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 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.

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.

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.

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.

A Review of Patient-Reported Outcome Measures in Childhood Cancer.

Patient-reported outcomes (PROs) are used in clinical work and research to capture the subjective experiences of childhood cancer patients and survivors. PROs encompass content domains relevant and important to this population, including health-related quality-of-life (HRQOL), symptoms, and functional status. To inform future efforts in the application of PRO measures, this review describes the existing generic and cancer-specific PRO measures for pediatric cancer populations and summarizes their characteristics, available language translations, content coverage, and measurement properties into tables for clinicians and researchers to reference before choosing a PRO measure that suits their purpose. We have identified often unreported measurement properties that could provide evidence about the clinical utility of the PRO measures. Routine PRO assessment in pediatric cancer care offers opportunities to facilitate clinical decision-making and improve quality of care for these patients. However, we suggest that before implementing PRO measures into research or clinical care, the psychometric properties and content coverage of the PRO measures must be considered to ensure that PRO measures are appropriately assessing the intended construct in childhood cancer patients.

Stronger association of perceived health with socio-economic inequality during COVID-19 pandemic than pre-pandemic era.

OBJECTIVE: The COVID-19 pandemic has changed peoples' routine of daily living and posed major risks to global health and economy. Few studies have examined differential impacts of economic factors on health during pandemic compared to pre-pandemic. We aimed to compare the strength of associations between perceived health and socioeconomic position (household income, educational attainment, and employment) estimated before and during the pandemic. METHODS: Two waves of nationwide survey [on 2018(T1;n = 1200) and 2021(T2;n = 1000)] were done for 2200 community adults. A balanced distribution of confounders (demographics and socioeconomic position) were achieved across the T2 and T1 by use of the inverse probability of treatment weighting. Distributions of perceived health [= (excellent or very good)/(bad, fair, or good)] for physical-mental-social-spiritual subdomains were compared between T1 and T2. Odds of bad/fair/good health for demographics and socioeconomic position were obtained by univariate logistic regression. Adjusted odds (aOR) of bad/fair/good health in lower household income(< 3000 U.S. dollars/month) were retrieved using the multiple hierarchical logistic regression models of T1 and T2. RESULTS: Perceived health of excellent/very good at T2 was higher than T1 for physical(T1 = 36.05%, T2 = 39.13%; P = 0.04), but were lower for mental(T1 = 38.71%, T2 = 35.17%; P = 0.01) and social(T1 = 42.48%, T2 = 35.17%; P < 0.001) subdomains. Odds of bad/fair/good health were significantly increased at T2 than T1 for household income (physical-mental-social; all Ps < 0.001) and educational attainment (social; P = 0.04) but not for employment (all Ps > 0.05). AORs of bad/fair/good health in lower household income were stronger in T2 than T1, for mental [aOR (95% CI) = 2.15(1.68-2.77) in T2, 1.33(1.06-1.68) in T1; aOR difference = 0.82(P < 0.001)], physical [aOR (95% CI) = 2.64(2.05-3.41) in T2, 1.50(1.18-1.90) in T1; aOR difference = 1.14(P < 0.001)] and social [aOR (95% CI) = 2.15(1.68-2.77) in T2, 1.33(1.06-1.68) in T1; aOR difference = 0.35(P = 0.049)] subdomains. CONCLUSIONS: Risks of perceived health worsening for mental and social subdomains in people with lower monthly household income or lower educational attainment became stronger during the COVID-19 pandemic compared to pre-pandemic era. In consideration of the prolonged pandemic as of mid-2022, policies aiming not only to sustain the monthly household income and compulsory education but also to actively enhance the perceived mental-social health status have to be executed and maintained.

The impact of COVID-19 on cancer care in a tertiary hospital in Korea: possible collateral damage to emergency care.

OBJECTIVES: We investigated the impact of the COVID-19 pandemic on cancer care in a tertiary hospital in Korea without specific lockdown measures. METHODS: A retrospective cohort of cancer patients from one of the largest tertiary hospitals in Korea was used to compare healthcare utilization in different settings (outpatient cancer clinic, the emergency department [ED], and admissions to the hematology/oncology ward) between January 1 and December 31, 2020 and the same time period in 2019. The percent changes in healthcare utilization between the 2 periods were calculated. RESULTS: A total of 448,833 cases from the outpatient cohort, 26,781 cases from the ED cohort, and 14,513 cases from the admission cohort were reviewed for 2019 and 2020. The total number of ED visit cases significantly decreased from 2019 to 2020 by 18.04%, whereas the proportion of cancer patients remained stable. The reduction in ED visits was more prominent in patients with symptoms suspicious for COVID-19, high-acuity cases, and those who lived in non-capital city areas. There were no significant changes in the number of total visits, new cases in the outpatient clinic, or the total number of hospitalizations between the 2 periods. CONCLUSIONS: During the pandemic, the number of ED visits significantly decreased, while the use of the outpatient clinic and hospitalizations were not affected. Cancer patients' ED visits decreased after the COVID-19 outbreak, suggesting the potential for collateral damage outside the hospital if patients cannot reach the ED in a timely manner.

