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BackgroundLong-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.MethodsWe 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.ResultsAnalysis 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).ConclusionsChildhood 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.

Purpose: Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors. Patients and Methods: We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data. Results: Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems. Conclusion: The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences. [ABSTRACT FROM AUTHOR]

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. 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. 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. [ABSTRACT FROM AUTHOR]

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. Adult siblings of long‐term childhood cancer survivors may experience greater aspects of financial hardship compared with the general population. A childhood cancer diagnosis and treatment may adversely affect entire households, with potentially lasting implications. [ABSTRACT FROM AUTHOR]