37.6; P = .004), or an unassigned ADI (RR, 15.7; 95% CI, 3.5-70.3; P < .001); and having frailty (RR, 2.3; 95% CI, 1.3-3.9; P = .004) were associated with significant increases in the risk of late all-cause death. Similar associations were observed for the risk of late health-related death (1 modifiable CHC of grade ≥2: RR, 2.2 [95% CI, 1.1-4.4; P = .02]; 2 modifiable CHCs of grade ≥2: RR, 2.5 [95% CI, 1.2-5.2; P = .01]; 3 modifiable CHCs of grade ≥2: RR, 4.0 [95% CI, 1.9-8.4; P < .001]; ADI in 51st-80th percentile: RR, 9.2 [95% CI, 1.2-69.7; P = .03]; ADI in 81st-100th percentile: RR, 16.2 [95% CI, 2.1-123.7; P = .007], unassigned ADI: RR, 27.3 [95% CI, 3.5-213.6; P = .002]; and frailty: RR, 2.3 [95% CI, 1.2-4.1; P = .009]). Conclusions and Relevance: In this cohort study of childhood cancer survivors, living in a Census block with a high ADI and having modifiable CHCs were independently associated with an increased risk of late death among survivors of childhood cancer. Future investigations seeking to mitigate these factors will be important to improving health outcomes and developing risk-stratification strategies to optimize care delivery to childhood cancer survivors.

Ten Considerations for Integrating Patient-Reported Outcomes into Clinical Care for Childhood Cancer Survivors.

Patient-reported outcome measures (PROMs) are subjective assessments of health status or health-related quality of life. In childhood cancer survivors, PROMs can be used to evaluate the adverse effects of cancer treatment and guide cancer survivorship care. However, there are barriers to integrating PROMs into clinical practice, such as constraints in clinical validity, meaningful interpretation, and technology-enabled administration of the measures. This article discusses these barriers and proposes 10 important considerations for appropriate PROM integration into clinical care for choosing the right measure (considering the purpose of using a PROM, health profile vs. health preference approaches, measurement properties), ensuring survivors complete the PROMs (data collection method, data collection frequency, survivor capacity, self- vs. proxy reports), interpreting the results (scoring methods, clinical meaning and interpretability), and selecting a strategy for clinical response (integration into the clinical workflow). An example framework for integrating novel patient-reported outcome (PRO) data collection into the clinical workflow for childhood cancer survivorship care is also discussed. As we continuously improve the clinical validity of PROMs and address implementation barriers, routine PRO assessment and monitoring in pediatric cancer survivorship offer opportunities to facilitate clinical decision making and improve the quality of survivorship care.