Efficacy and safety of rapid intermittent bolus compared with slow continuous infusion in patients with severe hypernatremia (SALSA II trial): a study protocol for a randomized controlled trial.

BACKGROUND: Hypernatremia is a common electrolyte disorder in children and elderly people and has high short-term mortality. However, no high-quality studies have examined the correction rate of hypernatremia and the amount of fluid required for correction. Therefore, in this study, we will compare the efficacy and safety of rapid intermittent bolus (RIB) and slow continuous infusion (SCI) of electrolyte-free solution in hypernatremia treatment. METHODS: This is a prospective, investigator-initiated, multicenter, open-label, randomized controlled study with two experimental groups. A total of 166 participants with severe hypernatremia will be enrolled and divided into two randomized groups; both the RIB and SCI groups will be managed with electrolyte-free water. We plan to infuse the same amount of fluid to both groups, for 1 hour in the RIB group and continuously in the SCI group. The primary outcome is a rapid decrease in serum sodium levels within 24 hours. The secondary outcomes will further compare the efficacy and safety of the two treatment protocols. CONCLUSION: This is the first randomized controlled trial to evaluate the efficacy and safety of RIB correction compared with SCI in adult patients with severe hypernatremia.

Attitudes toward the Legalization of Euthanasia or Physician-Assisted Suicide in South Korea: A Cross-Sectional Survey.

This study aimed to investigate the general South Korean public attitudes toward the legalization of euthanasia or physician-assisted suicide (EAS) and examine the reasons underpinning these attitudes. From March-April 2021, we conducted a cross-sectional survey of a representative national sample of 1000 South Koreans aged 19 years or older. Three in four participants (76.4%) expressed positive attitudes toward the legalization of EAS. Participants who agreed with this legalization reported "meaninglessness of the rest of life" and "right to a good death" as their main reasons. Participants who disagreed with the legalization of EAS reported "respect for life", "violation of the right to self-determination", "risk of abuse or overuse", and "violation of human rights" as theirs. In the multivariate logistic regression analyses, participants with poor physical status (adjusted odds ratio [aOR]: 1.41, 95%; confidence interval [CI]: 1.02-1.93) or comorbidity (aOR: 1.84, 95%; CI: 1.19-2.83) showed positive attitudes toward the legalization of EAS. In summary, most of the general South Korean population regards the legalization of EAS positively, especially participants with poor physical status or comorbidity.

Blood DNA methylation signatures are associated with social determinants of health among survivors of childhood cancer.

Social epigenomics is an emerging field in which social scientist collaborate with computational biologists, especially epigeneticists, to address the underlying pathway for biological embedding of life experiences. This social epigenomics study included long-term childhood cancer survivors enrolled in the St. Jude Lifetime Cohort. DNA methylation (DNAm) data were generated using the Illumina EPIC BeadChip, and three social determinants of health (SDOH) factors were assessed: self-reported educational attainment, personal income, and an area deprivation index based on census track data. An epigenome-wide association study (EWAS) was performed to evaluate the relation between DNAm at each 5'-cytosine-phosphate-guanine-3' (CpG) site and each SDOH factor based on multivariable linear regression models stratified by ancestry (European ancestry, n = 1,618; African ancestry, n = 258). EWAS among survivors of European ancestry identified 130 epigenome-wide significant SDOH-CpG associations (P < 9 × 10-8), 25 of which were validated in survivors of African ancestry (P < 0.05). Thirteen CpGs were associated with all three SDOH factors and resided at pleiotropic loci in cigarette smoking-related genes (e.g., CLDND1 and CPOX). After accounting for smoking and body mass index, these associations remained significant with attenuated effect sizes. Seven of 13 CpGs were associated with gene expression level based on 57 subsamples with blood RNA sequencing data available. In conclusion, DNAm signatures, many resembling the effect of tobacco use, were associated with SDOH factors among survivors of childhood cancer, thereby suggesting that biologically distal SDOH factors influence health behaviours or related factors, the epigenome, and subsequently survivors' health.

Impact of COVID-19 on the End-of-Life Care of Cancer Patients Who Died in a Korean Tertiary Hospital: A Retrospective Study.

Purpose: Social distancing and strict visitor restrictions at hospitals have been national policies since the onset of the coronavirus disease 2019 (COVID-19) pandemic. This has challenged the concept of a good death in comfort with the opportunity to say goodbye. Little is known about how these measures have influenced end-of-life (EOL) care among cancer patients who die in acute care hospitals. This study examined changes in the EOL care of cancer patients during the COVID-19 pandemic. Methods: We retrospectively analyzed 1,456 adult cancer patients who died in 2019 (n=752) and 2020 (n=704) at a tertiary hospital. Data on EOL care-symptom control and comfort care in an imminently dying state, preparation for death, place of death, and aggressive care in the last month-were reviewed. Results: The 1,456 patients had a median age of 67 years, and 62.5% were men. Patients who died in 2020 were more likely to experience agitation or delirium before death (17.2% vs. 10.9%), to use inotropes/vasopressors near death (59.2% vs. 52.3%), and to receive cardiopulmonary resuscitation in their last months (16.3% vs. 12.5%) than those who died in 2019. Additionally, the number of deaths in the emergency room doubled in 2020 compared to 2019 (from 7.1% to 14.1%). Conclusion: This study suggests that EOL care for cancer patients who died in a tertiary hospital deteriorated during the COVID-19 pandemic. The implementation of medical care at the EOL and the preferred place of death should be discussed carefully in advance for high-quality EOL care.

The Association of the Health Management Strategy with Subjective Health and Well-being Outcomes in General Population.

BACKGROUND: People face many obstacles to overcoming crisis in life and proactively manage life crises. This study aimed to evaluate the association of self-management strategy with subjective health and well-being for the general South Korean population. METHODS: We recruited 1,200 respondents using an equal-probability sampling method from March to May 2018. A questionnaire including life version of the Smart Management Strategy for Health Assessment Tool (SAT-Life), the five Health Status Questionnaire, Short Form-12, McGill Quality of Life Questionnaire (MQOL), Patient Health Questionnaire-9 (PHQ-9), and Satisfaction With Life Scale (SWLS) was administered to participants. RESULTS: In multiple stepwise logistic regression model adjusted with basic demographic variables (age, sex, region, education and monthly income level), core strategy was independently associated with physical, mental, social, spiritual, and general health status (adjusted odds ratios [aORs], 1.97-2.92). Preparation strategy was independently associated with physical, mental, spiritual, and general health status (aORs, 2.36-3.31). Implementation strategy was independently associated with physical, social, spiritual, and general health status (aORs, 2.22-2.42). Core strategy and implementation strategy were independently associated with higher Physical Component Score (aORs, 2.21-2.29) and higher Mental Component Score (aORs, 1.68-1.76). Core strategy and preparation strategy were independently associated with lower PHQ-9 (aORs, 2.63-3.74). Pearson's correlation coefficients between scores on SAT-Life and the other factors (MQOL social support, MQOL spiritual well-being, and SWLS) explain having significant correlations ranging from 0.41-0.43. CONCLUSION: Self-management strategies of health might be encouraged to manage subjective health and well-being outcomes.

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study.

BACKGROUND: Assessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship. OBJECTIVE: This study aims to test the validity of natural language processing (NLP) and machine learning (ML) algorithms in identifying different attributes of pain interference and fatigue symptoms experienced by child and adolescent survivors of cancer versus the judgment by PRO content experts as the gold standard to validate NLP/ML algorithms. METHODS: This cross-sectional study focused on child and adolescent survivors of cancer, aged 8 to 17 years, and caregivers, from whom 391 meaning units in the pain interference domain and 423 in the fatigue domain were generated for analyses. Data were collected from the After Completion of Therapy Clinic at St. Jude Children's Research Hospital. Experienced pain interference and fatigue symptoms were reported through in-depth interviews. After verbatim transcription, analyzable sentences (ie, meaning units) were semantically labeled by 2 content experts for each attribute (physical, cognitive, social, or unclassified). Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods. RESULTS: Compared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95% CI 0.905-0.957) and 0.916 (95% CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95% CI 0.903-0.953) and 0.917 (95% CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95% CI 0.879-0.997; 0.948, 95% CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95% CI 0.735-0.917; 0.855, 95% CI 0.791-0.930). CONCLUSIONS: The BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors.

Leveraging Text Mining Approach to Identify What People Want to Know About Mental Disorders From Online Inquiry Platforms.

Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. LDA identified four latent topics: (1) the understanding of general symptoms, (2) a disability grading system and welfare entitlement, (3) stressful life events, and (4) social adaptation with mental disorders. People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs.

A network analysis of suicidal ideation, depressive symptoms, and subjective well-being in a community population.

Although the relationship between subjective well-being (SWB) and suicidal ideation (SI) has been illustrated in previous research, few studies have conceptualized SWB as a comprehensive measure of life satisfaction in multiple domains, nor have they considered possible mediators such as depressive symptoms. Therefore, the present study aimed to identify dimensions of SWB correlated with SI, and to analyze associations among SWB sub-domains, depressive symptoms, and SI in a community population. A total of 1200 community adults in South Korea, aged 20-86 years, completed self-report questionnaires on demographics, depressive mood (Patient Health Questionnaire-9 [PHQ-9]), SI (item 9 of the PHQ-9), and 14 SWB sub-domains (Subjective Well-Being Inventory). Factors associated with SI, and interactions among SI, depressive mood, and SWB, were identified by logistic regression and phenotype network analyses, respectively. The five main factors influencing the regularized partial correlation network were life satisfaction, self-blame, job, hopelessness, and fatigue. Pathways were observed from work-life balance and life satisfaction to hopelessness; from self-blame and fatigue to safety and health; and from sleep disturbance, concentration difficulties, self-blame, and hopelessness to SI. Making job activities more emotionally rewarding, the potential for career progression and regular work hours could address anhedonia, hopelessness and sleep disturbance, respectively, thus enhancing SWB and reducing SI in the community population.

Development and validation of the Health-Friendly Activity Index: an assessment tool to comprehensively measure health-friendly activities of corporations or organisations.

OBJECTIVES: We developed the Health-Friendly Activity Index (HFAI) to comprehensively measure the health-friendly activities of corporations or organisations. We validated the developed tool and reported on its use as an assessment tool to improve consumers' health-related outcomes. DESIGN: This was a cross-sectional study. SETTING: Development of the HFAI questionnaire followed a three-phase process: item generation, item construction and validation with field testing. Using relevance and feasibility criteria, we developed a 105-item questionnaire with six domains (Governance and Infrastructure, Needs Assessment, Planning, Implementation, Monitoring and Feedback, and Outcomes). PARTICIPANTS: To assess the sensitivity and validity of the questionnaire, the HFAI and Contribution Assessment Tool for Consumer's Health (CATCH) were administered to 302 participants (151 employers and 151 employees) from 151 Korean companies. PRIMARY OUTCOME MEASURES: The CATCH measured the contribution of each company to the physical, mental, social and spiritual health of its consumers. To estimate the reliability and validity of all six HFAI domains and their respective scales, Cronbach's α coefficients and correlation coefficients were used. RESULTS: Each domain and scale of the HFAI exhibited a Cronbach's α coefficient between 0.80 and 0.98 for the employers and employees. The overall HFAI and its six domains correlated significantly and positively with all health outcomes such as physical, mental, social and spiritual status scores evaluated using the CATCH (Spearman's correlation range: 0.37-0.68). CONCLUSION: The HFAI, a unique assessment tool with acceptable psychometric properties, can help corporate managers assess their health-friendly activities.

Association of co-morbidities with financial hardship in survivors of adult cancer.

PURPOSE: To evaluate the impact of co-morbidities on financial hardship in adult cancer survivors and the role of health insurance and a usual source of care (i.e., a particular doctor's office/health center/other place that the person usually goes if he/she is sick or needs advice) in relieving this impact. METHODS: Using the Medical Expenditure Panel Survey, we estimated prevalence of two financial hardships, out-of-pocket (OOP) burden and financial worry, among 1196 adult cancer survivors. A modified Charlson co-morbidity index (CCI) assessed co-morbidities, which represent a medical event (e.g., a doctor's appointment) associated with co-morbid conditions within the past 1 year. Multivariable logistic regression tested the influence of health insurance and a usual source of care on associations of co-morbidities with financial hardship by middle/high vs. low-income families and by working vs. retirement-age individuals. RESULTS: Years since cancer diagnosis ranged from 0 to 76 years (mean: 10.3, SD: 9.8), 10 and 25% of survivors experienced OOP burden and financial worry. For OOP burden, increased CCI was a risk factor among survivors of low-income families, ORs: 1.91 (95% CI: 1.06, 3.46) for a CCI 1-2 and 3.37 (95% CI: 1.72, 6.61) for a CCI ≥ 3 vs. CCI of 0. For financial worry, increased CCI was a risk factor among working-age survivors, ORs: 1.58 (95% CI: 1.02, 2.47) for a CCI 1-2 and 2.15 (95% CI: 1.19, 3.87) for a CCI ≥ 3 vs. CCI of 0. However, having health insurance and a usual source of care did not mitigate impact of co-morbidities on financial hardship (P values > 0.05). CONCLUSIONS: Impact of co-morbidities on OOP and financial worry burden was greatest in survivors of low-income families and working-age, respectively. Health insurance and a usual source of care did not relieve the impact of co-morbidities on financial hardship. IMPLICATIONS: Effective strategies are warranted to mitigate financial hardship for survivors.

Persistent variations of blood DNA methylation associated with treatment exposures and risk for cardiometabolic outcomes in long-term survivors of childhood cancer in the St. Jude Lifetime Cohort.

BACKGROUND: It is well-established that cancer treatment substantially increases the risk of long-term adverse health outcomes among childhood cancer survivors. However, there is limited research on the underlying mechanisms. To elucidate the pathophysiology and a possible causal pathway from treatment exposures to cardiometabolic conditions, we conducted epigenome-wide association studies (EWAS) to identify the DNA methylation (DNAm) sites associated with cancer treatment exposures and examined whether treatment-associated DNAm sites mediate associations between specific treatments and cardiometabolic conditions. METHODS: We included 2052 survivors (median age 33.7 years) of European ancestry from the St. Jude Lifetime Cohort Study, a retrospective hospital-based study with prospective clinical follow-up. Cumulative doses of chemotherapy and region-specific radiation were abstracted from medical records. Seven cardiometabolic conditions were clinically assessed. DNAm profile was measured using MethylationEPIC BeadChip with blood-derived DNA. RESULTS: By performing multiple treatment-specific EWAS, we identified 935 5'-cytosine-phosphate-guanine-3' (CpG) sites mapped to 538 genes/regions associated with one or more cancer treatments at the epigenome-wide significance level (p < 9 × 10-8). Among the treatment-associated CpGs, 8 were associated with obesity, 63 with hypercholesterolemia, and 17 with hypertriglyceridemia (false discovery rate-adjusted p < 0.05). We observed substantial mediation by methylation at four independent CpGs (cg06963130, cg21922478, cg22976567, cg07403981) for the association between abdominal field radiotherapy (abdominal-RT) and risk of hypercholesterolemia (70.3%) and by methylation at three CpGs (cg19634849, cg13552692, cg09853238) for the association between abdominal-RT and hypertriglyceridemia (54.6%). In addition, three CpGs (cg26572901, cg12715065, cg21163477) partially mediated the association between brain-RT and obesity with a 32.9% mediation effect, and two CpGs mediated the association between corticosteroids and obesity (cg22351187, 14.2%) and between brain-RT and hypertriglyceridemia (cg13360224, 10.5%). Notably, several mediator CpGs reside in the proximity of well-established dyslipidemia genes: cg21922478 (ITGA1) and cg22976567 (LMNA). CONCLUSIONS: In childhood cancer survivors, cancer treatment exposures are associated with DNAm patterns present decades following the exposure. Treatment-associated DNAm sites may mediate the causal pathway from specific treatment exposures to certain cardiometabolic conditions, suggesting the utility of DNAm sites as risk predictors and potential mechanistic targets for future intervention studies.

The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.

The primary goal of this study was to evaluate the major roles of health-related quality of life (HRQOL) in a 5-year lung cancer survival prediction model using machine learning techniques (MLTs). The predictive performances of the models were compared with data from 809 survivors who underwent lung cancer surgery. Each of the modeling technique was applied to two feature sets: feature set 1 included clinical and sociodemographic variables, and feature set 2 added HRQOL factors to the variables from feature set 1. One of each developed prediction model was trained with the decision tree (DT), logistic regression (LR), bagging, random forest (RF), and adaptive boosting (AdaBoost) methods, and then, the best algorithm for modeling was determined. The models' performances were compared using fivefold cross-validation. For feature set 1, there were no significant differences in model accuracies (ranging from 0.647 to 0.713). Among the models in feature set 2, the AdaBoost and RF models outperformed the other prognostic models [area under the curve (AUC) = 0.850, 0.898, 0.981, 0.966, and 0.949 for the DT, LR, bagging, RF and AdaBoost models, respectively] in the test set. Overall, 5-year disease-free lung cancer survival prediction models with MLTs that included HRQOL as well as clinical variables improved predictive performance.